How to Code and Understand DeepMind's Neural Stack Machine - i am trask
Learning to Transduce with Unbounded Memory Posted by iamtrask on February 25, 2016 Summary: I learn best with toy code that I can play with. This tutorial teaches DeepMind's Neural Stack machine via a very simple toy example, a short python implementation. I will also explain my thought process along the way for reading and implementing research papers from scratch, which I hope you will find use
DeepLearningを使った実装を纏めてみた - のんびりしているエンジニアの日記
皆さんこんにちは お元気ですか。私は全然です。 Deep Learning 一言で言うとただの深層学習ですが、 作り手や用途によって構造が全然違います。 今回は逆引き辞典よろしく、Deep Learningの実装のリンク集を作ってみました。 今回はライブラリは問わず、掲載します。 Caffe、Theano（Lasagne）、Torch7、Chainerなんでもござれです。 後日、追記するかも・・・ Neural Network（Full Connected） Auto Encoder Auto Encoder Denoising AutoEncoder Convolutional AutoEncoder Convolutional Neural Network Convolutional Neural Network R-CNN Fast-RCNN Faster-RCNN Recurren
BinaryNetとBinarized Deep Neural Network | 射撃しつつ前転
BinaryNet が最近話題になっている。ニューラルネットワークの二値化については前から興味があったので読んでみた。 ニューラルネットワークの二値化についてはこれまでも色々と研究があるようである。大きな方向性としては、まずはじめに実数値で重みを学習してそれを二値化するというやり方と、最初から二値の重みを学習するやり方がある。BinaryNetはそのどちらとも明確に分類しづらい、ちょっと変わった手法である。 BinaryConnectBinaryNetの前にBinaryConnectに触れておくことにする。どちらもBengioグループの研究である。BinaryConnectは大雑把に説明すると、以下の様な手法である。 weight matrix自体は実数で値を持っていて、forward/backwardの計算時に二値化する。パラメーター更新時は実数で持っている側のweight matrix
Prioritized Experience Replay
Experience replay lets online reinforcement learning agents remember and reuse experiences from the past. In prior work, experience transitions were uniformly sampled from a replay memory. However, this approach simply replays transitions at the same frequency that they were originally experienced, regardless of their significance. In this paper we develop a framework for prioritizing experience,
Reducing Overfitting in Deep Networks by Decorrelating Representations
One major challenge in training Deep Neural Networks is preventing overfitting. Many techniques such as data augmentation and novel regularizers such as Dropout have been proposed to prevent overfitting without requiring a massive amount of training data. In this work, we propose a new regularizer called DeCov which leads to significantly reduced overfitting (as indicated by the difference between
Theoretical Motivations for Deep Learning
This post is based on the lecture “Deep Learning: Theoretical Motivations” given by Dr. Yoshua Bengio at Deep Learning Summer School, Montreal 2015. I highly recommend the lecture for a deeper understanding of the topic. Deep learning is a branch of machine learning algorithms based on learning multiple levels of representation. The multiple levels of representation corresponds to multiple levels
Teaching Deep Convolutional Neural Networks to Play Go
Mastering the game of Go has remained a long standing challenge to the field of AI. Modern computer Go systems rely on processing millions of possible future positions to play well, but intuitively a stronger and more 'humanlike' way to play the game would be to rely on pattern recognition abilities rather then brute force computation. Following this sentiment, we train deep convolutional neural n
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