Concepts and Programming in PyTorch

Learn to Demystify the neural networks with PyTorch

Key Features

● Basics concepts of PyTorch including CNN and RNN architecture are discussed in detailed manner.
● The worked out case studies are dealt in a detailed manner.
● Each chapter concludes with the observations of PyTorch to facilitate a better understanding of PyTorch.
● Abundant worked out coding examples.
● Highly self-explanatory and user-friendly approach.

Description

Book is written in a lucid manner to explain concepts in detail, with adequate emphasis on examples. To make clarity on the programming examples, logic is accurately explained and discussed through comments in the program itself. The topics covered in this book include starting the software through coding in software and writing programs.
The book features more on practical approach through ample examples covering simple to complex topics that address many core concepts and advanced topics.

What will you learn

● Linear Regression
● Convolution Neural Network (CNN)
● Recurrent Neural Network (RNN)
● PyTorch Datasets

Who this book is for

● Graduate Students- Computer Science/ CSE / IT/ Computer Applications
● Master Class Students—Msc (CS/IT)/ MCA/ M.Phil, M.Tech, M.S.
● Researcher’s—Ph.D Research Scholars

Table of Contents

1. Introduction to PyTorch
2. Linear Regression
3. Convolution Neural Network (CNN)
4. Recurrent Neural Network (RNN)
5. PyTorch Datasets
6. Observation in PyTorch