Book Image

Hands-On Neural Networks with TensorFlow 2.0

By : Paolo Galeone
Book Image

Hands-On Neural Networks with TensorFlow 2.0

By: Paolo Galeone

Overview of this book

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.
Table of Contents (15 chapters)
Free Chapter
1
Section 1: Neural Network Fundamentals
4
Section 2: TensorFlow Fundamentals
8
Section 3: The Application of Neural Networks

Supported deployment platforms

As shown in the diagram at the beginning of this chapter, SavedModel is the input for a vast ecosystem of deployment platforms, with each one being created to satisfy a different range of use cases:

  • TensorFlow Serving: This is the official Google solution for serving machine learning models. It supports model versioning, multiple models can be deployed in parallel, and it ensures that concurrent models achieve high throughput with low latency thanks to its complete support for hardware accelerators (GPUs and TPUs). TensorFlow Serving is not merely a deployment platform, but an entire ecosystem built around TensorFlow and written in highly efficient C++ code. Currently, this is the solution Google itself uses to run tens of millions of inferences per second on Google Cloud's ML platform.
  • TensorFlow Lite: This is the deployment platform of choice...