Character-Based Deep Convolutional Models | DeepDetect
Below are a range of character-based deep convolutional neural networks that are free, even for commercial use in your applications. These models have been trained over various corpuses, from sentiment analysis in many languages to advertizing link classification from just reading a URL. They should accomodate a range of applications. Training your own models is made easy too and can lead to even
Deep learning Libs @twm
6. 自己紹介 • 大è¦æ¨¡ãƒªã‚¢ãƒ«ã‚¿ã‚¤ãƒ ã®ãƒ‡ãƒ¼ã‚¿ãƒ—ラットフォームリアルタイムデータ å‰å‡¦ç† å‡¦ç† ã‚¹ãƒˆã‚¢ æä¾› tweets æ•´å½¢ 自然言語処ç†ãƒ»åˆ†é¡ž API, 検索, å¯è¦–化 気象データ å¤‰æ› åˆ†é¡žãƒ»ç•°å¸¸å€¤æ¤œçŸ¥ API, 検索,ストリー ミング ä½ç½®ãƒ‡ãƒ¼ã‚¿ å¤‰æ› åˆ†é¡ž API, 検索, スト リーミング 経済データ æ•´å½¢ãƒ»å¤‰æ› ç•°å¸¸å€¤æ¤œçŸ¥ API, 検索, å¯è¦–化 6 7. 自己紹介 • Zope3ã®é–‹ç™º • Python • ã„ãã¤ã‹ã®æœ¬ • PyCon JP 2015ã§ã®ãƒˆãƒ¼ã‚¯ • ãƒãƒƒã‚¯ã‚°ãƒ©ã‚¦ãƒ³ãƒ‰ã¯å®‡å®™ç‰©ç†å¦ • 大è¦æ¨¡ãƒ‡ãƒ¼ã‚¿çµ±è¨ˆè§£æžï¼Œç§‘å¦è¨ˆç®— • 深層å¦ç¿’ウオッãƒãƒ£ãƒ¼: 2012å¹´Hinton講義 https://goo.gl/GG4Bo8 https://www.coursera.org/course/neuralnets 7
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Emerging technology review シリコンãƒãƒ¬ãƒ¼ã‹ã‚‰ã®å…ˆç«¯æŠ€è¡“分æžãƒ¬ãƒãƒ¼ãƒˆã€‚先端技術をå¦ã³ã€æ—¥æœ¬ä¼æ¥ã®çµŒå–¶æˆ¦ç•¥ã¨è£½å“計画ç–定ã«å¯„与。未æ¥ã‚’生ã¿å‡ºã™ãƒ™ãƒ³ãƒãƒ£ãƒ¼ä¼æ¥ã‚„ファウンダーã¨æŽ¥ã—ã€ã‚¤ãƒŽãƒ™ãƒ¼ã‚·ãƒ§ãƒ³èª•ç”Ÿã®æ€æƒ³ã«è¿«ã‚‹ã€‚ “Emerging Technology Review†is research reports featuring the latest technologies. The articles analyze the new wave of the technologies and explores the Silicon Valley culture and innovations. â– featured stories シリコンãƒãƒ¬ãƒ¼ã‹ã‚‰ã®å…ˆç«¯æŠ€è¡“分æžãƒ¬ãƒãƒ¼ãƒˆ (ブãƒã‚°ã¸ã®ãƒªãƒ³ã‚¯) â– about シリコンãƒãƒ¬ãƒ¼ã§ç”Ÿã¾ã‚Œã‚‹å…ˆç«¯æŠ€è¡“ã‚’è§£æž VentureCl
The Unreasonable Effectiveness of Recurrent Neural Networks
May 21, 2015 There’s something magical about Recurrent Neural Networks (RNNs). I still remember when I trained my first recurrent network for Image Captioning. Within a few dozen minutes of training my first baby model (with rather arbitrarily-chosen hyperparameters) started to generate very nice looking descriptions of images that were on the edge of making sense. Sometimes the ratio of how simpl
Deep Learningã®éŽåŽ»ã¨æœªæ¥ ～黒é”è¡“ã‹ã‚‰ã®è„±å´ã¸å‘ã‘ã¦ï½ž
