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05/19/2025 10:07:13 AM
rwxr-xr-x
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00075c93132acf7a6e46e48d2291ce41.spc
5.69 KB
05/16/2025 04:32:22 AM
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0102169e52b6a27a410e7b237202fe84.spc
140.81 KB
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027d4dde1e82475da3d9afe4844afb1d.spc
2.63 KB
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03036edfece701eaa1537fea4014dd44.spc
56.35 KB
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0446f65691fba260d3eabbd1377240f8.spc
5.75 KB
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04d0c6cc2bf146b1318b78f84416b912.spc
124.45 KB
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0582678c8cfff117f770f9368b70c2b5.spc
19.33 KB
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0601d608f5e2ea8e198130b17fe6ef01.spc
157 bytes
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061ad7f2b0116c570fdc35c36824c7c6.spc
42.24 KB
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06e0c598a46c483b6b9d775e1ba1ecd4.spc
124.09 KB
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0802b12194f292de0e9d9617ac014785.spc
290.02 KB
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083aed319a0b5c8691e31d9150d8005e.spc
19.84 KB
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0a3bf48c84477cd58dbc2036a0331134.spc
70.63 KB
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0b5e5f226006af7e46d02ba8ce216a45.spc
54.71 KB
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0b73d04c6bba0acaf2f9a569f388313a.spc
33.59 KB
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0b8a46fca237497cfc90498f9eb909ab.spc
686.66 KB
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0ce2bdd7061489c6136e7614d421b874.spc
47.7 KB
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0de8a2204854bb5dd311607494c671e4.spc
828.58 KB
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0e15494dca4aeb24ea769582482c5162.spc
150.58 KB
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0eaec40cfb584fcb55fcdfb5d76684b9.spc
16.95 KB
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0ed254d4d9db6e3afe193b00bc6471bb.spc
89.85 KB
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0f079d9bb09fef940c38ee73b52b91d4.spc
34.42 KB
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0f5e21d9d8354d10ea23d99101259ba2.spc
42.06 KB
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0ffc1fa29a6bad7fb49e55940c374610.spc
75.61 KB
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1071b4a15b6c2fe6f7a96f194d0ba524.spc
196 bytes
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10ae571a6266a8e21b0fbb15f552a1cb.spc
13.15 KB
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118c129ff99a905e4e9325e388b841fe.spc
45.34 KB
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131a4ad07dda46888cbbc1cb4c710a91.spc
59.6 KB
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100.76 KB
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142d8795402a4e8a520be8ebea6f54f3.spc
22.7 KB
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1469d584e9747d132077c9df3cda6c97.spc
121.15 KB
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1479626ef8ef423638ca62f43b3e0f8d.spc
95.45 KB
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16e016e3ca27d793aa9172c1913c3f23.spc
26.74 KB
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19f3a21c36072f501f634db8e658bc9f.spc
16.6 KB
05/16/2025 04:32:23 AM
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1b8954ae7aab6fd9784cbcc827133f80.spc
186 bytes
05/16/2025 04:32:23 AM
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1c0bbac8beea30e555f26fd02994e7a5.spc
19.96 KB
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1c1a63fc25720b7c22c9c28fa2aa9379.spc
236.54 KB
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1f1672e0ecc5e7a6d278c930015520ab.spc
166 bytes
05/16/2025 04:32:23 AM
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1f4cf3ae9ba91935f556711c1cfc34d4.spc
88.33 KB
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1f5e96e3f1a01f95ab611ec1458fe470.spc
169.16 KB
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20a75b688975a2d5d342eae9f4c33411.spc
1.22 MB
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225d97aca36305a8b407ea6d8d5b187e.spc
55.08 KB
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242d3dabf79d13154fcc384ff8b2d25e.spc
113.19 KB
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25512b0d18ae6e4d20d027abbc467365.spc
31.2 KB
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25948504a82cd8da1985fddd4500c1c7.spc
153.7 KB
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26e0c631724f3653c10c3123546ab5e2.spc
110.09 KB
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2704664dff0e40e19de087fe00892bc2.spc
24.51 KB
05/16/2025 04:32:24 AM
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274ae07ff50cfde2bda57a71703b62f4.spc
2.54 KB
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2799184659106c88b5072a3e3f763a4d.spc
2.54 KB
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2801f3bdd649962fa663f608c2383280.spc
154.53 KB
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28099e26c5c9a06acb85a41ccd789efc.spc
500.36 KB
05/16/2025 04:32:24 AM
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2aabe0323264e3f60916621039be0e76.spc
42.37 KB
05/16/2025 04:32:24 AM
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2abcd685295b4a261ad2e866188e5e11.spc
125.3 KB
05/16/2025 04:32:24 AM
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2aed529f6407470bef913050a1d118ef.spc
151 bytes
05/16/2025 04:32:24 AM
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2b2654a64e8b0f5d9cf497e0883b2042.spc
96.1 KB
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2dae1abba28ecd05f3e1e91f308cf8c4.spc
87.25 KB
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2db16a36af8daf383cb739dd57a44d90.spc
147.19 KB
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2de250597c053bd81359233c14c51db4.spc
286.38 KB
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2fb670ecdcda7db936aa7d2f018a79e4.spc
23.75 KB
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30d5af6cd4c10ea02520bcaba31f3d1c.spc
141.02 KB
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31591159e55bceb27be71ce43cd1517e.spc
443.64 KB
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31f817c15425941589a9819216265501.spc
68.33 KB
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34661b0e5b23f423b303c946172b39f8.spc
20.99 KB
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3567037b5acd1842946ba40397edead4.spc
84.5 KB
05/16/2025 04:32:25 AM
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37cf2adae9335c54f1dbc436922e6cfc.spc
181 bytes
05/16/2025 04:32:25 AM
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389ae768f4ecb350b56b92da3b04c1ac.spc
180.5 KB
05/16/2025 04:32:25 AM
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3bcfb7838de30c68c7acc437c16935cc.spc
142.35 KB
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42.02 KB
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46.18 KB
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3e4e8d898fc42bca52bf888c3a33ef23.spc
614.85 KB
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3e804b49f84699d48348b3bee312090d.spc
25.24 KB
05/16/2025 04:32:25 AM
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3f92b590befbddc6f7237f2ff7a2ca21.spc
407.55 KB
05/16/2025 04:32:25 AM
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3f93802ae5a285cffaf04f22ceb596fb.spc
307.02 KB
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419e5468f73de12da7ac55b064ff6e04.spc
19.87 KB
05/16/2025 04:32:25 AM
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43cdef0c688f38c395285fd09bd1d8b6.spc
163 bytes
05/16/2025 04:32:25 AM
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445a8424173fb9de0f08493a09557c92.spc
39.14 KB
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3.18 KB
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103.66 KB
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67.86 KB
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170 bytes
05/16/2025 04:32:26 AM
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155 bytes
05/16/2025 04:32:26 AM
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22.31 KB
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88.77 KB
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31.56 KB
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87.42 KB
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181 bytes
05/16/2025 04:32:26 AM
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134 bytes
05/16/2025 04:32:26 AM
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42.22 KB
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148.14 KB
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35.08 KB
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22.11 KB
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720.35 KB
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18.89 KB
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134.37 KB
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22.07 KB
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31.16 KB
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29.23 KB
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154 bytes
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6.77 KB
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128 bytes
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41.86 KB
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56.94 KB
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124.66 KB
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602.71 KB
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67.49 KB
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186.19 KB
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100.02 KB
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19.59 KB
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41.42 KB
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32.47 KB
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123.73 KB
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28.3 KB
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280.88 KB
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99.77 KB
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46.29 KB
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32.55 KB
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150 bytes
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22.35 KB
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200.49 KB
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57.94 KB
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28.51 KB
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193 bytes
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60.73 KB
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1.8 MB
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136.69 KB
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2.63 KB
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266.75 KB
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159 bytes
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185.34 KB
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89.36 KB
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150 bytes
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56.61 KB
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50.63 KB
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3.92 KB
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33.47 KB
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162 bytes
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3.94 KB
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88.67 KB
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Editing: 850a6a7f962502618892db457e231fe7.spc
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How To Use It With Python Code";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:108:"https://analyticsindiamag.com/merlion-salesforces-latest-time-series-library-how-to-use-it-with-python-code/";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"pubDate";a:1:{i:0;a:5:{s:4:"data";s:31:"Thu, 30 Sep 2021 04:30:00 +0000";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:8:"category";a:8:{i:0;a:5:{s:4:"data";s:17:"Developers Corner";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:17:"anomaly detection";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:23:"artificial intelligence";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:17:"forecasting model";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:16:"machine learning";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:7:"Merlion";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:6;a:5:{s:4:"data";s:10:"Salesforce";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:7;a:5:{s:4:"data";s:11:"time series";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"guid";a:1:{i:0;a:5:{s:4:"data";s:41:"https://analyticsindiamag.com/?p=10050022";s:7:"attribs";a:1:{s:0:"";a:1:{s:11:"isPermaLink";s:5:"false";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:130:"Cloud-based software company, Salesforce released Merlion this month, an open-source Python library for time series intelligence. ";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:17:"Vijaysinh Lendave";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:15257:"<img width="1915" height="1436" src="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==" class="attachment-full size-full thb-lazyload lazyload wp-post-image" alt="" loading="lazy" style="float:right; margin:0 0 10px 10px;" sizes="(max-width: 1915px) 100vw, 1915px" data-src="https://analyticsindiamag.com/wp-content/uploads/2021/09/some-place-in-shanghai_t20_8g7QQB.jpg" data-sizes="auto" data-srcset="https://analyticsindiamag.com/wp-content/uploads/2021/09/some-place-in-shanghai_t20_8g7QQB.jpg 1915w, https://analyticsindiamag.com/wp-content/uploads/2021/09/some-place-in-shanghai_t20_8g7QQB-1024x768.jpg 1024w, https://analyticsindiamag.com/wp-content/uploads/2021/09/some-place-in-shanghai_t20_8g7QQB-768x576.jpg 768w, https://analyticsindiamag.com/wp-content/uploads/2021/09/some-place-in-shanghai_t20_8g7QQB-1536x1152.jpg 1536w, https://analyticsindiamag.com/wp-content/uploads/2021/09/some-place-in-shanghai_t20_8g7QQB-370x277.jpg 370w, https://analyticsindiamag.com/wp-content/uploads/2021/09/some-place-in-shanghai_t20_8g7QQB-800x600.jpg 800w, https://analyticsindiamag.com/wp-content/uploads/2021/09/some-place-in-shanghai_t20_8g7QQB-20x15.jpg 20w, https://analyticsindiamag.com/wp-content/uploads/2021/09/some-place-in-shanghai_t20_8g7QQB-185x139.jpg 185w, https://analyticsindiamag.com/wp-content/uploads/2021/09/some-place-in-shanghai_t20_8g7QQB-740x555.jpg 740w, https://analyticsindiamag.com/wp-content/uploads/2021/09/some-place-in-shanghai_t20_8g7QQB-400x300.jpg 400w, https://analyticsindiamag.com/wp-content/uploads/2021/09/some-place-in-shanghai_t20_8g7QQB-1600x1200.jpg 1600w, https://analyticsindiamag.com/wp-content/uploads/2021/09/some-place-in-shanghai_t20_8g7QQB-64x48.jpg 64w" /> <p>Cloud-based software company<a href="https://analyticsindiamag.com/merlion-an-open-source-library/" data-wpel-link="internal">, Salesforce</a> released Merlion this month, an open-source Python library for time series intelligence. It is used for time series analysis and provides an end-to-end machine learning framework that includes loading and transforming data, building and training models, post-processing model outputs, and evaluating model performance. Along with this, we will also learn to implement anomaly detection in time series using Merlion. The major points to be discussed in this article are listed below. </p> <h3 id="h-table-of-contents"><strong>Table of Contents</strong></h3> <ol><li>What is Merlion?