Stanford University CS224d: Deep Learning for Natural Language Processing
Please see cs224n.stanford.edu for the current (Winter 2017) version of this class. Natural language processing (NLP) is one of the most important technologies of the information age. Understanding complex language utterances is also a crucial part of artificial intelligence. Applications of NLP are everywhere because people communicate most everything in language: web search, advertisement, email
TensorFlow: Google 最新ã®æ©Ÿæ¢°å¦ç¿’ライブラリをオープンソース公開
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Neural networks and deep learning
Neural Networks and Deep Learning What this book is about On the exercises and problems Using neural nets to recognize handwritten digits How the backpropagation algorithm works Improving the way neural networks learn A visual proof that neural nets can compute any function Why are deep neural networks hard to train? Deep learning Appendix: Is there a simple algorithm for intelligence? Acknowledge
分散深層強化å¦ç¿’ã§ãƒãƒœãƒƒãƒˆåˆ¶å¾¡ - Preferred Networks Research & Development
新入社員ã®æ¾å…ƒã§ã™ã€‚ã¯ã˜ã‚ã¾ã—ã¦ã€‚ “分散深層強化å¦ç¿’â€ã®æŠ€è¡“デモを作æˆã—ã€å…¬é–‹ã„ãŸã—ã¾ã—ãŸã€‚ãƒãƒœãƒƒãƒˆã‚«ãƒ¼ãŒ0ã‹ã‚‰å‹•ä½œã‚’å¦ç¿’ã—ã¦ã„ãã¾ã™ï¼ ã¾ãšã¯ã“ã®å‹•ç”»ã‚’御覧ãã ã•ã„。 以下ã§ã€å‹•ç”»ã®è¦‹ã©ã“ã‚ã¨ã€ä½¿ã£ã¦ã„る技術を紹介ã—ã¾ã™ã€‚ å‹•ç”»ã®è¦‹ã©ã“ã‚ Car 0(â—‹ã®ä»˜ã„ã¦ã„る車)ãŒå³æŠ˜ã‚«ãƒ¼ãƒ–ã®æ‰‹å‰ã§æ¸›é€Ÿã™ã‚‹æ§˜å(å³ç”»é¢ã®ç™½ã„ãƒãƒ¼ã®ã¨ã“ã‚ãŒã€ãƒ–レーã‚ã®ã¨ã“ã‚ã§èµ¤ããªã£ã¦ã„る。ニューラルãƒãƒƒãƒˆã¯ãƒ–レーã‚ãŒæœ€ã‚‚多ãå ±é…¬ãŒå¾—られるã¨æŽ¨æ¸¬ã—ã¦ã„ã‚‹ã“ã¨ãŒã‚ã‹ã‚‹)。