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.

    The AWS Deep Learning AMIs is designed to equip data scientists, machine learning practitioners, and research scientists with the infrastructure and tools to accelerate work in deep learning, in the cloud, at any scale.

    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.

    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).

    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.

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

    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

    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.

    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.

    Hopfield Networks are a simple form of neutral network, that can be understood as a simplified model of memory.

    LambdaNet is an artificial neural network library written in Haskell that abstracts network creation, training, and use as higher order functions, it provides a framework in which users can: quickly iterate through network designs by using different functional components and experiment by writing small functional components to extend the library

    Lasagne is a lightweight library to build and train neural networks in Theano.

    MGL is a Common Lisp machine learning library that concentrates on various forms of neural networks (boltzmann machines, feed-forward and recurrent backprop nets).

    MLPNeuralNet is a fast multilayer perceptron neural network library for iOS and Mac OS X that predicts new examples through trained neural networks, it is built on top of Apple's Accelerate Framework using vectored operations and hardware acceleration (if available).

    Multi-Perceptron-NeuralNetwor is a Machine Learning that implemented multi-layer perceptrons neural network (MLP)and Back Propagation Neural Network (BPN), it designed unlimited hidden layers to do the training tasks and can be used in products recommendation, user behavior analysis, data mining and data analysis.

    neon is Nervana's Python-based deep learning library that provides ease of use while delivering the highest performance.

    NeuralN is a powerful Neural Network for Node.js with multiple advantages compared to existing solutions it works with extra large datasets (>1Go allowed by nodejs) and Multi-Threaded training available.

    Neurolab is a simple and powerful Neural Network Library for Python that contains based neural networks, train algorithms and flexible framework to create and explore other neural network types.

    ANN is a Python class for time series prediction it is a family of mathematical models inspired by biological neural networks, wich are used to estimate or approximate functions that can't be estimate analytically.

    Neuroph is lightweight Java neural network framework that 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 and has s GUI neural network editor to quickly create Java neural network components.

    nnet is a software for feed-forward neural networks with a single hidden layer, and for multinomial log-linear models.

    RSNNS is a Neural Networks in R using the Stuttgart Neural Network Simulator (SNNS) a library containing many standard implementations of neural networks, this package wraps the SNNS functionality to make it available from within R. Using the RSNNS low-level interface, all of the algorithmic functionality and flexibility of SNNS can be accessed and contains a convenient high-level interface, so that the most common neural network topologies and learning algorithms integrate seamlessly into R.

    RustNN is a feedforward neural network library that generates fully connected multi-layer artificial neural networks that are trained via backpropagation.

    uKit AI is a AI web design made to offer free design and technology upgrades in minutes.

    5Analytics helps enable companies to integrate, deploy and monitor their machine learning in a scalable, repeatable manner.

    AForge.Neuro is a namespace that contains interfaces and classes for neural networks computation, the namespace and its sub namespaces contain classes, which allow as creating of popular neural network architecture as classes to train this network.

    APEX is an AI-enhanced technology platform intended to provide solutions for your business end to end. With APEX you gain access to the same powerful AI capabilities and tools used by the tech unicorns at a fraction of the cost. APEX allows you to realize the full benefits of the AI technologies, while sustaining governance, flexibility, scalability, tool compatibility, and collaboration. Through the integration of the most advanced open source and proprietary 2021.AI technological components, APEX enhances data governance, increases maintainability and quality of the AI models. APEX can be installed either on-premises, or consumed in private or public cloud. APEX offers 3 editions: Front, Go, and Enterprise, all capable of delivering immediate business value for companies of all sizes, in all the stages of AI maturity and ambitions.

    AIToolbox is a toolbox of AI modules written in Swift: Graphs/Trees, Linear Regression, Support Vector Machines, Neural Networks, PCA, KMeans, Genetic Algorithms, MDP, Mixture of Gaussians, Logistic Regression

    Allspark is an AI framework designed to automate manual repetitive processes based on unstructured text data like invoices, contracts and emails.

    NeuroIntelligence is a neural networks software application designed to assist neural network, data mining, pattern recognition, and predictive modeling experts in solving real-world problems. NeuroIntelligence features only proven neural network modeling algorithms and neural net techniques; software is fast and easy-to-use

    Autobox adjusts for interventions like outliers, seasonal pulses, level shifts, local time trends, variance & parameters changes. Only Autobox adjusts the model for lead and lag relationships automatically!

    Deeplearning4j is the first commercial-grade, open-source, distributed deep-learning library written for Java and Scala it integrated with Hadoop and Spark, to be used in business environments on distributed GPUs and CPUs that aims to be cutting-edge plug and play, more convention than configuration, which allows for fast prototyping for non-researchers.

    DeepLearningKit is an open source Deep Learning Framework for Apple's iOS, OS X & tvOS. Developed in Swift & Metal (GPU acceleration).