Thushan Ganegedara - TensorFlow in Action

These days it is hard to find a real-world system or a product that is not driven or at least impacted by machine learning. Machine learning plays a paramount role in terms of boosting user experience as well as cutting costs and increasing savings for a company. TensorFlow is a machine learning framework that enables developers to develop machine learning solutions quickly for various bespoke use cases that can benefit from machine learning. If you are a machine learning practitioner or even a software engineer who touches on machine learning systems, it pays to have a well-grounded understanding of TensorFlow, as its used by millions of developers to build ML solutions.

This book takes you through an informative journey covering most popular machine learning tasks as well as state-of-the-art models. You will learn about image classification and segmentation, and various natural language processing tasks, such as language modeling and sentiment analysis. While doing so, we will try to maintain our code production quality. This means we will explore ways in which we can standardize our code and models, such as building robust end-to-end data pipelines that can wrangle common data types such as images and text. We will also pay attention to other important dimensions, such as model explainability, current state-of-the-art performance on similar tasks, and so forth. We conclude the book with how TensorFlow can be used to build production-level machine learning pipelines to deliver a smooth operational experience for developers.

TensorFlow has good documentation coverage (although certain topics can be better documented) that is available for free. You might be wondering why, then, you need this book. TensorFlow has evolved to become a complex ecosystem with many moving parts. For someone initially learning the technology, it is quite easy to get lost in the documentation and waste hours (if not days). The rapid pace at which new features and new releases come out exacerbates this problem. Therefore, it helps to have a resource that collates all the most up-to-date and important information and best practices of TensorFlow into a digestible, well-explained text.

After reading this book, you will know how to build most of the common machine learning models, such as convolutional neural networks, recurrent neural networks, and Transformers. You will learn about the general machine learning life cycle and how it can be applied across many different tasks. Furthermore, you will become familiar with building data pipelines that can perform complex transformations in just a few lines of code.

I wish readers all the success in their machine learning careers and sincerely hope they will immensely benefit from the wide variety of topics covered in this book.

First and foremost, I would like to thank my parents and my wife, Thushani, for supporting me throughout the journey and always standing by my side. I would also like to thank my editors at Manning for all the support and encouragement. Thanks also to the Manning production staff for the valuable guidance they provided.

To all the reviewers who took the time to read my manuscript and provide feedback: Alessandro Buggin, Amaresh Rajasekharan, Biswanath Chowdhury, Brian Griner, David Cronkite, Dhinakaran Venkat, Eduardo Paluzo Hidalgo, Francisco Rivas, Ganesh Swaminathan, Geoff Clark, Gherghe Georgios, Giri S, Jason Hales, Jos Antonio Quiles, Joshua A McAdams, Kaniskha Tyagi, Kelvin D. Meeks, Kim Falk Jrgensen, Krzysztof Jedrzejewski, Lawrence Nderu, Levi McClenny, Nguyen Cao, Nikos Kanakaris, Peter Morgan, Ryan Markoff, Sergio Govoni, Sriram Macharla, Tiklu Ganguly, Todd Cook, Tony Holdroyd, Vidhya Vinay, Vincent Ngo, Vipul Gupta, Vishwesh Ravi Shrimali, and Wei Luo, your suggestions are appreciated and helped make this a better book.

In this section, we will discuss who this book is for, the different chapters and their contents, and where you can find the code.

It is imperative that you are certain this book is for you. This book is written for a broad audience in the machine learning community to provide a low barrier for entry, so novices as well as machine learning practitioners with basic to medium knowledge and experience can push their TensorFlow skills further. To get the most out of this book, you need the following:

• Experience in the model development life cycle (through a research/industry project)

• Moderate knowledge in Python and object-oriented programming (OOP) (e.g., classes, generators, list comprehension)

• Basic knowledge of NumPy/pandas libraries (e.g., computing summary statistics, what pandas series DataFrame objects are)