Displaying 1 to 10 from 15 results

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.

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.

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.

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.

Caffe - Deep Learning Framework from Berkley Vision


Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by the Berkeley Vision and Learning Center (BVLC) and by community contributors.

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.

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.

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.

Apache Singa - Distributed Deep Learning Platform


SINGA is a distributed deep learning platform for big data analytics. It supports various deep learning models, and thus has the flexibility to allow users to customize the models that fit their business requirements. It provides a scalable architecture to train deep learning models from huge volumes of data and it makes the distributed training process transparent to users.