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fast.aiã® Cutting Edge Deep Learning For Coders, Part 2ã‚’å—ã‘ãŸ
fast.aiã® Cutting Edge Deep Learning For Coders, Part 2ã‚’å—ã‘㟠fast.aiãŒæä¾›ã™ã‚‹ ディープラーニングã®ã‚ªãƒ³ãƒ©ã‚¤ãƒ³ç„¡æ–™è¬›åº§ã€Cutting Edge Deep Learning for Coders ã‚’å—ã‘ãŸã®ã ãŒã€é›£ã—ãã¦å¾ŒåŠã‹ã‚‰æŒ«æŠ˜ã€‚。 Deep Learning For Coders—36 hours of lessons for free ã¨ã¦ã‚‚ã„ã„講座ãªã®ã§ã€å¸ƒæ•™ã®ãŸã‚ã«ã‚‚講座ã®ç´¹ä»‹ã¨ã€å„レクãƒãƒ£ãƒ¼ã®å†…容を書ã„ã¦ãŠãã“ã¨ã«ã™ã‚‹ã€‚ ã“ã‚Œã¯ã€fast.aiãŒæä¾›ã™ã‚‹Deep Learning コース㮠Part2ã«å½“ãŸã‚‹ã€‚Part1ã®æ„Ÿæƒ³ã¯ä»¥ä¸‹ã€‚ Fast.AI ã® Practical Deep Learning for Coders Part1 ã‚’å—ã‘㟠| Futurismo 講座ã®ç´¹ä»‹# 紹介記事: Cutting E
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何ã®è©±ã‹ã¨ã„ã†ã¨ qiita.com 上記ã®è¨˜äº‹ã§ã¯ã€ã€Œå€’立振åã€ã‚’é¡Œæã«ã—ãŸã€DQN（Deep Q Network）ã«ã‚ˆã‚‹å¼·åŒ–å¦ç¿’ã®è§£èª¬ãŒã‚ã‚Šã€éžå¸¸ã«ã‚ˆãã¾ã¨ã¾ã£ã¦ã„ã¾ã™ã€‚ 一方ã€ã“ã®è¨˜äº‹ã®ä¸ã§ã¯ã€å…¨çµåˆå±¤ã‚’4層ã«é‡ããŸãƒãƒƒãƒˆãƒ¯ãƒ¼ã‚¯ã‚’利用ã—ã¦ã„ã‚‹ã®ã§ã™ãŒã€å€’立振åã®å•é¡Œã«é™å®šã™ã‚Œã°ã€ã‚‚ã£ã¨ã‚·ãƒ³ãƒ—ルãªãƒãƒƒãƒˆãƒ¯ãƒ¼ã‚¯ã§ã‚‚対応ã§ãã‚‹æ°—ãŒã—ãªãã‚‚ã‚ã‚Šã¾ã›ã‚“。 ã¨ã„ã†ã‚ã‘ã§ã€ã€Œ0層（パーセプトãƒãƒ³ï¼‰ã€ã€Œ1層ã€ã€Œ2層ã€ã®ãƒãƒƒãƒˆãƒ¯ãƒ¼ã‚¯ã§ã©ã“ã¾ã§å¦ç¿’ã§ãã‚‹ã®ã‹ã€ãƒ¢ãƒ‡ãƒ«ã®è¤‡é›‘ã•ã¨å¦ç¿’内容ã®é–¢ä¿‚を確èªã—ã¦ã¿ãŸã‚ˆãƒ¼ã€ã¨ã„ã†ã®ãŒã“ã®ãƒ–ãƒã‚°ã®ãƒã‚¿ã«ãªã‚Šã¾ã™ã€‚ DQNã¨ã¯ï¼Ÿ ã¾ãšã¯ç°¡å˜ã«DQNを解説ã—ã¦ãŠãã¾ã™ã€‚ ビデオゲームã®è‡ªå‹•ãƒ—レイã§æœ‰åã«ãªã‚Šã¾ã—ãŸãŒã€ã€Œä¸Žãˆã‚‰ã‚ŒãŸç’°å¢ƒã«ãŠã„ã¦ã€æœ€å–„ã®è¡Œå‹•ã‚’é¸æŠžã™ã‚‹ã€ã¨ã„ã†å‡¦ç†ã‚’実ç¾ã™ã‚‹ã“ã¨ãŒç›®æ¨™ã§ã™ã€‚ã“ã“ã§è¨€ã†ã€Œè¡Œå‹•ã€ã¯ã€ãƒ“デオゲームã®æ“作ã®ã‚ˆã†ã«ã€ã€Œã©ã®ãƒœã‚¿ãƒ³ã‚’押ã™ã®ã‹ã€ã¨
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人工知能技術（Recurrent Neural Networkã®1ã¤ã§ã‚ã‚‹GRU）を使ã£ã¦ã€ãƒ©ã‚¤ãƒ³ãƒˆãƒ¬ãƒ¼ã‚¹ãƒãƒœãƒƒãƒˆã®ãŸã‚ã®åˆ¶å¾¡ãƒ—ãƒã‚°ãƒ©ãƒ を作ã£ã¦ã¿ã¾ã—ãŸã€‚訓練を繰り返ã™ã“ã¨ã«ã‚ˆã‚Šã€æ»‘らã‹ãªãƒ©ã‚¤ãƒ³ãƒˆãƒ¬ãƒ¼ã‚¹ã‚’ã§ãるよã†ã«ãªã‚Šã¾ã™ã€‚- プãƒã‚°ãƒ©ãƒ ã®å…¬é–‹å¦ç¿’ã®ãŸã‚ã®ãƒ—ãƒã‚°ãƒ©ãƒ ã¨ã‚·ãƒŸãƒ¥ãƒ¬ãƒ¼ã‚¿ã‚’公開予定ã§ã™(ç¾åœ¨ã€æº–å‚™ä¸ã§ã™)。