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ç¬¬57å›ž ãƒ‡ãƒ¼ã‚¿ãƒžã‚¤ãƒ‹ãƒ³ã‚°+WEB ï¼ æ±äº¬( #TokyoWebmining 57th )ãƒ¼ ãƒ“ã‚¸ãƒã‚¹æ´»ç”¨ ç¥ã‚Šãƒ¼ã‚’é–‹å‚¬ã—ã¾ã—ãŸ - hamadakoichi blog
2016/10/29 "ç¬¬57å›ž ãƒ‡ãƒ¼ã‚¿ãƒžã‚¤ãƒ‹ãƒ³ã‚°+WEB ï¼ æ±äº¬( #TokyoWebmining 57th ) ãƒ¼ ãƒ“ã‚¸ãƒã‚¹æ´»ç”¨ ç¥ã‚Šãƒ¼" ã‚’é–‹å‚¬ã—ã¾ã—ãŸã€‚ ç¬¬57å›ž ãƒ‡ãƒ¼ã‚¿ãƒžã‚¤ãƒ‹ãƒ³ã‚°+WEB ï¼ æ±äº¬( #TokyoWebmining 57th ) ãƒ¼ ãƒ“ã‚¸ãƒã‚¹æ´»ç”¨ ç¥ã‚Š ãƒ¼Eventbrite Google ã‚°ãƒ«ãƒ¼ãƒ— ä¼šå ´æä¾›ã—ã¦ä¸‹ã•ã£ãŸ FreakOut ã•ã‚“ã€ã©ã†ã‚‚ã‚ã‚ŠãŒã¨ã†ã”ã–ã„ã¾ã—ãŸã€‚ç´ æ•µãªãƒˆãƒ¼ã‚¯ã‚’æä¾›ã—ã¦ãã‚ŒãŸè¬›å¸«ãƒ¡ãƒ³ãƒãƒ¼ã«æ„Ÿè¬ã—ã¾ã™ã€‚å¤šãã®æ–¹ã€…ã®å‚åŠ ã‚’å¬‰ã—ãæ€ã£ã¦ã„ã¾ã™ã€‚ å‚åŠ è€…IDãƒ»ãƒãƒƒã‚¯ã‚°ãƒ©ã‚¦ãƒ³ãƒ‰ä¸€è¦§ï¼š å‚åŠ è€…ã‚»ã‚ã‚³ã‚³ï¼šç¬¬57å›ž ãƒ‡ãƒ¼ã‚¿ãƒžã‚¤ãƒ‹ãƒ³ã‚°+WEB ï¼ æ±äº¬ ã‚»ã‚ã‚³ã‚³ (ä½œæˆã—ã¦ãã‚ŒãŸ [Twitter:@komiya_atsushi] ã•ã‚“ã«æ„Ÿè¬ï¼‰ ä»¥ä¸‹ã€å…¨è¬›å¸«è³‡æ–™ã€é–¢é€£è³‡æ–™ã€ãƒ„ã‚¤ãƒ¼ãƒˆã¾ã¨ã‚ã§ã™ã€‚ AGENDAï¼š â– Opening Talkï¼š O1.ã€Œãƒ‡ãƒ¼ã‚¿ãƒžã‚¤
chezou 2016/10/29
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1999/07/07 æ›´æ–° çŸ³äº• å‹ ã¯ã˜ã‚ã« ã“ã“ã§ã¯ï¼Œã‚ªãƒ–ã‚¸ã‚§ã‚¯ãƒˆæŒ‡å‘ã«å‡ºã¦ãã‚‹æ³•å‰‡ãƒ»åŽŸå‰‡ã‚’ã¾ã¨ã‚ã¾ã—ãŸï¼Žãƒ‘ã‚¿ãƒ¼ãƒ³ã«æ¯”ã¹ã¦ã»ã¨ã‚“ã©çŸ¥ã‚‰ã‚Œã¦ã„ãªã„ã®ãŒç¾çŠ¶ã§ã™ãŒï¼Œå„ªã‚ŒãŸã‚ªãƒ–ã‚¸ã‚§ã‚¯ãƒˆæŒ‡å‘é–‹ç™ºè€…ã‚’ç›®æŒ‡ã™ãªã‚‰ãƒ‡ã‚¶ã‚¤ãƒ³ãƒ‘ã‚¿ãƒ¼ãƒ³ã‚ˆã‚Šã¾ãšã“ã£ã¡ã‚’ç†è§£ã—è¦šãˆã¦ã—ã¾ã„ã¾ã—ã‚‡ã†ï¼Ž ã“ã‚Œã‚‰ã®æ³•å‰‡ã¯ï¼Œçµ¶å¯¾å®ˆã‚‰ãªã‘ã‚Œã°ãªã‚‰ãªã„ã¨ã„ã†ã‚‚ã®ã§ã¯ã‚ã‚Šã¾ã›ã‚“ï¼Žé–‹ç™ºä¸ã«æ³•å‰‡ãŒå®ˆã‚‰ã‚Œã¦ã„ã‚‹ã‹æ„è˜ã™ã‚‹ã“ã¨ãŒé‡è¦ã§ã™ï¼Žã¤ã¾ã‚Š ä»Šè¡Œã£ã¦ã„ã‚‹è¨è¨ˆã¯ãã®æ³•å‰‡ãŒå®ˆã‚‰ã‚Œã¦ã„ã‚‹ã ã‚ã†ã‹ ãã®æ³•å‰‡ã‚’ç ´ã£ã¦ã„ã‚‹å ´åˆï¼Œç ´ã‚‹ã¹ãæ£å½“ãªç†ç”±ãŒã‚ã‚‹ã ã‚ã†ã‹ ã¨çµ¶ãˆãšè€ƒãˆã‚‹ã‚ˆã†ã«ã—ã¾ã—ã‚‡ã†ï¼Žãã†ã™ã‚‹ã¨ãã‚Œã¯è‡ªç„¶ã«å„ªã‚ŒãŸã‚ªãƒ–ã‚¸ã‚§ã‚¯ãƒˆæŒ‡å‘è¨è¨ˆã«ãªã‚‹ã®ã§ã™ï¼Žã¤ã¾ã‚Šã“ã‚Œã‚‰ã®æ³•å‰‡ã¯ï¼Œå„ªã‚ŒãŸã‚ªãƒ–ã‚¸ã‚§ã‚¯ãƒˆæŒ‡å‘é–‹ç™ºã®ãŸã‚ã®æŒ‡é‡ãªã®ã§ã™ï¼Ž Robert C. Martin ã® Principles of OOD Robert C. Martinã¯ï¼Œã‚ªãƒ–ã‚¸ã‚§ã‚¯ãƒˆæŒ‡å‘è¨è¨ˆã®
