[B! machinelearning] nhayatoã®ãƒ–ãƒƒã‚¯ãƒžãƒ¼ã‚¯

nhayato id:nhayato

machinelearningã«é–¢ã™ã‚‹nhayatoã®ãƒ–ãƒƒã‚¯ãƒžãƒ¼ã‚¯ (75)

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nhayato 2015/11/17
machinelearning
ãƒªãƒ³ã‚¯
ãƒ†ã‚ã‚¹ãƒˆåˆ†é¡žã§ã¯ã˜ã‚ã‚‹æ©Ÿæ¢°å¦ç¿’ / A tutorial of text classification with Golang. // Speaker Deck
ã‚†ã‚‹ãµã‚åˆ†é¡žå•é¡Œã“ã¨ã¯ã˜ã‚ @ Idea Quest (http://idea-quest.info/) ã©ã†ã‚„ã£ã¦æœ€é©åŒ–ã—ã¦ã„ã‚‹ã®ã‹ãªã©ã®ç†è«–çš„ãªè©±ã¯ç„¡ã—ã§ã€ãªãœæ©Ÿæ¢°å¦ç¿’ã‚’ã—ãŸããªã‚‹ã®ã‹ã€é”æ³•ã§å‹•ã„ã¦ã‚‹ã‚“ã˜ã‚ƒãªã„ã‚“ã§ã™ã€ã¨ã„ã†ã¨ã“ã‚ã«ãƒ•ã‚©ãƒ¼ã‚«ã‚¹ã—ã¤ã¤ã€ç„¡ç†ã‚„ã‚Š Go ãƒã‚¿ã‚’æŒã¡è¾¼ã‚€è©±ã€‚æ¬¡å›žã¯æœ€é©åŒ–ã®è©±ã¨ã‹ã‚‚ã—ãŸã„æ‰€ã€‚
nhayato 2015/09/08
machinelearning
ãƒªãƒ³ã‚¯
AWS News Blog
Join AWS Hybrid Cloud & Edge Day to Learn How to Deploy Your Applications in the Everywhere Cloud In his keynote of AWS re:Invent 2021, Dr. Werner Vogels shared the insight of how â€œthe everywhere cloudâ€ is bringing AWS to new locales through AWS hardware and services and spotlighted it as one of his tech predictions for 2022 and beyond in his blog post. â€œWhat we will see in 2022, and even more [â€¦]
nhayato 2015/04/11
machinelearning
ãƒªãƒ³ã‚¯
Episodes â€” Talking Machines
In the first episode of Talking Machines we meet our hosts, Katherine Gorman (nerd,Â journalist) and Ryan Adams (nerd,Â Harvard computer science professor), and explore some of the interviews you'll be able to hear this season. Today we hear some short clips on big issues, we'll get technical, but the today is all about introductions.We start with Kevin Murphy of Google talking about his textbook th
nhayato 2015/03/27
machinelearning
ãƒªãƒ³ã‚¯
æ©Ÿæ¢°ã®ä»£ã‚ã‚Šã«äººé–“ãŒå¦ç¿’å…¥é–€
[ç¬¬ï¼“å›žåˆ†æžã‚³ãƒ³ãƒšLTä¼š ã€ã‚ªãƒ³ãƒ©ã‚¤ãƒ³é–‹å‚¬] (https://kaggle-friends.connpass.com/event/220927/) ã§ã®ç™ºè¡¨è³‡æ–™ã§ã™ã€‚ ç”»åƒã‚³ãƒ³ãƒšã«å‡ºã‚‹ã†ãˆã§ä¾¿åˆ©éŽãŽã‚‹ timm(pytorch image models) ã®ç´¹ä»‹ã‚’ã—ã¾ã—ãŸã€‚
nhayato 2015/01/09
machinelearning
ãƒªãƒ³ã‚¯
æ¡ä»¶ä»˜ãç¢ºçŽ‡å ´ã®æŽ¨è«–ã¨å¦ç¿’
