{"title":"深度学习:架构、算法、应用","authors":"R. Memisevic","doi":"10.1109/HOTCHIPS.2015.7477319","DOIUrl":null,"url":null,"abstract":"This article consists of a collection of slides from the author's conference presentation. Some of the topics covered include: Machine learning 101: Neural nets, backprop, RNNs; Applications; Structured prediction; Unsupervised learning; \"Neural Programs\"; Architecture exploration; Towards hardware-friendlier DL; and Software.","PeriodicalId":6666,"journal":{"name":"2015 IEEE Hot Chips 27 Symposium (HCS)","volume":"18 1","pages":"1-127"},"PeriodicalIF":0.0000,"publicationDate":"2015-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Deep learning: Architectures, algorithms, applications\",\"authors\":\"R. Memisevic\",\"doi\":\"10.1109/HOTCHIPS.2015.7477319\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This article consists of a collection of slides from the author's conference presentation. Some of the topics covered include: Machine learning 101: Neural nets, backprop, RNNs; Applications; Structured prediction; Unsupervised learning; \\\"Neural Programs\\\"; Architecture exploration; Towards hardware-friendlier DL; and Software.\",\"PeriodicalId\":6666,\"journal\":{\"name\":\"2015 IEEE Hot Chips 27 Symposium (HCS)\",\"volume\":\"18 1\",\"pages\":\"1-127\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 IEEE Hot Chips 27 Symposium (HCS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/HOTCHIPS.2015.7477319\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE Hot Chips 27 Symposium (HCS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/HOTCHIPS.2015.7477319","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Deep learning: Architectures, algorithms, applications
This article consists of a collection of slides from the author's conference presentation. Some of the topics covered include: Machine learning 101: Neural nets, backprop, RNNs; Applications; Structured prediction; Unsupervised learning; "Neural Programs"; Architecture exploration; Towards hardware-friendlier DL; and Software.