By Daniel Graupe
Deep studying Neural Networks is the quickest turning out to be box in computing device studying. It serves as a robust computational device for fixing prediction, choice, prognosis, detection and selection difficulties according to a well-defined computational structure. it's been effectively utilized to a extensive box of functions starting from machine protection, speech attractiveness, photograph and video reputation to business fault detection, clinical diagnostics and finance.
This accomplished textbook is the 1st within the new rising box. a variety of case reports are succinctly proven within the textual content. it really is meant to be used as a one-semester graduate-level collage textual content and as a textbook for examine and improvement institutions in undefined, drugs and fiscal research.
- Deep studying Neural Networks: technique and Scope
- Basic thoughts of Neural Networks
- Back Propagation
- The Cognitron and Neocognitron
- Deep studying Convolutional Neural Networks
- LAMSTAR-1 and LAMSTAR-2 Neural Networks
- Other Neural Networks for Deep Learning
- Case Studies
- Concluding Comments
- Appendices to Case experiences of bankruptcy 8
- Author Index
- Subject Index
Readership: Researchers, lecturers, pros, graduate and undergraduate scholars in computer studying, synthetic intelligence, neural networks/networking, software program engineering, and of their purposes in drugs, protection engineering and monetary engineering.
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DEEP LEARNING NEURAL NETWORKS: DESIGN AND CASE STUDIES by Daniel Graupe