1. Nakayama Lab. Machine Perception Group The University of Tokyo æ±äº¬å¤§å¦ 大å¦é™¢æƒ…å ±ç†å·¥å¦ç³»ç ”究科 å‰µé€ æƒ…å ±å¦å°‚æ”» 講師 ä¸å±± 英樹 2. Nakayama Lab. Machine Perception Group The University of Tokyo ï½ ï¼‘ï¼Žç”»åƒèªè˜åˆ†é‡Žã«ãŠã‘ã‚‹deep learningã®æ´å² ï½ ï¼’ï¼Žä¸€èˆ¬ç”»åƒèªè˜ï¼šDeep learning 以å‰ã¨ä»¥å¾Œã§ä½•ãŒå¤‰ã‚ã£ãŸã‹ â—¦ Bag-of-visual-words â—¦ 畳ã¿è¾¼ã¿ãƒ‹ãƒ¥ãƒ¼ãƒ©ãƒ«ãƒãƒƒãƒˆ (ConvNets) ï½ ï¼“ï¼ŽDeep learningã®æ•°ç† â—¦ ãªãœå„ªã‚ŒãŸæ€§èƒ½ãŒå®Ÿç¾ã§ãã‚‹ã®ã‹ï¼Ÿ â—¦ ブレークスルーを生んã å„è¦ç´ 技術 ï½ ï¼”ï¼Žå®Ÿè·µã™ã‚‹ã«ã‚ãŸã£ã¦ â—¦ é©åˆ‡åˆ©ç”¨ã™ã‚‹ãŸã‚ã«å¿…è¦ãªçŸ¥è˜ â—¦ ãƒãƒ¥ãƒ¼ãƒ‹ãƒ³ã‚°ã®å‹˜æ‰€ ï½ ï¼•ï¼Žä»Šå¾Œã®å±•æœ›ãƒ»ç ”究動
A Brief Overview of Deep Learning
(This is a guest post by Ilya Sutskever on the intuition behind deep learning as well as some very useful practical advice. Many thanks to Ilya for such a heroic effort!) Deep Learning is really popular these days. Big and small companies are getting into it and making money off it. It’s hot. There is some substance to the hype, too: large deep neural networks achieve the best results on speech re
深層å¦ç¿’入門
4. 深層å¦ç¿’ • 英語ã§ã¯Deep Learning • 特徴ã®æ•°æ®µã®çµ„ã¿åˆã‚ã›ã‚’考慮ã™ã‚‹ã“ã¨ã§ã‚ˆã‚Šè¤‡é›‘ ãªç¾è±¡ã‚’å¦ç¿’ã™ã‚‹ä»•çµ„㿠• 神経網回路(ニューラル・ãƒãƒƒãƒˆãƒ¯ãƒ¼ã‚¯)ã®å±¤ã‚’é‡ãã‚‹ ã“ã¨ã§å¦ç¿’ã™ã‚‹ã“ã¨ãŒæ®†ã© • 人間ã®è„³ã®ä»•çµ„ã¿ã«ä¼¼ã¦ã„る？ • ã¨ã«ã‹ã，様々ãªèªè˜ã‚¿ã‚¹ã‚¯ã§å¤§å¹…ã«è‰¯ã„精度を示 ã—ã¦ãŠã‚Šï¼ŒGoogle, Facebook,Microsoft,…ãªã©å¤šãã®ä¼ æ¥ãŒç ”究開発をã—ã¦ã„る． 4 6. 6 å ±é“ Deep Learning in the News 13! Researcher Dreams Up Machines That Learn Without Humans 06.27.13 Scientists See Promise in Deep-Learning Programs John Markoff November 23, 2012 Google!taps!
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2. ⾃自⼰己紹介 lï¬ å¾—å±… èª ä¹Ÿ (Seiya Tokui) lï¬ Preferred Networks リサーãƒãƒ£ãƒ¼ lï¬ Jubatus ã®ã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ 開発 – Jubatus: NTTã¨PFIã§å…±åŒé–‹ç™ºã—ã¦ã„るオープンソースã®åˆ†æ•£ リアルタイム機械å¦ç¿’基盤 http://jubat.us/ lï¬ ç¾åœ¨ã¯æ˜ åƒè§£æžã¨ãƒ‡ã‚£ãƒ¼ãƒ—ラーニングã®ç ”究開発ã«å¾“事 2 3. ニューラルãƒãƒƒãƒˆã®åŸºç¤Žã€å®Ÿè£…ã€å®Ÿé¨“ã«ã¤ã„ã¦è©±ã— ã¾ã™ lï¬ ãƒ‹ãƒ¥ãƒ¼ãƒ©ãƒ«ãƒãƒƒãƒˆã®é“å…· – 全体ã®æ§‹æˆã€â¾è¡Œï¨ˆåˆ—列ã«ã‚ˆã‚‹è¡¨ç¾ã€æ失関数ã€èª¤å·®é€†ä¼æ’ã€SGD lï¬ ä¸»è¦ãªå®Ÿè£… – Pylearn2, Torch7, Caffe, Cuda-â€â€‘‒convnet2 lï¬ ãƒ•ãƒ¬ãƒ¼ãƒ ãƒ¯ãƒ¼ã‚¯ã®åŸºæœ¬çš„ãªè¨è¨ˆ – テンソルデータã€ãƒ¬ã‚¤ãƒ¤ãƒ¼ã€ãƒãƒƒãƒˆã€æœ€é©åŒ–ルーãƒãƒ³ – アーã‚テクãƒãƒ£ã®è¨˜è¿°â½…方法（宣⾔言的ã€ã‚¹ã‚¯ãƒªãƒ—ティング） lï¬ å®Ÿé¨“ã®é€²ã‚â½…æ–¹
