Displaying 1 to 20 from 43 results

MXNet - A Deep Learning Framework


MXNet is an open-source deep learning framework that allows you to define, train, and deploy deep neural networks on a wide array of devices, from cloud infrastructure to mobile devices. It is highly scalable, allowing for fast model training, and supports a flexible programming model and multiple languages. MXNet allows you to mix symbolic and imperative programming flavors to maximize both efficiency and productivity.

CNTK - Computational Network Toolkit (CNTK)


The Microsoft Cognitive Toolkit is a free, easy-to-use, open-source, commercial-grade toolkit that trains deep learning algorithms to learn like the human brain. It is a unified deep-learning toolkit that describes neural networks as a series of computational steps via a directed graph.

keras - Deep Learning library for Python. Runs on TensorFlow, Theano, or CNTK.


Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.

pytorch-tutorial - PyTorch Tutorial for Deep Learning Researchers


This repository provides tutorial code for deep learning researchers to learn PyTorch. In the tutorial, most of the models were implemented with less than 30 lines of code. Before starting this tutorial, it is recommended to finish Official Pytorch Tutorial.




StarGAN - Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation


PyTorch implementation of StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation. StarGAN can flexibly translate an input image to any desired target domain using only a single generator and a discriminator.

DeepSpeech - A TensorFlow implementation of Baidu's DeepSpeech architecture


Project DeepSpeech is an open source Speech-To-Text engine. It uses a model trained by machine learning techniques, based on Baidu's Deep Speech research paper. Project DeepSpeech uses Google's TensorFlow project to make the implementation easier.

keras-rl - Deep Reinforcement Learning for Keras.


keras-rl implements some state-of-the art deep reinforcement learning algorithms in Python and seamlessly integrates with the deep learning library Keras. Just like Keras, it works with either Theano or TensorFlow, which means that you can train your algorithm efficiently either on CPU or GPU. Furthermore, keras-rl works with OpenAI Gym out of the box. This means that evaluating and playing around with different algorithms is easy. Of course you can extend keras-rl according to your own needs. You can use built-in Keras callbacks and metrics or define your own. Even more so, it is easy to implement your own environments and even algorithms by simply extending some simple abstract classes. In a nutshell: keras-rl makes it really easy to run state-of-the-art deep reinforcement learning algorithms, uses Keras and thus Theano or TensorFlow and was built with OpenAI Gym in mind.

Bender - Easily craft fast Neural Networks on iOS! Use TensorFlow models. Metal under the hood.


Bender is an abstraction layer over MetalPerformanceShaders useful for working with neural networks. Bender is an abstraction layer over MetalPerformanceShaders which is used to work with neural networks. It is of growing interest in the AI environment to execute neural networks on mobile devices even if the training process has been done previously. We want to make it easier for everyone to execute pretrained networks on iOS.


chainer - A flexible framework of neural networks for deep learning


Chainer is a Python-based deep learning framework aiming at flexibility. It provides automatic differentiation APIs based on the define-by-run approach (a.k.a. dynamic computational graphs) as well as object-oriented high-level APIs to build and train neural networks. It also supports CUDA/cuDNN using CuPy for high performance training and inference. For more details of Chainer, see the documents and resources listed above and join the community in Forum, Slack, and Twitter. The stable version of current Chainer is separated in here: v3.

TensorFlow - Artificial Intelligence Library from Google


TensorFlow is a library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) that flow between them. This flexible architecture lets you deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device without rewriting code.

tensorpack - A Neural Net Training Interface on TensorFlow


Tensorpack is a training interface based on TensorFlow. It's Yet Another TF high-level API, with speed, readability and flexibility built together.

ml-agents - Unity Machine Learning Agents


Unity Machine Learning Agents (ML-Agents) is an open-source Unity plugin that enables games and simulations to serve as environments for training intelligent agents. Agents can be trained using reinforcement learning, imitation learning, neuroevolution, or other machine learning methods through a simple-to-use Python API. We also provide implementations (based on TensorFlow) of state-of-the-art algorithms to enable game developers and hobbyists to easily train intelligent agents for 2D, 3D and VR/AR games. These trained agents can be used for multiple purposes, including controlling NPC behavior (in a variety of settings such as multi-agent and adversarial), automated testing of game builds and evaluating different game design decisions pre-release. ML-Agents is mutually beneficial for both game developers and AI researchers as it provides a central platform where advances in AI can be evaluated on Unity’s rich environments and then made accessible to the wider research and game developer communities. For more information, in addition to installation and usage instructions, see our documentation home. If you have used a version of ML-Agents prior to v0.3, we strongly recommend our guide on migrating to v0.3.

DeepDetect - Deep Learning Server


DeepDetect is an Instant Machine Learning for your Applications. It can classify images, text and numerical data from your application or the command line by series of simple calls to the deep learning server. A simple yet powerful and generic API for use of Machine Learning.

H2O - Fast Scalable Machine Learning API For Smarter Applications


H2O is for data scientists and application developers who need fast, in-memory scalable machine learning for smarter applications. H2O is an open source parallel processing engine for machine learning. Unlike traditional analytics tools, H2O provides a combination of extraordinary math, a high performance parallel architecture, and unrivaled ease of use.

Amazon DSSTNE: Deep Scalable Sparse Tensor Network Engine


DSSTNE (pronounced "Destiny") is an open source software library for training and deploying recommendation models with sparse inputs, fully connected hidden layers, and sparse outputs. Models with weight matrices that are too large for a single GPU can still be trained on a single host. DSSTNE has been used at Amazon to generate personalized product recommendations for our customers at Amazon's scale.

Sonnet - Library built on top of TensorFlow for building complex neural networks


Sonnet is a library built on top of TensorFlow for building complex neural networks. The library uses an object-oriented approach, similar to Torch/NN, allowing modules to be created which define the forward pass of some computation. Modules are called with some input Tensors, which adds ops to the Graph and returns output Tensors.

merlin - This is now the official location of the Merlin project.


This repository contains the Neural Network (NN) based Speech Synthesis System developed at the Centre for Speech Technology Research (CSTR), University of Edinburgh.Merlin is a toolkit for building Deep Neural Network models for statistical parametric speech synthesis. It must be used in combination with a front-end text processor (e.g., Festival) and a vocoder (e.g., STRAIGHT or WORLD).

Kur - Descriptive Deep Learning


Kur is a system for quickly building and applying state-of-the-art deep learning models to new and exciting problems. Kur was designed to appeal to the entire machine learning community, from novices to veterans. It uses specification files that are simple to read and author, meaning that you can get started building sophisticated models without ever needing to code. Even so, Kur exposes a friendly and extensible API to support advanced deep learning architectures or workflows.

ConvNetJS - Javascript implementation of Neural networks


ConvNetJS is a Javascript implementation of Neural networks, It currently supports Common Neural Network modules, Classification (SVM/Softmax) and Regression (L2) cost functions, A MagicNet class for fully automatic neural network learning (automatic hyperparameter search and cross-validatations), Ability to specify and train Convolutional Networks that process images, An experimental Reinforcement Learning module, based on Deep Q Learning.