</li><li>Key Features of Merlion</li><li>Architectural Arrangement of Merlion</li><li>Implementation of Anomaly Detection using Merlion</li></ol> <p> Let’s start with understanding the Merlion package.</p> <h4 id="h-what-is-merlion"><strong>What is Merlion?</strong></h4> <p>It is an open-source time-series machine learning library that has a uniform interface for various commonly used models and datasets for anomaly detection and forecasting on univariate and multivariate time series, as well as conventional pre/post-processing layers. It includes numerous modules to increase use, such as visualization, anomaly score calibration to improve interoperability, AutoML for hyperparameter tuning and model selection, and model assembly. </p> <p>Merlion also offers a one-of-a-kind evaluation system that replicates live model deployment and re-training in production. This library intends to provide engineers and researchers with a one-stop solution for fast developing and benchmarking models for their specific time series needs across numerous time-series datasets.</p> <h4 id="h-key-features-of-merlion"><strong>Key Features of Merlion</strong></h4> <p>It provides an end-to-end machine learning framework that covers data loading and transformation, model development and training, model output post-processing, and model performance evaluation. Apart from these Merlion is:</p> <ol><li>A standardized and easily expandable framework for data loading, pre-processing, and benchmarking has been designed to support a wide range of time series forecasting and anomaly detection operations.</li><li>A set of models for anomaly detection and forecasting that are linked through a common interface. Among the models are traditional statistical approaches, tree ensembles, and deep learning methods. Advanced users can tailor each model to their preferences.</li><li>Abstracts that are efficient, robust, and provide a starting point for new users Models such as DefaultDetector and DefaultForecaster.</li><li>AutoML is a model selection and hyperparameter tuning tool. </li><li>Practical, industry-inspired post-processing rules for anomaly detectors that improve the interpretability of anomaly scores while lowering the false positive rate. </li><li>Ensembles that are simple to use and integrate the results of numerous models to generate more robust performance. </li><li>Model predictions can be visualized natively.</li></ol> <h4 id="h-architectural-arrangement-of-merlion"><strong>Architectural Arrangement of Merlion</strong></h4> <p>Merlion’s module architecture is divided into five layers:-</p> <ul><li>The data layer loads raw data, converts it to Merlion’s TimeSeries data structure, and performs any desired pre-processing. </li><li>The modelling layer supports a variety of models for forecasting and anomaly detection, including autoML for automated hyperparameter tuning. </li><li>The postprocessing layer offers practical solutions for improving interoperability and lowering the false positive rate of anomaly detection models. </li><li>The next ensemble layer allows for transparent model selection and combining. </li><li>The final evaluation layer includes important evaluation metrics and algorithms that emulate a model’s live deployment in production.</li></ul> <div class="wp-block-image"><figure class="aligncenter"><img src="https://lh4.googleusercontent.com/9mYpX4kg1IVOk6h3V0AR_6QKWhct4Cy4CkbDD_nQXzOUMRQmQYh6RKmVtmfT7QOtFtqXaVyKON6ePftOnz__V0UjAfkQ1P8HWd49DeJH1zrj8uzTjpc0NpG940204Hi0RrjTXBQ7=s0" alt=""/><figcaption><a href="https://arxiv.org/pdf/2109.09265.pdf" data-wpel-link="external" target="_blank" rel="nofollow">Architecture of Merlion</a></figcaption></figure></div> <p>Merlion employs a wide range of models for forecasting and anomaly detection. Among these are statistical methods, tree-based models, and deep learning approaches. To transparently expose all of these possibilities to an end-user, the engineering team has unified all Merlion models under two common APIs, one for forecasting and the other for anomaly detection. All models start with a config object containing implementation-specific hyperparameters and support a model. method train(time series). Now let’s move to the implementation part where we implement anomaly detection and Forecasting a series. </p> <h4 id="h-implementation-of-anomaly-detection-using-merlion"><strong>Implementation of Anomaly Detection using Merlion</strong></h4> <p>Merlion includes a number of models that are optimized for detecting univariate time series anomalies. These are classified into two types: forecasting-based and statistical. Forecasters in Merlion are simple to modify for anomaly identification because they predict the value of a specified univariate in a generic time series. The anomaly score is just the difference between the expected and true-time series values, optionally normalized by the predicted standard error of the underlying forecaster (if it produces one).</p> <p>To start using merlion first we need to install it, we can install it either by using the PIP command or by cloning the <a href="https://github.com/salesforce/Merlion" data-wpel-link="external" target="_blank" rel="nofollow">repository</a>. Check <a href="https://opensource.salesforce.com/Merlion/v1.0.0/index.html" data-wpel-link="external" target="_blank" rel="nofollow">here</a> for the instructions for installing the package. </p> <p>Merlion comes with a data loader package called ts_dataset it basically implements certain python-based Classes which help to manipulate numerous time-series datasets into standardized pandas data frames. The submodules of it like ts_dataset.anomaly and ts_dataset.forecast are used to load the dataset for anomaly detection and forecasting a series respectively. </p> <p>For anomaly detection, we are using the NAB<a href="https://github.com/numenta/NAB" data-wpel-link="external" target="_blank" rel="nofollow">(Numenta Anomaly Benchmark)</a> dataset. NAB is a new benchmark for evaluating algorithms in streaming, real-time applications for anomaly detection. It consists of more than 50 labeled real-world and artificial time series data files. We are using Merlion’s standard data class called TimeSeries from the subpackage <em>utils</em> which can handle both univariate and multivariate time series data. This class wraps a collection of Univariate time series in a single class. </p> <p>The below code shows the use case of both ts_dataset and TimeSeries class, and we are splitting the NAB train and test set and will take a glimpse of the obtained time series.</p> <pre class="wp-block-preformatted">from merlion.utils import TimeSeries from ts_datasets.anomaly import NAB time_series, metadata = NAB(subset='realTweets')[5] train_data = TimeSeries.from_pd(time_series[metadata.trainval]) test_data = TimeSeries.from_pd(time_series[~metadata.trainval]) test_labels = TimeSeries.from_pd(metadata.anomaly[~metadata.trainval]) </pre> <div class="wp-block-image"><figure class="aligncenter size-full"><img loading="lazy" width="253" height="276" src="https://analyticsindiamag.com/wp-content/uploads/2021/09/anomaly.png" alt="" class="wp-image-10050023" srcset="https://analyticsindiamag.com/wp-content/uploads/2021/09/anomaly.png 253w, https://analyticsindiamag.com/wp-content/uploads/2021/09/anomaly-185x202.png 185w, https://analyticsindiamag.com/wp-content/uploads/2021/09/anomaly-20x22.png 20w, https://analyticsindiamag.com/wp-content/uploads/2021/09/anomaly-44x48.png 44w" sizes="(max-width: 253px) 100vw, 253px" /></figure></div> <p>Merlion’s DefaultDetector, which is an anomaly detection model that balances performance and efficiency, may now be initialized and trained. On the test split, we also get its predictions.</p> <pre class="wp-block-preformatted">from merlion.models.defaults import DefaultDetectorConfig, DefaultDetector # initialize,train, and test the detector model = DefaultDetector(DefaultDetectorConfig()) model.train(train_data=train_data) test_pred = model.get_anomaly_label(time_series=test_data) </pre> <p>Now visualize the prediction, for visualization merlion comes with a visualization package that gives us a very interactive and informative visualization of our predictions.</p> <pre class="wp-block-preformatted">from merlion.plot import plot_anoms import matplotlib.pyplot as plt fig, ax = model.plot_anomaly(time_series=test_data) plot_anoms(ax=ax, anomaly_labels=test_labels) plt.show() </pre> <div class="wp-block-image"><figure class="aligncenter size-full"><img loading="lazy" width="777" height="465" src="https://analyticsindiamag.com/wp-content/uploads/2021/09/anomaly-visualization.png" alt="" class="wp-image-10050024" srcset="https://analyticsindiamag.com/wp-content/uploads/2021/09/anomaly-visualization.png 777w, https://analyticsindiamag.com/wp-content/uploads/2021/09/anomaly-visualization-768x460.png 768w, https://analyticsindiamag.com/wp-content/uploads/2021/09/anomaly-visualization-370x221.png 370w, https://analyticsindiamag.com/wp-content/uploads/2021/09/anomaly-visualization-20x12.png 20w, https://analyticsindiamag.com/wp-content/uploads/2021/09/anomaly-visualization-185x111.png 185w, https://analyticsindiamag.com/wp-content/uploads/2021/09/anomaly-visualization-740x443.png 740w, https://analyticsindiamag.com/wp-content/uploads/2021/09/anomaly-visualization-400x239.png 400w, https://analyticsindiamag.com/wp-content/uploads/2021/09/anomaly-visualization-80x48.png 80w" sizes="(max-width: 777px) 100vw, 777px" /></figure></div> <p>Finally, we may assess the model quantitatively by using the evaluate package. Merlion’s evaluation implements utility and metrics by which we can evaluate the performance of our time series task.</p> <p>As we can see in the plot, the model fired three alarms, with three true positives, and one false negative, resulting in precision and recall. By using the evaluation package we can also look at the average time it took the model to accurately detect each abnormality as shown below.</p> <pre class="wp-block-preformatted">from merlion.evaluate.anomaly import TSADMetric #Precision Score p = TSADMetric.Precision.value(ground_truth=test_labels, predict=test_pred) # Recall Score r = TSADMetric.Recall.value(ground_truth=test_labels, predict=test_pred) # F1 Score f1 = TSADMetric.F1.value(ground_truth=test_labels, predict=test_pred) # returns mean time taken to detect anomaly mttd = TSADMetric.MeanTimeToDetect.value(ground_truth=test_labels, predict=test_pred) print(f"Precision: {p:.4f}, Recall: {r:.4f}, F1: {f1:.4f}\n" f"Mean Time To Detect: {mttd}") </pre> <p>Output:</p> <div class="wp-block-image"><figure class="aligncenter size-full"><img loading="lazy" width="499" height="56" src="https://analyticsindiamag.com/wp-content/uploads/2021/09/score.png" alt="" class="wp-image-10050025" srcset="https://analyticsindiamag.com/wp-content/uploads/2021/09/score.png 499w, https://analyticsindiamag.com/wp-content/uploads/2021/09/score-370x42.png 370w, https://analyticsindiamag.com/wp-content/uploads/2021/09/score-185x21.png 185w, https://analyticsindiamag.com/wp-content/uploads/2021/09/score-20x2.png 20w, https://analyticsindiamag.com/wp-content/uploads/2021/09/score-400x45.png 400w, https://analyticsindiamag.com/wp-content/uploads/2021/09/score-428x48.png 428w" sizes="(max-width: 499px) 100vw, 499px" /></figure></div> <h4 id="h-conclusion"><strong>Conclusion</strong> </h4> <p>We have seen how seamlessly we can implement an anomaly detection task. For a wide range of models and datasets, it provides uniform, easily expandable interfaces, and implementations. Similarly, we can forecast a series just by changing the configuration for the forecasting model and dataset for which we want a forecast. You can check the implementation in the Colab notebook. </p> <h4 id="h-references"><strong>References</strong> </h4> <ul><li><a href="https://arxiv.org/pdf/2109.09265.pdf" data-wpel-link="external" target="_blank" rel="nofollow">Official Research Paper</a></li><li><a href="https://github.com/salesforce/Merlion" data-wpel-link="external" target="_blank" rel="nofollow">Official Git-Hub Repository</a></li><li><a href="https://opensource.salesforce.com/Merlion/v1.0.0/index.html" data-wpel-link="external" target="_blank" rel="nofollow">Documentation</a></li><li><a href="https://colab.research.google.com/drive/1MakziNd9HdLjufejiqy_9ryewxYJO14_?usp=sharing" data-wpel-link="external" target="_blank" rel="nofollow">Link for above codes</a></li></ul> ";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:1;a:6:{s:4:"data";s:84:" ";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:3:{s:0:"";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:55:"Amazon Reveals AZ2 CPU Chip At Fall Hardware Event 2021";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:86:"https://analyticsindiamag.com/amazon-reveals-az2-cpu-chip-at-fall-hardware-event-2021/";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"pubDate";a:1:{i:0;a:5:{s:4:"data";s:31:"Wed, 29 Sep 2021 13:54:35 +0000";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:8:"category";a:10:{i:0;a:5:{s:4:"data";s:4:"News";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:2:"AI";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:6:"amazon";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:11:"Amazon Echo";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:9:"analytics";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:23:"artificial intelligence";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:6;a:5:{s:4:"data";s:3:"AZ1";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:7;a:5:{s:4:"data";s:12:"data science";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:8;a:5:{s:4:"data";s:13:"deep learning";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:9;a:5:{s:4:"data";s:16:"machine learning";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"guid";a:1:{i:0;a:5:{s:4:"data";s:41:"https://analyticsindiamag.com/?p=10050029";s:7:"attribs";a:1:{s:0:"";a:1:{s:11:"isPermaLink";s:5:"false";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:137:"The AZ2 possesses the ability to process machine-learning-based speech models "significantly faster," according to Amazon's announcement.