pylearn2 入門ã—ãŸã„ç·¨ - laughingã®ãƒ–ãƒã‚°
vagrant install ã¨ã‹ pylearn2 install 済ã¿ã® vm 作æˆãªã© vagrant install ã™ã‚‹ 手早ã vagrant 環境を整ãˆã¦*1 (ref:http://blog.papix.net/entry/2013/04/27/141608 )， github ã«ã‚ã‚‹ pylearn2 用㮠box を使ã£ã¦ pylearn2 ãŒå®Ÿè¡Œå‡ºæ¥ã‚‹ç’°å¢ƒã‚’作る *2 vagrant up ã™ã‚‹ git clone https://github.com/ironchief/pylearn2_vagrant.git cd pylearn2_vagrant vagrant upを実行ã™ã‚‹ã ã‘ã ãŒï¼Œcpu ã«ã‚ˆã£ã¦ã¯ vt-x ãŒä½¿ãˆãªã‹ã£ãŸã‚Šã™ã‚‹ã®ã§*3， 下記ã®ã‚ˆã†ã« 32bit OS を見るよã†ã« Vagrantfile を編集ã™ã‚‹ *4 diff --git a/
一般å‘ã‘ã®Deep Learning
PFI 全体セミナーã§ç™ºè¡¨ã—ãŸã€å°‚門家å‘ã‘ã§ã¯ãªã一般å‘ã‘ã®Deep Learning（深層å¦ç¿’）ã®è§£èª¬ã§ã™ã€‚ã©ã®ã‚ˆã†ãªå ´é¢ã§æ´»èºã—ã¦ã„ã‚‹ã®ã‹ã€ä»Šã¾ã§ã®å¦ç¿’手法ã¨ä½•ãŒé•ã†ã®ã‹ã‚’解説ã—ã¦ã„ã¾ã™ã€‚Read less
データマイニングコンペティションサイト Kaggle ã«ã‚‚ Deep Learning ブームãŒãã¦ã‚‹ã‹ã¨æ€ã£ãŸã®ã§ã¾ã¨ã‚ã‚‹ - 糞糞糞ãƒãƒƒãƒˆå¼æ…¶
æ¦‚è¦ æœ€è¿‘è©±é¡Œã® Deep Learning，NIPS ã‚„ ICML，CVPR ã¨ã„ã£ãŸä¸–ç•Œã®è©±ã ã‚ã†ã¨æ€ã£ã¦ã„ãŸã‚‰ Kaggle 㧠Deep learning ãŒåŽ»å¹´ä¸€ä»¶ï¼Œä»Šå¹´ã«å…¥ã£ã¦æ›´ã«ä¸€ä»¶å„ªå‹ã—ã¦ã„ãŸã®ã§ã¾ã¨ã‚る． Kaggle Kaggle: Your Home for Data Science ãŠãªã˜ã¿ã®ãƒ‡ãƒ¼ã‚¿ãƒžã‚¤ãƒ‹ãƒ³ã‚°ã‚³ãƒ³ãƒšãƒ†ã‚£ã‚·ãƒ§ãƒ³ã‚µã‚¤ãƒˆï¼Žãƒ‡ãƒ¼ã‚¿ã¨ç›®çš„関数ãŒä¸Žãˆã‚‰ã‚ŒãŸä¸Šã§æœ€ã‚‚高ã„スコアを出ã—ãŸãƒãƒ¼ãƒ ã«è³žé‡‘ãŒå‡ºã‚‹ï¼Ž 最近ã§ã¯ KDD Cup ã‚„ http://www.kaggle.com/c/challenges-in-representation-learning-the-black-box-learning-challenge:title=ICML2013 workshop competition]，や RecSys2013 Competition，レストランレビューサイ
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身内ã§deep learningã®å‹‰å¼·ä¼šã‚’ã‚„ã£ãŸã‚‰ã§ããã†ã ã£ãŸã®ã§å®Ÿè£…ã—ãŸï¼Ž èªã‚“ã ã®ã¯å¤§ä½“ã“ã“らã¸ã‚“． NEURAL NETS FOR VISION(CVPR2012 tutorial) CS294A Lecture notes Sparse autoencoder ImageNet Classification with Deep Convolutional Neural Networks autoencoder autoencoderã¯unsupervised feature learningã®ä¸€ç¨®ï¼ŽConvolutional Neural Netã¨ã¯é•ã£ã¦ï¼Œæœ€å¾Œã®åˆ¤åˆ¥å™¨ã®äºˆæ¸¬èª¤å·®ã‚’back propagationã•ã›ã‚‹ï¼Œã¨ã„ã†äº‹ã¯ã›ãšã‚る種特徴抽出ã§å®Œçµã•ã›ã¦ã„る． autoencoderを一言ã§ã¾ã¨ã‚ã‚‹ã¨ï¼Œã€Œæ¬¡å…ƒå‰Šæ¸›ã‚’ç¹°ã‚Šè¿”ã™Neural Netを多段ã«ç¹‹ã’ã¦ç‰¹å¾´æŠ½å‡ºã‚’è¡Œã†æ‰‹æ³•
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