Matlab
1. ConvNet convolutional neural network is a class of deep learning classification algorithms, which can autonomously learn useful features from raw data by adjusting weight values.
2. DeepLearnToolBox is a Matlab/Octave toolbox for deep learning, which includes deep belief network (DBN), stacked autoencoder (stacked AE), convolutional neural network (CNN) and other algorithms.
3. cuda-convet is a set of convolutional neural network (CNN) code, which is also suitable for feed-forward neural networks and uses C++/CUDA for operations. It can model multilayer neural networks of arbitrary depth. As long as it is a directed acyclic graph network structure, it can be used. The training process adopts the back propagation algorithm (BP algorithm).
4. MatConvNet is a convolutional neural network (CNN) Matlab toolbox for computer vision applications. It is simple and efficient, capable of running and learning state-of-the-art machine learning algorithms.
CPP
1. eblearn is an open source machine learning C++ package library developed by the New York University Machine Learning Lab led by Yann LeCun. It implements convolutional neural networks with energy-based models and provides a visual interface (GUI), examples, and demonstration tutorials.
2. SINGA is a project supported by the Apache Software Foundation, and its design goal is to provide a general distributed model training algorithm on existing systems.
3. NVIDIA DIGITS is a new system for developing, training, and visualizing deep neural networks. It brings the power of deep learning into a browser interface, allowing data scientists and researchers to visualize neural network behavior in real-time and quickly design deep neural networks that best fit the data.
4. The Intel® Deep Learning Framework provides a unified platform for the Intel® platform to accelerate deep convolutional neural networks.
https://github.com/fchollet/keras
https://github.com/tflearn/tflearn
https://github.com/beniz/deepdetect
https://github.com/tensorflow/fold
https: //github.com/leriomaggio/deep-learning-keras-tensorflow
https://github.com/tensorflow/models
https://github.com/aymericdamien/TensorFlow-Examples
https://github.com/donnemartin/data-science-ipython-notebooks
https://github.com/jtoy/awesome-tensorflow
https://github.com/jikexueyuanwiki/tensorflow-zh
https://github.com/nlintz/TensorFlow-Tutorials
https://github.com/pkmital/tensorflow_tutorials
https://github.com/deepmind/learning-to-learn
https://github.com/BinRoot/TensorFlow-Book
https://github.com/jostmey/NakedTensor
https://github.com/alrojo/tensorflow-tutorial
https://github.com/CreatCodeBuild/TensorFlow-and-DeepLearning-Tutorial
https://github.com/sjchoi86/Tensorflow-101
https://github.com/chiphuyen/tf-stanford-tutorials
https://github.com/google/prettytensor
https://github.com/ahangchen/GDLnotes
https://github.com/Hvass-Labs/TensorFlow-Tutorials
https://github.com/NickShahML/tensorflow_with_latest_papers
https://github.com/nfmcclure/tensorflow_cookbook
https://github.com/ppwwyyxx/tensorpack
https://github.com/rasbt/deep-learning-book
https://github.com/pkmital/CADL
https://github.com/tensorflow/skflow
https://github.com/dennybritz/reinforcement-learning
https://github.com/zsdonghao/tensorlayer
https://github.com/matthiasplappert/keras-rl
https://github.com/nivwusquorum/tensorflow-deepq
https://github.com/devsisters/DQN-tensorflow
https://github.com/coreylynch/async-rl
https://github.com/carpedm20/deep-rl-tensorflow
https://github.com/yandexdataschool/Practical_RL
文本分类
https://github.com/silicon-valley-data-science/RNN-Tutorial
语音合成
https://github.com/pannous/tensorflow-speech-recognition
风格转换
https://github.com/ericjang/tdb
https://github.com/samjabrahams/tensorflow-on-raspberry-pi
https://github.com/rstudio/tensorflow
https://github.com/fluxcapacitor/pipeline
https://github.com/yahoo/TensorFlowOnSpark
https://github.com/ethereon/caffe-tensorflow
关于文本分类：https://github.com/ChengjinLi/machine_learning
关于聊天机器人：https://github.com/MarkWuNLP/MultiTurnResponseSelection