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- Open-Closed Principle ã¨ãƒ‡ã‚¶ã‚¤ãƒ³ãƒ‘ã‚¿ãƒ¼ãƒ³
1999/09/03 æ›´æ–° çŸ³äº• å‹ ã•ã¦ï¼Œã“ã®ã‚»ã‚¯ã‚·ãƒ§ãƒ³ã§ã¯ãƒ‡ã‚¶ã‚¤ãƒ³ãƒ‘ã‚¿ãƒ¼ãƒ³ã‚’çµ±ä¸€çš„ã«ç†è§£ã™ã‚‹ãŸã‚ã«ï¼Œã€Œ Open-Closed Principle (OCP) ã€ ã¨ã„ã†è¨è¨ˆãƒ«ãƒ¼ãƒ«ã«åŸºã¥ã„ã¦ãƒ‘ã‚¿ãƒ¼ãƒ³ã‚’çœºã‚ã¦ã¿ã‚‹ã“ã¨ã«ã—ã¾ã™ï¼Žã¾ãš OCP ã®æ„å‘³ã¨è§£èª¬ã‚’è¡Œã„ï¼Œãã®å¾Œãƒ‡ã‚¶ã‚¤ãƒ³ãƒ‘ã‚¿ãƒ¼ãƒ³ã‚’ OCP ã®è¦³ç‚¹ã‹ã‚‰è¦‹ã¦ã¿ã¾ã™ï¼Žå®Ÿã¯ï¼Œãƒ‡ã‚¶ã‚¤ãƒ³ãƒ‘ã‚¿ãƒ¼ãƒ³ã®ã†ã¡ã®å¤šãã¯ OCP ã‚’æº€ãŸã™ãŸã‚ã«ç”¨æ„ã•ã‚ŒãŸã‚‚ã®ã¨è€ƒãˆã‚‹ã“ã¨ãŒã§ãã‚‹ã®ã§ã™ï¼Žã“ã®ã‚»ã‚¯ã‚·ãƒ§ãƒ³ã§ã¯ï¼Œ OCP ã‚’ç†è§£ã—ï¼Œæ•°ã‚ã‚‹ãƒ‡ã‚¶ã‚¤ãƒ³ãƒ‘ã‚¿ãƒ¼ãƒ³ã®ä¸ã‹ã‚‰ã©ã†ã„ã†å ´åˆã«ã©ã®ãƒ‘ã‚¿ãƒ¼ãƒ³ã‚’ä½¿ã†ã®ãŒä¸€ç•ªåŠ¹æžœçš„ãªã®ã‹ã‚’è€ƒãˆã¾ã™ï¼Ž GoF ã®ãƒ‡ã‚¶ã‚¤ãƒ³ãƒ‘ã‚¿ãƒ¼ãƒ³ã¯ï¼Œå…¨éƒ¨ã§ 23 å€‹ã‚‚ã®ãƒ‘ã‚¿ãƒ¼ãƒ³ãŒã‚ã‚Šã¾ã™ï¼Žã“ã®ãƒ‡ã‚¶ã‚¤ãƒ³ãƒ‘ã‚¿ãƒ¼ãƒ³ã¯ï¼Œå¤šãã®å±€é¢ã§ç¹°ã‚Šè¿”ã—ç¾ã‚Œã‚‹è¨è¨ˆã‚’æŠ½å‡ºã—ãŸã‚‚ã®ã§ã™ã‹ã‚‰ï¼Œã‚ªãƒ–ã‚¸ã‚§ã‚¯ãƒˆæŒ‡å‘ã®ã‚¨ãƒƒã‚»ãƒ³ã‚¹ã‚’é›†ã‚ãŸã‚‚ã®ã ã¨è¨€ãˆã‚‹ã§ã—ã‚‡ã†ï¼Žã‚ªãƒ–ã‚¸ã‚§ã‚¯ãƒˆæŒ‡å‘ã«ã¯ï¼Œã‚«ãƒ—ã‚»ãƒ«åŒ–ï¼Œç¶™
chezou 2015/12/08
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Code Smells
18 May 2006 Code Smells I'm often asked why the book Refactoring isn't included in my recommended developer reading list. Although I own the book, and I've read it twice, I felt it was too prescriptive â€“ if you see (x), then you must do (y). Any programmer worth his or her salt should already be refactoring aggressively. It's so essential to the craft that if you have to read a book to understand
