Marvin L. ... 418 pages -
Publisher: CreateSpace Independent Publishing Platform; (October, 2016) ...
Language: English -
ISBN-10: 1539701956 -
ISBN-13: 978-1539701958 ...
Neural Network Toolbox 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 importan features
are de next: •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.
