A compact guide to mastering TensorFlow 2, covering essential APIs, datasets, and practical applications for efficient machine learning and deep learning projects.

Key Features

Comprehensive guide to TensorFlow 2

Practical examples and code samples

Companion files with source code and figures

Book Description

As part of the best-selling Pocket Primer series, this book introduces beginners to basic machine learning algorithms using TensorFlow 2. It provides a fast-paced introduction to TensorFlow, covering core features and machine learning basics with Python code samples. An appendix includes Keras-based code samples and explores MLPs, CNNs, RNNs, and LSTMs. The chapters illustrate how to solve various tasks, encouraging further reading to deepen your knowledge. The journey begins with an introduction to TensorFlow 2, followed by essential APIs and datasets. You’ll explore linear regression and classifiers, learning to apply TensorFlow to practical problems. The comprehensive appendix covers advanced topics like NLPs and deep learning architectures, enhancing your understanding of machine learning. Understanding these concepts is crucial for modern AI applications. This book transitions readers from basic TensorFlow use to advanced machine learning techniques, blending theory with practical examples. Companion files with source code and figures enhance learning, making this an essential resource for mastering TensorFlow and machine learning.

What you will learn

Master TensorFlow 2 APIs

Implement linear regression

Work with classifiers

Use TensorFlow 2 datasets

Understand eager execution

Convert TF 1.x code to TF 2

Who this book is for

Developers with a basic understanding of Python and machine learning concepts will find this book ideal. It assumes familiarity with basic programming and data handling. Prior knowledge of TensorFlow 1.x is beneficial but not required.