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Hacker News Comments on
Neural Networks for Pattern Recognition (Advanced Texts in Econometrics (Paperback))

Christopher M. Bishop · 1 HN comments
HN Books has aggregated all Hacker News stories and comments that mention "Neural Networks for Pattern Recognition (Advanced Texts in Econometrics (Paperback))" by Christopher M. Bishop.
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Amazon Summary
This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts, the book examines techniques for modeling probability density functions and the properties and merits of the multi-layer perceptron and radial basis function network models. Also covered are various forms of error functions, principal algorithms for error function minimalization, learning and generalization in neural networks, and Bayesian techniques and their applications. Designed as a text, with over 100 exercises, this fully up-to-date work will benefit anyone involved in the fields of neural computation and pattern recognition.
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Hacker News Stories and Comments

All the comments and stories posted to Hacker News that reference this book.
This book is a little old but you should learn whatever math it takes to understand it

https://www.amazon.com/Networks-Recognition-Advanced-Econome...

Another oldie-but-goodie is

https://www.amazon.com/Neural-Networks-Lecture-Computer-Scie...

which tells the secret of when to stop when you're doing "early stopping", something I've seen many modern deep learners fail to get right.

Off the top of my head I would say the fundamental math about deep networks is not really new. Most of the work in that field is pretty ad-hoc and not a lot is proven; probably people that are proving things are using difficult graduate-level math but you don't need to go there.

⬐ IWantToRelocate
thank you Paul for the references, will def take a look mainly in the early stopping problem.
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