3 Must-Own Books for Deep Learning Practitioners
Last Updated on August 6, 2019
Developing neural networks is often referred to as a dark art.
The reason for this is that being skilled at developing neural network models comes from experience. There are no reliable methods to analytically calculate how to design a “good” or “best” model for your specific dataset. You must draw on experience and experiment in order to discover what works on your problem.
A lot of this experience can come from actually developing neural networks on test problems.
Nevertheless, many people have come before and recorded their discoveries, best practices, and preferred techniques. You can learn a lot about how to design and configure neural networks from some of the best books on the topic.
In this post, you will discover the three books that I recommend reading and having next to you when developing neural networks for your datasets.
Kick-start your project with my new book Better Deep Learning, including step-by-step tutorials and the Python source code files for all examples.
Let’s get started.
Three Recommended Books on Neural Networks
There are three books that I think you must own physical copies of if you are a neural network practitioner.