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Deep Learning in Speech Synthesis Heiga Zen Google August 31st, 2013 Outline Background Deep Learning Deep Learning in Speech Synthesis Motivation Deep learning-based approaches DNN-based statistical parametric speech synthesis Experiments Conclusion Text-to-speech as sequence-to-sequence mapping • Automatic speech recognition (ASR) Speech (continuous time series) → Text (discrete symbol sequence)
Please see cs224n.stanford.edu for the current (Winter 2017) version of this class. Natural language processing (NLP) is one of the most important technologies of the information age. Understanding complex language utterances is also a crucial part of artificial intelligence. Applications of NLP are everywhere because people communicate most everything in language: web search, advertisement, email
Answer (1 of 13): 1. A simple course in neural networks should help (Geoff Hintons course on coursera) 2. Gradient Descent/Stochastic Gradient Descent 3. Logistic Regression 4. Ability to code in a standard programming language (Python, Java, C++ etc) 5. An understanding of probability, linear al...
Caffe Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR) and by community contributors. Yangqing Jia created the project during his PhD at UC Berkeley. Caffe is released under the BSD 2-Clause license. Check out our web image classification demo! Why Caffe? Expressive architecture encourages application and innovat
概要 最近話題の Deep Learning,NIPS や ICML,CVPR といった世界の話だろうと思っていたら Kaggle で Deep learning が去年一件,今年に入って更に一件優勝していたのでまとめる. Kaggle Kaggle: Your Home for Data Science おなじみのデータマイニングコンペティションサイト.データと目的関数が与えられた上で最も高いスコアを出したチームに賞金が出る. 最近では KDD Cup や 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
岡野原です。Deep Learningが各分野のコンペティションで優勝し話題になっています。Deep Learningは7、8段と深いニューラルネットを使う学習手法です。すでに、画像認識、音声認識、最も最近では化合物の活性予測で優勝したり、既存データ・セットでの最高精度を達成しています。以下に幾つか例をあげます。 画像認識 LSVRC 2012 [html] 優勝チームスライド [pdf], まとめスライド[pdf] Googleによる巨大なNeuralNetを利用した画像認識(猫認識として有名)[paper][slide][日本語解説] また、各分野のトップカンファレンスでDeep Learningのチュートリアルが行われ、サーベイ論文もいくつか出ました。おそらく来年以降こうした話が増えてくることが考えられます。 ICML 2012 [pdf] ACL 2012 [pdf] CVPR
Deep Learning Methods for Vision CVPR 2012 Tutorial 9:00am-5:30pm, Sunday June 17th, Ballroom D (Full day) Rob Fergus (NYU), Honglak Lee (Michigan), Marc'Aurelio Ranzato (Google) Ruslan Salakhutdinov (Toronto), Graham Taylor (Guelph), Kai Yu (Baidu) Overview Hand-designed features such as SIFT and HOG underpin many successful object recognition approaches. However, these only capture low-level edg
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