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Deep Learning ã‚’å®Ÿè£…ã™ã‚‹
2. ç›®æ¬¡ â€¢â€¯ Deep Learning ã¨ã¯" â€“â€¯ æ©Ÿæ¢°å¦ç¿’ã«ã¤ã„ã¦" â€“â€¯ å¾“æ¥ã® NN ã¨ã®ã¡ãŒã„" â€“â€¯ Deep Learning ã®ãƒ–ãƒ¬ã‚¤ã‚¯ã‚¹ãƒ«ãƒ¼" â€¢â€¯ dA (Denoising Autoencoders) ã‚’ã†ã”ã‹ã™" â€“â€¯ æ•°ç†ãƒ¢ãƒ‡ãƒ«ã®è§£èª¬" â€“â€¯ Python ã§å®Ÿè£…ã™ã‚‹å‰æº–å‚™" â€“â€¯ ã‚³ãƒ¼ãƒ‰ãƒ¬ãƒ“ãƒ¥ãƒ¼" â€“â€¯ å®Ÿè¡Œçµæžœ" â€¢â€¯ RBM (Restricted Boltzmann Machines) ã‚’ã†ã”ã‹ã™" â€“â€¯ æ•°ç†ãƒ¢ãƒ‡ãƒ«ã®è§£èª¬" â€“â€¯ å®Ÿè¡Œçµæžœ" â€¢â€¯ ã¾ã¨ã‚ 4. Deep Learning ã¨ã¯ â€¢â€¯ å…¥åŠ›ä¿¡å·ã‹ã‚‰ã‚ˆã‚ŠæŠ½è±¡çš„ãªæ¦‚å¿µã‚’å¦ã¶ãƒ»ç‰¹å¾´ã‚’æŠ½å‡ºã™ã‚‹ æ©Ÿæ¢°å¦ç¿’ã®æ‰‹æ³•ã®é›†åˆã§ã™ " â€œãƒ‹ãƒ¥ãƒ¼ãƒ©ãƒ«ãƒãƒƒãƒˆã¨ã©ã†é•ã†ã®ï¼Ÿâ€! â€¢â€¯ ãƒ‹ãƒ¥ãƒ¼ãƒ©ãƒ«ãƒãƒƒãƒˆã‚’å¤šå±¤ã«ã—ãŸã‚“ã§ã™ " â€œå¾“æ¥ã®ãƒ‹ãƒ¥ãƒ¼ãƒ©ãƒ«ãƒãƒƒãƒˆãƒ¯ãƒ¼ã‚¯ã¨ä½•ãŒé•ã†ã®ï¼Ÿâ€! â€¢â€¯ ã²ã¨ã¤ã²ã¨ã¤ã®ãƒ¬ã‚¤ãƒ¤ãƒ¼é–“ã§ãƒ‘ãƒ©
peketamin 2015/02/14
æ©Ÿæ¢°å¦ç¿’

deeplearning
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Deep learningã®è»½ã„ç´¹ä»‹
2. ç›®çš„ã¨æ³¨æ„ â€¢ æœ€è¿‘ Deep Learning ãŒç†±ã„ã§ã™ã â€¢ ãã“ã§ Deep Learning ã‚’åˆå¿ƒè€…ã®æ–¹ã§ã‚‚ã‚ ã‹ã‚Šã‚„ã™ã„ã‚ˆã†ã«ã¾ã¨ã‚ã¦ã¿ã¾ã—ãŸ â€¢ è‡ªåˆ†è‡ªèº«æ©Ÿæ¢°å¦ç¿’ã¯è«–æ–‡ã‚„æœ¬ã§ã®ç‹¬å¦ãªã® ã§ã€é–“é•ã£ã¦ã‚‹ç‚¹ãŒã‚ã‚Œã°æ•™ãˆã¦ãã ã•ã„ 4. â€¢ Deep Learning â€¢ Deep Neural Network(DNN) â€¢ Neural Network(NN) â€¢ Hidden Markov Model(HMM) â€¢ Gaussian Mixture Model - Hidden Markov Model(GMM-HMM) â€¢ Deep Neural Network - Hidden Markov Model(DNN-HMM) â€¢ æ©Ÿæ¢°å¦ç¿’ â€¢ ãƒ‘ã‚¿ãƒ¼ãƒ³èªè˜ â€¢ æ™‚ç³»åˆ—ãƒ‡ãƒ¼ã‚¿ã®èªè˜ â€¢ éŸ³å£°èªè˜ â€¢ ç”»åƒèªè˜ â€¢ ãƒ¢ãƒ¼ã‚·ãƒ§ãƒ³èªè˜ â€¢ éŸ³æ¥½ãƒªã‚ºãƒ èªè˜ â€¢ çµŒæ¸ˆãƒ¢ãƒ‡ãƒ« â€¢ æ„Ÿ
peketamin 2015/02/14
DeepLearning

