Download DEEP LEARNING NEURAL NETWORKS: DESIGN AND CASE STUDIES by Daniel Graupe PDF
By Daniel Graupe
Deep studying Neural Networks is the quickest becoming box in laptop studying. It serves as a robust computational device for fixing prediction, choice, prognosis, detection and choice difficulties in keeping with a well-defined computational structure. it's been effectively utilized to a vast box of functions starting from desktop defense, speech attractiveness, photo and video reputation to business fault detection, clinical diagnostics and finance.
This complete textbook is the 1st within the new rising box. various case reviews are succinctly tested within the textual content. it's meant to be used as a one-semester graduate-level college textual content and as a textbook for study and improvement institutions in undefined, medication and fiscal research.
Contents:
- Acknowledgements
- Preface
- Deep studying Neural Networks: method and Scope
- Basic innovations 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
- Problems
- Appendices to Case experiences of bankruptcy 8
- Author Index
- Subject Index
Readership: Researchers, lecturers, execs, graduate and undergraduate scholars in desktop studying, synthetic intelligence, neural networks/networking, software program engineering, and of their purposes in drugs, protection engineering and monetary engineering.
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