速ã„速度ã»ã©å ±é…¬ã¯å¤§ãã„ãŒã€ã‚«ãƒ¼ãƒ–を曲ãŒã‚Šãã‚Œãšå£ã«ã¶ã¤ã‹ã£ã¦ã—ã¾ã†ã®ã§å¦ç¿’ãŒé€²ã‚€ã¨ã‚«ãƒ¼ãƒ–手å‰ã§ã®ã¿æ¸›é€Ÿã™ã‚‹ã‚ˆã†ã«ãªã‚‹ã€‚ ç›®ã®å‰ã«è»ŠãŒã„ã‚‹ã¨ãã¯ä¸€æ™‚åœæ¢ã—ã¦ã€ã„ãªããªã£ã¦ã‹ã‚‰åŠ 速ã™ã‚‹ã€‚ エãƒã‚ªãƒ”ã‚¢ã«ã¯æœ¬å½“ã«ã“ã®ã‚ˆã†ãªäº¤å·®ç‚¹ãŒã‚るらã—ã„。 ãŽã‚ŠãŽã‚Šã§ã™ã‚Œé•ã†2å°ã€‚å¦ç¿’途ä¸ã§ã¯ã™ã‚Œé•ã„ãã‚Œãšã«ã¶ã¤ã‹ã£ã¦å€’ã‚Œã¦ã—ã¾ã†ã“ã¨ã‚‚ã‚ã‚‹(æ—©é€ã‚Šã‚·ãƒ¼ãƒ³ä¸ã«äººãŒå†™ã£ã¦
ゼãƒã‹ã‚‰å§‹ã‚ã‚‹DeepLearning_ãã®1_ニューラルãƒãƒƒãƒˆã¨ã¯ - 分ã‹ã‚‰ã‚“ã“ã¨å¤šã™ãŽ
対象ã¨ã™ã‚‹äºº ディープラーニングã™ã”ã„ï¼ã€€â†èžã飽ã㟠ãƒãƒ¥ãƒ¼ãƒˆãƒªã‚¢ãƒ«ã‚るよï¼ã€€â†ãµã‚ã£ã¨ã—ã™ãŽã¦å…·ä½“çš„ãªã¨ã“ã‚ãŒåˆ†ã‹ã‚‰ã‚“ ã“ã†ã„ã†è«–æ–‡ã‚るよï¼ã€€â†èªã‚ã‚‹ã‚ã‘ãªã„ã ã‚ ãã†ã„ã†äººå‘ã‘。（ãŸã¶ã‚“å¦éƒ¨å››å¹´ç¨‹åº¦å‘ã‘） ニューラルãƒãƒƒãƒˆåˆå¦è€…ãŒã€æ›¸ããã‚‹ã¾ã§æ€ªã—ã„ã¨ã“ã‚満載ã§ã‚‚çªã£èµ°ã‚Šã¾ã™ã€‚ ニューラルãƒãƒƒãƒˆï¼ˆã“ã®è¨˜äº‹ï¼‰ →（AutoEncoder） →（DenoisingAutoEncoder） →ホップフィールドãƒãƒƒãƒˆãƒ¯ãƒ¼ã‚¯ →ボルツマンマシン →Restrictedボルツマンマシン →（Gaussian Binary - Restricted Boltzmann Machines） →（DeepBeliefNetwork） →（DeepNeuralNetworks） →畳ã¿è¾¼ã¿ãƒ‹ãƒ¥ãƒ¼ãƒ©ãƒ«ãƒãƒƒãƒˆï¼ˆå¾Œæ—¥ï¼‰ ã¾ã§ã‚„る。 太線以外ã¯èªã¿é£›ã°ã—ã¦OK 本文ä¸ã§ã¯æ€–ã„å¼ã¯ä½¿ã‚ãªã„。（Appendixã«æ›¸
Deep Learning技術ã®ä»Š
2. ⾃自⼰己紹介 lï¬ å¾—å±… Â èª ä¹Ÿ  (Seiya  Tokui) æ ªå¼ä¼šç¤¾Preferred  Infrastructure,  Jubatus  Pj.  リサーãƒãƒ£ãƒ¼ lï¬ å°‚â¾¨é–€ã¯æ©Ÿæ¢°å¦ç¿’（修⼠士ã€ç¾è·ï¼‰ lï¬ â€“ 系列列ラベリング→ãƒãƒƒã‚·ãƒ¥ãƒ»è¿‘å‚探索索→深層å¦ç¿’ lï¬ ä»Šã®èˆˆå‘³ã¯æ·±å±¤å¦ç¿’ã€è¡¨ç¾å¦ç¿’ã€åˆ†æ•£å¦ç¿’ã€æ˜ åƒè§£æž lï¬ @beam2d  (Twitter,  Github,  etc.) 2 /  47 3. 2011年年:  ⾳音声èªè˜ï§¼ã«ãŠã‘ã‚‹æˆåŠŸ lï¬ lï¬ 3 /  47 DNN-â€â€‘‒HMM  を使ã£ãŸâ¼¿æ‰‹æ³•ãŒã€â¾³éŸ³å£°èªè˜ï§¼ã®  word  error  rate  ã§å¾“æ¥ æ³•  (GMM)  より  10%  å‰å¾Œã‚‚改善 æºå¸¯ç«¯æœ«ã«ãŠã‘る⾳音声æ“作㫠 Deep  Learning  ãŒåˆ©ï§â½¤ç”¨ã•ã‚Œã‚‹ã‚ˆã†ã« F. Seide, G. Li and D. Yu.
