deep learning.md

Deep Learning

Tutorials of Tensorflow

• https://github.com/jtoy/awesome-tensorflow *****   TensorFlow - A curated list of dedicated resources http://tensorflow.org.

• https://github.com/aymericdamien/TensorFlow-Examples *****   TensorFlow Tutorial and Examples for beginners.

• https://github.com/zsdonghao/tensorlayer ****   TensorLayer: A Deep Learning and Reinforcement Learning Library for TensorFlow.

• https://github.com/pkmital/tensorflow_tutorials ****   From the basics to slightly more interesting applications of Tensorflow.

• https://github.com/nlintz/TensorFlow-Tutorials ****   Simple tutorials using Google's TensorFlow Framework. ipynb.

• https://github.com/awjuliani/TF-Tutorials  ***   A collection of deep learning tutorials using Tensorflow and Python(ipynb), including DCGAN, InfoGAN, Deep Layer Visualization, Deep Network Comparison, RNN-TF, t-SNE Tutorial.

• https://github.com/alrojo/tensorflow-tutorial ***   Practical tutorials and labs for TensorFlow used by Nvidia, FFN, CNN, RNN, Kaggle, AE.

• https://github.com/sjchoi86/Tensorflow-101 ***   Tensorflow Tutorials using Jupyter Notebook.

• https://github.com/MorvanZhou/tutorials ***   莫烦 Python机器学习视频教程代码。

• https://github.com/ahangchen/GDLnotes ***   Google Deep Learning Notes（TensorFlow教程） Udacity.

• https://github.com/tensorflow/skflow ***   Simplified interface for TensorFlow (mimicking Scikit Learn) for Deep Learning.

• https://github.com/tensorflow/models ***   Models built with TensorFlow

• https://github.com/CreatCodeBuild/TensorFlow-Chinese-Tutorial  **   Tensorflow Chinese Tutorial.

Tutorials of Other Frameworks

• https://github.com/fchollet/keras-resources *****   A directory of tutorials and open-source code repositories for working with Keras.

• https://github.com/Lasagne/Lasagne ***   Lightweight library to build and train neural networks in Theano.

• https://github.com/tflearn/tflearn ***   Deep learning library featuring a higher-level API for TensorFlow.

• https://github.com/transcranial/keras-js   ***   Run Keras models (tensorflow backend) in the browser, with GPU support.

• https://github.com/facebookresearch/CommAI-env ***   CommAI-env is a platform for training and testing an AI system. Python

• https://github.com/baidu/Paddle *** PaddlePaddle (PArallel Distributed Deep LEarning) Baidu.

• https://github.com/Microsoft/CNTK ***   Microsoft Cognition Toolkit (CNTK).

• https://github.com/ipod825/keraflow **   Reimplement the core of Keras.

Deep Learning Models

• https://github.com/fchollet/deep-learning-models *****   Keras code and weights files for popular deep learning models.

• https://github.com/fchollet/keras-blog ****   Blog with Keras news, tutorials, and demos.

• https://github.com/karpathy/char-rnn ****   Multi-layer Recurrent Neural Networks (LSTM, GRU, RNN) for character-level language models in Torch

• https://github.com/zo7/deconvfaces ***   Generating faces with deconvolution networks. keras

• https://github.com/hardmaru/write-rnn-tensorflow ***   Generative Handwriting using LSTM Mixture Density Network with TensorFlow.

• https://github.com/RobRomijnders/RNN_basketball ***   LSTM + MDN for basketball trajectories. ACL2016, Tensorflow.

• https://github.com/songhan/SqueezeNet-Deep-Compression ***   SqueezeNet-Deep-Compression

• https://github.com/LeavesBreathe/tensorflow_with_latest_papers ***   Implementation of RNN and NLP Related Neural Network Papers.

• https://github.com/samjabrahams/tensorflow-white-paper-notes **   Annotated notes and summaries of the TensorFlow white paper, along with SVG figures and links to documentation.

GAN

• https://github.com/ofirnachum/sequence_gan *** Tensorflow implementation of generative adversarial networks (GAN) applied to sequential data via recurrent neural networks (RNN).

• https://github.com/buriburisuri/ac-gan *** A tensorflow implementation of google's AC-GAN ( Auxiliary Classifier GAN ).

• https://github.com/phreeza/keras-GAN *** Generative Adversarial Networks with Keras

• https://github.com/openai/InfoGAN **** Code for reproducing key results in the paper InfoGAN:

• https://github.com/ikostrikov/TensorFlow-VAE-GAN-DRAW **** A collection of generative methods implemented with TensorFlow (Deep Convolutional Generative Adversarial Networks (DCGAN), Variational Autoencoder (VAE) and DRAW: A Recurrent Neural Network For Image Generation).

• https://github.com/openai/improved-gan *** Code for the paper "Improved Techniques for Training GANs"

• https://github.com/junyanz/iGAN **** iGAN: Interactive Image Generation via Generative Adversarial Networks

• https://github.com/Newmu/dcgan_code **** Deep Convolutional Generative Adversarial Networks

• https://github.com/soumith/ganhacks **** starter from "How to Train a GAN?" at NIPS2016

• https://github.com/AYLIEN/gan-intro ** This is the code that we used to generate our GAN 1D Gaussian approximation.

Conclusion

• https://github.com/ChristosChristofidis/awesome-deep-learning *****   A curated list of awesome Deep Learning tutorials, projects and communities.

• https://github.com/terryum/awesome-deep-learning-papers *****   A curated list of the most cited deep learning papers (since 2010)

• https://github.com/leriomaggio/deep-learning-keras-tensorflow *****   Deep Learning with Keras and Tensorflow.

• https://github.com/dennybritz/deeplearning-papernotes ***   Summaries and notes on Deep Learning research papers. A good method worthing learning.

• https://github.com/wepe/MachineLearning ***   一些常见的机器学习算法的实现代码，学习过程中做的总结，资历尚浅，如有错误请不吝指出。

• https://github.com/imistyrain/OpenDL ***   开源的深度学习资料整理. Caffe.

• https://github.com/carpedm20/awesome-torch ***   A curated list of awesome Torch tutorials, projects and communities.

• https://github.com/karpathy/paper-notes **   Random notes on papers, likely a short-term repo.

Miscellaneous

• https://github.com/josephmisiti/awesome-machine-learning *****
  A curated list of awesome Machine Learning frameworks, libraries and software.

• https://github.com/vhf/free-programming-books ****   Freely available programming books

• https://github.com/openai/requests-for-research ****   A living collection of deep learning problems https://openai.com/requests-for-research.

• https://github.com/soumith/convnet-benchmarks ****   Easy benchmarking of all publicly accessible implementations of convnets.

• https://github.com/jatinshah/ufldl_tutorial ****   Stanford Unsupervised Feature Learning and Deep Learning Tutorial

• https://github.com/kjw0612/awesome-deep-vision ****   A curated list of deep learning resources for computer vision

• https://github.com/ty4z2008/Qix ***   里面有很多干货,差不多每天都在更新。

• https://github.com/sjchoi86/dl_tutorials ***   Deep learning tutorials (2nd ed.) slides.

• https://github.com/ujjwalkarn/Machine-Learning-Tutorials ***   machine learning and deep learning tutorials, articles and other resources.