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

Summary

In this chapter, we introduced the problem of semantic segmentation and implemented U-Net: a deep encoder-decoder architecture used to tackle this problem. A short introduction about the possible use cases and the challenges this problem poses has been presented, followed by an intuitive introduction of the deconvolution (transposed convolution) operation, used to build the decoder part of the architecture. Since, at the time of writing, there is not a dataset for semantic segmentation that's ready to use in TensorFlow Datasets, we took the advantage of this to show the architecture of TensorFlow Datasets and show how to implement a custom DatasetBuilder. Implementing it is straightforward, and it is something that's recommended to every TensorFlow user since it is a handy way of creating a high-efficiency data input pipeline (tf.data.Dataset). Moreover, by implementing...