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Hands-On Neural Networks with TensorFlow 2.0

Hands-On Neural Networks with TensorFlow 2.0

By : Galeone
3.7 (7)
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Hands-On Neural Networks with TensorFlow 2.0

Hands-On Neural Networks with TensorFlow 2.0

3.7 (7)
By: 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)
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1
Section 1: Neural Network Fundamentals
4
Section 2: TensorFlow Fundamentals
8
Section 3: The Application of Neural Networks

Convolutional neural networks

Convolutional Neural Networks (CNNs) are the fundamental building blocks of modern computer vision, speech recognition, and even natural language processing applications. In this section, we are going to describe the convolution operator, how it is used in the signal analysis domain, and how convolution is used in machine learning.

The convolution operator

Signal theory gives us all the tools we need to properly understand the convolution operation: why it is so widely used in many different domains and why CNNs are so powerful. The convolution operation is used to study the response of certain physical systems when a signal is applied to their input. Different input stimuli can make a system...

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Hands-On Neural Networks with TensorFlow 2.0
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