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MATLAB + Deep Learning Toolbox (formally Neural Network Toolbox) MathWorks: 1992 Proprietary: No Linux, macOS, Windows: C, C++, Java, MATLAB: MATLAB: No No Train with Parallel Computing Toolbox and generate CUDA code with GPU Coder [23] No Yes [24] Yes [25] [26] Yes [25] Yes [25] Yes With Parallel Computing Toolbox [27] Yes Microsoft Cognitive ...
Matlab: The neural network toolbox has explicit functionality designed to produce a time delay neural network give the step size of time delays and an optional training function. The default training algorithm is a Supervised Learning back-propagation algorithm that updates filter weights based on the Levenberg-Marquardt optimizations.
Neural Engineering Object (Nengo) is a graphical and scripting software for simulating large-scale neural systems. [1] As the neural network software Nengo is a tool for modelling neural networks with applications in cognitive science , psychology , artificial intelligence and neuroscience .
Networks such as the previous one are commonly called feedforward, because their graph is a directed acyclic graph. Networks with cycles are commonly called recurrent. Such networks are commonly depicted in the manner shown at the top of the figure, where is shown as dependent upon itself. However, an implied temporal dependence is not shown.
An echo state network (ESN) [1] [2] is a type of reservoir computer that uses a recurrent neural network with a sparsely connected hidden layer (with typically 1% connectivity). The connectivity and weights of hidden neurons are fixed and randomly assigned.
Neural network software is used to simulate, research, develop, and apply artificial neural networks, software concepts adapted from biological neural networks, and in some cases, a wider array of adaptive systems such as artificial intelligence and machine learning.
In the field of mathematical modeling, a radial basis function network is an artificial neural network that uses radial basis functions as activation functions.The output of the network is a linear combination of radial basis functions of the inputs and neuron parameters.
FAN supports cross-platform execution of single and multilayer networks. It also supports fixed-point and floating-point arithmetic. It includes functions that simplify the creating, training and testing of neural networks. It has bindings for over 20 programming languages, including commonly used languages such as PHP, C# and Python.