Supervised Learning in Feedforward Artificial Neural Networks (A Bradford Book)
by Russell Reed
Recommended by Kirk Borne
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Recommended by 1 source.
Artificial neural networks are nonlinear mapping systems whose structure is loosely based on principles observed in the nervous systems of humans and animals. The basic idea is that massive systems of simple units linked together in appropriate ways can generate many complex and interesting behaviors. This book focuses on the subset of feedforward ...
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Why recommended
Recommended by 1 source.
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Kirk Borne
“Explore 80+ articles & resources for #NeuralNetworks here: #abdsc ————— +Learn ANN deeply from this classic #MachineLearning book: “Neural Smithing — Supervised Learning...” ————— #DataScience #BigData #DeepLearning #AI #Algorithms”
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Consider Deep Learning by Ian Goodfellow. Recommended by 10 sources.
“Equation-forward introduction covering probability, linear-algebra foundations, optimization methods, model families, and common architectures. Sections trade short conceptual summaries for formal derivations and algorithm descriptions; occasional practical notes appear but runnable code is rare. Most useful for building a technical picture of why methods behave as they do and for informed follow-up experimentation. Main limitation: dense notation and extended proofs demand slow, focused study, so readers seeking hands-on walkthroughs will be left wanting.”
Each recommendation is collected from a public source — interviews, articles, or curated lists — and linked to its original URL. Books with many verifiable recommendations from respected people rank higher.
