Share:

9 Best Neural Networks Books

Goal: Find the best Neural Networks books according to the internet (not just one person's opinion).

Method:

  1. Search for "best neural networks books" and study the top 5+ pages.
  2. Add only the books mentioned 2+ times.
  3. Rank the results neatly here.

9 Best Neural Networks Books

As an Amazon Associate, we earn from qualifying purchases (at no extra cost to you).

1

Deep Learning

by Ian Goodfellow

2

Neural Networks for Pattern Recognition

by Christopher M. Bishop

3

Deep Learning with Python

by Francois Chollet

4

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow

Concepts, Tools, and Techniques to Build Intelligent Systems

by Aurélien Géron

5

Deep Learning

A Practitioner's Approach

by Josh Patterson

6

Hands-On Deep Learning Algorithms with Python

Master Deep Learning Algorithms with Extensive Math by Implementing Them Using TensorFlow

by Sudharsan Ravichandiran

7

Neural Networks and Deep Learning

A Textbook

by Charu C. Aggarwal

8

Neural Smithing

Supervised Learning in Feedforward Artificial Neural Networks

by Russell Reed

9

TensorFlow 1.x Deep Learning Cookbook

Over 90 Unique Recipes To Solve Artificial-Intelligence Driven Problems With Python

by Antonio Gulli

Sources

Edited by

Richard Reis

Software engineer whose passion for tracking book recommendations from podcasts inspired the creation of MRB.

Anurag Ramdasan

Lead investor at 3one4 Capital whose startup expertise and love for books helped shaped MRB and its growth.

Comments

Did we miss something? Have feedback?

Help us improve this page by sharing your thoughts

We only use your email to notify you about replies.

All comments are moderated.