Noisy Activation Functions
Common nonlinear activation functions used in neural networks can cause training difficulties due to the saturation behavior of the activation function, which may hide dependencies that are not visible to vanilla-SGD (using first order gradients only). Gating mechanisms that use softly saturating activation functions to emulate the discrete switching of digital logic circuits are good examples of
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
Contents — DeepLearning 0.1 documentation
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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
Crazy Stone
If you have any problem buying or using Crazy Stone, please contact Unbalance, not me. History 2016-03-21: Crazy Stone took the third place of the UEC Cup. It is considerably stronger thanks to deep learning. It reached 7d on KGS, and entered the KGS top 100! 2015-03-21: Crazy Stone won the UEC Cup, and lost a 3-stone game against Cho Chikun in the Densei-sen. 2014-03-24: Recently, Crazy Stone est
Deeply Moving: Deep Learning for Sentiment Analysis
Deeply Moving: Deep Learning for Sentiment Analysis This website provides a live demo for predicting the sentiment of movie reviews. Most sentiment prediction systems work just by looking at words in isolation, giving positive points for positive words and negative points for negative words and then summing up these points. That way, the order of words is ignored and important information is lost.
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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How DeepMind Conquered Go With Deep Learning (AlphaGo) | Two Minute Papers #42
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Deeplearning 4 j のクイックスタートガイド - Deeplearning4j: Open-source, Distributed Deep Learning for the JVM