By Daniel Graupe
Deep studying Neural Networks is the quickest growing to be box in computer studying. It serves as a strong computational software for fixing prediction, determination, prognosis, detection and choice difficulties according to a well-defined computational structure. it's been effectively utilized to a huge box of purposes starting from laptop defense, speech attractiveness, picture and video attractiveness to business fault detection, scientific diagnostics and finance.
This entire textbook is the 1st within the new rising box. quite a few case reports are succinctly proven within the textual content. it's meant to be used as a one-semester graduate-level collage textual content and as a textbook for learn and improvement institutions in undefined, drugs and monetary research.
- Deep studying Neural Networks: technique and Scope
- Basic techniques 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 stories of bankruptcy 8
- Author Index
- Subject Index
Readership: Researchers, teachers, execs, graduate and undergraduate scholars in desktop studying, synthetic intelligence, neural networks/networking, software program engineering, and of their functions in medication, safety engineering and monetary engineering.
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Extra resources for DEEP LEARNING NEURAL NETWORKS: DESIGN AND CASE STUDIES
DEEP LEARNING NEURAL NETWORKS: DESIGN AND CASE STUDIES by Daniel Graupe