Name: Neural networks
ISBN: 9780132733502

Neural networks

This book represents the most comprehensive treatment available of neural networks from an engineering perspective. Thorough, well-organized, and completely up to date, it examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks. Written in a concise and fluid manner, by a foremost engineering textbook author, to make the material more accessible, this book is ideal for professional engineers and graduate students entering this exciting field. Computer experiments, problems, worked examples, a bibliography, photographs, and illustrations reinforce key concepts.

What Hacker News says

mfrieswykJan 2023

I've picked up the following, just wondering what everyone's thoughts are on the best books for a strong foundation:

Pattern Recognition and Machine Learning - Bishop

Deep Learning - Goodfellow, Bengio, Courville

Neural Smithing - Reed, Marks

Neural Networks - Haykin

Artificial Intelligence - Haugeland

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wslhFeb 2018

There is something in Canada because the best book (I tried to understand) about neural networks back in the 90s was "Neural Networks: A Comprehensive Foundation" by Simon Hayking [1]

[1] https://en.wikipedia.org/wiki/Simon_Haykin

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physcabJul 2009

It does take a wide-variety of knowledge. But I don't think you need to be an expert in all these fields. You just have to have a passion for presenting information to others. As far as what tools are needed? Here's my take.

Computer Science (Data Mining, Machine Learning):

- Hard science book like PRML from Chris Bishop or Neural Networks from Haykin

- Fun implementations from Programming Collective Intelligence by Toby Segaran

- See practical implementations by these people: http://www.doloreslabs.com/

Statistics:

- Good foundation of probability theory and calculus

- Understand these slides: http://www.autonlab.org/tutorials/index.html

Visualization (something I'm working on but not good at yet):

- Check out http://processingjs.org/

- Dabble in Adobe Flash/Flex Framework

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qubexMay 2020

I do not exempt those who make such statements just because they have authority. If anything, they should know better.

I started off along much the same path (I bought and read Neural Networks: A Comprehensive Foundation (1994) by Simon Haykin) when I was in high school, read it all and thought I knew everything there was to know on the topic, and then started to read about neurobiology and was stunned to discover I was living a lie. Then I read The Computational Beauty of Nature (1998) by Gary William Flake and decided I was going to dedicate myself to understanding the complexities we brush under the rugs.

I think the Hacker News/Silicon Valley/Machine Learning/Developer crowd really need to look beyond the virtuosismo of their implementations. It’s a very introverted and self-referential mindset.

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