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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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.
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.
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.
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 .