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:10:"Victor Dey";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:3499:"<img width="1497" height="763" src="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==" class="attachment-full size-full thb-lazyload lazyload wp-post-image" alt="" loading="lazy" style="float:right; margin:0 0 10px 10px;" sizes="(max-width: 1497px) 100vw, 1497px" data-src="https://analyticsindiamag.com/wp-content/uploads/2021/09/AZ2.png" data-sizes="auto" data-srcset="https://analyticsindiamag.com/wp-content/uploads/2021/09/AZ2.png 1497w, https://analyticsindiamag.com/wp-content/uploads/2021/09/AZ2-1024x522.png 1024w, https://analyticsindiamag.com/wp-content/uploads/2021/09/AZ2-768x391.png 768w, https://analyticsindiamag.com/wp-content/uploads/2021/09/AZ2-370x189.png 370w, https://analyticsindiamag.com/wp-content/uploads/2021/09/AZ2-800x408.png 800w, https://analyticsindiamag.com/wp-content/uploads/2021/09/AZ2-185x94.png 185w, https://analyticsindiamag.com/wp-content/uploads/2021/09/AZ2-740x377.png 740w, https://analyticsindiamag.com/wp-content/uploads/2021/09/AZ2-20x10.png 20w, https://analyticsindiamag.com/wp-content/uploads/2021/09/AZ2-400x204.png 400w, https://analyticsindiamag.com/wp-content/uploads/2021/09/AZ2-94x48.png 94w" /> <p>Amazon has unveiled its new smart home device Echo Show 15. 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The two-day long influential conference on deep learning featured 30 keynote speakers and over 500 attendees, where some of the leading professionals and researchers from 200 organisations presented their feature talks and research papers. A full-day workshop on different deep learning technologies was also held on Day 2 of the event, with certificates being provided to all the attendees. </p> <p>The conference was virtually held, yet allowing fellow attendees to network with each other, providing opportunities to meet companies and the chance to talk with the speakers. It covered both technical and business aspects of trending developments in the domain of deep learning and attracted developers and executives from all over India. The sessions held at DLDC helped uncover a wide range of different approaches to the problems that are currently faced by the tech industry in the areas of deep learning, <a href="https://analyticsindiamag.com/my-small-project-on-animating-images-videos-using-gansnroses/" data-wpel-link="internal">artificial intelligence</a> and machine learning. The workshops and research paper presentations also garnered a large number of attendees and focused on insights to the latest in deep learning with hands-on experience. </p> <p>DLDC 2021 was sponsored by some of the most highly known tech conglomerates. The presenting sponsor being <a href="https://timestsw.com/" data-wpel-link="external" target="_blank" rel="nofollow">TSW</a>, Gold sponsors <a href="https://www.wearemiq.com/" data-wpel-link="external" target="_blank" rel="nofollow">MIQ</a>, <a href="https://www.ugamsolutions.com/" data-wpel-link="external" target="_blank" rel="nofollow">Ugam, A Merkle Company</a>, and Platinum sponsors <a href="https://www.americanexpress.com/en-in/" data-wpel-link="external" target="_blank" rel="nofollow">American Express</a> and <a href="https://www.genpact.com/" data-wpel-link="external" target="_blank" rel="nofollow">Genpact</a>. </p> <h2 id="h-how-it-happened"><strong>How It Happened</strong></h2> <p>The exciting first day started with a highly anticipated and insightful session by <a href="https://www.linkedin.com/in/mohan-silaparasetty/" data-wpel-link="external" target="_blank" rel="nofollow">Mohan Silaparasetty</a>, Head of Technology Programs at Times Professional Learning, where he spoke on “The State of AI and Deep Learning”. This keynote talked about the latest advances in deep learning and its current applications in artificial intelligence worldwide. Mohan dived deep into the details of emerging trends and technologies in <a href="https://analyticsindiamag.com/generating-piano-music-with-score2perf/" data-wpel-link="internal">deep learning</a>, which featured textless NLP and <a href="https://analyticsindiamag.com/beginners-guide-to-generative-adversarial-networks-ga" data-wpel-link="internal">generative networks</a>, to name a few. </p> <p>The morning session was followed by tech talks from <a href="https://www.linkedin.com/in/manoj-kumar-rajendran-93191668/" data-wpel-link="external" target="_blank" rel="nofollow">Manoj Kumar Rajendran</a>, Principal Data Scientist at MiQ Digital India, and another from <a href="https://www.linkedin.com/in/vbehrani/" data-wpel-link="external" target="_blank" rel="nofollow">Vikas Behrani</a>, Vice President, Data Science, at Genpact. Manoj talked about understanding and leveraging differential data privacy and how the tech world can be prepared for a differentially private world of data. The following talk from Vikas Behrani elaborated details on the Lap Estimate Optimizer and how race-day strategies for Formula E can be transformed using AI. </p> <p>In a subsequent talk, <a href="https://www.linkedin.com/in/radhakrishnan-g-7111515/" data-wpel-link="external" target="_blank" rel="nofollow">Radhakrishnan G</a>, VP and Global Head of Commercial Risk Decision Science at American Express guided the attendees on helping small businesses with real-time credit decisioning using ML and AI, where he also described how Amex is leveraging machine learning techniques to enhance marketing. This talk wrapped up the morning sessions at DevCon. </p> <p>The post-lunch sessions started with a talk on how to deal with data imbalance in classification problems by <a href="https://www.linkedin.com/in/raghavendra-nagaraja-rao-a9440952/" data-wpel-link="external" target="_blank" rel="nofollow">Raghavendra Nagaraja Rao</a>, Data Science, Academic Lead at Times Professional Learning, where he enlightened the attendees on how different techniques can be used to deal with imbalanced data. </p> <p>Swagata Maiti, Technology Architect, IP & Data Products, and Shaji Thomas, Vice President, Cloud & Data Engineering at Ugam, A Merkle Company, later continued the post-lunch session with their presentation on seven techniques that help create a scalable data platform, answering the question on “To data prep or data science?”. </p> <p>Data Scientists at CRED, <a href="https://www.linkedin.com/in/rk0912/" data-wpel-link="external" target="_blank" rel="nofollow">Ravi Kumar</a> and <a href="https://www.linkedin.com/in/samiranroy/" data-wpel-link="external" target="_blank" rel="nofollow">Samiran Roy</a>, later explained the essence of using graph neural networks and how the emerging technology is being utilised by <a href="https://cred.club/" data-wpel-link="external" target="_blank" rel="nofollow">CRED</a> in their post-lunch presentation on deep learning applications with graph neural networks.</p> <p>As a huge volume of information in an enterprise flows through documents, <a href="https://www.linkedin.com/in/rahul-ghosh-a20382b/" data-wpel-link="external" target="_blank" rel="nofollow">Rahul Ghosh</a>, VP of AI Research and Services at American Express AI Labs, helped understand how AI-powered document intelligence for enterprises can drive innovation and efficiency at scale during his time at the DLDC 2021. Hitesh Prakash Nahata, Senior Manager of Advanced Analytics at MiQ, presented a detailed talk on driving incremental outcomes through hyper-relevance that can aid analytics and how to gain deeper insights using such methodologies. </p> <p>DLDC 2021 also touched upon a brief history and key trends in deep learning for Computer Vision by speaker <a href="https://www.linkedin.com/in/drangshu/" data-wpel-link="external" target="_blank" rel="nofollow">Angshuman Ghosh</a>, Head of Data Science at <a href="https://www.linkedin.com/company/sonyresearchindia/" data-wpel-link="external" target="_blank" rel="nofollow">Sony Research India</a>, where he shed light on Computer Vision technologies and some breakthrough models that led to massive progress in the field.</p> <p>Other prominent names included <a href="https://www.linkedin.com/in/ram-seshadri-nyc-nj/" data-wpel-link="external" target="_blank" rel="nofollow">Ram Seshadri</a>, Machine Learning Program Manager at Google, who gave an insightful discussion on his Deep Learning AutoML library, Deep AutoViML. The agenda of the talk included top features of the library and how the library can be used in the context of MLOps. Kaggle Grandmaster and Senior Data Scientist <a href="https://www.linkedin.com/in/lmassaron/" data-wpel-link="external" target="_blank" rel="nofollow">Luca Massaron</a> talked about his recent work on using deep learning techniques to predict credit ratings for global corporate entities in his tech talk on Deep Learning for Credit Rating. </p> <h2 id="h-exciting-workshops-and-paper-presentations"><strong>Exciting Workshops and Paper Presentations</strong></h2> <p>The second day was filled with workshops and paper presentations, starting with a workshop on text classification with vectorisers and pre-trained neural net models by Raghavendra Nagaraja Rao. A line up of key paper presentations included: <strong>Time Expression Extraction and Normalisation in Industrial Setting</strong> by Piyush Arora, Senior AI researcher at American Express AI Labs,<strong> Classification of Quasars, Galaxies and Stars using Multi-modal deep learning</strong> by Bharath Kumar Bolla, Senior Data Scientist at Verizon, <strong>Hyper localisation of leaks in piping and cabling systems using reinforcement learning</strong> by Indrajit Kar, Head of AI at Siemens Advanta, <strong>Global-Local Scalable Explanations Using Linear Model Tree</strong> by Narayanan Unny E., Head of Machine Learning Research, American Express AI Lab, <strong>Predicting Custom Ad Performance Metric using Contextual Features</strong> by Prateek Kulkarni, Data Science Team Lead at MiQ Digital and Divyaprabha M, Data Scientist at MiQ Digital and <strong>Analysis of Sectoral Profitability of the Indian Stock Market Using an LSTM Regression Model</strong> by Jaydip Sen, Professor of Data Science and Artificial Intelligence at Praxis Business School.</p> <p>Dipyaman Sanyal, Head of Academics and Learning at Hero Vired, conducted a tech talk on “Explainable and Interpretable Deep Learning” and Jatindra Singh Deo, Senior Technical Architect at Genpact along with Abhilash NVS, Data Scientist for Genpact, conducted a two-hour detailed workshop on industrialising AI/ML: Hands-on Model Deployment, post-lunch at day two. </p> <p>During the two days of DevCon 2021, the speakers also interacted with the attendees by answering their questions and doubts regarding the presentation through a live interactive chatbox and post-presentation discussion being conducted after each talk. This helped the attendees not only connect better with the speakers but also gain deeper knowledge and understanding of the topic. </p> <h2 id="h-summing-up"><strong>Summing up </strong></h2> <p>The Deep Learning Developers Conference 2021 set an example of how even during times of a global pandemic, an insightful yet engaging symposium can be held virtually. The conference included some of the biggest names in the tech industry who covered almost every aspect of Deep learning. Speaking on the success of the event, <a href="https://www.linkedin.com/in/bhaskergupta/" data-wpel-link="external" target="_blank" rel="nofollow">Bhasker Gupta</a>, Founder & CEO, Analytics India Magazine, said, “It was great to see such an overwhelming response knowing about tough times the whole world is going through. DLDC 2021 would not have been possible without the tireless efforts from the entire Analytics India Magazine team and the sheer support of our sponsors”.</p> ";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:3;a:6:{s:4:"data";s:57:" ";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:3:{s:0:"";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:65:"Meet The New Player In The US-China AI Arms Race: United Kingdom.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:94:"https://analyticsindiamag.com/meet-the-new-player-in-the-us-china-ai-arms-race-united-kingdom/";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"pubDate";a:1:{i:0;a:5:{s:4:"data";s:31:"Wed, 29 Sep 2021 11:30:00 +0000";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:8:"category";a:1:{i:0;a:5:{s:4:"data";s:8:"Opinions";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"guid";a:1:{i:0;a:5:{s:4:"data";s:41:"https://analyticsindiamag.com/?p=10049965";s:7:"attribs";a:1:{s:0:"";a:1:{s:11:"isPermaLink";s:5:"false";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:121:"The UK plans not only to be a top AI country but also to do that with ethical and regulated AI that the citizens desire. ";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:10:"Avi Gopani";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:9580:"<img width="1600" height="900" src="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==" class="attachment-full size-full thb-lazyload lazyload wp-post-image" alt="" loading="lazy" style="float:right; margin:0 0 10px 10px;" sizes="(max-width: 1600px) 100vw, 1600px" data-src="https://analyticsindiamag.com/wp-content/uploads/2021/09/rce-01.jpg" data-sizes="auto" data-srcset="https://analyticsindiamag.com/wp-content/uploads/2021/09/rce-01.jpg 1600w, https://analyticsindiamag.com/wp-content/uploads/2021/09/rce-01-1024x576.jpg 1024w, https://analyticsindiamag.com/wp-content/uploads/2021/09/rce-01-768x432.jpg 768w, https://analyticsindiamag.com/wp-content/uploads/2021/09/rce-01-1536x864.jpg 1536w, https://analyticsindiamag.com/wp-content/uploads/2021/09/rce-01-370x208.jpg 370w, https://analyticsindiamag.com/wp-content/uploads/2021/09/rce-01-800x450.jpg 800w, https://analyticsindiamag.com/wp-content/uploads/2021/09/rce-01-20x11.jpg 20w, https://analyticsindiamag.com/wp-content/uploads/2021/09/rce-01-185x104.jpg 185w, https://analyticsindiamag.com/wp-content/uploads/2021/09/rce-01-740x416.jpg 740w, https://analyticsindiamag.com/wp-content/uploads/2021/09/rce-01-400x225.jpg 400w, https://analyticsindiamag.com/wp-content/uploads/2021/09/rce-01-85x48.jpg 85w" /> <p>The United Kingdom government has released its 10-year plan to make the country the global “artificial intelligence superpower”. This comes right after BCS proposed ‘gold standards’ to make the UK a leading ethical AI superpower. The latest developments in the AI sector in the UK begs us to ask the question – is the UK competing in the AI arms race with China and the US?</p> <p>“Today we’re laying the foundations for the next ten years’ growth with a strategy to help us seize the potential of artificial intelligence and play a leading role in shaping the way the world governs it,” Chris Philp, a minister of the Department for Digital, Culture, Media and Sport, said in a statement.