LEGO Mindstorms ã®åˆ¶å¾¡ãƒ—ãƒã‚°ãƒ©ãƒ ã®æ–¹ã¯ETãƒãƒœã‚³ãƒ³ã®ãƒŽã‚¦ãƒã‚¦ãŒå«ã¾ã‚Œã¦ã„ã‚‹ã®ã§éžå…¬é–‹ã§ã™ã€‚- å‰å›žå‹•ç”»DQN（コンピュータ）ã§CODE VS 5.0ã«æŒ‘戦 : mylist/56938120å‰ç·¨ã‚’ nu様 ã«å®£ä¼ã—ã¦ã„ãŸã ãã¾ã—ãŸã€‚ã‚ã‚ŠãŒã¨ã†ã”ã–ã„ã¾ã—ãŸã€‚
GitHub - yandexdataschool/Practical_RL: A course in reinforcement learning in the wild
The syllabus is approximate: the lectures may occur in a slightly different order and some topics may end up taking two weeks. week01_intro Introduction Lecture: RL problems around us. Decision processes. Stochastic optimization, Crossentropy method. Parameter space search vs action space search. Seminar: Welcome into openai gym. Tabular CEM for Taxi-v0, deep CEM for box2d environments. Homework d
Deep Learningã‚’ã‚„ã‚‹ãªã‚‰TensorflowよりもPyTorch! | ITANDI Blog
ã“ã‚“ã«ã¡ã¯ã€ã‚¨ãƒ³ã‚¸ãƒ‹ã‚¢ã®å»ºä¸‰ã§ã™ã€‚ Deep Learningã®ãƒ©ã‚¤ãƒ–ラリã¨è¨€ãˆã°TensorflowãŒæœ‰åã§ã™ã‚ˆã。１年åŠå‰ã«ãƒªãƒªãƒ¼ã‚¹ã•ã‚Œã¦ä»¥æ¥ã€ä¸€çž¬ã«ã—ã¦çŸ¥å度を手ã«ã—ã¾ã—ãŸã€‚僕ã¯ãã®é ƒDeep Learningを勉強ã—ã¦ã„ãŸã®ã§ã€Hacker Newsã§TensorflowãŒãƒã‚ºã£ã¦ã‚‹ã®ã‚’見ã¦ä½•ã¨ãªã僕も興奮ã—ã¦ã„ãŸã®ã‚’覚ãˆã¦ã„ã¾ã™ã€‚ã—ã‹ã—早速使ã£ã¦ã¿ã‚ˆã†ã¨æ€ã„ãƒãƒ¥ãƒ¼ãƒˆãƒªã‚¢ãƒ«ã‚’進ã‚ãŸã‚‚ã®ã®ã€LSTMã‚„CNNã®ä½œã‚Šæ–¹ãŒåˆ†ã‹ã‚‰ãšæ–念ã—ã¾ã—ãŸã€‚ 僕㯠Stanfordã®ã‚³ãƒ¼ã‚¹ ã§Deep Learningを勉強ã—ãŸã‚“ã§ã™ãŒï¼ˆç„¡æ–™ã§ã‚¯ã‚ªãƒªãƒ†ã‚£è¶…高ã„ã§ã™ï¼‰ã‚¤ãƒ³ã‚¹ãƒˆãƒ©ã‚¯ã‚¿ãƒ¼ã®Andrej Karpathyã¯TensorflowよりもTorchを勧ã‚ã¦ã„ã¾ã—ãŸã€‚ホームワークã¯numpyを使ã†ã‚“ã§ã™ãŒã€ã‚¢ãƒ¼ã‚テクãƒãƒ£ãŒæ£ã«Torchã®Python版ã¨ã„ã†æ„Ÿã˜ã§ã€ã™ã”ãã—ã£ãã‚Šãã‚‹ã‚“ã§ã™ã‚ˆã。ãã‚Œã¾ã§
NeuPy — NeuPy
NeuPy is a python library for prototyping and building neural networks. NeuPy uses Tensorflow as a computational backend for deep learning models. User Guide Install NeuPy Check the tutorials Learn more about NeuPy in the documentation Explore lots of different neural network algorithms. Read articles and learn more about Neural Networks.
