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.
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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.
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.
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.
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.
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
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.
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
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 .
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.
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.
nDimensional's product, nD, is a full stack application development Platform-as-a-Service (PaaS) that allows companies to rapidly design, develop, deploy and operate AI, big data and IoT applications. nD empowers data-rich industries to drive real-time actions that quantitatively improve business outcomes, proven at enterprise scale, to bring the power of predictive insights and optimization to all vertical markets and value chains.
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.
NeuroSolutions is a neural network software for Windows that combines a modular, icon-based network design interface with an implementation of advanced artificial intelligence and learning algorithms using intuitive wizards or an Excel interface it perform cluster analysis, sales forecasting, sports predictions, medical classification, and much more
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.