Neural networks and deep learning
Neural Networks and Deep Learning What this book is about On the exercises and problems Using neural nets to recognize handwritten digits How the backpropagation algorithm works Improving the way neural networks learn A visual proof that neural nets can compute any function Why are deep neural networks hard to train? Deep learning Appendix: Is there a simple algorithm for intelligence? Acknowledge
Deep Learningã®æŠ€è¡“ã¨æœªæ¥
PFIセミナー2013å¹´6月6日分ã§ã™ã€‚Deep Learningã®æŠ€è¡“的基礎ã‹ã‚‰Bengioã®æ€ªã—ã’ãªå¦„想ã¾ã§ã€‚Read less
Deep Learning 101
Deep learning has become something of a buzzword in recent years with the explosion of 'big data', 'data science', and their derivatives mentioned in the media. Justifiably, deep learning approaches have recently blown other state-of-the-art machine learning methods out of the water for standardized problems such as the MNIST handwritten digits dataset. My goal is to give you a layman understandin
Hello Autoencoder — KiyuHub
Hello Autoencoder 最近，身内ã§Deep Learningã‚’é¡Œæã«å«ã‚“ã 勉強会を行ãªã£ã¦ã„る． メインã¯å°‚é–€ã§ã‚る自然言語処ç†ã¾ã‚ã‚Šã ãŒï¼Œ ã¨ã‚Šã‚ãˆãšå®Ÿè£…(ã¨ã„ã†ã‚ˆã‚Šnumpy)ã®å°Žå…¥ã«ãªã‚‹ä¸Šï¼Œçµæžœã‚’視覚化ã§ãã‚‹ã“ã¨ã‹ã‚‰ï¼Œ ç”»åƒãƒ‡ãƒ¼ã‚¿ã‚’利用ã—ãŸAutoencoderã®å®Ÿè£…ã«ã¤ã„ã¦å–り扱ã£ãŸï¼Ž 軽ã„説明ã¨å…±ã«ã‚³ãƒ¼ãƒ‰ã¨ï¼Œè‰²ã€…ãªçµæžœã‚’ Autoencoder Autoencoderã¨ã¯,Neural Networkã®ç‰¹æ®Šç³»ã§ï¼ŒåŸºæœ¬çš„ã«ã¯ 入力層ã¨å‡ºåŠ›å±¤ã®ãƒ¦ãƒ‹ãƒƒãƒˆæ•°ãŒåŒã˜ã§ã‚る． 教師信å·ã¨ã—ã¦å…¥åŠ›ãã®ã‚‚ã®ã‚’与ãˆã‚‹ï¼Ž ã¨ã„ã†ç‰¹å¾´ã‚’æŒã¤ï¼Ž 入力ã¨å‡ºåŠ›ãŒå…±ã«4次元ã§ï¼Œéš れ層ãŒ2次元ãªAutoencoderã®å›³ Autoencoderã¯ï¼Œå…¥åŠ›ã®æƒ…å ±ã‚’ã‚’ä¸€åº¦éš ã‚Œå±¤ã®ç©ºé–“ã«å†™åƒ(encode) ã—ãŸã‚ã¨ï¼Œ å…ƒã®ä¿¡å·ã‚’復元(decode)ã™ã‚‹ã‚ˆã†ãªãƒ‘ラメータをå¦ç¿’ã™ã‚‹ï¼Ž 図ã®ã‚ˆã†ã«ï¼Œã‚‚ã—ã‚‚éš ã‚Œ
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Research Blog: TensorFlow - Google’s latest machine learning system, open sourced for everyone
ディープラーニングï¼ç•³ã¿è¾¼ã¿ãƒ‹ãƒ¥ãƒ¼ãƒ©ãƒ«ãƒãƒƒãƒˆãƒ¯ãƒ¼ã‚¯ã¨Pythonã«ã‚ˆã‚‹ç‰¹å¾´æŠ½å‡º | POSTD
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