Computer Engineering Machine Learning and Neural Networks

Introduction

This is the first textbook focusing on practicality of machine learning (ML) and deep neural networks (DNN), by introducing methods that enable engineering applications of ML and DNN models.

Content Overview

The authors describe many methodologies that are widely used in designing, training, and deploying of these models and discuss their applicability under various contexts.

Coverage

Coverage begins with the basic knowledge of machine learning and deep neural networks and their applications in solving practical engineering problems. It then proceeds through a series of computer engineering methods commonly used in developing machine learning and deep neural network models.

Performance Improvement

The book also explains how to improve the training and inference performance in terms of model accuracy, size, runtime, etc. by considering various requirements and availability of data in the applications.

Techniques and Practice

Techniques that are widely adopted in both industry and academia are discussed. Tutorials and projects designed to practice the introduced techniques are provided using popular development frameworks of machine learning.

Features

Emphasizes practice over theoretical foundations, making content accessible to engineering students and engineers; Includes in-depth discussion of popular DNN models and their applications; Discusses engineering methods and tricks widely adopted in practice for using ML and DNN to solve engineering problems.