This demo trains a Convolutional Neural Network on the MNIST digits dataset in your browser, with nothing but Javascript.手書き数字の画像データを JavaScript のプログラムで学習するデモ。
学習中の各層（conv, pool, softmax）の重みやアクティベーション値をリアルタイムで見ることができる。
2015-05-26
ConvNetJS MNIST demo
http://cs.stanford.edu/people/karpathy/convnetjs/demo/mnist.html
学習中の各層（conv, pool, softmax）の重みやアクティベーション値をリアルタイムで見ることができる。
学習中の各層（conv, pool, softmax）の重みやアクティベーション値をリアルタイムで見ることができる。
The Unreasonable Effectiveness of Recurrent Neural Networks
http://karpathy.github.io/2015/05/21/rnn-effectiveness/
We'll train RNNs to generate text character by character and ponder the question "how is that even possible?"
karpathy/convnetjs · GitHub
https://github.com/karpathy/convnetjs
ConvNetJS is a Javascript implementation of Neural networks, together with nice browser-based demos.
ConvNetJS: Deep Learning in your browser
http://cs.stanford.edu/people/karpathy/convnetjs/
ConvNetJS is a Javascript library for training Deep Learning models (mainly Neural Networks) entirely in your browser. Open a tab and you're training. No software requirements, no compilers, no installations, no GPUs, no sweat.
2015-05-16
2015-05-01
2015-04-27
GloVe: Global Vectors for Word Representation
http://nlp.stanford.edu/projects/glove/
GloVe is an unsupervised learning algorithm for obtaining vector representations for words. Training is performed on aggregated global word-word co-occurrence statistics from a corpus, and the resulting representations showcase interesting linear substructures of the word vector space.
2015-04-20
Echo state network - Wikipedia, the free encyclopedia
http://en.wikipedia.org/wiki/Echo_state_network
The echo state network (ESN) is a recurrent neural network with a sparsely connected hidden layer (with typically 1% connectivity). The connectivity and weights of hidden neurons are randomly assigned and are fixed. The weights of output neurons can be learned so that the network can (re)produce specific temporal patterns.
Reservoir computing - Wikipedia, the free encyclopedia
http://en.wikipedia.org/wiki/Reservoir_computing
Reservoir computing is a framework for computation like a neural network.
The “echo state” approach to analysing and training recurrent neural networks – with an Erratum note
http://web.info.uvt.ro/~dzaharie/cne2013/proiecte/tehnici/ReservoirComputing/EchoStatesTechRep.pdf
This is a corrected version of the technical report H. Jaeger(2001): The ”echo state” approach to analysing and training recurrent neural networks. GMD Report 148, German National Research Center for Information Techno logy, 2001.
An overview of reservoir computing: theory, applications and implementations
https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2007-8.pdf
This tutorial will give an overview of current research on theory, application and implementations of Reservoir Computing.
Frontiers | MACOP modular architecture with control primitives | Frontiers in Computational Neuroscience
http://journal.frontiersin.org/article/10.3389/fncom.2013.00099/full
2.4.2. Echo state networks
We use an Echo State Network (ESN) (Jaeger, 2001) as inverse model. An ESN is composed of a discrete-time recurrent neural network [commonly called the reservoir because ESNs belong to the class of Reservoir Computing techniques (Schrauwen et al., 2007)] and a linear readout layer which maps the state of the reservoir to the desired output.
Download PDF
http://journal.frontiersin.org/article/10.3389/fncom.2013.00099/pdf
2015-04-14
青空朗読 | 青空文庫に所蔵されている本を朗読しています
http://aozoraroudoku.jp/
この音声データと「青空文庫」のテキストを合わせれば、日本語音声コーパスとして使えるだろう。
「青空朗読」はインターネット上の図書館である「青空文庫」に掲載されている本を朗読する ...「青空文庫」を朗読した mp3 をダウンロードできる。
この音声データと「青空文庫」のテキストを合わせれば、日本語音声コーパスとして使えるだろう。
2015-04-13
2015-04-08
[1504.00941] A Simple Way to Initialize Recurrent Networks of Rectified Linear Units
http://arxiv.org/abs/1504.00941
In this paper, we propose a simpler solution that use recurrent neural networks composed of rectified linear units.
Key to our solution is the use of the identity matrix or its scaled version to initialize the recurrent weight matrix.再帰ネットに ReLU (rectified linear units) を使って長期の時間的構造を学習する。
[1501.00092] Image Super-Resolution Using Deep Convolutional Networks
http://arxiv.org/abs/1501.00092
The mapping is represented as a deep convolutional neural network (CNN) that takes the low-resolution image as the input and outputs the high-resolution one.畳み込みニューラルネットを使って、低解像度の画像を入力して高解像度の画像を出力する。
Random Ponderings: A Brief Overview of Deep Learning
http://yyue.blogspot.ca/2015/01/a-brief-overview-of-deep-learning.html
(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!)