cvpaper.challenge ã® ãƒ¡ã‚¿ã‚µãƒ¼ãƒ™ã‚¤ç™ºè¡¨ã‚¹ãƒ©ã‚¤ãƒ‰ã§ã™ã€‚ cvpaper.challengeã¯ã‚³ãƒ³ãƒ”ãƒ¥ãƒ¼ã‚¿ãƒ“ã‚¸ãƒ§ãƒ³åˆ†é‡Žã®ä»Šã‚’æ˜ ã—ã€ãƒˆãƒ¬ãƒ³ãƒ‰ã‚’å‰µã‚Šå‡ºã™æŒ‘æˆ¦ã§ã™ã€‚è«–æ–‡ã‚µãƒžãƒªä½œæˆãƒ»ã‚¢ã‚¤ãƒ‡ã‚£ã‚¢è€ƒæ¡ˆãƒ»è°è«–ãƒ»å®Ÿè£…ãƒ»è«–æ–‡æŠ•ç¨¿ã«å–ã‚Šçµ„ã¿ã€å‡¡ã‚†ã‚‹çŸ¥è˜ã‚’å…±æœ‰ã—ã¾ã™ã€‚2020ã®ç›®æ¨™ã¯ã€Œãƒˆãƒƒãƒ—ä¼šè°30+æœ¬æŠ•ç¨¿ã€ã™ã‚‹ã“ã¨ã§ã™ã€‚ http://xpaperchallenge.org/cv/
nhayato 2014/12/25
machinelearning
ãƒªãƒ³ã‚¯
How To Get Started In Machine Learning: A Self-Study Blueprint - MachineLearningMastery.com
How do you get started in machine learning, specifically Deep Learning? This question was asked recently in the machine learning sub-reddit. Specifically, the original poster of the question had completed the Coursera Machine Learning course but felt like they did not have enough of a background to get started in Deep Learning. I wrote a lengthy reply that I think may be helpful more generally, fo
nhayato 2014/12/21
machinelearning
ãƒªãƒ³ã‚¯
ç”·ã®ãŸã‚ã®æ©Ÿæ¢°å¦ç¿’ã€œRBMã§Aâ—¯å¥³å„ªã•ã‚“ã®å…±é€šç‰¹å¾´é‡ã‚’å¾—ã‚ˆã†ã€œ - æ–°kensuke-miã®æ—¥è¨˜
ã„ã¾ã€å··ã§è©±é¡Œï¼ˆï¼“å¹´ãã‚‰ã„å‰ã‹ã‚‰ã ã„ã¶è©±é¡Œã ã‘ã©ï¼‰ã®Deep learningã‚’ã‚¢â—¯ã§ã‚‚ä½¿ãˆã‚‹pylearn2ã‚’ä½¿ã£ã¦è¦‹ã‚‹ã€‚ Deep learningã¨ã¯ï¼Ÿ ä¸€è¨€ã§è¨€ã†ã¨ã€Œæ•™å¸«ãªã—ã®ãƒ‹ãƒ¥ãƒ¼ãƒ©ãƒ«ãƒãƒƒãƒˆã‚’ã„ã£ã±ã„ã¤ãªã’ã¦å¤šå±¤ãƒãƒƒãƒˆãƒ¯ãƒ¼ã‚¯åŒ–ã—ãŸã‚‚ã®ã€‚ã€ ã„ã¾ã¾ã§ã¯ å‰å‡¦ç†è·äººã®ä¸¹ç²¾ã‚’è¾¼ã‚ãŸç‰¹å¾´é‡ä½œã‚Šã€€â†’ã€€åˆ†é¡žå™¨ï¼ˆSVMã¨ã‹ã€ãƒã‚¸ã‚¹ãƒ†ã‚£ãƒƒã‚¯å›žå¸°ã¨ã‹ã€ãªã‚“ã§ã‚‚ã„ã„ã®ã§é–¢æ•°ï¼‰ã®å¦ç¿’ ã ã£ãŸã®ãŒã€ é»’é”è¡“ã§ç‰¹å¾´é‡ä½œã‚Šã€€â†’ã€€åˆ†é¡žå™¨ï¼ˆSVMã¨ã‹ã€ãƒã‚¸ã‚¹ãƒ†ã‚£ãƒƒã‚¯å›žå¸°ã¨ã‹ã€ãªã‚“ã§ã‚‚ã„ã„ã®ã§é–¢æ•°ï¼‰ã®å¦ç¿’ ã«ã§ãã‚‹ã€‚ é»’é”è¡“ã‚’ã‚‚ã†ã¡ã‚‡ã£ã¨ç´è§£ã ã€Œé»’é”è¡“ã€ã£ã¦è¨€ã£ã¦ã‚‹ã¨ã“ã‚ã§ã‚„ã£ã¦ã„ã‚‹ã“ã¨ã¯ã€Œãƒ™ã‚¹ãƒˆãªå†™åƒé–¢æ•°ã®å¦ç¿’ã€ ã‚‚ã†å°‘ã—ã€è¨€è‘‰ã‚’åŽ³å¯†ã«è¡¨ç¾ã™ã‚‹ã¨ã€ã€Œå…¥åŠ›/ãƒŽã‚¤ã‚ºä»˜ãå‡ºåŠ›ã®å¤‰æ›ã‚’ã†ã¾ãè¡¨ç¾ã§ãã‚‹é–¢æ•°ã®å¦ç¿’ã€ ã“ã®é–¢æ•°ã¯ã§ãã‚‹ã ã‘è‰¯ã„[1]æ¡ä»¶ã§åˆ¥ã®ç©ºé–“ã«å†™åƒã™ã‚‹ã€‚ ãªã®ã§ã€ã“ã®é–¢æ•°ãŒå¦ç¿’ã§ãã‚‹ã¨ã€ç‰¹å¾´é‡ç©ºé–“ã«å°„
nhayato 2014/11/07
machinelearning
ãƒªãƒ³ã‚¯
Private Presentation
Private content!This content has been marked as private by the uploader.
