Jason's Machine Learning 101
Jason Mayes Senior Creative Engineer, Google Machine Learning 101 Feel free to share this deck with others who are learning! Send me feedback here. Dec 2017 Welcome! If you are reading the notes there are a few extra snippets down here from time to time. But more for my own thoughts, feel free to...
Deep Learning
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NVIDIA ディープラーニング用語集
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Understanding deep learning requires re-thinking generalization
the morning paper a random walk through Computer Science research, by Adrian Colyer Understanding deep learning requires re-thinking generalization Zhang et al., ICLR’17 This paper has a wonderful combination of properties: the results are easy to understand, somewhat surprising, and then leave you pondering over what it all might mean for a long while afterwards! The question the authors set out
Deep Learning
Deep Learning is a rapidly growing area of machine learning. To learn more, check out our deep learning tutorial. (There is also an older version, which has also been translated into Chinese; we recommend however that you use the new version.) Machine learning has seen numerous successes, but applying learning algorithms today often means spending a long time hand-engineering the input feature rep
Deep Learning 2016: The Year in Review
Bringing you everything new and exciting in the world of
 deep learning from academia to the grubby depth
 of industry every week right to your inbox. Free. In order to understand trends in the field, I find it helpful to think of developments in deep learning as being driven by three major frontiers that limit the success of artificial intelligence in general and deep learning in particular. Firs
The Neural Network Zoo - The Asimov Institute
With new neural network architectures popping up every now and then, it’s hard to keep track of them all. Knowing all the abbreviations being thrown around (DCIGN, BiLSTM, DCGAN, anyone?) can be a bit overwhelming at first. So I decided to compose a cheat sheet containing many of those architectures. Most of these are neural networks, some are completely different beasts. Though all of these archi
What’s the Difference Between Deep Learning Training and Inference?
What’s the Difference Between Deep Learning Training and Inference? Editor’s note: This post was updated in October 2025. School’s in session. That’s how to think about deep neural networks going through the “training†phase. Neural networks, also known as AI models, get an education for the same reason most people do — to learn to do a job. Generative AI models use neural networks to identify the
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個人的ã«ã¯æœ€è¿‘èžã„ãŸè©±ã®ä¸ã§ã²ã•ã³ã•ã«ãƒ¯ã‚¯ãƒ¯ã‚¯ã—ãŸè©±ã€‚ 「医療ã€ã€Œæ•™è‚²ã€ã€Œè¾²æ¥ã€ã®ã‚ˆã†ãªIT未開ã®åˆ†é‡Žã«é»™ã€…ã¨å–り組んã§ã„る人é”ã¯ãƒ’ーãƒãƒ¼ã«è¦‹ãˆã‚‹ Google Cloud Platform Japan å…¬å¼ãƒ–ãƒã‚°: ã‚ュウリ農家ã¨ãƒ‡ã‚£ãƒ¼ãƒ—ラーニングをã¤ãªã TensorFlow 約三行è¦ç´„ エンジニアè·ã ã£ãŸæ–¹ãŒå®Ÿå®¶ã®ã‚ãƒ¥ã‚¦ãƒªè¾²å ´ã§tensorflowを使ã£ãŸè‡ªå‹•ã®ã€Œã‚ュウリã®å“質仕分ã‘æ©Ÿã€ã‚’自作ã—ã¦ã„ã‚‹ 家æ—（仕分ã‘担当ã¯ãŠæ¯ã•ã‚“）ãŒ9段階ã«ä»•åˆ†ã‘ã—ãŸã‚ュウリを撮影ã—å¦ç¿’用画åƒãƒ‡ãƒ¼ã‚¿ï¼ˆ80×80px）ã¨ã—ã¦ã„る。画åƒ7000枚分。 åŽç©«ã®ãƒ”ーク時ã«ã¯ä¸€æ—¥ 8 時間ãšã£ã¨ä»•åˆ†ã‘作æ¥ã«è¿½ã‚れる。ãれを自動化ã—ãŸã„。 Web カメラã«ã‚ˆã‚‹ç”»åƒæ’®å½±ã¯ Raspberry Pi 3 ã§åˆ¶å¾¡ã—ã€ãã“㧠TensorFlow ã«ã‚ˆã‚‹å°è¦æ¨¡ãªãƒ‹ãƒ¥ãƒ¼ãƒ©ãƒ«ãƒãƒƒãƒˆã«ã‚ˆã£ã¦ã‚ュウリã®ã‚り・ãªã—ã‚’åˆ¤æ– å¦ç¿’ã¨è¨ˆ
ディープラーニングã®æœ‰åライブラリ5種を最çŸè·é›¢ã§è©¦ã™åŠæ—¥ã‚³ãƒ¼ã‚¹ï¼ˆTensorFlow, Chainer, Caffe, DeepDream, 画風変æ›ï¼‰ - Qiita
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Deep Learning | Udacity
Course 1: Constructing and Training Neural Networks Course OverviewExplore course objectives, prerequisites, and your instructor as you prepare to build, train, and evaluate neural networks from scratch in deep learning. Introduction to Deep LearningDiscover deep learning: its place in AI, how neural networks learn patterns from data, why it's so powerful, and when to use it for complex, large-sca
Microsoft releases CNTK, its open source deep learning toolkit, on GitHub - The AI Blog
Microsoft releases CNTK, its open source deep learning toolkit, on GitHub Microsoft is making the tools that its own researchers use to speed up advances in artificial intelligence available to a broader group of developers by releasing its Computational Network Toolkit on GitHub. The researchers developed the open-source toolkit, dubbed CNTK, out of necessity. Xuedong Huang, Microsoft’s chief spe
ディープラーニングã®æœ€æ–°å‹•å‘
NVIDIA Deep Learning Dayã§ã®è¬›æ¼”内容ã§ã™ï¼Ž ディープラーニングã®æœ€æ–°ã®ç ”究æˆæžœã¨ã—ã¦å¼·åŒ–å¦ç¿’ã«ã‚ˆã‚‹ãƒãƒœãƒƒãƒˆã‚«ãƒ¼ã®åˆ¶å¾¡ï¼Œãƒãƒ©ç©ã¿ãƒãƒœãƒƒãƒˆã®èªè˜ï¼Œé§è»Šå ´ã®æ¤œå‡ºï¼Œã‚»ãƒ³ã‚µãƒ‡ãƒ¼ã‚¿ã‹ã‚‰ã®ç•°å¸¸æ¤œçŸ¥ï¼Œç”»åƒç”Ÿæˆã‚’紹介ã—ã¦ã„ã¾ã™ã€‚
Ian Goodfellow --- Redirect
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The 4 things Google believes are key to the future of search
Behshad Behzadi, director of search innovation at Google's Zurich lab. TOULOUSE, France — Before talking about the future of search, one of Google’s top researchers wants you to understand just how dramatically search has changed in the past two years. Speaking at the Futurapolis conference in Toulouse, Behshad Behzadi, director of search innovation at Google’s Zurich lab, pointed out that the maj
NVIDIA Launches New GPUs For Deep Learning Applications, Partners With Mesosphere | TechCrunch
The days when GPUs were only about letting you play Crysis with a higher framerate are long over. Many of the most transformative new computational techniques now rely on GPUs’ ability to quickly run certain algorithms in parallel. One area where GPUs are especially helpful (besides video encoding) is machine learning, and NVIDIA is making a big bet on this. The company is launching two new hardwa
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A Guide to Deep Learning
Baidu follows US tech giants and open sources its deep learning tools
Google Prediction API — Google Developers
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