Description
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
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
