Best Artificial Neural Network Software

Artificial neural networks (ANNs) are models based on the neural networks in the human brain that react and adapt to information, learning to make decisions based off that information, in theory, the same way a human would. ANNs require a data pool as a baseline for learning. The more data available, the more connections a neural network can make and the more it can learn. As an ANN learns, it can consistently give accurate outputs based on the solution a user is seeking. Deep neural networks (DNNs) are ANNs that have hidden layers between input and output. Developers use DNNs when building an intelligent application with deep learning functionality. Artificial neural networks are the basis for other deep learning algorithms, such as image recognition, natural language processing, and voice recognition, among others.

To qualify for inclusion in the Artificial Neural Networks category, a product must:

  • Provide a network based on interconnected neural units to create learning capabilities
  • Offer a backbone for deeper learning algorithms
  • Link to data sources to feed the neural network information
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    Keras is a neural networks library, written in Python and capable of running on top of either TensorFlow or Theano.

    Mocha is a Deep Learning framework for Julia, inspired by the C++ framework Caffe that is efficient implementations of general stochastic gradient solvers and common layers, it could be used to train deep / shallow (convolutional) neural networks, with (optional) unsupervised pre-training via (stacked) auto-encoders.

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    Clarifai offers a suite of tools that make it easy for anyone to quickly and accurately train, customize, and use machine learning-powered image and video recognition in their products.

    DeepPy is a MIT licensed deep learning framework that tries to add a touch of zen to deep learning as it allows for Pythonic programming based on NumPy's ndarray,has a small and easily extensible codebase, runs on CPU or Nvidia GPUs and implements the following network architectures feedforward networks, convnets, siamese networks and autoencoders.

    TFlearn is a modular and transparent deep learning library built on top of Tensorflow that provide a higher-level API to TensorFlow in order to facilitate and speed-up experimentations, while remaining fully transparent and compatible with it.

    Torch is a scientific computing framework with wide support for machine learning algorithms that puts GPUs first.

    ConvNetJS is a Javascript library for training Deep Learning models (Neural Networks) entirely in a browser.

    brain is a JavaScript neural network library to recognize color contrast.

    NVIDIA Deep Learning GPU Training System (DIGITS) deep learning for data science and research to quickly design deep neural network (DNN) for image classification and object detection tasks using real-time network behavior visualization.

    julia-ann is the implementation of backpropagation artificial neural networks in Julia that allow users to build multilayer networks and accept DataFrames as inputs. fit! and predict currently require Float64 matrices and vectors.

    Knet (pronounced "kay-net") is a deep learning framework implemented in Julia that allows the definition and training of machine learning models using the full power and expressivity of Julia.

    BrainCore is a neural network framework written in Swift that uses Metal which makes it fast.

    Cortex is a neural networks, regression and feature learning in Clojure.

    cuda-convnet2 is a fast C++/CUDA implementation of convolutional (or more generally, feed-forward) neural networks that can model arbitrary layer connectivity and network depth, any directed acyclic graph of layers will do it required fermi-generation GPU (GTX 4xx, GTX 5xx, or Tesla equivalent).

    Hebel is a library for deep learning with neural networks in Python using GPU acceleration with CUDA through PyCUDA that implements the important types of neural network models and offers a variety of different activation functions and training methods such as momentum, Nesterov momentum, dropout, and early stopping.

    NeuralTalk2 is an Efficient Image Captioning code in Torch that runs on GPU

    Swift Brain is a neural network / machine learning library written in Swift for AI algorithms in Swift for iOS and OS X development it includes algorithms focused on Bayes theorem, neural networks, SVMs, Matrices, etc.

    BackpropNeuralNet.jl is a neural network in Julia that initialize a network of 2 inputs, 1 hidden layer with 3 neurons, and 2 outputs.

    BPN-NeuralNetwork is a Machine Learning that implemented 3 layers ( Input Layer, Hidden Layer and Output Layer ) neural network and implemented Back Propagation Neural Network (BPN), QuickProp theory and Kecman's theory (EDBD). KRBPN can be used in products recommendation user behavior analysis, data mining and data analysis .

    Chainer is a powerful, flexible, and intuitive framework of neural networks that bridge the gap between algorithms and implementations.

    Darknet is an open source neural network framework written in C and CUDA that supports CPU and GPU computation.

    deeplearn-rs is a deep learning in rust that can be used to build trainable matrix compptation graphs that are configurable at runtime.

    gobrain is a neural networks written in go that includes just basic Neural Network functions such as Feed Forward and Elman Recurrent Neural Network.

    GoNN is an implementation of Neural Network in Go Language, which includes BPNN, RBF, PCN

    HNN (stands for Haskell Neural Network library) is an attempt at providing a simple but powerful and efficient library to deal with feed-forward neural networks in Haskell.

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