Neural networks for pattern recognition
by Christopher M. Bishop · first published 1995
8mentions
3recommended by
6HN commenters
2008–2018first – last mentioned
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…recommendations (for neural networks) would be as follows: - parallel distributed processing (PDP group vol 1 & 2) - simon haykin (Neural Networks and Learning Machines) - christopher bishop (Neural Networks for Pattern Recognition)
on “Neural Networks Virtual Study Group” ↗…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) Neural Networks for Pattern Recognition ( Bishop). At this point you are in good shape to read any papers in NN. My reccomendations - anything by Yann LeCun and Geoffrey Hinton. Both do amazing research. Reinforcement Learning (s) Reinforcement…
on “Learning About Statistical Learning” ↗…for beginners. 1. Linear Algebra by Gilbert Strang 2. Calculus by Gilbert Strang 3. Artificial Intelligence A Modern Approach By Russell and Norvig 4. Machine Learning By Tom Mitchell 5. Neural Networks for Pattern Recognition by Chris Bishop 6. Paradigms of Artificial Intelligence Programming by Peter Norvig (ok, this one is not quite an cutting edge AI book, but imo this is the best book on programming ever written and…
on “Self-learning is overrated (in programming)” ↗I agree about #2. I have Russell and Norvig and agree that it is an excellent book, but I am not sure how much overlap there truly is here. I also do not have PRML, but Neural Networks for Pattern Recognition by Bishop is excellent (and includes many non-NN related items). ESL is excellent and, to me, the best modern text in machine learning. It covers many of the topics in PCI both at a reasonable level…
on “Critique: "Programming Collective Intelligence"” ↗…different perspective from (most uses of) neural networks, which do not have this clear separation between the model and how to reason about it. It's funny that Chris Bishop in 1995 wrote the textbook "Neural Networks for Pattern Recognition" and now is effectively arguing against using neural networks. You can use both by using neural networks as "factors" (the black squares) in probabilistic models.
on “Model-Based Machine Learning” ↗His review of Black's 'Neural Networks for Pattern Recognition' made me chuckle, and accorded with my own experience of the book. To the reviewer who said "I was looking forward to a detailed insight into neural networks in this book. Instead, almost every…
on “Peter Norvig's book reviews on Amazon” ↗It's funny that Chris Bishop in 1995 wrote the textbook "Neural Networks for Pattern Recognition" and now is effectively arguing against using neural networks. I haven't read "Neural Networks for Pattern Recognition", but his "Pattern Recognition and Machine Learning"[1] is the text for ML work…
on “Model-Based Machine Learning” ↗indeed ! the 'neural networks for pattern recognition' book (bishop) has exercises in chapter-01 on the same subject.
on “Counterintuitive Properties of High Dimensional Space” ↗