Building Artificial Neural Networks

Artificial neural networks (ANNs) or connectionist systems are computing systems vaguely inspired by the biological neural networks that constitute animal brains[1]. Such systems "learn" (i.e. progressively improve performance on) tasks by considering examples, generally without task-specific programming. For example, in image recognition, they might learn to identify images that contain cats by analyzing example images that have been manually labeled as "cat" or "no cat" and using the results to identify cats in other images. They do this without any a priori knowledge about cats, e.g., that they have fur, tails, whiskers and cat-like faces. Instead, they evolve their own set of relevant characteristics from the learning material that they process.

An ANN is based on a collection of connected units or nodes called artificial neurons (a simplified version of biological neurons in an animal brain). Each connection (a simplified version of a synapse) between artificial neurons can transmit a signal from one to another. The artificial neuron that receives the signal can process it and then signal artificial neurons connected to it.

This list focuses on books that help you learn to build ANNs.

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1	Machine Learning with Neural Networks: An In-depth Visual Introduction with Python: Make Your Own Neural Network in Python: A Simple Guide on Machine Learning with Neural Networks. by Michael Taylor really liked it 4.00 avg rating — 174 ratings score: 498, and 5 people voted	Error rating book. Refresh and try again. Rate this book Clear rating 1 of 5 stars 2 of 5 stars 3 of 5 stars 4 of 5 stars 5 of 5 stars
2	The Math of Neural Networks by Michael Taylor 4.17 avg rating — 110 ratings score: 399, and 4 people voted	Error rating book. Refresh and try again. Rate this book Clear rating 1 of 5 stars 2 of 5 stars 3 of 5 stars 4 of 5 stars 5 of 5 stars
3	Machine Learning: An Introduction Math Guide for Beginners to Understand Data Science Through the Business Applications by Samuel Hack 4.39 avg rating — 23 ratings score: 300, and 3 people voted	Error rating book. Refresh and try again. Rate this book Clear rating 1 of 5 stars 2 of 5 stars 3 of 5 stars 4 of 5 stars 5 of 5 stars
4	Pattern Recognition and Machine Learning by Christopher M. Bishop 4.32 avg rating — 1,889 ratings score: 289, and 3 people voted	Error rating book. Refresh and try again. Rate this book Clear rating 1 of 5 stars 2 of 5 stars 3 of 5 stars 4 of 5 stars 5 of 5 stars
5	Machine Learning Mathematics: Study Deep Learning Through Data Science. How to Build Artificial Intelligence Through Concepts of Statistics, Algorithms, Analysis and Data Mining by Samuel Hack 4.28 avg rating — 32 ratings score: 198, and 2 people voted	Error rating book. Refresh and try again. Rate this book Clear rating 1 of 5 stars 2 of 5 stars 3 of 5 stars 4 of 5 stars 5 of 5 stars
6	Machine Learning: 4 Books in 1: A Complete Overview for Beginners to Master the Basics of Python Programming and Understand How to Build Artificial Intelligence Through Data Science by Samuel Hack 4.80 avg rating — 20 ratings score: 196, and 2 people voted	Error rating book. Refresh and try again. Rate this book Clear rating 1 of 5 stars 2 of 5 stars 3 of 5 stars 4 of 5 stars 5 of 5 stars
7	Neural Networks And Learning Machines by Simon Haykin 4.09 avg rating — 45 ratings score: 194, and 2 people voted	Error rating book. Refresh and try again. Rate this book Clear rating 1 of 5 stars 2 of 5 stars 3 of 5 stars 4 of 5 stars 5 of 5 stars
7	Neural Networks for Pattern Recognition (Advanced Texts in Econometrics (Paperback)) by Christopher M. Bishop 4.11 avg rating — 170 ratings score: 194, and 2 people voted	Error rating book. Refresh and try again. Rate this book Clear rating 1 of 5 stars 2 of 5 stars 3 of 5 stars 4 of 5 stars 5 of 5 stars
9	An Introduction to Neural Networks by Kevin Gurney 3.71 avg rating — 45 ratings score: 192, and 2 people voted	Error rating book. Refresh and try again. Rate this book Clear rating 1 of 5 stars 2 of 5 stars 3 of 5 stars 4 of 5 stars 5 of 5 stars
10	Neural Network Design by Martin T. Hagan 4.33 avg rating — 60 ratings score: 190, and 2 people voted	Error rating book. Refresh and try again. Rate this book Clear rating 1 of 5 stars 2 of 5 stars 3 of 5 stars 4 of 5 stars 5 of 5 stars
11	Neural Networks: A Comprehensive Foundation by Simon Haykin 3.89 avg rating — 206 ratings score: 187, and 2 people voted	Error rating book. Refresh and try again. Rate this book Clear rating 1 of 5 stars 2 of 5 stars 3 of 5 stars 4 of 5 stars 5 of 5 stars
12	Compassionate Artificial Superintelligence AI 5.0 by Amit Ray 4.73 avg rating — 51 ratings score: 100, and 1 person voted	Error rating book. Refresh and try again. Rate this book Clear rating 1 of 5 stars 2 of 5 stars 3 of 5 stars 4 of 5 stars 5 of 5 stars
12	Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks by Russell D. Reed 3.61 avg rating — 18 ratings score: 100, and 1 person voted	Error rating book. Refresh and try again. Rate this book Clear rating 1 of 5 stars 2 of 5 stars 3 of 5 stars 4 of 5 stars 5 of 5 stars
12	A Primer to the 42 Most commonly used Machine Learning Algorithms (With Code Samples). (Artificial Intelligence Book 1) by Murat Durmus (Goodreads Author) 4.20 avg rating — 10 ratings score: 100, and 1 person voted	Error rating book. Refresh and try again. Rate this book Clear rating 1 of 5 stars 2 of 5 stars 3 of 5 stars 4 of 5 stars 5 of 5 stars
12	Machine Learning for Imbalanced Data: Tackle imbalanced datasets using machine learning and deep learning techniques by Kumar Abhishek 4.86 avg rating — 7 ratings score: 100, and 1 person voted	Error rating book. Refresh and try again. Rate this book Clear rating 1 of 5 stars 2 of 5 stars 3 of 5 stars 4 of 5 stars 5 of 5 stars
16	Neural Networks: Tricks of the Trade by Klaus-Robert Müller (Editor) 3.33 avg rating — 6 ratings score: 98, and 1 person voted	Error rating book. Refresh and try again. Rate this book Clear rating 1 of 5 stars 2 of 5 stars 3 of 5 stars 4 of 5 stars 5 of 5 stars