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satojkovic 2013/02/10

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Mahoutã«ãƒ‘ãƒƒãƒã‚’é€ã£ã¦ã¿ãŸ

3. ä»Šæ—¥ã¯çš†æ—¢æœˆé£Ÿ â€¢ 11å¹´æŒ¯ã‚Šã®å¥½æ¡ä»¶ã€æ¬¡ã®ãƒãƒ£ãƒ³ã‚¹ã¯2030 å¹´1æœˆ31æ—¥ã‚‰ã—ã„ â€“ éƒ¨åˆ†é£Ÿå§‹ã¾ã‚Šï¼š 21æ™‚54åˆ† â€“ çš†æ—¢é£Ÿå§‹ã¾ã‚Šï¼š 23æ™‚5åˆ† â€“ çš†æ—¢é£Ÿæœ€å¤§: 23æ™‚31åˆ† â€“ çš†æ—¢é£Ÿçµ‚ã‚ã‚Šï¼š 23æ™‚58åˆ† 4. æµã‚Œ â€¢ å®Ÿè£…å¯„ã‚Šã®å‹‰å¼·ä¼šã¨ã„ã†ã“ã¨ãªã® ã§ãƒ»ãƒ»ãƒ» â€¢ MahoutãŒã©ã®ã‚ˆã†ã«ã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ ã‚’ MapReduceã§å®Ÿè£…ã—ã¦ã„ã‚‹ã‹ã‚’ã²ãŸã™ã‚‰è§£ èª¬ â€¢ æ•™å¸«ã‚ã‚Šå¦ç¿’ã®ã¿ â€¢ é€ã£ãŸãƒ‘ãƒƒãƒã«ã¤ã„ã¦ç´¹ä»‹

satojkovic 2013/02/04

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Deep Learning ã®æ¦‚è¦ - Sideswipe

ã¯ã˜ã‚ã« åŽ»å¹´ã® æ©Ÿæ¢°å¦ç¿’Ã—ãƒ—ãƒã‚°ãƒ©ãƒŸãƒ³ã‚°å‹‰å¼·ä¼š vol.2 ã§ã€Deep Learning ã®æ¦‚è¦ã«ã¤ã„ã¦ç™ºè¡¨ã•ã›ã¦ã„ãŸã ãã¾ã—ãŸã€‚èª˜ã£ã¦ã„ãŸã ã„ãŸ @07c00 ã•ã‚“ã€ã‚ã‚ŠãŒã¨ã†ã”ã–ã„ã¾ã—ãŸã€‚ Deep learning from Kazuya Gokita è©³ã—ãã¯ä¸Šè¨˜ã®ã‚¹ãƒ©ã‚¤ãƒ‰ã‚’å¾¡è¦§ãã ã•ã„ï¼ˆæ³¨ï¼šAuto Encoder ã¨ DBM ã‚’ã”ã£ã¡ã‚ƒã«èª¬æ˜Žã—ã¦ã„ã‚‹ã®ã§æ£ã—ãã‚ã‚Šã¾ã›ã‚“ã€ãã®ã†ã¡ç›´ã—ã¾ã™ï¼‰ã€‚ Deep Learning ã¨ã¯ Deep Learning ã¯ã€ãƒ‹ãƒ¥ãƒ¼ãƒ©ãƒ«ãƒãƒƒãƒˆãƒ¯ãƒ¼ã‚¯*1ã®ã²ã¨ã¤ã§ã€5å±¤ã¨ã‹10å±¤ã¨ã‹ã€å¾“æ¥ã®æ‰‹æ³•ã§ã¯ã†ã¾ãå¦ç¿’ã§ããªã‹ã£ãŸæ·±ã„å±¤ã®å¦ç¿’ã‚’ã†ã¾ãã§ãã‚‹ã‚ˆã†ã«ã—ãŸã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ ã§ã™ã€‚ Deep Learning ãŒç›®æŒ‡ã—ãŸã¨ã“ã‚ å…¥åŠ›ã«è¿‘ã„å±¤ã§ã¯ã€å˜ç´”ãªç‰¹å¾´æŠ½å‡ºã—ã‹ã§ãã¾ã›ã‚“ãŒã€ãã‚Œã‚‰ã®é‡ã¿ä»˜ã‘å’Œã‚’ã¨ã‚‹ã¨è¡¨ç¾èƒ½åŠ›ãŒä¸ŠãŒã‚Šã¾ã™ã€‚ãã‚Œã‚’ã•ã‚‰ã«ä¸Šä½ã®å±¤ã«å…¥åŠ›ã—

