BigDL. A distributed open-source Apache Spark deep learning library designed for efficient scale-out to multiple nodes. CPU-optimized via MKL. Python support. Only works on CPUs. Developed by Intel.
Cuda-Convnet – A fast C++/CUDA implementation of convolutional (or more generally, feed-forward) neural networks. It can model arbitrary layer connectivity and network depth. Any directed acyclic graph of layers will do. Training is done using the back-propagation algorithm.
RNNLM– Tomas Mikolov’s Recurrent Neural Network based Language models Toolkit.
RNNLIB-RNNLIB is a recurrent neural network library for sequence learning problems. Applicable to most types of spatiotemporal data, it has proven particularly effective for speech and handwriting recognition.
matrbm. Simplified version of Ruslan Salakhutdinov’s code, by Andrej Karpathy (Matlab).
The LUSH programming language and development environment, which is used @ NYU for deep convolutional networks
Eblearn.lsh is a LUSH-based machine learning library for doing Energy-Based Learning. It includes code for “Predictive Sparse Decomposition” and other sparse auto-encoder methods for unsupervised learning. Koray Kavukcuoglu provides Eblearn code for several deep learning papers on this page.
deepmat– Deepmat, Matlab based deep learning algorithms.
MShadow – MShadow is a lightweight CPU/GPU Matrix/Tensor Template Library in C++/CUDA. The goal of mshadow is to support efficient, device invariant and simple tensor library for machine learning project that aims for both simplicity and performance. Supports CPU/GPU/Multi-GPU and distributed system.
CXXNET – CXXNET is fast, concise, distributed deep learning framework based on MShadow. It is a lightweight and easy extensible C++/CUDA neural network toolkit with friendly Python/Matlab interface for training and prediction.
Nengo-Nengo is a graphical and scripting based software package for simulating large-scale neural systems.
Eblearn is a C++ machine learning library with a BSD license for energy-based learning, convolutional networks, vision/recognition applications, etc. EBLearn is primarily maintained by Pierre Sermanet at NYU.
cudamat is a GPU-based matrix library for Python. Example code for training Neural Networks and Restricted Boltzmann Machines is included.
Gnumpy is a Python module that interfaces in a way almost identical to numpy, but does its computations on your computer’s GPU. It runs on top of cudamat.
The CUV Library (github link) is a C++ framework with python bindings for easy use of Nvidia CUDA functions on matrices. It contains an RBM implementation, as well as annealed importance sampling code and code to calculate the partition function exactly (from AIS lab at University of Bonn).
Apache Singa is an open source deep learning library that provides a flexible architecture for scalable distributed training. It is extensible to run over a wide range of hardware, and has a focus on health-care applications.
Lightnet is a lightweight, versatile and purely Matlab-based deep learning framework. The aim of the design is to provide an easy-to-understand, easy-to-use and efficient computational platform for deep learning research.
SimpleDNN is a machine learning lightweight open-source library written in Kotlin whose purpose is to support the development of feed-forward and recurrent Artificial Neural Networks.
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