nhayato 2014/11/02
machinelearning
ãƒªãƒ³ã‚¯
å®Ÿè·µ æ©Ÿæ¢°å¦ç¿’ã‚·ã‚¹ãƒ†ãƒ
æœ¬æ›¸ã¯ã€å®Ÿéš›ã«æ‰‹ã‚’å‹•ã‹ã—ãªãŒã‚‰ã‚·ã‚¹ãƒ†ãƒ ã‚’ä½œæˆã—ã€ãã®ã‚¨ãƒƒã‚»ãƒ³ã‚¹ã‚’èº«ã«ã¤ã‘ã‚‹ã“ã¨ã‚’ç›®çš„ã¨ã—ãŸæ©Ÿæ¢°å¦ç¿’ã‚·ã‚¹ãƒ†ãƒ ã®å®Ÿè·µçš„ãªè§£èª¬æ›¸ã§ã™ã€‚ã€Œãƒ‡ãƒ¼ã‚¿ã¨ã„ã‹ã«å‘ãåˆã†ã‹ã€ã¨ã„ã†è¦–ç‚¹ã‹ã‚‰ã€ç”Ÿã®ãƒ‡ãƒ¼ã‚¿ã‹ã‚‰ãƒ‘ã‚¿ãƒ¼ãƒ³ã‚’è¦‹ã¤ã‘ã‚‹æ–¹æ³•ã‚’è§£èª¬ã—ã¾ã™ã€‚Pythonã¨æ©Ÿæ¢°å¦ç¿’ã®åŸºæœ¬ã€ãƒ©ã‚¤ãƒ–ãƒ©ãƒªã®ä½¿ã„æ–¹ã‚’ã¯ã˜ã‚ã€å…·ä½“çš„ãªä¾‹ã«åŸºã¥ã„ãŸãƒ‡ãƒ¼ã‚¿ã‚»ãƒƒãƒˆã€ãƒ¢ãƒ‡ãƒ«åŒ–ã€ãƒ¬ã‚³ãƒ¡ãƒ³ãƒ‰ã¨ã€ãã®æ”¹è‰¯ã€éŸ³å£°ã‚„ç”»åƒã®å‡¦ç†ãªã©ã€ã‚ˆã‚Šé‡è¦ãªå•é¡Œã«ã¤ã„ã¦ã‚‚è§£èª¬ã—ã¾ã™ã€‚ã•ã‚‰ã«ã€ãƒ†ã‚ã‚¹ãƒˆã‚„ç”»åƒã€éŸ³å£°ã«å¯¾ã—ã¦æ©Ÿæ¢°å¦ç¿’ã®æ‰‹æ³•ã‚’é©ç”¨ã™ã‚‹æ–¹æ³•ã‚’å¦ã³ã€æ©Ÿæ¢°å¦ç¿’é–¢é€£æŠ€è¡“ã®è©•ä¾¡æ–¹æ³•ã‚„ã€æœ€é©ãªé¸æŠžã‚’è¡Œã†ãŸã‚ã®æ¯”è¼ƒæ–¹æ³•ã«ã¤ã„ã¦å¦ã³ã¾ã™ã€‚æœ¬æ›¸ã§å¦ã‚“ã ãƒ„ãƒ¼ãƒ«ã¨çŸ¥è˜ãŒã‚ã‚Œã°ã€å®Ÿéš›ã®å•é¡Œã‚’è§£æ±ºã§ãã‚‹ç‹¬è‡ªã®ã‚·ã‚¹ãƒ†ãƒ ã‚’ä½œæˆã§ãã‚‹ã‚ˆã†ã«ãªã‚‹ã§ã—ã‚‡ã†ã€‚ è¬è¾ž åŽŸæ›¸ã®ç›£ä¿®è€…ã«ã¤ã„ã¦ ã¯ã˜ã‚ã« 1ç« ã€€ Pythonã§ã¯ã˜ã‚ã‚‹æ©Ÿæ¢°å¦ç¿’ 1.1ã€€æ©Ÿæ¢°å¦ç¿’ã¨ Pythonã¯ãƒ‰ãƒªãƒ¼ãƒ ãƒãƒ¼ãƒ 1.2ã€€æœ¬æ›¸
nhayato 2014/10/25
machinelearning
ãƒªãƒ³ã‚¯
å®Ÿè·µ æ©Ÿæ¢°å¦ç¿’ã‚·ã‚¹ãƒ†ãƒ | Willi Richert, Luis Pedro Coelho, æ–Žè—¤ åº·æ¯… |æœ¬ | é€šè²© | Amazon
nhayato 2014/10/25
machinelearning

book
ãƒªãƒ³ã‚¯
Classias - A collection of machine-learning algorithms for classification
A collection of machine-learning algorithms for classification Classias is a collection of machine-learning algorithms for classification. Currently, it supports the following formalizations: L1/L2-regularized logistic regression (aka. Maximum Entropy) L1/L2-regularized L1-loss linear-kernel Support Vector Machine (SVM) Averaged perceptron
