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Books like Neural networks by Simon S. Haykin


Simon S. Haykin’s book offers a thorough, up‑to‑date engineering perspective on neural networks. It examines learning methods, back‑propagation, radial‑basis function networks, self‑organizing and modular systems, temporal processing, neurodynamics, and VLSI implementation. The concise text is supplemented with computer experiments, worked examples, problems, illustrations, and a bibliography, making it suitable for professional engineers and graduate students.
First publish date: 1994
Genre: Technology & Computing/Artificial Intelligence
Authors: Simon S. Haykin
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Books similar to Neural networks (12 similar books)

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📘 Deep Learning
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Deep Learning is a comprehensive textbook that equips students and practitioners with the foundational knowledge and practical skills needed to enter the fields of machine learning and deep learning.

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 by Gail A. Carpenter


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 by Bart Kosko


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Talking nets by 10178315|Anderson, James A. book cover

📘 Talking nets
 by Anderson, James A.

Since World War II, a group of scientists has been attempting to understand the human nervous system and to build computer systems that emulate the brian's abilities. Many of the workers in this field of neural networks came from cybernetics; others came from neuroscience, physics, electrical engineering, mathematics, psychology, even economics. In this collection of interviews, those who helped to shape the field share their childhood memories, their influences, how they became interested in neural networks, and how they envision its future.

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📘 Pattern Recognition and Machine Learning
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📘 Artificial neural networks
 by Robert J. Schalkoff

An accessible guide to the foundations, techniques, and applications of artificial neural networks, designed for advanced undergraduates, graduate students, engineers, and scientists.

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Bioinformatics by 8328148|Pierre Baldi book cover

📘 Bioinformatics
 by Pierre Baldi

Pierre Baldi and Soren Brunak present the key machine learning approaches and apply them to the computational problems encountered in the analysis of biological data. The book is aimed at two types of researchers and students. First are the biologists and biochemists who need to understand new data-driven algorithms, such as neural networks and hidden Markov models, in the context of biological sequences and their molecular structure and function. Second are those with a primary background in physics, mathematics, statistics, or computer science who need to know more about specific applications in molecular biology.

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📘 Darwin among the machines
 by George Dyson

George Dyson charts how early thinkers anticipated the rise of a globally networked, sentient system—an information revolution that would birth artificial intelligence, artificial life, and a global mind.

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Neural Networks by 3285357|Manav Surya book cover

📘 Neural Networks
 by Manav Surya


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Some Other Similar Books

Machine Learning: A Probabilistic Perspective by Kevin P. Murphy
Artificial Neural Networks: A Beginner's Guide by Kevin Gurney
Neural Networks and Deep Learning: A Textbook by Charu C. Aggarwal
Introduction to Neural Networks and Deep Learning by James D. Hinselwood
Foundations of Neural Networks, Fuzzy Systems, and Evolutionary Computing by Nikhil D. Banthia

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