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January 21, 2017

Neuroph

 Neuroph is lightweight Java neural network framework to develop common neural network architectures. It contains well designed, open source Java library with small number of basic classes which correspond to basic NN concepts. Also has nice GUI neural network editor to quickly create Java neural network components. It has been released as open source under the Apache 2.0 license. Neuroph simplifies the development of neural networks by providing Java neural network library and GUI tool that supports creating, training and saving neural networks. Neuroph is best for beginners with neural networks, especially if the user just wanted to try how it works [...]

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January 21, 2017

DNNGraph

DNNGraph is a deep neural network model generation DSL in Haskell. It is a DSL for specifying the model. This uses the lens library for elegant, composable constructions, and the fgl graph library for specifying the network layout. A set of optimization passes that run over the graph representation to improve the performance of the model. For example, users can take advantage of the fact that several layers types (ReLU, Dropout) can operate in-place. DNNGraph also offers a set of backends to generate code for the platform. Currently, DNNGraph generate Caffe (by generating model prototxt files) and Torch (by generating Lua scripts). It also has a set of [...]

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January 21, 2017

neon

Neon is Nervana’s Python-based deep learning library. It provides ease of use while delivering the highest performance. Some of the features that neon has would be the support for commonly used models including convnets, RNNs, LSTMs, and autoencoders, tight integration with neon’s state-of-the-art GPU kernel library, 3s/macrobatch (3072 images) on AlexNet on Titan X (Full run on 1 GPU ~ 32 hrs), basic automatic differentiation support, framework for visualization and swappable hardware backends: write code once and deploy on CPUs, GPUs, or Nervana hardware. Neon supports loading of both common and custom datasets. Data should be loaded as a python [...]

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January 21, 2017

HNN

HNN stands for Haskell Neural Network library; it is an attempt at providing a simple but powerful and efficient library to deal with feed-forward neural networks in Haskell. It is a neural network library implemented purely in Haskell, relying on the hmatrix library. This library provides a straight and simple feed-forward neural networks implementation which is way better than the one in hnn-0.1, in all aspects.Haskell is an advanced purely-functional programming language. An open-source product of more than twenty years of cutting-edge research, it allows rapid development of robust, concise, correct software. With strong support for integration with [...]

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January 21, 2017

Keras

Keras is a deep learning library for Theano and TensorFlow. It is a high-level neural networks library, written in Python and capable of running on top of either TensorFlow 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. Keras deep learning library allows for easy and fast prototyping (through total modularity, minimalism, and extensibility). It supports both convolutional networks and recurrent networks, as well as combinations of the two. Keras also supports arbitrary connectivity schemes (including multi-input and multi-output [...]

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