A comprehensive guide to developing neural network-based solutions using TensorFlow 2.0
Key Features
- Understand the basics of machine learning and discover the power of neural networks and deep learning
- Explore the structure of the TensorFlow framework and understand how to transition to TF 2.0
- Solve any deep learning problem by developing neural network-based solutions using TF 2.0
Book Description
TensorFlow, the most popular and widely used machine learning framework, has made it possible for almost anyone to develop machine learning solutions with ease. With TensorFlow (TF) 2.0, you'll explore a revamped framework structure, offering a wide variety of new features aimed at improving productivity and ease of use for developers.
This book covers machine learning with a focus on developing neural network-based solutions. You'll start by getting familiar with the concepts and techniques required to build solutions to deep learning problems. As you advance, you'll learn how to create classifiers, build object detection and semantic segmentation networks, train generative models, and speed up the development process using TF 2.0 tools such as TensorFlow Datasets and TensorFlow Hub.
By the end of this TensorFlow book, you'll be ready to solve any machine learning problem by developing solutions using TF 2.0 and putting them into production.
What you will learn
- Grasp machine learning and neural network techniques to solve challenging tasks
- Apply the new features of TF 2.0 to speed up development
- Use TensorFlow Datasets (tfds) and the tf.data API to build high-efficiency data input pipelines
- Perform transfer learning and fine-tuning with TensorFlow Hub
- Define and train networks to solve object detection and semantic segmentation problems
- Train Generative Adversarial Networks (GANs) to generate images and data distributions
- Use the SavedModel file format to put a model, or a generic computational graph, into production
Who this book is for
If you're a developer who wants to get started with machine learning and TensorFlow, or a data scientist interested in developing neural network solutions in TF 2.0, this book is for you. Experienced machine learning engineers who want to master the new features of the TensorFlow framework will also find this book useful.
Basic knowledge of calculus and a strong understanding of Python programming will help you grasp the topics covered in this book.
Table of Contents
- What is Machine Learning?
- Neural Networks and Deep Learning
- TensorFlow Graph Architecture
- TensorFlow 2.0 Architecture
- Efficient Data Input Pipelines and Estimator API
- Image Classification using TensorFlow Hub
- Introduction to Object Detection
- Semantic Segmentation and Custom Dataset Builder
- Generative Adversarial Networks
- Bringing a Model to Production
Paolo Galeone is a computer engineer with strong practical experience. After getting his MSc degree, he joined the Computer Vision Laboratory at the University of Bologna, Italy, as a research fellow, where he improved his computer vision and machine learning knowledge working on a broad range of research topics. Currently, he leads the Computer Vision and Machine Learning laboratory at ZURU Tech, Italy.
In 2019, Google recognized his expertise by awarding him the title of Google Developer Expert (GDE) in Machine Learning. As a GDE, he shares his passion for machine learning and the TensorFlow framework by blogging, speaking at conferences, contributing to open-source projects, and answering questions on Stack Overflow.
