SigmaQuan

rnn-tutorial-gru-lstm

Language Model GRU with Python and Theano

convergent_learning

Code for paper "Convergent Learning: Do different neural networks learn the same representations?"

DARQN

Deep Attention Recurrent Q-Network

algorithm-learning

bAbI-tasks

Task generation for testing text understanding and reasoning

BOOK-CODE-Learning.Python.The.Hard.Way

version 0.52

CTC-LSTM

Theano implementation of CTC. Applied on TIMIT and a Toy dataset.

cuda-convnet2-1

Automatically exported from code.google.com/p/cuda-convnet2

cxxnet

fast, concise, distributed deep learning framework

DeepLearningBibliography

Bibliography for Publications about Deep Learning using GPU

DeepLearnToolbox

Matlab/Octave toolbox for deep learning. Includes Deep Belief Nets, Stacked Autoencoders, Convolutional Neural Nets, Convolutional Autoencoders and vanilla Neural Nets. Each method has examples to get you started.

Dynamic-memory-networks-in-Theano

Implementation of Dynamic memory networks by Kumar et al. http://arxiv.org/abs/1506.07285

embeddings

Code for AMIA CRI 2016 paper "Learning Low-Dimensional Representations of Medical Concepts" (http://cs.nyu.edu/~dsontag/papers/ChoiChiuSontag_AMIA_CRI16.pdf)

fbcunn

Facebook's extensions to torch/cunn.

hed-dlg

Hierarchical Encoder Decoder for Dialog Modelling

kaggle_allen

keras_qa

Keras solution to the bAbI tasks using recurrent neural networks - merged as an example into Keras mainline

learningSimpleAlgorithms

We present an approach for learning simple algorithms such as copying, multi-digit addition and single digit multiplication directly from examples. Our framework consists of a set of interfaces, accessed by a controller. Typical interfaces are 1-D tapes or 2-D grids that hold the input and output data. For the controller, we explore a range of neural network-based models which vary in their ability to abstract the underlying algorithm from training instances and generalize to test examples with many thousands of digits. The controller is trained using Q-learning with several enhancements and we show that the bottleneck is in the capabilities of the controller rather than in the search incurred by Q-learning.

MemNN

Memory Networks implementations

NAMAS

Neural Attention Model for Abstractive Summarization

Pinyin2Hanzi

拼音转汉字， 拼音输入法引擎， pin yin -> 拼音

practical6

Practical 6: LSTM language models

python-docx

Create and modify Word documents with Python

rlntm

An implementation of the RL-NTM from http://arxiv.org/abs/1505.00521

rnn-lm

Standard Recurrent Language Model

SCRNNs

This is a self contained software accompanying the paper titled: Learning Longer Memory in Recurrent Neural Networks: http://arxiv.org/abs/1412.7753.

seuthesis

Automatically exported from code.google.com/p/seuthesis

TestNvidiaCuda6_5

Test NIDAIA CUDA 6.5

theano_exercises

Exercises for my tutorials on Theano

Weibo

Python Web框架 Flask开发的微博程序