chezou 2015/12/07
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Autogradã¨ã„ã†é‡ŽéƒŽãŒä¹—ã‚Šè¾¼ã‚“ã§ããŸã®ã§ã‚¬ã‚¯ãƒ–ãƒ«ãªä»¶ - Qiita
Autogradã¨ã„ã†é‡ŽéƒŽãŒä¹—ã‚Šè¾¼ã‚“ã§ãã¾ã—ãŸã€‚ã¯ã„ã€ãã‚Šã‚ƒã‚‚ã†ã„ããªã‚Šã€‚è¤‡é›‘ãªç¢ºçŽ‡ãƒ¢ãƒ‡ãƒ«ã‚„æå¤±é–¢æ•°ã ã¨ã—ã¦ã‚‚ã€ãƒ‘ãƒ©ãƒ¡ãƒ¼ã‚¿ã«é–¢ã™ã‚‹å‹¾é…ã‚’ã“ã‚Œã§ã‚‚ã‹ã¨ã„ã†ãã‚‰ã„ç°¡å˜ã«è¨ˆç®—ã§ãã¡ã‚ƒã†ã®ã§ã€æ©Ÿæ¢°å¦ç¿’ã®ä¸–ç•Œã«å¤§ããªå½±éŸ¿ã‚’ä¸Žãˆãã†ã§ã™ã€‚ç¾æ™‚ç‚¹ã§ã¯ã€Pythonã¨Torchã§ã®å®Ÿè£…ãŒå…¬é–‹ã•ã‚Œã¦ã„ã‚‹ã‚ˆã†ã§ã™ãŒã€ã“ã‚Œã‹ã‚‰Juliaãªã©ä»–ã®è¨€èªžã§ã‚‚å®Ÿè£…ã•ã‚Œã¦ã„ããã†ã§ã™ãã€‚ ï¼ˆè£œè¶³ï¼šã“ã®è¨˜äº‹ã‚’æ›¸ã„ãŸã™ãå¾Œã«GoogleãŒTensorFlowãªã‚‹ã‚‚ã®ã‚’å‡ºã—ã¦ãã¾ã—ã¦ã€ãã¡ã‚‰ã§ã‚‚è‡ªå‹•å¾®åˆ†ãŒã—ã£ã‹ã‚Šå®Ÿè£…ã•ã‚Œã¦ã‚‹ã¿ãŸã„ã§ã™ã€œã€‚æ©Ÿæ¢°å¦ç¿’é–¢é€£ã®ãƒ•ãƒ¬ãƒ¼ãƒ ãƒ¯ãƒ¼ã‚¯ã¯ç§»ã‚Šå¤‰ã‚ã‚ŠãŒæ¿€ã—ã„ã§ã™ããƒ¼ ^^; ï¼‰ ã¡ãªã¿ã«å§‹ã¾ã‚Šã¯ã“ã‚“ãªæ„Ÿã˜ã§ã—ãŸã€‚ ã‚†ã‚‹ã„ã§ã™ãã€‚ ã¨ã‚Šã‚ãˆãšãƒãƒ¥ãƒ¼ãƒˆãƒªã‚¢ãƒ«ã‚„ã‚ŠãªãŒã‚‰ã€Pythonç‰ˆãƒãƒ¥ãƒ¼ãƒˆãƒªã‚¢ãƒ«ã®å‰åŠéƒ¨åˆ†ã«ãƒ†ã‚ãƒˆãƒ¼ãªæ—¥æœ¬èªžè¨³ã‚’ã¤ã‘ãŸã®ã§ã€ã“ã“ã§ã‚·ã‚§ã‚¢ã—ã¦ãŠãã¾ã™ã€‚è‹±èªžãŒèªã‚ã‚‹æ–¹ã¯ã€åƒ•ã®ãƒ˜ãƒ³ãƒ†ã‚³ãªæ—¥æœ¬èªž
chezou 2015/11/08
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Ad Click Prediction: a View from the Trenches
Philosophy We strive to create an environment conducive to many different types of research across many different time scales and levels of risk. Learn more about our Philosophy Learn more
chezou 2015/02/13
"8. AUTOMATED FEATURE MANAGEMENT"

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machine learning
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Significant terms aggregation | Elasticsearch Guide [8.16] | Elastic