</p> <h3 id="h-"> </h3> <h3 id="h-the-strategy"><strong>The Strategy</strong></h3> <p>The national AI strategy includes several programs, reports and initiatives to boost the country’s long term capabilities in and around ML technologies by prioritising and levelling up the development of AI applications in the UK. This is to strengthen its position as a global science superpower. This includes a new National AI Research and Innovation program to improve coordination and collaboration between the country’s researchers. </p> <p>The UK’s first <a href="https://www.gov.uk/government/publications/national-ai-strategy" data-wpel-link="external" target="_blank" rel="nofollow">AI strategy</a> plans to launch a new national programme and a positive approach to support R&D in AI and publish a white paper on the governance and regulation of AI to ensure public confidence in the technology. This will allow organisations in various regions and sectors to capitalise on the power of AI technologies.</p> <p>“The UK already punches above its weight internationally, and we are ranked third in the world behind the USA and China in the list of top countries for AI,” <a href="https://www.gov.uk/government/news/new-ten-year-plan-to-make-britain-a-global-ai-superpower" data-wpel-link="external" target="_blank" rel="nofollow">said</a> DCMS Minister Chris Philp. </p> <h3 id="h--1"> </h3> <h3 id="h-desired-ai"><strong>Desired AI</strong></h3> <p>The United Kingdom plans not only to be a top AI country but also to do that with ethical and regulated AI that the citizens desire. The New National AI, Research and Innovation Programme is set up to discover the latest developments and AI innovations. In addition, the country’s plans include a white paper on AI regulation to use modern technologies to improve people’s lives and solve global challenges such as climate change and public health.</p> <p>The government plans on encouraging and developing AI students by supporting postgraduate learning and retraining and ensuring that students from wide backgrounds can access specialist courses. </p> <p>The latest data shows £13.5 billion investment by global investors into more than 1,400 UK private technology firms between January and June in 2021. This is accompanied by more than £2.3 billion government investment into AI since 2014 to support AI working with clear rules, applying ethical principles, and a pro-innovation regulatory environment. The three pillars of AI growth in the UK are: </p> <ul><li>Benefiting all sectors of the economy</li><li>Governing with rules to encourage innovation, investment </li><li>Protecting the public and the country’s fundamental values.</li></ul> <p>“The UK is already a world leader in certain aspects of AI – and this strategy helps to define how to enhance those capabilities further to ensure that the UK can both develop and use AI for the benefit of citizens,” <a href="https://www.gov.uk/government/news/new-ten-year-plan-to-make-britain-a-global-ai-superpower" data-wpel-link="external" target="_blank" rel="nofollow">said</a> Government Chief Scientific Adviser Sir Patrick Vallance. </p> <p>The government also announced a review into the availability and capacity of computing power for researchers and organisations, along with a consultation on copyrights and patents for AI to assess the development. </p> <h3 id="h-ai-arm-s-race-china-and-the-us"><strong>AI Arm’s Race: China and the US</strong></h3> <h2 id="h-according-to-the-world-intellectual-property-organisation-the-us-has-filed-more-ai-patent-applications-than-any-other-country-in-the-past-two-decades-closely-following-china-had-41-000-over-the-same-period-the-uk-filed-less-than-2-000">According to the World Intellectual Property Organisation, the US has filed more AI patent applications than any other country in the past two decades. Closely following, <a href="https://analyticsindiamag.com/china-overtakes-the-us-to-bag-most-awards-in-ai-city-challenge/" data-wpel-link="internal">China</a> had 41,000 over the same period. The UK filed less than 2,000.</h2> <p>The US government’s Competition and Innovation Act involves the country investing billions of dollars in chips, AI, and supply chain reliability in building smart cities. In comparison, China has invested <a href="https://www.nytimes.com/2021/06/08/briefing/investment-senate-china-bill.html" data-wpel-link="external" target="_blank" rel="nofollow">twice as much</a> as the US in R&D (research and development). The Carnegie Endowment for International Peace declared China and the US as the two globally leading exporters of technology.</p> <p>Up until 2020, research based on patents and research publications ranked China as the top country for AI development. However, China’s recent developments and the government’s <a href="https://analyticsindiamag.com/big-tech-regulations-its-impact-on-ai/" data-wpel-link="internal">crackdown</a> on big tech companies may <a href="https://analyticsindiamag.com/chinas-self-curbs-are-endangering-chinas-tech-industry/" data-wpel-link="internal">harm China’s status</a> in the AI arms race. </p> <p>Giving the UK a possibly good standing in the AI arms race, BCS’ Report – “<a href="https://www.bcs.org/media/7562/national-ai-strategy.pdf" data-wpel-link="external" target="_blank" rel="nofollow">Priorities for the National AI Strategy</a>“, found the country capable of leading the world in creating AI that cares about humanity. </p> <p>“The public has become extremely distrustful of AI systems,” said Bill Mitchell, lead report author and director of policy at BCS. “You need to prove to them that you are competent, ethical and accountable.” The BCS report encouraged the UK to set the ‘<a href="https://analyticsindiamag.com/uks-ambition-to-create-gold-standards-for-ethical-ai/" data-wpel-link="internal">gold standard</a>‘ in AI professionalism through a pro-innovation, pro-ethical, pro-competition, and fair competition-based regulatory framework. </p> <p>Some of the largest AI companies like Graphcore, Darktrace, DeepMind, BenevolentAI and more are situated in the UK. The country is also brimming with leading universities, research centres and institutions that the government aims to tap into to become a leader in creating an empathetic AI. </p> <h3 id="h-with-a-pinch-of-salt"><strong>With a pinch of salt</strong></h3> <p>It’s also important to note that while the ideas of clear rules, ethical AI, and a pro-innovation regulatory environment for AI are still strategies and plans by the UK government. While the EU has a <a href="https://techcrunch.com/2021/04/21/europe-lays-out-plan-for-risk-based-ai-rules-to-boost-trust-and-uptake/" data-wpel-link="external" target="_blank" rel="nofollow">comprehensive proposal</a> for regulating high-risk applications of AI on the table, the UK still lacks a formal regulatory framework.</p> <p>The public response to the announcement has been varied until now; while some are sceptical about the proceedings given the government’s history of lack of research incentive, others are quite optimistic about the plan. All in all, for now, we just have ideas and strategies and are still to see how they map out in reality. </p> ";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:4;a:6:{s:4:"data";s:84:" ";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:3:{s:0:"";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:58:"Analytics For Small/Medium Scale Quick Service Restaurants";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:89:"https://analyticsindiamag.com/analytics-for-small-medium-scale-quick-service-restaurants/";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"pubDate";a:1:{i:0;a:5:{s:4:"data";s:31:"Wed, 29 Sep 2021 11:00:42 +0000";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:8:"category";a:10:{i:0;a:5:{s:4:"data";s:9:"Education";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:9:"analytics";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:33:"analytics for fast food reatilers";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:39:"analytics for quick service restaurants";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:25:"analytics for restaurants";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:48:"analytics for small and medium scale restaurants";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:6;a:5:{s:4:"data";s:33:"analytics problem for restaurants";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:7;a:5:{s:4:"data";s:19:"fast food retailers";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:8;a:5:{s:4:"data";s:25:"quick service restaurants";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:9;a:5:{s:4:"data";s:28:"small and medium restaurants";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"guid";a:1:{i:0;a:5:{s:4:"data";s:41:"https://analyticsindiamag.com/?p=10050007";s:7:"attribs";a:1:{s:0:"";a:1:{s:11:"isPermaLink";s:5:"false";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:166:"The findings of this study would help the fast-food retailers to shore up their strengths and remedy their shortcomings and have substantial growth in their business.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:22:"Praxis Business School";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:11364:"<img width="1600" height="900" src="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==" class="attachment-full size-full thb-lazyload lazyload wp-post-image" alt="Analytics For Small/Medium Scale Quick Service Restaurants" loading="lazy" style="float:right; margin:0 0 10px 10px;" sizes="(max-width: 1600px) 100vw, 1600px" data-src="https://analyticsindiamag.com/wp-content/uploads/2021/09/QSR-1600-x-900.jpeg" data-sizes="auto" data-srcset="https://analyticsindiamag.com/wp-content/uploads/2021/09/QSR-1600-x-900.jpeg 1600w, https://analyticsindiamag.com/wp-content/uploads/2021/09/QSR-1600-x-900-1024x576.jpeg 1024w, https://analyticsindiamag.com/wp-content/uploads/2021/09/QSR-1600-x-900-768x432.jpeg 768w, https://analyticsindiamag.com/wp-content/uploads/2021/09/QSR-1600-x-900-1536x864.jpeg 1536w, https://analyticsindiamag.com/wp-content/uploads/2021/09/QSR-1600-x-900-370x208.jpeg 370w, https://analyticsindiamag.com/wp-content/uploads/2021/09/QSR-1600-x-900-800x450.jpeg 800w, https://analyticsindiamag.com/wp-content/uploads/2021/09/QSR-1600-x-900-20x11.jpeg 20w, https://analyticsindiamag.com/wp-content/uploads/2021/09/QSR-1600-x-900-185x104.jpeg 185w, https://analyticsindiamag.com/wp-content/uploads/2021/09/QSR-1600-x-900-740x416.jpeg 740w, https://analyticsindiamag.com/wp-content/uploads/2021/09/QSR-1600-x-900-400x225.jpeg 400w, https://analyticsindiamag.com/wp-content/uploads/2021/09/QSR-1600-x-900-85x48.jpeg 85w" /> <p>Quick service restaurant (QSR) offers food items that require minimal preparation time and are delivered through quick services. Typically, a quick-service restaurant caters to fast food items over a limited menu as they can be cooked in lesser time with a minimum possible variation. As a result, QSRs have been one of the highest revenue-generating verticals in the food & beverages industry.</p> <p>We choose a few problems which can be solved by analytics and can help the QSR chains to create more revenue. </p> <h3 id="h-creating-better-food-combinations"><strong>Creating Better Food Combinations</strong></h3> <p>Increase restaurant revenue by providing better food combinations in restaurant menus, which can dynamically change according to seasons, demand and inventory. We can also improve the customer experience by providing personalized recommendations to specific customers based on their previous restaurant orders and providing loyalty points or discounts based on their purchase frequency and for new customers by using a recommendation system, and different kitchens can be recommended to increase customer experience. Restaurants have issues with giving saleable framing combos to their customers. This is because the combos are not optimized and are given in menu cards. However, people prefer individual dishes over combos as they feel combos are either designed for excess items or insufficient items. Alongside, the variety of combos are also generally less. Therefore, we will frame the best combos and increase their customer experience.</p> <h3 id="h-analytics-problem"><strong>Analytics Problem</strong></h3> <p>The business wants to identify their best combos for customers, resulting in a decrease in cost per plate and increasing revenue and customer satisfaction from a business perspective. Therefore, it is required to find the best association of items available in the QSR with complete sales data. Based on the past consumers’ data, personalized combos are created using affinity analysis. Therefore, one should find the best association of items from the individual customer’s past data. The customized combos will make the customer experience better and also would increase the revenue for the business. </p> <h3 id="h-data"><strong>Data</strong></h3> <p>We will use all past customer records. </p> <h3 id="h-modelling"><strong>Modelling</strong></h3> <p>Apriori is tabular-based and involves more computation time as it has to be tabulated across all possible rules of the association, and the space consumed by it is also too high. </p> <h3 id="h-algorithm"><strong>Algorithm</strong></h3> <p>FPGrowth will give a Tree the computation time is also less, space consumed is less, and also calculation metrics is pretty easy as the visualization is a tree-like structure. </p> <ol><li>We will use association rules to find the support of each dish in the QSR. We will have a threshold for support upon which we can find the best possible item that can go together for the dish crossing the threshold. We will use Lift measure on items for framing combos.</li><li>We will use their past data to calculate the confidence or Lift metric for existing customers and frame the best combos. In our scenario, both give better results on R & D. We suggest FPGrowthTree better over Apriori.