nhayato 2014/09/24
machinelearning
ãƒªãƒ³ã‚¯
Sign in - Google Accounts
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nhayato 2014/09/18
SE

machinelearning
ãƒªãƒ³ã‚¯
Machine learning textbook
Machine Learning: a Probabilistic Perspective by Kevin Patrick Murphy. MIT Press, 2012. See new web page.
nhayato 2014/09/17
machinelearning
ãƒªãƒ³ã‚¯
ãã¾ãã‚Œæ—¥è¨˜: ã‚¿ã‚°ã¨ãƒžãƒ«ãƒãƒ©ãƒ™ãƒ«å•é¡Œã¨æ©Ÿæ¢°å¦ç¿’
ãƒãƒƒãƒˆä¸Šã®ã‚µãƒ¼ãƒ“ã‚¹ã‚’è¦‹ã¦ã„ã‚‹ã¨ã€ãƒ¡ãƒ¼ãƒ«ãªã‚ŠWebãƒšãƒ¼ã‚¸ã‚’ã‚ã‚‹ä¸€æ„ã®ã‚«ãƒ†ã‚´ãƒªã«åˆ†é¡žã™ã‚‹ã¨ã„ã†æ•´ç†æ³•ã‹ã‚‰ã€ã‚¿ã‚°ï¼ˆãƒ©ãƒ™ãƒ«ï¼‰ã‚’ã¤ã‘ã‚‹ã¨ã„ã†æ•´ç†æ³•ã«å¤‰ã‚ã£ã¦ãã¦ã„ã‚‹ã‚ˆã†ã§ã™ã€‚ ä»£è¡¨çš„ãªä¾‹ã¯ Gmailã€‚ãƒ•ã‚©ãƒ«ãƒ€ã¨ã„ã†æ¦‚å¿µã¯ãªããƒ¡ãƒ¼ãƒ«ã«ãƒ©ãƒ™ãƒ«ã‚’ä»˜ä¸Žã—ã¦ã„ãã¾ã™ã€‚ç§ãŒè‰¯ãä½¿ã†æ–¹æ³•ã¯ã€ã€Œãƒªãƒžã‚¤ãƒ³ãƒ€ãƒ¼ã€ã®ãƒ©ãƒ™ãƒ«ï¼ˆãƒ¡ãƒ¼ãƒ«ã®é‡è¦ã•ã¨ã„ã†è¦³ç‚¹ï¼‰ã¨ã€Œå†…å®¹ã€ã®ãƒ©ãƒ™ãƒ«ã§ã™ã€‚äºŒã¤ã¯ãã‚Œãžã‚Œç‹¬ç«‹ã—ãŸåˆ†é¡žæ–¹æ³•ã§ã™ãŒã€ãƒ•ã‚©ãƒ«ãƒ€ã ã¨åŒå±…ã§ãã¾ã›ã‚“ã€‚ä»–ã®ä¾‹ã ã¨ã€Œã¯ã¦ãªãƒ–ãƒƒã‚¯ãƒžãƒ¼ã‚¯ã€ãŒã‚ã‚Šã¾ã™ã€‚ãƒ¦ãƒ¼ã‚¶ãŒä»»æ„ã®ã‚¿ã‚°ã‚’ä»˜ä¸Žã™ã‚‹ã“ã¨ãŒã§ãã¾ã™ã€‚ æ©Ÿæ¢°å¦ç¿’ã®è¨€è‘‰ã‚’ä½¿ãˆã°ã€å¾“æ¥ã®ãƒ•ã‚©ãƒ«ãƒ€ã¯ã€Œã‚·ãƒ³ã‚°ãƒ«ãƒ©ãƒ™ãƒ«ã€ã®åˆ†é¡žå•é¡Œã€å¾Œè€…ã®ã‚¿ã‚°ã¯ã€Œãƒžãƒ«ãƒãƒ©ãƒ™ãƒ«ã€åˆ†é¡žå•é¡Œã¨ãªã‚Šã¾ã™ã€‚æ–‡å—ã©ãŠã‚Šã€å‰è€…ã¯ã‚¤ãƒ³ã‚¹ã‚¿ãƒ³ã‚¹ã«å¯¾ã—1ã¤ã®ãƒ©ãƒ™ãƒ«ã®ã¿ã‚’ä»˜ä¸Žã™ã‚‹å•é¡Œã€å¾Œè€…ã¯è¤‡æ•°ã®ãƒ©ãƒ™ãƒ«ã‚’ä»˜ä¸Žã™ã‚‹å•é¡Œã§ã™ã€‚ ã•ã¦ã€æ©Ÿæ¢°å¦ç¿’ã®åˆ†é‡Žã§ãƒžãƒ«ãƒãƒ©ãƒ™ãƒ«å•é¡Œã¯ã©ã†é€²å±•ã—ã¦ã‚‹ã®ã§ã—ã‚‡ã†ã‹ï¼Ÿå®Ÿéš›
nhayato 2014/09/13
machinelearning
ãƒªãƒ³ã‚¯
ç¬¬3å›žIBISMLãƒãƒ¥ãƒ¼ãƒˆãƒªã‚¢ãƒ« | IBIS2014
nhayato 2014/09/04
machinelearning
ãƒªãƒ³ã‚¯
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©
nhayato 2014/09/04
machinelearning