An aggregation that returns interesting or unusual occurrences of terms in a set. Example use cases: Suggesting "H5N1" when users search for "bird flu" in text Identifying the merchant that is the "common point of compromise" from the transaction history of credit card owners reporting loss Suggesting keywords relating to stock symbol $ATI for an automated news classifier Spotting the fraudulent d
chezou 2015/02/06
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NIPS2014èªã¿ä¼šã‚’é–‹å‚¬ã—ã¾ã—ãŸ - Preferred Networks Research & Development
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chezou 2015/01/28
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Machine Learning: The High Interest Credit Card of Technical Debt
Philosophy We strive to create an environment conducive to many different types of research across many different time scales and levels of risk. Learn more about our Philosophy Learn more
chezou 2015/01/25
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machine learning
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Y Combinator å‰µæ¥è€… Paul Graham ã‹ã‚‰ã®ã‚¹ã‚¿ãƒ¼ãƒˆã‚¢ãƒƒãƒ—ã¸ã®ã‚¢ãƒ‰ãƒã‚¤ã‚¹ï¼ˆã‚¹ã‚¿ãƒ¼ãƒˆã‚¢ãƒƒãƒ—ãŒè¿·ã£ãŸæ™‚ã«èªã‚€ Paul Graham ã‹ã‚‰ã®ã‚¢ãƒ‰ãƒã‚¤ã‚¹ï¼‰
1. ä¸–ç•Œæœ€é«˜ã®ã‚¹ã‚¿ãƒ¼ãƒˆã‚¢ãƒƒãƒ— ãƒ¡ãƒ³ã‚¿ãƒ¼ Paul Grahamã€ç çŽ‰ã®ã‚¢ãƒ‰ãƒã‚¤ã‚¹é›† ã‚¹ã‚¿ãƒ¼ãƒˆã‚¢ãƒƒãƒ—ãŒè¿·ã£ãŸæ™‚ã«èªã‚€ Paul Graham ã‹ã‚‰ã®ã‚¢ãƒ‰ãƒã‚¤ã‚¹ Takaaki Umada, Microsoft Ventures January 12th, 2015 https://medium.com/@tumada/ / tumada@microsoft.com Summary of Paul Graham Essays 1 2. Y Combinatorã®ãƒ‘ãƒ¼ãƒˆãƒŠãƒ¼ã¨è‹¥ã„èµ·æ¥å®¶ã®ä¼šè©±ã®å¤šãã¯ ã€Œã©ã†ã™ã‚Œã°â€¦ï¼Ÿã€ ã¨ã„ã†è³ªå•ã§å§‹ã¾ã‚Šã€ ãã—ã¦ãƒ‘ãƒ¼ãƒˆãƒŠãƒ¼ã¯ ã€ŒãŸã (just) ... ã‚’ã—ã‚ˆã†ã€ ã¨ç”ãˆã¾ã™ã€‚ http://www.paulgraham.com/before.html 2 So many of the conversations YC partners have with
chezou 2015/01/19