</li></ol> <h3 id="h-evaluation-metrics"><strong>Evaluation metrics</strong></h3> <p>There are some evaluation metrics for framing good combos – Lift and Confidence. </p> <h3 id="h-inventory-management"><strong>Inventory Management </strong></h3> <p>Every product or raw material has its own shelf life, so the restaurant has to keep an eye on inventory to effectively use the fresh stock and restock it in time to meet the demands required for the food preparation. Therefore, we will build an inventory alert system that notifies the management about the stock refilling and maintenance. Quick service restaurants have been facing many challenges managing inventory and reducing waste. In fact, improper inventory management leads to wastage of raw materials and excess purchase or dump of inventory; thus, we will end up with financial challenges like cash flow of payments biweekly or weekly. </p> <h3 id="h-analytics-problem-1"><strong>Analytics Problem</strong></h3> <p>Our analytical approach to solving this inventory management is by estimating the consumption of each ingredient based on the previous six months or yearly daily data so that we will compare the consumption to inventory and generate purchase orders(Po’s). So since we are estimating the consumption of each ingredient, it falls under the regression problem. </p> <h3 id="h-modelling-1"><strong>Modelling</strong></h3> <p>We are using a linear regression model to predict the consumption of each ingredient on a daily/biweekly/weekly basis. We have models separately for Daily/Biweekly/Weekly Data Structure. We have past data as Daily POS Data for 6months or yearly; here, the independent variable is time (Day/BiWeekly/Weekly) and try to predict the consumption of each ingredient, which is our target variable. We are trying to generate the best-fitting regression line and try to predict the consumption of each ingredient. </p> <h3 id="h-waste-reduction"><strong>Waste Reduction</strong></h3> <p>Wastage reduction is also a daily challenge in quick-service restaurants if prepared food is not going to sell before getting spoiled, which leads to huge revenue loss. </p> <h3 id="h-analytics-problem-2"><strong>Analytics Problem</strong></h3> <p>Our analytical approach to solving this waste management is by estimating the consumption of each dish based on the previous six months or yearly data Modeling: We are identifying this as a regression problem where we are trying to estimate the quantity of food(dishes) to be cooked for a day. Here we are using a Linear Regression model to estimate the quantity of food against time (Daily) by the regression line.</p> <p>Evaluation Metrics for Regression Problem</p> <p>There are some metrics for regression to evaluate the model: Root Mean Square Error (RMSE)</p> <h3 id="h-summary"><strong>Summary</strong></h3> <p>The Current QSR food chains in India have not been using analytics as widely as the other western QSR giants. In this research, we have found few traits on how analytics keeps them on top of the Leaderboard. If they use analytics, they will be even more successful. Customers tend to show a variety-seeking behaviour in terms of outlets and variety of food. This has created a large potential. The industry is also growing rapidly, with some of the formats growing at more than 20% per annum. Applying few traits to medium and small scale QSRs can make them survive and expand their business.</p> <p class="has-text-align-center">______________________________________</p> <h3><strong>Group Details:</strong></h3> <figure class="wp-block-table is-style-regular"><table class="has-background" style="background-color:#ffffff"><tbody><tr><td><img loading="lazy" width="110" height="147" src="https://lh4.googleusercontent.com/roJ8ghwZN58ZjxBga85ZJnUN4e4ha4hq-KFXrLwlhpM24K_SU9rfaDuvTiL43GQQg7ZSX2Y37uKn8BZiYUaRVcAonXEHUxwwp0GBE_5lZlk1II_XJywZweHTbX8uoTIz4yRJTsI=s0"> <strong>N.Raghuram Reddy</strong><br><br>Pursuing Post Graduate Program in Data Science at Praxis Business School, Jan 2021 batch. Certified NASSCOM technology Explorer, enthusiastic learner of IoT, Machine Learning and Artificial Intelligence.</td></tr><tr><td></td></tr></tbody></table></figure> <figure class="wp-block-table"><table><tbody><tr><td><meta charset="utf-8"><img loading="lazy" width="109" height="142" src="https://lh5.googleusercontent.com/n-lK1-YMcFNKmYz_JSa5rJo65DlHnkN-tY-JlY0i-u2-MTkkaHDYUD2aephb0dCnSAwx9gGUHSUnqmTuiS-TZNJrdq0IabNUkpRKp2F4fQJNXePtWEq9X2sd7uXZbJlZxBAX2vM=s0"> <strong><meta charset="utf-8"><strong>Koushik Tulasi</strong> <br><br></strong>Koushik is a student of the Post Graduate Program in Data Science at Praxis Business School, Jan 2021 batch. He has completed his graduation in Computer Science and Engineering from RMK College of Engineering and Technology in the year 2019. He has 18 months of experience in Cognizant Technology and Solutions as a Programmer Analyst. </td></tr></tbody></table></figure> <figure class="wp-block-table"><table><tbody><tr><td><img loading="lazy" width="106" height="150" src="https://lh6.googleusercontent.com/34C-vmxcl0_l7AhsIjOaQ4dGog-ltXnj0F-37lWmJx0UpNdoE31whBA8yKtkW1MVS4F6wYG_xxzmWGVjMJA_ANmu_ufrOPb6JzckSLToU0T5hgV_2ZlQl3Pu-fa1HJ2ABRkyWEI=s0"> <strong>Mansoor Ali Shaik</strong><br><br>Mansoor is a student of the Post Graduate Program in Data Science at Praxis Business School, Jan 2021 Batch. He has completed his graduation in Electronics & Communication Engineering from Avanthi Institute of Engineering & Technology. He has also worked as a Data Science Intern at Widhya. </td></tr></tbody></table></figure> <figure class="wp-block-table"><table><tbody><tr><td><meta charset="utf-8"><img loading="lazy" width="110" height="143" src="https://lh4.googleusercontent.com/Hj1AzWW_S_duViQQG9j4wi9ymGgMaMgrgmOcm8DTGyxXhskhTICXvCaX0sCMIub7_cPRUsgc9Jst2zChgGRgn0wbzZspVFBdzuIyMo-zfSOb0N_DbV47WumXU-COvHxUlMwFpYI=s0"> <strong><meta charset="utf-8"><strong>Kaushik Muthu Krishnan S G</strong></strong><br><strong><br></strong>Kaushik is a student of the Post Graduate Program in Data Science at Praxis Business School, Jan 2021 batch. He has completed his graduation in Computer Science and Engineering from SRM Institute of Science and Technology in the year 2019. He has 20 months of experience in Cognizant Technology and Solutions as a Programmer Analyst. </td></tr></tbody></table></figure> ";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:5;a:6:{s:4:"data";s:90:" ";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:3:{s:0:"";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:80:"Salesforce CodeT5 vs Github Copilot: A Comparative Guide to Auto-code Generators";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:110:"https://analyticsindiamag.com/salesforce-codet5-vs-github-copilot-a-comparative-guide-to-auto-code-generators/";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"pubDate";a:1:{i:0;a:5:{s:4:"data";s:31:"Wed, 29 Sep 2021 10:30:00 +0000";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:8:"category";a:12:{i:0;a:5:{s:4:"data";s:17:"Developers Corner";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:9:"analytics";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:23:"artificial intelligence";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:10:"Automation";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:12:"data science";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:14:"data scientist";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:6;a:5:{s:4:"data";s:17:"Development tools";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:7;a:5:{s:4:"data";s:6:"GitHub";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:8;a:5:{s:4:"data";s:16:"machine learning";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:9;a:5:{s:4:"data";s:6:"python";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:10;a:5:{s:4:"data";s:10:"Salesforce";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:11;a:5:{s:4:"data";s:25:"software developers India";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"guid";a:1:{i:0;a:5:{s:4:"data";s:41:"https://analyticsindiamag.com/?p=10049974";s:7:"attribs";a:1:{s:0:"";a:1:{s:11:"isPermaLink";s:5:"false";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:217:"Automatic code generation can act as an amazing tool with potential use cases for enterprise settings. Capabilities that can evolve within programming languages and IDEs that work at compile time are being discovered.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:10:"Victor Dey";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:11999:"<img width="2240" height="1260" src="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==" class="attachment-full size-full thb-lazyload lazyload wp-post-image" alt="" loading="lazy" style="float:right; margin:0 0 10px 10px;" sizes="(max-width: 2240px) 100vw, 2240px" data-src="https://analyticsindiamag.com/wp-content/uploads/2021/09/Article-17.jpg" data-sizes="auto" data-srcset="https://analyticsindiamag.com/wp-content/uploads/2021/09/Article-17.jpg 2240w, https://analyticsindiamag.com/wp-content/uploads/2021/09/Article-17-1024x576.jpg 1024w, https://analyticsindiamag.com/wp-content/uploads/2021/09/Article-17-768x432.jpg 768w, https://analyticsindiamag.com/wp-content/uploads/2021/09/Article-17-1536x864.jpg 1536w, https://analyticsindiamag.com/wp-content/uploads/2021/09/Article-17-2048x1152.jpg 2048w, https://analyticsindiamag.com/wp-content/uploads/2021/09/Article-17-370x208.jpg 370w, https://analyticsindiamag.com/wp-content/uploads/2021/09/Article-17-800x450.jpg 800w, https://analyticsindiamag.com/wp-content/uploads/2021/09/Article-17-20x11.jpg 20w, https://analyticsindiamag.com/wp-content/uploads/2021/09/Article-17-185x104.jpg 185w, https://analyticsindiamag.com/wp-content/uploads/2021/09/Article-17-740x416.jpg 740w, https://analyticsindiamag.com/wp-content/uploads/2021/09/Article-17-400x225.jpg 400w, https://analyticsindiamag.com/wp-content/uploads/2021/09/Article-17-1600x900.jpg 1600w, https://analyticsindiamag.com/wp-content/uploads/2021/09/Article-17-85x48.jpg 85w" /> <p>Creating large codes for software programs can sometimes be a time consuming and tedious task. Developers today are looking for methods and tools that can aid coding and improve turnaround times and accuracy for software development productivity. Therefore, automatic code generation capabilities are being discovered that can evolve within programming languages and IDEs that work at compile time. Automatic code generation can act as an amazing tool with potential use cases for enterprise settings. This article will discuss two of the most recently developed tools for automatic code generation, the Salesforce CodeT5 and Github Copilot.</p> <h2 id="h-salesforce-code-t5"><strong>Salesforce Code T5</strong></h2> <p>The <a href="https://github.com/salesforce/CodeT5" data-wpel-link="external" target="_blank" rel="nofollow">CodeT5 </a>by Salesforce is an open-source <a href="https://analyticsindiamag.com/what-is-graph-analytics-its-top-tools/" data-wpel-link="internal">machine learning tool</a> that can understand and readily generate code in real-time. It is an identifier-aware unified pre-trained coder-encoder tool that enables a wide range of code intelligence applications. The tool aims to reduce time spent writing software as well as reduction of computational and operational costs. It consists of software code pre-training methods that boost a range of downstream applications in the <a href="https://analyticsindiamag.com/everything-as-a-service-xaas-the-all-in-one-cloud-model/" data-wpel-link="internal">software development lifecycle</a>. CodeT5 possesses an uninformed model for natural language processing tasks, which reframes text-to-text with input, and output data always being strings of texts. </p> <p>The existing code pre-training methods had two major limitations that CodeT5 addressed. First, they often rely on either an encoder-only model similar to BERT or a decoder-only model like GPT, which is suboptimal for generation and understanding tasks. Second, current methods can only adopt the conventional <a href="https://analyticsindiamag.com/guide-to-feed-forward-network-using-pytorch-with-mnist-dataset/" data-wpel-link="internal">NLP pre-training</a> techniques on source code by regarding it as a sequence of tokens like natural language, which largely ignores the rich structural information present in the programming language, information which is vital to fully comprehend code semantics.</p> <h2 id="h-codet5-s-architecture-and-working"><strong>CodeT5’s Architecture and Working</strong></h2> <p>Salesforce’s CodeT5 is built on a similar architectural scheme of Google’s T5 framework, but it incorporates better code specific knowledge, which endows the model with a better code understanding. It takes the code to be worked upon and its accompanying comments as a sequence to build and generate upon. </p> <p>Some of the pre-training tasks of CodeT5 include:</p> <ul><li>Masked Span Prediction: Randomly masks span with lengths, and the decoder recovers the original input. Captures syntactic information of the NL-PL input and learns robust cross-lingual representations.</li><li>Identifier Tagging: The encoder distinguishes whether each code is an identifier or not. </li><li>Masked Identifier Prediction: Employs the same mask placeholder for all occurrences of one unique identifier. Comprehends the code semantics based on the obfuscated code.</li><li>Bimodal Dual Generation: Jointly optimises conversions from code to its comments and vice versa. This encourages a better alignment between the NL and PL counterparts. </li></ul> <div class="wp-block-image"><figure class="aligncenter size-large"><img loading="lazy" width="1024" height="406" src="https://analyticsindiamag.com/wp-content/uploads/2021/09/image-179-1024x406.png" alt="" class="wp-image-10049975" srcset="https://analyticsindiamag.com/wp-content/uploads/2021/09/image-179-1024x406.png 1024w, https://analyticsindiamag.com/wp-content/uploads/2021/09/image-179-768x305.png 768w, https://analyticsindiamag.com/wp-content/uploads/2021/09/image-179-370x147.png 370w, https://analyticsindiamag.com/wp-content/uploads/2021/09/image-179-800x317.png 800w, https://analyticsindiamag.com/wp-content/uploads/2021/09/image-179-185x73.png 185w, https://analyticsindiamag.com/wp-content/uploads/2021/09/image-179-740x293.png 740w, https://analyticsindiamag.com/wp-content/uploads/2021/09/image-179-20x8.png 20w, https://analyticsindiamag.com/wp-content/uploads/2021/09/image-179-400x159.png 400w, https://analyticsindiamag.com/wp-content/uploads/2021/09/image-179-121x48.png 121w" sizes="(max-width: 1024px) 100vw, 1024px" /></figure></div> <p>Image Source: Salesforce Code T5</p> <h2 id="h-features-of-code-t5"><strong>Features of Code T5 </strong></h2> <p>Some features of CodeT5 include:</p> <ul><li>Text-to-code generation: Can generate code based on the natural language description.</li><li>Code autocompletion: Can complete the whole function of code, given the target function name.</li><li>Code summarisation: It can generate the summary of a function in natural language description.</li></ul> <h2 id="h-risks-with-codet5"><strong>Risks with CodeT5</strong></h2> <p>Although CodeT5 can be a potential tool for auto code generation, there are still some ethical risks involved that one should consider beforehand. CodeT5 team says that they are still working on improving the following risks:</p> <ul><li>Automation Bias: Sometimes, the system might produce functions that superficially appear correct but might not actually align with the developer’s intents. If developers adopt these incorrect code suggestions, it might harm the schema and cause a much longer debugging time with significant safety issues.</li></ul> <ul><li>Security Implications: Pre-trained models might encode some sensitive information from the training data. It is possible that the tool might be unable to completely remove certain sensitive information and might produce code that harmfully affects the software. </li></ul> <h2 id="h-github-copilot"><strong>Github Copilot </strong></h2> <p><a href="https://copilot.github.com/" data-wpel-link="external" target="_blank" rel="nofollow">Github Copilot</a> is a service tool created by GitHub and OpenAI and is described as an AI pair programmer. It is a plugin to Visual Studio Code and auto-generates code based on the current file’s contents and the current cursor location. Copilot can generate entire multiline functions and can even create documentation and tests based on the context of a file of code.