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shogun | A Large Scale Machine Learning Toolbox
Shogun - A Large Scale Machine Learning Toolbox This is the official homepage of the SHOGUN machine learning toolbox. The machine learning toolbox's focus is on large scale kernel methods and especially on Support Vector Machines (SVM) [1]. It provides a generic SVM object interfacing to several different SVM implementations, among them the state of the art OCAS [21], Liblinear [20], LibSVM [2], S
nhayato 2014/09/01
machinelearning
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Go machine learning & NLP libraries | Biosphe|re
The Go programming language is getting increasingly popular for the purpose of natural language processing. At the moment there seems to be no central directory of useful libraries. Therefore, I decided to publish a list of corresponding packages that I am using or have been stumbled upon. Natural Language Processing go-stem: Go implementation of the Porter stemming algorithm snowball: Cgo wrapper
nhayato 2014/09/01
nlp

machinelearning

golang
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SVM-rank: Support Vector Machine for Ranking
SVMrank Support Vector Machine for Ranking Author: Thorsten Joachims <[email protected]> Cornell University Department of Computer Science Version: 1.00 Date: 21.03.2009 Overview SVMrank is an instance of SVMstruct for efficiently training Ranking SVMs as defined in [Joachims, 2002c]. SVMrank solves the same optimization probl em as SVMlight with the '-z p' option, but it is much faster. On the
nhayato 2014/06/03
machinelearning
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