NEURAL NETWORKS using MATLAB. FUNCTION APPROXIMATION and REGRESSION

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NEURAL NETWORKS using MATLAB. FUNCTION APPROXIMATION and REGRESSION by K. Taylor
English | 7 Feb. 2017 | ASIN: B06VSC445V | 155 Pages | PDF (conv) | 5.48 MB
MATLAB has the tool Neural Network Toolbox that provides algorithms, functions, and apps to create, train, visualize, and simulate neural networks.

MATLAB has the tool Neural Network Toolbox that provides algorithms, functions, and apps to create, train, visualize, and simulate neural networks. You can perform
classification, regression, clustering, dimensionality reduction, time-series forecasting, and dynamic system modeling and control.

The toolbox includes convolutional neural network and autoencoder deep learning algorithms for image classification and feature learning tasks. To speed up training of large data sets, you can distribute computations and data across multicore processors, GPUs, and computer clusters using Parallel Computing Toolbox.

The more important features are the following:

*Deep learning, including convolutional neural networks and autoencoders
*Parallel computing and GPU support for accelerating training (with Parallel Computing Toolbox)
*Supervised learning algorithms, including multilayer, radial basis, learning vector quantization (LVQ), time-delay, nonlinear autoregressive (NARX), and recurrent neural network (RNN)
*Unsupervised learning algorithms, including self-organizing maps and competitive layers
*Apps for data-fitting, pattern recognition, and clustering
*Preprocessing, postprocessing, and network visualization for improving training efficiency and assessing network performance
*Simulink® blocks for building and evaluating neural networks and for control systems applications

This book delves into the applications of neural networks to fit functions and regression models
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