</p> <p>It is powered by a deep neural network language model called Codex, trained on several public code repositories on Github. It can help fine-tune and get state-of-the-art results on a wide range of NLP problems. </p> <h2 id="h-how-does-it-work"><strong>How does it work?</strong></h2> <p>The Visual Studio Code sends comments and code typed by the developer to the Copilot service, which synthesises and suggests the implementation. Github states that the Copilot tool acts as a pen for generating code. The former claims that the Copilot understands more context than most of the currently available code assistants. It uses the provided context and synthesises a code to match. Copilot can work with a wide range of frameworks and languages such as <a href="https://analyticsindiamag.com/exploring-simfin-api-using-exploratory-data-analysis/" data-wpel-link="internal">Python</a>, Javascript, TypeScript, Ruby, and Go. Alternative suggestions can be cycled through, and suggestions can be either accepted or rejected with an option to also manually edit the suggested code. </p> <div class="wp-block-image"><figure class="aligncenter size-large"><img loading="lazy" width="1024" height="492" src="https://analyticsindiamag.com/wp-content/uploads/2021/09/image-180-1024x492.png" alt="" class="wp-image-10049976" srcset="https://analyticsindiamag.com/wp-content/uploads/2021/09/image-180-1024x492.png 1024w, https://analyticsindiamag.com/wp-content/uploads/2021/09/image-180-768x369.png 768w, https://analyticsindiamag.com/wp-content/uploads/2021/09/image-180-370x178.png 370w, https://analyticsindiamag.com/wp-content/uploads/2021/09/image-180-800x384.png 800w, https://analyticsindiamag.com/wp-content/uploads/2021/09/image-180-185x89.png 185w, https://analyticsindiamag.com/wp-content/uploads/2021/09/image-180-740x355.png 740w, https://analyticsindiamag.com/wp-content/uploads/2021/09/image-180-20x10.png 20w, https://analyticsindiamag.com/wp-content/uploads/2021/09/image-180-400x192.png 400w, https://analyticsindiamag.com/wp-content/uploads/2021/09/image-180-100x48.png 100w" sizes="(max-width: 1024px) 100vw, 1024px" /></figure></div> <p>Image Source: Github Copilot </p> <h2 id="h-features-of-copilot"><strong>Features of Copilot </strong></h2> <p>Some features of Github Copilot include:</p> <ul><li>Converting comments to code: Write a comment that describes the logic, and Copilot assembles the code.</li><li>Easy Autofill: Copilot can help quickly produce repetitive code patterns. When fed with a few examples, the Copilot learns and does the rest. </li><li>Aids in testing: Copilot automatically suggests tests that match the code implementation.</li></ul> <h2 id="h-risks-with-copilot"><strong>Risks with Copilot </strong></h2> <p>Github Copilot might come along with unknown issues at implementation, which can be a potential risk factor, some of which include:</p> <ul><li>Bugs at implementation: A few developers who got their hands on the Copilot complained that it generated a number of bugs at runtime when trained on a large size of Github projects.</li></ul> <ul><li>Unwanted outputs: Github Copilot may sometimes produce undesired outputs that might include biased, discriminatory, abusive or offensive outputs. </li></ul> <h2 id="h-summing-up"><strong>Summing Up</strong></h2> <p>Although auto code generators are tools that aim to automate tedious and time-consuming coding work for developers, they come with their own set of limitations and risk factors. These issues seem to be still at work and require strong attention. In the coming future, this technology will enable existing engineers to be more productive, reducing manual tasks and helping them focus on other interesting aspects of work. </p> ";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:6;a:6:{s:4:"data";s:105:" ";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:3:{s:0:"";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:64:"Addressing The Vanishing Gradient Problem: A Guide For Beginners";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:94:"https://analyticsindiamag.com/addressing-the-vanishing-gradient-problem-a-guide-for-beginners/";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"pubDate";a:1:{i:0;a:5:{s:4:"data";s:31:"Wed, 29 Sep 2021 09:30:00 +0000";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:8:"category";a:17:{i:0;a:5:{s:4:"data";s:17:"Developers Corner";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:9:"analytics";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:3:"ANN";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:23:"artificial intelligence";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:12:"data science";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:13:"deep learning";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:6;a:5:{s:4:"data";s:26:"Exploding Gradient Problem";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:7;a:5:{s:4:"data";s:17:"gradient boosting";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:8;a:5:{s:4:"data";s:16:"gradient descent";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:9;a:5:{s:4:"data";s:16:"gradient problem";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:10;a:5:{s:4:"data";s:29:"lstm recurrent neural network";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:11;a:5:{s:4:"data";s:16:"machine learning";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:12;a:5:{s:4:"data";s:15:"neural networks";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:13;a:5:{s:4:"data";s:6:"python";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:14;a:5:{s:4:"data";s:6:"resnet";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:15;a:5:{s:4:"data";s:27:"stochastic gradient descent";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:16;a:5:{s:4:"data";s:18:"vanishing gradient";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"guid";a:1:{i:0;a:5:{s:4:"data";s:41:"https://analyticsindiamag.com/?p=10049945";s:7:"attribs";a:1:{s:0:"";a:1:{s:11:"isPermaLink";s:5:"false";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:146:"when the elements of the gradient become exponentially small so that the update of the parameters with the gradient becomes almost insignificant ";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:12:"Yugesh Verma";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:15494:"<img width="1280" height="720" src="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==" class="attachment-full size-full thb-lazyload lazyload wp-post-image" alt="" loading="lazy" style="float:right; margin:0 0 10px 10px;" sizes="(max-width: 1280px) 100vw, 1280px" data-src="https://analyticsindiamag.com/wp-content/uploads/2021/09/Problem-solving.jpg" data-sizes="auto" data-srcset="https://analyticsindiamag.com/wp-content/uploads/2021/09/Problem-solving.jpg 1280w, https://analyticsindiamag.com/wp-content/uploads/2021/09/Problem-solving-1024x576.jpg 1024w, https://analyticsindiamag.com/wp-content/uploads/2021/09/Problem-solving-768x432.jpg 768w, https://analyticsindiamag.com/wp-content/uploads/2021/09/Problem-solving-370x208.jpg 370w, https://analyticsindiamag.com/wp-content/uploads/2021/09/Problem-solving-800x450.jpg 800w, https://analyticsindiamag.com/wp-content/uploads/2021/09/Problem-solving-20x11.jpg 20w, https://analyticsindiamag.com/wp-content/uploads/2021/09/Problem-solving-185x104.jpg 185w, https://analyticsindiamag.com/wp-content/uploads/2021/09/Problem-solving-740x416.jpg 740w, https://analyticsindiamag.com/wp-content/uploads/2021/09/Problem-solving-400x225.jpg 400w, https://analyticsindiamag.com/wp-content/uploads/2021/09/Problem-solving-85x48.jpg 85w" /> <p>Despite having many important applications, <a href="https://analyticsindiamag.com/6-types-of-artificial-neural-networks-currently-being-used-in-todays-technology/" data-wpel-link="internal">artificial neural networks</a> often face a number of problems. One such problem is the Vanishing Gradient Problem. When the neural networks are trained with gradient-based learning methods and backpropagation, they encounter the vanishing gradient problem. In this problem, at the time of training, the gradient starts getting smaller in size which prevents the neural networks from getting trained by not letting the network weights be changed. In this article, we will try to understand the vanishing gradient problem in detail along with the approach to resolve this problem. The major points to be covered in this article are listed below.</p> <p><strong>Table of Contents</strong></p> <ol><li>What is a Vanishing Gradient Problem?</li><li>How to identify the Vanishing Gradient Problem?</li><li>Recognizing The Vanishing Gradients</li><li>How to Resolve the Vanishing Gradient Problem?</li></ol> <p>Let us begin with understanding the vanishing gradient problem.</p> <p><strong>What is a Vanishing Gradient Problem?</strong></p> <p>As we know, using more layers in any <a href="https://analyticsindiamag.com/a-beginners-guide-to-neural-network-pruning/" data-wpel-link="internal">neural network</a> causes more activation function with them and as we increase the number or activation function increases the gradient of loss function leads to zero. Let’s take a simple example of any neural network where we are using any layer in a layered neural network with hyperbolic tangent function, and have gradients in the range between 0 to 1. This function will be multiplying n of these(0-1) small numbers to compute gradients of the preceding layers, meaning that the gradient decreases exponentially with n. So the function gives the output between the range of 0 to 1. This means that the output from the tanh activation function does not depend on the size of input data. The image represents a hyperbolic tangent activation function.</p> <figure class="wp-block-image size-large"><img loading="lazy" width="1024" height="1024" src="https://analyticsindiamag.com/wp-content/uploads/2021/09/image-175-1024x1024.png" alt="" class="wp-image-10049947" srcset="https://analyticsindiamag.com/wp-content/uploads/2021/09/image-175.png 1024w, https://analyticsindiamag.com/wp-content/uploads/2021/09/image-175-300x300.png 300w, https://analyticsindiamag.com/wp-content/uploads/2021/09/image-175-150x150.png 150w, https://analyticsindiamag.com/wp-content/uploads/2021/09/image-175-768x768.png 768w, https://analyticsindiamag.com/wp-content/uploads/2021/09/image-175-180x180.png 180w, https://analyticsindiamag.com/wp-content/uploads/2021/09/image-175-90x90.png 90w, https://analyticsindiamag.com/wp-content/uploads/2021/09/image-175-370x370.png 370w, https://analyticsindiamag.com/wp-content/uploads/2021/09/image-175-800x800.png 800w, https://analyticsindiamag.com/wp-content/uploads/2021/09/image-175-20x20.png 20w, https://analyticsindiamag.com/wp-content/uploads/2021/09/image-175-185x185.png 185w, https://analyticsindiamag.com/wp-content/uploads/2021/09/image-175-740x740.png 740w, https://analyticsindiamag.com/wp-content/uploads/2021/09/image-175-400x400.png 400w, https://analyticsindiamag.com/wp-content/uploads/2021/09/image-175-48x48.png 48w" sizes="(max-width: 1024px) 100vw, 1024px" /></figure> <p><a href="https://upload.wikimedia.org/wikipedia/commons/thumb/7/76/Sinh_cosh_tanh.svg/1024px-Sinh_cosh_tanh.svg.png" class="mfp-image" data-wpel-link="external" target="_blank" rel="nofollow">Image source</a></p> <p>So while using the function we can say that a large change in the input space will be very small in the output. The vanishing gradients problem is one example of the unstable behaviour of a multilayer neural network. Networks are unable to backpropagate the gradient information to the input layers of the model.</p> <p> In a multi-layer network, gradients for deeper layers are calculated as products of many gradients (of activation functions). When those gradients are small or zero, they will easily vanish. (On the other hand, when they’re bigger than 1, it will possibly explode.) So it becomes very hard to calculate and update.</p> <p>The VGP occurs when the elements of the gradient (the partial derivatives with respect to the parameters of the NN) become exponentially small so that the update of the parameters with the gradient becomes almost insignificant </p> <p><strong>Recognizing The Vanishing Gradients</strong></p> <ul><li>We can detect it by analysing the kernel weight distribution. There is a vanishing gradient if the weights are falling regularly near zero.</li></ul> <ul><li>This problem can be recognised when a neural network is very slow in training.</li></ul> <ul><li> Neural networks are not well trained with the data which we are using or showing unusual behaviour regarding results.</li></ul> <p><strong>How to Resolve the Vanishing Gradient Problem?</strong></p> <p>There are various methods that help in overcoming the vanishing gradient problems:</p> <ul><li>Multi-level hierarchy</li><li>The long short term memory</li><li>Residual neural network</li><li>ReLU </li></ul> <p>Let us understand these approaches one by one.</p> <p><strong>Multi-Level Hierarchy</strong></p> <p>It is one of the most basic and older solutions for a multilayer neural network model facing the vanishing gradient problem. It is simply a method that follows the procedure of training one level at a time and fine-tuning the level by backpropagation. So that every layer learns a compressed observation which goes ahead for the next level.</p> <p><strong>Long Short-Term Memory(LSTM)</strong></p> <p>So as of now, we have seen there are two major factors that affect the gradient size – weights and their derivatives of the activation function. A simple <a href="https://analyticsindiamag.com/a-complete-guide-to-lstm-architecture-and-its-use-in-text-classification/" data-wpel-link="internal">LSTM</a> helps the gradient size to remain constant. The activation function we use in the LSTM often works as an identity function which is a derivative of 1. So in gradient backpropagation, the size of the gradient does not vanish. </p> <p>Let’s understand the image below.</p> <figure class="wp-block-image size-full"><img loading="lazy" width="440" height="720" src="https://analyticsindiamag.com/wp-content/uploads/2021/09/image-176.png" alt="" class="wp-image-10049948" srcset="https://analyticsindiamag.com/wp-content/uploads/2021/09/image-176.png 440w, https://analyticsindiamag.com/wp-content/uploads/2021/09/image-176-370x605.png 370w, https://analyticsindiamag.com/wp-content/uploads/2021/09/image-176-185x303.png 185w, https://analyticsindiamag.com/wp-content/uploads/2021/09/image-176-20x33.png 20w, https://analyticsindiamag.com/wp-content/uploads/2021/09/image-176-400x655.png 400w, https://analyticsindiamag.com/wp-content/uploads/2021/09/image-176-29x48.png 29w" sizes="(max-width: 440px) 100vw, 440px" /></figure> <p><a href="https://qphs.fs.quoracdn.net/main-qimg-4359968f2dd46aaa1cf862d60724b453" data-wpel-link="external" target="_blank" rel="nofollow">Image source</a></p> <p>According to the above image, the effective weight of the gradient is equal to the <em>forget gate</em> activation. So, if the <em>forget gate</em> is on (activation close to 1.0), then the gradient does not vanish. That is why LSTM is one of the best options to deal with long-range dependencies. More powerful in the recurrent neural network.