Top 25 Neural Networks Books to Read in 2024 – Free Download

Neural networks have revolutionized the field of artificial intelligence, powering advancements in various domains such as image recognition, natural language processing, and autonomous systems.

As we step into 2024, the landscape of neural networks continues to evolve, with new architectures and techniques emerging to push the boundaries of what is possible.

To help you stay ahead in this rapidly changing field, we have curated a list of the top 25 neural networks books you should read about in 2024. The best part? All these resources are available for free download through a single link at the end of this article.

S. No.	Book Name
1	Applied Deep Learning
2	Artificial Intelligence for Humans – Volume 3 – Deep Learning and Neural Networks
3	Artificial Neural Networks – A Practical Course
4	Artificial Neural Networks
5	Convolutional Neural Networks in Visual Computing
6	Deep Learning – Practical Neural Networks with Java
7	Deep Learning and Convolutional Neural Networks for Medical Image Computing
8	Design of Intelligent Systems Based on Fuzzy Logic, Neural Networks and Nature-Inspired Optimization
9	FPGA Implementations of Neural Networks
10	Fuzzy Logic and Expert Systems Applications
11	Guide To Convolutional Neural Networks
12	Introduction To Artificial Intelligence
13	Learn Keras for Deep Neural Networks
14	Math Neural Networks
15	Neural Network Toolbox – MATLAB
16	Neural Networks – A Visual Introduction For Beginners
17	Neural Networks – Methodology and Applications
18	Neural Networks and Deep Learning
19	Neural Networks and Learning Machines
20	Neural Networks and Statistical Learning
21	Neural Networks for Pattern Recognition
22	Neural Networks with R
23	New Directions in Neural Networks
24	TensorFlow – Learn in 1 Day
25	The Application of Neural Networks in the Earth System Sciences

These top 25 neural networks books represent the forefront of deep learning research and applications. Whether you are a researcher, a practitioner, or an enthusiast, understanding these resources will provide valuable insights into the capabilities and future directions of neural networks.