Neural network design
by Martin T. Hagan · first published 1996
8mentions
6recommended by
6HN commenters
2010–2026first – last mentioned
100% recommend
Why HN recommends it
Covers neural network fundamentals with mathematics introduced step by step, including linear algebra and optimization; suits readers who want a serious, in-depth text rather than a quick tutorial.
Summarised by Claude from recommending comments
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…places. Edit: A couple of other suggestions. Managing Gigabytes: Compressing and Indexing Documents and Images[2] Mining of Massive Datasets[3] Algorithm Design Manual[4] Network Algorithmics[5] Neural Network Design[6] I think all of these fall into the category of "Won't be applicable to everyone, but can be good for those who need this kind of stuff." [1]: https://github.com/clever-algorithms/CleverAlgorithms…
on “Ask HN: Most interesting, mildly impractical, well-written books on software?” ↗…AI (m)AIMA 3d Edition (I prefer this to Mitchell) Machine Learning (n) "Pattern Recognition and Machine Learning" by Christopher Bishop, (o)"Elements of Statistical Learning" (free download). (p) Neural Network Design by Hagan Demuth and Kneale, (q) Neural Networks, A Comprehensive Foundation (2nd edition) - By Haykin (there is a newer edition out but I don't know anything about that, this is the one I used) (r)…
on “Learning About Statistical Learning” ↗An excellent resource to get a more comprehensive review of ANNs is Neural Network Design (http://hagan.okstate.edu/nnd.html). Unfortunately, the book itself is out of print, but that link has some sample chapters and used copies are floating around. The book was written by the authors of…
on “Introduction to Artifical Neural Networks” ↗…especially after "Scala for the Impatient" "Learning Spark" - the best book on Spark so far "Assessing and Improving Prediction and Classification" - couple of interesting ideas for ML and Data Science "Neural Network Design" - Quite basic, but I like the flow and introducing mathematical concepts just before they're needed.
on “Ask HN: Books you read in 2015?” ↗…think is talked about much, is that many tutorials, even if they show the backpropagation, only limit it to a single RNN layer (this is also true for LSTM/GRU). Some time ago, I read this book called "Neural Network design" by M. Hagan, and I found the explanations of the book to be quite good (even though the book is not new). The book explains things clearly enough for you to build everything and does not handwaive anything.…
on “Show HN: I implemented a RNN from scratch by reading a dense neural network book” ↗…other courses you can pick up on Coursera or Edx or whatever. If you get really interested in neural networks (which are all the rage these days) after the Ng class, there's a freely available book on Neural Network design that you could look at. It doesn't cover all the very latest techniques, but it would help you build the foundation of understanding. http://hagan.okstate.edu/nnd.html There's also a MOOC around the…
on “Ask HN: AI is going to be big. How should we learn?” ↗[EDIT] Updated original comment to include matrix dimensions. If you want a serious text that goes through the relevant linear algebra and optimization mathematics in depth up front, Neural Network Design, 2nd edition is a good one. [Disclaimer, co-author]. We took great pains to walk through every conceptual and mathematical topic before we apply those concepts to machine learning. We use MATLAB a lot,…
on “Show HN: The Hessian of tall-skinny networks is easy to invert” ↗…bit dated, is Fundamentals of Neural Networks: Architectures, Algorithms And Applications by Laurene Fausett. https://www.amazon.com/gp/product/0133341860/ref=oh_aui_deta... I've also been looking at Neural Network Design by Martin Hagan. https://www.amazon.com/gp/product/0971732116/ref=oh_aui_deta... That one is also freely available online: http://hagan.okstate.edu/nnd.html
on “Understanding Machine Learning: From Theory to Algorithms [pdf]” ↗