</p> <p><strong>Residual Neural Network</strong></p> <p>The residual neural networks were not introduced to solve the vanishing gradient problem but they have special connections which makes it different from the other neural networks that are a residual connection, residual connection in the neural network make the model learn well and the batch normalization feature makes sure that the gradients will not be vanishing. These batch normalization features are obtained by the skip connection.</p> <p>The skip or bypass connection is useful in any network to bypass the data from a few layers. Basically, it allows information to skip the layers. Using these connections, information can be transferred from layer n to layer n+t. Here to perform this thing we need to connect the activation function of layer n to the activation function of n+t. This causes the gradient to pass between the layers without any modification in size. </p> <p>As we have discussed, activation functions keep multiplying the finite small numbers to the weights. For example, ½*½=¼ and then ½*¼=⅛ and so on. Here in the example, we can say the number of layers increases the chances of VGP. The skip of layers will help the weight of information pass from layers without vanishing.</p> <p>Therefore, skip connections can mitigate the VGP, and so they can be used to train deeper NNs.</p> <figure class="wp-block-image size-large"><img loading="lazy" width="1024" height="579" src="https://analyticsindiamag.com/wp-content/uploads/2021/09/image-177-1024x579.png" alt="" class="wp-image-10049949" srcset="https://analyticsindiamag.com/wp-content/uploads/2021/09/image-177-1024x579.png 1024w, https://analyticsindiamag.com/wp-content/uploads/2021/09/image-177-768x434.png 768w, https://analyticsindiamag.com/wp-content/uploads/2021/09/image-177-370x209.png 370w, https://analyticsindiamag.com/wp-content/uploads/2021/09/image-177-800x452.png 800w, https://analyticsindiamag.com/wp-content/uploads/2021/09/image-177-20x11.png 20w, https://analyticsindiamag.com/wp-content/uploads/2021/09/image-177-185x105.png 185w, https://analyticsindiamag.com/wp-content/uploads/2021/09/image-177-740x418.png 740w, https://analyticsindiamag.com/wp-content/uploads/2021/09/image-177-400x226.png 400w, https://analyticsindiamag.com/wp-content/uploads/2021/09/image-177-85x48.png 85w" sizes="(max-width: 1024px) 100vw, 1024px" /></figure> <p>The above image represents the architecture of the ResNet. ResNet stands for residual network.</p> <p><strong>Rectified Linear Unit (ReLU) Activation Function</strong></p> <p>ReLU is an activation function more deeply it is a linear activation function. Which is like a sigmoid and tanh activation function but better than them. The basic function for ReLU conversion of input can be represented as </p> <p>f(x) = max(0,x)</p> <p>Where the ReLU function is its derivatives are constant. If in input the function gets a negative it returns 0 or if the input is greater than 0 it returns a similar value back. That is why we can say the output from the ReLU has ranged between 0 to infinity.\</p> <figure class="wp-block-image size-full"><img loading="lazy" width="700" height="318" src="https://analyticsindiamag.com/wp-content/uploads/2021/09/image-178.png" alt="" class="wp-image-10049950" srcset="https://analyticsindiamag.com/wp-content/uploads/2021/09/image-178.png 700w, https://analyticsindiamag.com/wp-content/uploads/2021/09/image-178-370x168.png 370w, https://analyticsindiamag.com/wp-content/uploads/2021/09/image-178-185x84.png 185w, https://analyticsindiamag.com/wp-content/uploads/2021/09/image-178-20x9.png 20w, https://analyticsindiamag.com/wp-content/uploads/2021/09/image-178-400x182.png 400w, https://analyticsindiamag.com/wp-content/uploads/2021/09/image-178-106x48.png 106w" sizes="(max-width: 700px) 100vw, 700px" /></figure> <p>The above image represents the output of the ReLU function. Now let’s see how it helps in vanishing gradient problems.</p> <p>When we talk about the backpropagation procedure, whichever gradient gets updated by multiplying with the multiple factors. As the information goes towards the start of the network the more factors are multiplied together to update the gradient. Many of these factors can be considered as the activation function. The activation function derivatives can be considered as a kind of tuning parameter, designed to get the accurate gradient descent.</p> <p>In the above we have seen that if we multiply a bunch of numbers with a value less than 1, they will start to tend to zero hence the gradient we get from the output layer will be negligible. In this scenario, if we multiply a number with a greater value than 1 they will tend towards infinity. So where the values are less than one we will get a slope that will be less than one and here comes the vanishing gradient problem.</p> <p>But if somehow we get the contribution of these derivatives of the activation function as 1 we can resolve the gradient vanishing problem of the model. Basically, in this situation, we can say that every gradient update is contributing to the model from input to the output or model. Here for this ReLU comes in the picture which has only two gradients 0 or 1.</p> <ul><li>Gradient one when the output of the function is > 0. </li><li>Gradient zero when the output of the function is < 0.</li></ul> <p>Hence these bunch of derivatives give either 0 or either 1 when multiplying together. The backpropagation equation will have only two options of either being 1 or being 0. The update is either nothing or takes contributions entirely from the other weights and biases.</p> <p><strong>Final Words</strong></p> <p>This article is aimed to discuss the issue that we can face while training the neural network in following the backpropagation procedure. We have seen how the problem occurs when the weights recurring are very less and tend towards zero. This kind of issue often occurs with the network with many numbers of layers, it barely occurs when the network is shallow. So if the long time taking problem is there but the network has low layers we should check for the computer configuration and if the 25% of your kernel weights are falling down to zero it should not be considered as the vanishing gradient problem. <br></p> ";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:7;a:6:{s:4:"data";s:81:" ";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:3:{s:0:"";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:46:"Top AI & ML Courses Offered By Tech Giants";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:71:"https://analyticsindiamag.com/top-ai-ml-courses-offered-by-tech-giants/";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"pubDate";a:1:{i:0;a:5:{s:4:"data";s:31:"Wed, 29 Sep 2021 08:55:36 +0000";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:8:"category";a:9:{i:0;a:5:{s:4:"data";s:9:"Education";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:10:"AI courses";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:6:"amazon";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:23:"artificial intelligence";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:8:"facebook";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:6:"google";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:6;a:5:{s:4:"data";s:5:"intel";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:7;a:5:{s:4:"data";s:16:"machine learning";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:8;a:5:{s:4:"data";s:9:"Microsoft";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"guid";a:1:{i:0;a:5:{s:4:"data";s:41:"https://analyticsindiamag.com/?p=10049993";s:7:"attribs";a:1:{s:0:"";a:1:{s:11:"isPermaLink";s:5:"false";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:77:"Here’s a list of the top AI and ML courses offered by tech giants in 2021. ";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:15:"Debolina Biswas";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:8819:"<img width="1200" height="630" src="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==" class="attachment-full size-full thb-lazyload lazyload wp-post-image" alt="Data scientist" loading="lazy" style="float:right; margin:0 0 10px 10px;" sizes="(max-width: 1200px) 100vw, 1200px" data-src="https://analyticsindiamag.com/wp-content/uploads/2020/04/free-digital-courses-female-entrepreneurs.jpg" data-sizes="auto" data-srcset="https://analyticsindiamag.com/wp-content/uploads/2020/04/free-digital-courses-female-entrepreneurs.jpg 1200w, https://analyticsindiamag.com/wp-content/uploads/2020/04/free-digital-courses-female-entrepreneurs-1024x538.jpg 1024w, https://analyticsindiamag.com/wp-content/uploads/2020/04/free-digital-courses-female-entrepreneurs-768x403.jpg 768w, https://analyticsindiamag.com/wp-content/uploads/2020/04/free-digital-courses-female-entrepreneurs-770x404.jpg 770w, https://analyticsindiamag.com/wp-content/uploads/2020/04/free-digital-courses-female-entrepreneurs-20x11.jpg 20w, https://analyticsindiamag.com/wp-content/uploads/2020/04/free-digital-courses-female-entrepreneurs-385x202.jpg 385w" /> <p>Artificial intelligence is no longer limited to science fiction books and movies. Whether we realise it or not, every day and for almost all usual activities, artificial intelligence has been influencing us in some way or the other. The world has made immense progress in the field, and the global AI market size is predicted to grow from $58.3 billion today to $309.6 billion by 2026, <a href="https://www.marketsandmarkets.com/Market-Reports/artificial-intelligence-market-74851580.html#:~:text=%5B430%20Pages%20Report%5D%20MarketsandMarkets%20forecasts,39.7%25%20during%20the%20forecast%20period." data-wpel-link="external" target="_blank" rel="nofollow">growing at a CAGR of 39.7 per cent</a> during the forecast period. </p> <p>A couple of years ago, there was a <a href="https://analyticsindiamag.com/why-talent-shortage-in-ai-may-end-soon/" data-wpel-link="internal">talent shortage in AI</a>. However, tech giants have now realised the need for more data scientists and analysts, and subsequently, have <a href="https://analyticsindiamag.com/amazon-india-launches-machine-learning-summer-school/" data-wpel-link="internal">launched courses</a> to cater to this need. At Analytics India Magazine, we take a look at the top AI and ML courses offered by tech giants. </p> <p><em>The list is in no particular order. </em></p> <h2 id="h-ml-and-ai-courses-google-cloud">ML and AI courses: Google Cloud</h2> <p>The five-course programme offered by <a href="https://analyticsindiamag.com/google-kubernetes-engine/" data-wpel-link="internal">Google Cloud</a> teaches about the implementation of the latest AI and ML technologies exploring training on AI Platforms Notebooks, Cloud Dataflow, BigQuery, BigQuery ML, TensorFlow, Cloud Vision, Kubeflow Pipelines and Natural Language API. </p> <p>The programme is divided into courses and skill badge modules. It covers the following topics: </p> <ul><li>Big Data and ML fundamentals</li><li>Performing foundational data, ML and AI tasks on Google Cloud </li><li>ML on Google cloud </li><li>Automating interactions with Contact Center AI </li><li>Advanced ML with TensorFlow on Google Cloud platform </li><li>Exploring ML models with Explainable AI </li><li>MLOps fundamentals </li><li>ML pipelines on Google Cloud </li></ul> <p>To know about the additional courses and practices, click <a href="https://cloud.google.com/training/machinelearning-ai#data-scientist-learning-path" data-wpel-link="external" target="_blank" rel="nofollow">here</a>. </p> <h2 id="h-deep-learning-institute-nvidia">Deep Learning Institute: NVIDIA </h2> <p>The <a href="https://analyticsindiamag.com/nvidias-deep-learning-institute-sets-aim-train-100000-developers-deep-learning-ai/" data-wpel-link="internal">NVIDIA Deep Learning Institute</a> offers resources including learning materials and live training programmes for teams, organisations, educators and students to advance their knowledge in artificial intelligence, data science, accelerated computing, graphics and simulation. </p> <p>The online training courses offered by NVIDIA can be categorised into four parts — Deep Learning, Accelerated Computing, Data Science, Graphics and Simulation. These courses are self-paced and provide NVIDIA Deep Learning Institute certificate on completion. Additionally, it teaches how to build deep learning, accelerating data science applications for healthcare, robotics and manufacturing industries. To know about the latest courses offered by NVIDIA, check this <a href="https://www.nvidia.com/en-in/training/" data-wpel-link="external" target="_blank" rel="nofollow">link</a>. </p> <h2 id="h-intel-ai-academy">Intel AI Academy </h2> <p><a href="https://analyticsindiamag.com/intel-ai-proposes-novel-rl-for-teaching-robots-teamwork/" data-wpel-link="internal">Intel</a>’s AI Academy offers free courses for software developers, data scientists and students. The courses cover topics, explore tools and optimise libraries. The courses are categorised as: </p> <ul><li>Theory: These courses cover the basics of AI and graduate-level topics in technical AI theory. According to Intel AI Academy, these courses explain the mathematics behind AI. The courses include ML, deep learning and introduction to AI, NLP, Time Series Analysis, Deep Learning for Robotics, and Anomaly Detection. </li><li>Hardware: These courses are focused on training to use Intel hardware from high-performance data centre processors and accelerators to fast edge inference devices. The courses include — AI on PC, AI on the Edge with Computer Vision, and Deep Learning Inference with Intel FPGAs. </li><li>Software: These training courses are aimed at teaching to use the best software frameworks to develop AI applications. Intel AI Academy offers a course on Applied Deep Learning with TensorFlow. </li></ul> <p>Learn more about the courses <a href="https://software.intel.com/content/www/us/en/develop/topics/ai/training/courses.html" data-wpel-link="external" target="_blank" rel="nofollow">here</a>. </p> <h2 id="h-the-ai-residency-program-facebook">The AI Residency Program: Facebook </h2> <p><a href="https://ai.facebook.com/join-us/residency-program/" data-wpel-link="external" target="_blank" rel="nofollow">Facebook’s Artificial Intelligence Residency Program</a> is a one-year research training program to provide hands-on experience with AI research while working in Facebook AI. During the programme, students get paired with an AI Researcher and Engineer to help solve a research