Аннотация

Google has been a pioneer in introducing groundbreaking technology and products. TensorFlow is no exception, when it comes to efficiency and scale, yet there have been some adoption challenges that have convinced Google’s TensorFlow team to implement changes to facilitate ease of use.
Therefore, the idea of writing this book was simply to introduce to readers these important changes made by the TensorFlow core team. This book focuses on different aspects of TensorFlow, in terms of machine learning, and goes deeper into the internals of the recent changes in approach. This book is a good reference point for those who seek to migrate to TensorFlow to perform machine learning.
This book is divided into three sections. The first offers an introduction to data processing using TensorFlow 2.0. The second section discusses using TensorFlow 2.0 to build machine learning and deep learning models.
It also includes neuro-linguistic programming (NLP) using TensorFlow 2.0.
The third section covers saving and deploying TensorFlow 2.0 models in production. This book also is useful for data analysts and data engineers, as it covers the steps of big data processing using TensorFlow 2.0. Readers who want to transition to the data science and machine learning fields will also find that this book provides a practical introduction that can lead to more complicated aspects later. The case studies and examples given in the book make it really easy to follow and understand the relevant fundamental concepts. Moreover, there are very few books available on TensorFlow 2.0, and this book will certainly increase the readers’ knowledge. The strength of this book lies in its simplicity and the applied machine learning to meaningful data sets.
We have tried our best to inject our entire experience and knowledge into this book and feel it is specifically relevant to what businesses are seeking to solve real challenges. We hope you gain some useful takeaways from it.