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The Airbnb Tech Blog â€“ Medium
The Airbnb Tech BlogA deeper look into how our Engineers and Data Scientists build a world of belonging
chezou 2015/01/07
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JSAI's AI Tool Introduction - Deep Learning, Pylearn2 and Torch7
JSAI's AI Tool lecture series part 6,7,8. Deep Learning, the glance, Pylearn2 and Torch7Read less
chezou 2014/12/18
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deep learning
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Lessons Learned from ï¿½Building Machine Learning Software at Netflix
Lessons Learned from ï¿½Building Machine Learning Software at Netflix Talk from Software Engineering for Machine Learning Workshop (SW4ML) at the Neural Information Processing Systems (NIPS) 2014 conference in Montreal, Canada on 2014-12-13. Abstract: Building a real system that incorporates machine learning as a part can be a difficult effort, both in terms of the algorithmic and engineering challe
chezou 2014/12/16
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machine learning
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What's Wrong with Statistics in Julia?
Introduction Several months ago, I promised to write an updated version of my old post, â€œThe State of Statistics in Juliaâ€, that would describe how Juliaâ€™s support for statistical computing has evolved since December 2012. Iâ€™ve kept putting off writing that post for several reasons, but the most important reason is that all of my attention for the last few months has been focused on whatâ€™s wrong w
chezou 2014/12/03
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