satojkovic 2013/02/03

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PRMLã‚¬ãƒ¼ãƒ« ã€œ æ–‡èŠ¸éƒ¨ã®ãƒžãƒãƒ¼ã‚¸ãƒ£ãƒ¼ãŒã€Œãƒ‘ã‚¿ãƒ¼ãƒ³èªè˜ã¨æ©Ÿæ¢°å¦ç¿’ã€ã‚’èªã‚“ã ã‚‰ ã€œ - æœ¨æ›œä¸è¶³

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satojkovic 2013/01/18

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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©

satojkovic 2013/01/12

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A List of Data Science and Machine Learning Resources - Conductrics

A List of Data Science and Machine Learning Resources By Matt GershoffÂ Â | Monday, December 17th, 2012 Every now and then I get asked for some help or for some pointers on a machine learning/data science topic.Â I tend respond with links to resources by folks that I consider to be experts in the topic area.Â Â Over time my list has gotten a little larger so I decided to put it all together in a blog

satojkovic 2012/12/17

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Think Stats: Probability and Statistics for Programmers

by Allen B. Downey, published by O'Reilly Media. The second edition of this book is available now. We recommend you switch to the new (and improved) version! Order Think Stats from Amazon.com. Download this book in PDF. Read this book online. Code examples and solutions are available from this zip file. Download data files for use with the book. Read the related blog Probably Overthinking It. Desc

satojkovic 2012/11/26

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Charming Python: Functional programming in Python, Part 3

IBM Developer is your one-stop location for getting hands-on training and learning in-demand skills on relevant techno logies such as generative AI, data science, AI, and open source.

satojkovic 2012/11/26

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Deep Learning Tutorials â€” DeepLearning 0.1 documentation

Deep Learning TutorialsÂ¶ Deep Learning is a new area of Machine Learning research, which has been introduced with the objective of moving Machine Learning closer to one of its original goals: Artificial Intelligence. See these course notes for a brief introduction to Machine Learning for AI and an introduction to Deep Learning algorithms. Deep Learning is about learning multiple levels of represen

satojkovic 2012/11/26

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ãƒ‹ãƒ¥ãƒ¼ãƒ©ãƒ«ãƒãƒƒãƒˆã®é€†è¥² - Preferred Networks Research & Development

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satojkovic 2012/11/05

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mlpy (Machine Learning Python) ã® SVM ã‚’è©¦ã™ - (ä¸»ã«)ãƒ—ãƒã‚°ãƒ©ãƒŸãƒ³ã‚°ã®ãƒ¡ãƒ¢

mlpy : http://mlpy.sourceforge.net/ ä¾‹é¡Œã¨ã—ã¦ï¼ŒUCI Repository ã§æä¾›ã•ã‚Œã¦ã„ã‚‹ "Breast Cancer Wisconsin (Original) Data Set" ã‚’ç”¨ã„ã‚‹ã€‚ http://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Original) ã‚µãƒ³ãƒ—ãƒ«æ•°ï¼š683 (æœ¬æ¥ã®ã‚µãƒ³ãƒ—ãƒ«æ•° 699å€‹ã‹ã‚‰ missing value ã‚’ã‚‚ã¤ 16å€‹ã‚’é™¤ã„ãŸ) æ¬¡å…ƒæ•° : 9 æœ€åˆã« numpy ã‚’åˆ©ç”¨ã—ã¦ãƒ‡ãƒ¼ã‚¿ã‚’èªã¿è¾¼ã‚€ã€‚ >>> import numpy as np >>> f = np.loadtxt("breast-cancer-wisconsin.data",dtype='int',delimiter=',') >>> print f [[1000025

satojkovic 2012/10/25

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satojkovic 2012/10/15

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satojkovic 2012/10/15

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satojkovic 2012/09/03

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