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2015å¹´ç›®æ¨™ - äººå·¥çŸ¥èƒ½ã«é–¢ã™ã‚‹æ–å‰µéŒ²
2015å¹´ã«ã‚„ã‚ŠãŸã„ã“ã¨ã€‚ (1) Deep Learning Deep Learningã®å‹‰å¼·ã€‚è«–æ–‡ã‚’ã‚µãƒ¼ãƒ™ã‚¤ã—ã¦ç†è«–ã‚’æ·±ã‚ã‚‹ã¨ã¨ã‚‚ã«ã€ãƒ©ã‚¤ãƒ–ãƒ©ãƒªï¼ˆTheano, Pylearn2, Caffeï¼‰ã‚’ä½¿ã£ã¦ã„ã‚ã„ã‚ãªå®Ÿé¨“ã‚’ã‚„ã‚ŠãŸã„ã€‚æœ€è¿‘ã¯ã€å…ˆäººã«ã‚ˆã‚‹ãƒãƒ¥ãƒ¼ãƒˆãƒªã‚¢ãƒ«è¨˜äº‹ã‚‚å¤šããªã£ã¦ããŸã®ã§éžå¸¸ã«å‚è€ƒã«ãªã‚Šãã†ã€‚ã¾ãšã¯è¿½è©¦ã‹ã‚‰å§‹ã‚ãŸã„ã€‚ (2) Pythonã«ã‚ˆã‚‹ãƒ¢ãƒ³ãƒ†ã‚«ãƒ«ãƒæ³•å…¥é–€ åŽ»å¹´ã‚„ã‚Šå§‹ã‚ãŸãƒ—ãƒã‚¸ã‚§ã‚¯ãƒˆã‚’å®Œæˆã•ã›ãŸã„ã€‚ã‚µãƒ³ãƒ—ãƒªãƒ³ã‚°ã‚„MCMCã®ç†è§£ã‚‚æ·±ã‚ãŸã„ã€‚ (3) pandasã¨scikit-learnã®ç¿’å¾—ã¨æ´»ç”¨ Pythonã®ãƒ‡ãƒ¼ã‚¿åˆ†æžãƒ©ã‚¤ãƒ–ãƒ©ãƒªï¼ˆpandasï¼‰ã€æ©Ÿæ¢°å¦ç¿’ãƒ©ã‚¤ãƒ–ãƒ©ãƒªï¼ˆscikit-learnï¼‰ã‚’ç¿’å¾—ãƒ»æ´»ç”¨ã—ã¦ã„ããŸã„ã€‚ Pythonã«ã‚ˆã‚‹ãƒ‡ãƒ¼ã‚¿åˆ†æžå…¥é–€ â€•NumPyã€pandasã‚’ä½¿ã£ãŸãƒ‡ãƒ¼ã‚¿å‡¦ç† ä½œè€…: Wes McKinney,å°æž—å„€åŒ¡,éˆ´æœ¨å®å°š,ç€¬æˆ¸å±±é›…äºº,æ»
peketamin 2015/01/16
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Python

DeepLearning
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1. Word2vecã®ä¸¦åˆ—å®Ÿè¡Œæ™‚ã® å¦ç¿’é€Ÿåº¦ã®æ”¹å–„ å²¡å´Ž ç›´è¦³1,2 ä¹¾ å¥å¤ªéƒŽ1,3 1 æ±åŒ—å¤§å¦å¤§å¦é™¢æƒ…å ±ç§‘å¦ç ”ç©¶ç§‘ 2 JSTæˆ¦ç•¥çš„å‰µé€ ç ”ç©¶æŽ¨é€²äº‹æ¥ã€Œã•ããŒã‘ã€ 3 JSTæˆ¦ç•¥çš„å‰µé€ ç ”ç©¶æŽ¨é€²äº‹æ¥ã€ŒCRESTã€ 2014-07-04 Word2vecã®ä¸¦åˆ—å®Ÿè¡Œæ™‚ã®å¦ç¿’é€Ÿåº¦ã®æ”¹å–„ 1 3. ACL2014ã§ã‚‚word2vecç¥ã‚Š â€¢ J. Andreas and D. Klein: How much do word embeddings encode about syntax? â€¢ M. Bansal, K. Gimpel, and K. Livescu: Tailoring Continuous Word Representations for Dependency Parsing â€¢ M. Baroni, G Dinu, and G Kruszewski: Donâ€™t count,
peketamin 2014/07/04
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nlp

deeplearning

word2vec
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peketamin 2014/02/26
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DeepLearning

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peketamin 2013/05/09
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Deep Learning

DeepLearning

machinelearning

machine learning
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