Wise.io – Machine Learning for the Industrial Internet of Things
Machine Learning for the Industrial Internet of Things Minds+Machines is the premier Industrial Internet event featuring keynotes from industry luminaries, insightful forums, and hands-on demos. Wise.io hosted its first Industrial Machine Learning Workshop (IMLW17) on October 24, 2017. View our presenters. Our flagship product, Wise Support, is now part of AnswerIQ! Wise.io is a well-resourced, r
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自慢気ã«Deep Learning使ã£ã¦ã‚‹ã‚„ã¤ã¯å¤§ä½“クソ
色んãªãƒ©ã‚¤ãƒ–ラリãŒå‡ºã¦ã‚‹ã‹ã‚‰ã€Deep Learningを使ã†ã“ã¨è‡ªä½“ã¯å…¨ç„¶é›£ã—ããªã„。 ãŠã‹ã’ã§æš‡äººãŒDeep Learningã‚’ã¡ã‚‡ã£ã¨è©¦ã—ã¦ã¿ãŸã¿ãŸã„ãªè¨˜äº‹ãŒã‚ˆãホッテントリã«ä¸ŠãŒã£ã¦ãる。ブクマをã¤ã‘ã¦ã‚‹ã‚„ã¤ã‚‰ã¯å¤§æŠµã€è‡ªåˆ†ã§è©¦ã—ã¦ã¿ã‚‹æ°—力ã¨ã‹èƒ½åŠ›ã®ãªã„残念ãªäººãŸã¡ã€‚ ã¨ã“ã‚ãŒã€è¨˜äº‹ã®å†…容を見ã¦ã¿ã‚‹ã¨ãƒžã‚¸ã§å˜ã«ä½¿ã£ã¦ã¿ã¦ã‚‹ã ã‘ãªã‚“ã よãªã€‚ãªã‚“ã§Deep Learningを使ã‚ãªãゃã„ã‘ãªã„ã®ã‹ã•ãˆè€ƒãˆã¦ãªã„æ€è€ƒåœæ¢ã€‚ã»ã¨ã‚“ã©ã®å ´åˆã¯ã€æ˜”ã‹ã‚‰ã‚ã‚‹3層ã®ãƒ‹ãƒ¥ãƒ¼ãƒ©ãƒ«ãƒãƒƒãƒˆã¨ã‹SVMã§å分ãªæ€§èƒ½ãŒå‡ºã‚‹å•é¡Œã«ãªã‚“ã¨ãªãDeep Learningを当ã¦ã¯ã‚ã¦ã„ã‚‹ã ã‘。 ã‚ã®ãªã€Deep Learningã¿ãŸã„ã«è†¨å¤§ãªãƒ‘ラメータをæŒã£ã¦ã‚‹å¦ç¿’モデルã¯ç°¡å˜ã«éŽå¦ç¿’ã‚’èµ·ã“ã™ã‹ã‚‰ã€ç´ 人ãŒä¸‹æ‰‹ã«æ‰±ã†ã¨ã‚€ã—ã‚精度ãŒè½ã¡ã‚‹ã‚‚ã‚“ã 。ãã“ãã“難ã—ã„タスクã«å¯¾ã—ã¦ä¸Šæ‰‹ãå¦ç¿’ã™ã‚‹ã«ã¯ã€ã¨ã‚“ã§ã‚‚ãªã„é‡ã®è¨“練データãŒå¿…è¦
Music Generation with Deep Learning
The use of deep learning to solve problems in literary arts has been a recent trend that has gained a lot of attention and automated generation of music has been an active area. This project deals with the generation of music using raw audio files in the frequency domain relying on various LSTM architectures. Fully connected and convolutional layers are used along with LSTM's to capture rich featu
