TensorFlow 2 Pocket Reference

Chapter 1. Introduction to TensorFlow 2

TensorFlow has long been the most popular open source Python machine learning (ML) library. It was developed by the Google Brain team as an internal tool, but in 2015 it was released under an Apache License. Since then, it has evolved into an ecosystem full of important assets for model development and deployment. Today it supports a wide variety of APIs and modules that are specifically designed to handle tasks such as data ingestion and transformation, feature engineering, and model construction and serving, as well as many more.

TensorFlow has become increasingly complex. The purpose of this book is to help simplify the common tasks that a data scientist or ML engineer will need to perform during an end-to-end model development process. This book does not focus on data science and algorithms; rather, the examples here use prebuilt models as a vehicle to teach relevant concepts.

This book is written for readers with basic experience in and knowledge about building ML models. Some proficiency in Python programming is highly recommended. If you work through the book from beginning to end, you will gain a great deal of knowledge about the end-to-end model development process and the major tasks involved, including data engineering, ingestion, and preparation; model training; and serving the model.

The source code for the examples in the book was developed and tested with Google Colaboratory (Colab, for short) and a MacBook Pro running macOS ...