Author: Shanqing Cai, Stanley Bileschi, Eric D. Nielsen, François Chollet
Year: 2020
Publisher: Manning Publications
ISBN 9781617296178
Pages: 561
Language: Eng
Format: PDF
Size: 11 Mb

Content: The book you have in your hands will guide your grand tour through this multidimensional space of capabilities. We’ve chosen a path that primarily cuts through the first dimension (modeling tasks), enriched by excursions along the remaining dimensions.

We start from the relatively simpler task of predicting numbers from numbers (regression) to the more complex ones such as predicting classes from images and sequences, ending our trip on the fascinating topics of using neural networks to generate new images and training agents to make decisions (reinforcement learning).

We wrote the book not just as a recipe for how to write code in TensorFlow.js, but as an introductory course in the foundations of machine learning in the native language of JavaScript and web developers.

The field of deep learning is a fast-evolving one. It is our belief that a firm understanding of machine learning is possible without formal mathematical treatment, and this understanding will enable you to keep yourself up-to-date in future evolution of the techniques.