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DeepBach: a Steerable Model for Bach Chorales Generation
This paper introduces DeepBach, a graphical model aimed at modeling polyphonic music and specifically hymn-like pieces. We claim that, after being trained on the chorale harmonizations by Johann Sebastian Bach, our model is capable of generating highly convincing chorales in the style of Bach. DeepBach's strength comes from the use of pseudo-Gibbs sampling coupled with an adapted representation of
Composing Music With Recurrent Neural Networks
(Update: A paper based on this work has been accepted at EvoMusArt 2017! See here for more details.) It's hard not to be blown away by the surprising power of neural networks these days. With enough training, so called "deep neural networks", with many nodes and hidden layers, can do impressively well on modeling and predicting all kinds of data. (If you don't know what I'm talking about, I recomm
fast.ai ã® Practical Deep Learning for Coders, Part1 ã‚’å—ã‘ãŸ
fast.ai ã® Practical Deep Learning for Coders, Part1 ã‚’å—ã‘㟠fast.ai ãŒæä¾›ã™ã‚‹ MOOC, “Practical Deep Learning for Coders Part1†をå—ã‘ãŸã€‚ Practical Deep Learning For Coders — 18 hours of lessons for free 特徴# プãƒã‚°ãƒ©ãƒžã®ãŸã‚ã®å®Ÿè·µãƒ‡ã‚£ãƒ¼ãƒ—ラーニング入門# ã“ã®è¬›åº§ã¯ã€ãƒ—ãƒã‚°ãƒ©ãƒžã®ãŸã‚ã«ã‚ã‚‹ã€‚ç´ æ™´ã‚‰ã—ã„ç†å¿µã®åºæ–‡ã‚’引用ã—ãŸã„。 The purpose of this course is to make deep learning accessible to those individuals who may or may not possess a strong background in machine le
ディープラーニングã®ãŸã‚ã®ç·šå½¢ä»£æ•°å…¥é–€ï¼šä¸€èˆ¬çš„演算ã®åˆå¦è€…å‘ã‘ガイド | POSTD
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Deep Reinforcement Learning
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems ã‚’èªã‚“ã 
How to learn Machine Learning from Scratch
“Lisl’s Stisâ€: Recurrent Neural Networks for Folk Music Generation
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