Authors: Shanqing Cai, Stan Bileschi and Eric Nielsen
Part One: Motivation and Basic Concepts, which consists of a single chapter, gets you started with TensorFlow.js by way of a small example - linear regression. Don't be fooled into thinking that linear regression is a deep learning technique, but it is a good, simple, way of getting started. What the book doesn't help with is getting things setup. You are assumed to have Tensorflow.js already working in a development environment of your choice. If you aren't up to this then you are going to struggle with even the simple stuff.
Part 3: Advanced Deep Learning with TensorFlow.js consists of six chapters and occupies the bulk of the book. At first this is more about learning some of the characteristics of deep learning - data, visualization and the problem of over and under fitting. The final three chapters are on what most would consider advanced techniques including sequence learning, i.e. recurrent neural networks, generative netows and reinforcement learning. As these are big topics in their own right the treatment is practical and not deeply theoretical.
Part 4: Summary and Closing Words is just two chapters. The first is on testing, optimization and deployment. The second is a general discussion of where to go next.
To keep up with our coverage of books for programmers, follow @bookwatchiprog on Twitter or subscribe to I Programmer's Books RSS feed for each day's new addition to Book Watch and for new reviews.
|Last Updated ( Saturday, 16 July 2022 )|