problem and devise new deep learning algorithms and techniques. The research is then communicated to the communities, including CVPR, ICML, ICCV, ACL and EMNLP, and as open-source code. </p> <h2 id="h-deep-learning-on-aws-amazon">Deep Learning on AWS: Amazon </h2> <p>It is a one-day course offered by Amazon to introduce cloud-based deep learning (DL) solutions on AWS. The <a href="https://www.aws.training/SessionSearch?pageNumber=1&courseId=13854&languageId=1&trainingProviderId=1" data-wpel-link="external" target="_blank" rel="nofollow">Deep Learning on AWS</a> course details how deep learning is useful and explains its different concepts. Additionally, the course trains one on how to run models on the cloud using MNext and Amazon SageMaker frameworks. Students of the course will also get to deploy learning models using services such as Amazon EC2 Container Service and AWS Lambda. </p> <h2 id="h-ai-school-microsoft">AI School: Microsoft </h2> <p>The <a href="https://analyticsindiamag.com/microsoft-ai-for-earth-7-indian-grantees/" data-wpel-link="internal">Microsoft AI</a> School offers different learning paths for candidates at different skill levels to learn the latest technologies at their own pace. Microsoft AI School currently offers the following learning paths, besides a number of others: </p> <ul><li>AI on Azure </li><li>Using visual tools to create ML models </li><li>Computer vision in Microsoft Azure </li><li>Exploring NLP </li><li>AI Edge Engineer </li><li>Creating ML models </li><li>Preparing for AI Engineering </li></ul> <p>To learn new skills or discover learning paths that best suit you, click <a href="https://docs.microsoft.com/en-gb/learn/browse/?roles=ai-engineer%2Cdata-scientist&products=azure&resource_type=learning%20path&WT.mc_id=sitertzn_homepage_mslearn-banner-aischool" data-wpel-link="external" target="_blank" rel="nofollow">here</a>. </p> ";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:8;a:6:{s:4:"data";s:84:" ";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:3:{s:0:"";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:55:"The New Windows Store To Support Third-party App Stores";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:86:"https://analyticsindiamag.com/the-new-windows-store-to-support-third-party-app-stores/";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"pubDate";a:1:{i:0;a:5:{s:4:"data";s:31:"Wed, 29 Sep 2021 08:51:09 +0000";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:8:"category";a:10:{i:0;a:5:{s:4:"data";s:4:"News";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:9:"app store";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:5:"Apple";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:23:"artificial intelligence";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:10:"epic games";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:19:"microsoft app store";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:6;a:5:{s:4:"data";s:14:"Microsoft Edge";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:7;a:5:{s:4:"data";s:5:"Opera";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:8;a:5:{s:4:"data";s:7:"updates";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:9;a:5:{s:4:"data";s:10:"windows 11";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"guid";a:1:{i:0;a:5:{s:4:"data";s:41:"https://analyticsindiamag.com/?p=10049990";s:7:"attribs";a:1:{s:0:"";a:1:{s:11:"isPermaLink";s:5:"false";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:119:"Developers keep 100 per cent of the revenue from apps, with the sole exception that this policy doesn't apply to games.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:10:"Victor Dey";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:5144:"<img width="1920" height="1080" src="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==" class="attachment-full size-full thb-lazyload lazyload wp-post-image" alt="" loading="lazy" style="float:right; margin:0 0 10px 10px;" sizes="(max-width: 1920px) 100vw, 1920px" data-src="https://analyticsindiamag.com/wp-content/uploads/2021/09/W11-P1-Store-Hero-Image.jpg" data-sizes="auto" data-srcset="https://analyticsindiamag.com/wp-content/uploads/2021/09/W11-P1-Store-Hero-Image.jpg 1920w, https://analyticsindiamag.com/wp-content/uploads/2021/09/W11-P1-Store-Hero-Image-1024x576.jpg 1024w, https://analyticsindiamag.com/wp-content/uploads/2021/09/W11-P1-Store-Hero-Image-768x432.jpg 768w, https://analyticsindiamag.com/wp-content/uploads/2021/09/W11-P1-Store-Hero-Image-1536x864.jpg 1536w, https://analyticsindiamag.com/wp-content/uploads/2021/09/W11-P1-Store-Hero-Image-370x208.jpg 370w, https://analyticsindiamag.com/wp-content/uploads/2021/09/W11-P1-Store-Hero-Image-800x450.jpg 800w, https://analyticsindiamag.com/wp-content/uploads/2021/09/W11-P1-Store-Hero-Image-20x11.jpg 20w, https://analyticsindiamag.com/wp-content/uploads/2021/09/W11-P1-Store-Hero-Image-185x104.jpg 185w, https://analyticsindiamag.com/wp-content/uploads/2021/09/W11-P1-Store-Hero-Image-740x416.jpg 740w, https://analyticsindiamag.com/wp-content/uploads/2021/09/W11-P1-Store-Hero-Image-400x225.jpg 400w, https://analyticsindiamag.com/wp-content/uploads/2021/09/W11-P1-Store-Hero-Image-1600x900.jpg 1600w, https://analyticsindiamag.com/wp-content/uploads/2021/09/W11-P1-Store-Hero-Image-85x48.jpg 85w" /> <p>Microsoft Corp. has announced that it will allow other firms to integrate their app stores into its marketplace, giving more options to third-party developers. The Washington based company also said it would not take a cut from app developers’ revenue when the apps manage their own in-app payment systems. </p> <p>“Just like any other app, third-party storefront apps will have a product detail page, which can be found via search or by browsing, so that users can easily find and install it with the same confidence as any other app in the Microsoft Store on Windows. Today, we are sharing that Amazon and Epic Games will bring their storefront apps to the Microsoft Store over the next few months, and we look forward to welcoming other stores as well in the future,” said <a href="https://www.linkedin.com/in/gisardo/" data-wpel-link="external" target="_blank" rel="nofollow">Giorgio Sardo</a>, general manager of the <a href="https://www.microsoft.com/en-in/store/apps/windows" data-wpel-link="external" target="_blank" rel="nofollow">Microsoft Store</a>.</p> <p>Microsoft also announced some more <a href="https://analyticsindiamag.com/microsoft-to-skill-college-students-in-ai-cloud-and-cybersecurity-heres-how-to-apply/" data-wpel-link="internal">major changes</a>, such as the store being revamped for Windows 11 and Windows 10. Developers keep 100 per cent of the revenue from apps, with the sole exception that this policy doesn’t apply to games.</p> <p>The move comes after <a href="https://www.epicgames.com/store/en-US/" data-wpel-link="external" target="_blank" rel="nofollow">Epic Games</a> and <a href="https://www.apple.com/in/app-store/" data-wpel-link="external" target="_blank" rel="nofollow">Apple</a> were locked in a legal dispute since last year when the “Fortnite” creator tried to get around Apple’s 30 per cent fee for some of the in-app purchases on the App Store by launching its own in-app payment system.</p> <p>Several high-quality Progressive Web Apps (PWA) are also now available in the Microsoft Store on Windows, including <a href="https://www.microsoft.com/store/productId/9NS3RBQ5HV5F" data-wpel-link="external" target="_blank" rel="nofollow">Reddit</a>, <a href="https://www.microsoft.com/store/productId/9WZDNCRFHWM4" data-wpel-link="external" target="_blank" rel="nofollow">Wikipedia</a>, <a href="https://www.microsoft.com/en-us/p/tiktok/9nh2gph4jzs4?activetab=pivot:overviewtab" data-wpel-link="external" target="_blank" rel="nofollow">TikTok</a>, <a href="https://www.microsoft.com/store/productId/9PJKS5V4V997" data-wpel-link="external" target="_blank" rel="nofollow">Lyft</a>, <a href="https://www.microsoft.com/store/productId/9PLHCT3922NQ" data-wpel-link="external" target="_blank" rel="nofollow">Quizlet</a> and <a href="https://www.microsoft.com/store/productId/9NTDQP5CQG07" data-wpel-link="external" target="_blank" rel="nofollow">Tumblr</a>.</p> <p>The company has loosened policies on rival browsers’ paying dividends. Opera and Yandex Browser are coming to the Microsoft Store to provide an alternative to Edge. </p> <p>With Windows 11’s new business model, Microsoft also wants to make sure the business terms are fair and help promote innovation. Windows 11 is set to roll out to everyone on 5 October this year, but is available right now to everyone in the <a href="https://insider.windows.com/en-us/" data-wpel-link="external" target="_blank" rel="nofollow">Windows Insider program</a>.</p> ";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:9;a:6:{s:4:"data";s:78:" ";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:3:{s:0:"";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:56:"Qlik Launches A No-Code Solution For Optimised Workflows";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:87:"https://analyticsindiamag.com/qlik-launches-a-no-code-solution-for-optimised-workflows/";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"pubDate";a:1:{i:0;a:5:{s:4:"data";s:31:"Wed, 29 Sep 2021 08:15:55 +0000";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:8:"category";a:8:{i:0;a:5:{s:4:"data";s:4:"News";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:32:"automated data science solutions";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:2;a:5:{s:4:"data";s:27:"automated saas intelligence";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:3;a:5:{s:4:"data";s:18:"automated workflow";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:4;a:5:{s:4:"data";s:10:"Automation";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:5;a:5:{s:4:"data";s:21:"business intelligence";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:6;a:5:{s:4:"data";s:12:"optimization";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:7;a:5:{s:4:"data";s:16:"SaaS-based model";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"guid";a:1:{i:0;a:5:{s:4:"data";s:41:"https://analyticsindiamag.com/?p=10049985";s:7:"attribs";a:1:{s:0:"";a:1:{s:11:"isPermaLink";s:5:"false";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:157:"Qlik Application Automation cuts down the time needed for programming of repetitive tasks and gives more time back to deliver compelling real-time analytics.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:10:"Victor Dey";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:4882:"<img width="927" height="532" src="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==" class="attachment-full size-full thb-lazyload lazyload wp-post-image" alt="" loading="lazy" style="float:right; margin:0 0 10px 10px;" sizes="(max-width: 927px) 100vw, 927px" data-src="https://analyticsindiamag.com/wp-content/uploads/2021/09/Qlik.png" data-sizes="auto" data-srcset="https://analyticsindiamag.com/wp-content/uploads/2021/09/Qlik.png 927w, https://analyticsindiamag.com/wp-content/uploads/2021/09/Qlik-768x441.png 768w, https://analyticsindiamag.com/wp-content/uploads/2021/09/Qlik-370x212.png 370w, https://analyticsindiamag.com/wp-content/uploads/2021/09/Qlik-800x459.png 800w, https://analyticsindiamag.com/wp-content/uploads/2021/09/Qlik-20x11.png 20w, https://analyticsindiamag.com/wp-content/uploads/2021/09/Qlik-185x106.png 185w, https://analyticsindiamag.com/wp-content/uploads/2021/09/Qlik-740x425.png 740w, https://analyticsindiamag.com/wp-content/uploads/2021/09/Qlik-400x230.png 400w, https://analyticsindiamag.com/wp-content/uploads/2021/09/Qlik-84x48.png 84w" /> <p>Analytics cloud platform and SaaS company Qlik recently announced the debut of<a href="https://www.qlik.com/us/products/application-automation" data-wpel-link="external" target="_blank" rel="nofollow"> Qlik Application Automation</a>, a no-code solution designed to drive action from context-aware insights through streamlined and optimised workflows between SaaS applications and Qlik Cloud. The Qlik Application Automation helps invoke downstream processes, trigger alerts and enhance collaboration that shortens time to value. </p> <p>“With Qlik Application Automation’s no-code, drag and drop approach to automating tasks and data workflows between Qlik Cloud and hundreds of SaaS apps, teams can more easily create the scalable connections and data flows that compel action and shorten time to value from data throughout their entire organisation,” said <a href="https://www.linkedin.com/in/jamesafisheruk/" data-wpel-link="external" target="_blank" rel="nofollow">James Fisher</a>, Chief Product Officer of Qlik. </p> <p><a href="https://www.linkedin.com/in/cabrunton/" data-wpel-link="external" target="_blank" rel="nofollow">Chris Brunton</a>, Business Intelligence Manager at Dorel Home, said, “With Qlik Application Automation, we’ll be able to take the next step in activating our data to drive smart processes that can help us respond to changing conditions across the globe”.</p> <p>Qlik Application Automation cuts down the time needed for programming of repetitive back-office tasks and gives more time back to teams to deliver compelling <a href="https://analyticsindiamag.com/creating-a-market-trading-bot-using-open-ai-gym-anytrading/" data-wpel-link="internal">real-time analytics</a> with</p> <ul><li><strong>Smart connectivity and blocks:</strong> Quickly connect to market-leading SaaS applications such as Salesforce, Slack and MS Teams, represented as smart blocks, removing the need to technically understand an application’s low-level API.</li><li><strong>No-code user interface (UI)</strong>: Simple for business users, yet also offers advanced features like conditions, variables, loops, data mapping, error handlers and templates that IT specialists can use to accelerate flow development.</li><li><strong>Native Qlik Cloud integration:</strong> Helps easily build flows that leverage Qlik’s native APIs to automate your analytics DevOps processes.</li><li><strong>Dynamic automation triggers:</strong> Allows to invoke automation flows from various mechanisms to suit specific business needs. Trigger automations on-demand, use webhooks to activate flows from events, or schedule automations to execute at specific times.</li><li><strong>Central management and robust runtime</strong>: Manage and monitor the execution of your automations via Qlik Cloud to ensure flows are running and meeting your enterprise-wide SLAs.</li></ul> <p>Qlik Application Automation creates a seamless connection between the market’s leading analytics platform and the 3rd party SaaS applications that drive every business. With automated analytics environments, applications, and <a href="https://analyticsindiamag.com/all-you-need-to-know-about-data-fabric/" data-wpel-link="internal">workflows</a>, IT and data teams can dramatically increase data-driven actions with context-aware insights throughout the organisation. 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