Deep learning neural networks : design and case studies
- Singapore : World Scientific Publishing, 2022
- xvi, 263 p. ; ill., 25 cm
Includes bibliographical references and index.
Deep Learning Neural Networks is the fastest growing field in machine learning. It serves as a powerful computational tool for solving prediction, decision, diagnosis, detection and decision problems based on a well-defined computational architecture. It has been successfully applied to a broad field of applications ranging from computer security, speech recognition, image and video recognition to industrial fault detection, medical diagnostics and finance. This comprehensive textbook is the first in the new emerging field. Numerous case studies are succinctly demonstrated in the text. It is intended for use as a one-semester graduate-level university text and as a textbook for research and development establishments in industry, medicine and financial research.
9780000988546
ART neural network Back propogation Boltzmmann machine CNN applications Convolutional Neural networks Data analysis Deep learning neural networks Excitory neuron Fingerprint recognition Gibbs-Boltzmann-distribution Hopfield neural network Input matrix K-lines LAMSTAR network Link-weights Neocognition Output layer Restricted Boltzmann machine Speech recognition Support vector machines Winning neuron