Deep Learning for Engineers

Tariq M. Arif, Md Adilur Rahim

• Publisher: Chapman and Hall/CRC
• Year: 2024
• eBook ISBN: 9781003849827
• Topic: Computer Science
• Subtopic: Computer Science General
• Index: Computer Science

Deep Learning for Engineers introduces the fundamental principles of deep learning along with an explanation of the basic elements required for understanding and applying deep learning models.

As a comprehensive guideline for applying deep learning models in practical settings, this book features an easy-to-understand coding structure using Python and PyTorch with an in-depth explanation of four typical deep learning case studies on image classification, object detection, semantic segmentation, and image captioning. The fundamentals of convolutional neural network (CNN) and recurrent neural network (RNN) architectures and their practical implementations in science and engineering are also discussed.

This book includes exercise problems for all case studies focusing on various fine-tuning approaches in deep learning. Science and engineering students at both undergraduate and graduate levels, academic researchers, and industry professionals will find the contents useful.

Table of contents

• Cover Page
• Half-Title Page
• Title Page
• Copyright Page
• Contents
• About the Authors
• Chapter 1 ◾ Introduction
• Chapter 2 ◾ Basics of Deep Learning
• Chapter 3 ◾ Computer Vision Fundamentals
• Chapter 4 ◾ Natural Language Processing Fundamentals
• Chapter 5 ◾ Deep Learning Framework Installation: Pytorch and Cuda
• Chapter 6 ◾ Case Study I: Image Classification
• Chapter 7 ◾ Case Study II: Object Detection
• Chapter 8 ◾ Case Study III: Semantic Segmentation
• Chapter 9 ◾ Case Study IV: Image Captioning
• Index