deeplearn.js
deeplearn.js is an open-source library that brings performant machine learning building blocks to the web, allowing you to train neural networks in a browser or run pre-trained models in inference mode. We provide an API that closely mirrors the TensorFlow eager API. deeplearn.js was originally developed by the Google Brain PAIR team to build powerful interactive machine learning tools for the bro
Andrew Ng先生ã®æ©Ÿæ¢°å¦ç¿’講義を解説ã™ã‚‹å‹‰å¼·ä¼šè³‡æ–™ã‚’公開 - エンタープライズギークス (Enterprise Geeks)
シリコンãƒãƒ¬ãƒ¼ã®æœ‰å大å¦ã§ã‚るスタンフォード大å¦ã§ã€Andrew Ng先生ã®æ•™ãˆã‚‹æ©Ÿæ¢°å¦ç¿’ã®è¬›ç¾©ãŒäººæ°—を集ã‚ã¦ã„る。ã“ã®è¬›ç¾©ã¯å½¢ã‚’変ãˆã€courseraã¨ã„ã†ç„¡æ–™ã®Web上オンラインコースã¨ã—ã¦ã‚‚å—講ã§ãã€ã“ã“日本ã§ã‚‚機械å¦ç¿’ã®å‹‰å¼·ãŒã—ãŸã„人é”ã®é–“ã§ã‚‚人気ã®è¬›ç¾©ã¨ãªã£ã¦ã„る。 [Machine Learning by Stanford University] https://www.coursera.org/learn/machine-learning/ 人気ã®ç§˜å¯† Andrew Ng先生ã®è¬›ç¾©ã¯å†…容ãŒéžå¸¸ã«å®Ÿç”¨çš„ã§ã‚ã‚‹ã“ã¨ãŒäººæ°—ã®ç†ç”±ã®1ã¤ã¨ã—ã¦æŒ™ã’られるã ã‚ã†ã€‚Andrew Ng先生ã¯ã‚·ãƒªã‚³ãƒ³ãƒãƒ¬ãƒ¼ã®ä¼æ¥ã‚’相手ã«æ©Ÿæ¢°å¦ç¿’ã®æŒ‡å°Žã‚’ã—ã¦ã„る。ãŠãらããã®çµŒé¨“ã‹ã‚‰ã€ç†è«–ã«èµ°ã‚Šã™ãŽãšã€æ©Ÿæ¢°å¦ç¿’ã‚’é©ç”¨ã™ã‚‹ãƒ—ãƒã‚¸ã‚§ã‚¯ãƒˆã«ãŠã„ã¦æœ¬å½“ã«æœ‰æ„義ãªå†…容ã«å¼·ãフォーカスã—ã¦ã„ã‚‹ã“ã¨ãŒæ„Ÿã˜ã‚‰ã‚Œã‚‹ã®ã 。Andr
ConvNetJS: Deep Learning in your browser
Description The library allows you to formulate and solve Neural Networks in Javascript, and was originally written by @karpathy (I am a PhD student at Stanford). However, the library has since been extended by contributions from the community and more are warmly welcome. Current support includes: Common Neural Network modules (fully connected layers, non-linearities) Classification (SVM/Softmax)
ConvNetJS - ディープラーニングをWebブラウザã§å®Ÿç¾ã™ã‚‹JavaScriptライブラリ MOONGIFT
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ディープラーニングã®åˆ¤æ–æ ¹æ‹ ã‚’ç†è§£ã™ã‚‹æ‰‹æ³• - Qiita
Deleted articles cannot be recovered. Draft of this article would be also deleted. Are you sure you want to delete this article? ディープラーニングã¯ç‰¹å®šåˆ†é‡Žã§éžå¸¸ã«é«˜ã„精度ãŒå‡ºã›ã‚‹ã“ã¨ã‚‚ã‚ã‚Šã€ãã®å¿œç”¨ç¯„囲ã¯ã©ã‚“ã©ã‚“広ãŒã£ã¦ã„ã¾ã™ã€‚ ã—ã‹ã—ã€ãã‚“ãªãƒ‡ã‚£ãƒ¼ãƒ—ラーニングã«ã‚‚弱点ã¯ã‚ã‚Šã¾ã™ã€‚ãã®ä¸ã§ã‚‚大ãã„å•é¡Œç‚¹ãŒã€ã€Œä½•ã‚’æ ¹æ‹ ã«åˆ¤æ–ã—ã¦ã„ã‚‹ã‹ã‚ˆãã‚ã‹ã‚‰ãªã„ã€ã¨ã„ã†ã“ã¨ã§ã™ã€‚ ディープラーニングã¯ã€å¦ç¿’ã®éŽç¨‹ã§ãƒ‡ãƒ¼ã‚¿å†…ã®ç‰¹å¾´ãれ自体をå¦ç¿’ã™ã‚‹ã®ãŒå¾—æ„ã¨ã„ã†ç‰¹æ€§ãŒã‚ã‚Šã¾ã™ã€‚ã“ã‚Œã«ã‚ˆã‚Šã€ŒäººãŒç‰¹å¾´ã‚’抽出ã™ã‚‹å¿…è¦ãŒãªã„ã€ã¨è¨€ã‚ã‚ŒãŸã‚Šã‚‚ã—ã¾ã™ãŒã€é€†ã«è¨€ãˆã°ã©ã‚“ãªç‰¹å¾´ã‚’抽出ã™ã‚‹ã‹ã¯ãƒãƒƒãƒˆãƒ¯ãƒ¼ã‚¯ä»»ã›ã¨ã„ã†ã“ã¨ã§ã™ã€‚抽出ã•ã‚ŒãŸç‰¹å¾´ã¯ãã®åã®é€šã‚Šãƒ‡ã‚£ãƒ¼ãƒ—ãªãƒãƒƒãƒˆãƒ¯ãƒ¼ã‚¯ã®ä¸ã®é‡ã¿ã«æ½œåœ¨ã—ã¦ãŠ
Essential Cheat Sheets for Machine Learning and Deep Learning Engineers
Machine learning is complex. For newbies, starting to learn machine learning can be painful if they don’t have the right resources to learn from. Most of the machine learning libraries are difficult to understand and the learning curve can be a bit frustrating. I am creating a repository on GitHub (cheatsheets-ai) containing cheatsheets for different machine learning frameworks, gathered from diff
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