Pradeepta Mishra - PyTorch Recipes: A Problem-Solution Approach

Any source code or other supplementary material referenced by the author in this book is available to readers on GitHub via the books product page, located at www.apress.com/978-1-4842-4257-5 . For more detailed information, please visit http://www.apress.com/source-code .

Development of artificial intelligent products and solutions has recently become a norm; hence, the demand for graph theorybased computational frameworks is on the rise. Making the deep learning models work in real-life applications is possible when the modeling framework is dynamic, flexible, and adaptable to other frameworks.

PyTorch is a recent entrant to the league of graph computation tools/programming languages. Addressing the limitations of previous frameworks, PyTorch promises a better user experience in the deployment of deep learning models, and the creation of advanced models using a combination of convolutional neural networks, recurrent neural networks, LSTMs, and deep neural networks.

PyTorch was created by Facebooks Artificial Intelligence Research division, which seeks to make the model development process simple, straightforward, and dynamic, so that developers do not have to worry about declaring objects before compiling and executing the model. It is based on the Torch framework and is an extension of Python.

This book is intended for data scientists, natural language processing engineers, artificial intelligence solution developers, existing practitioners working on graph computation frameworks, and researchers of graph theory. This book will get you started with understanding tensor basics, computation, performing arithmetic-based operations, matrix algebra, and statistical distribution-based operations using the PyTorch framework.

Chapters provide detailed descriptions on neural network basics. Advanced neural networks, such as convolutional neural networks, recurrent neural networks, and LSTMs are explored. Readers will be able to implement these models using PyTorch functions.

Chapters discuss fine-tuning the models, hyper parameter tuning, and the refinement of existing PyTorch models in production. Readers learn how to choose the hyper parameters to fine-tune the model.

In Chapter , natural language processing is explained. The deep learning models and their applications in natural language processing and artificial intelligence is one of the most demanding skill sets in the industry. Readers will be able to benchmark the execution and performance of PyTorch implementation in deep learning models to execute and process natural language. They will be able to compare PyTorch with other graph computationbased deep learning programming tools.

I would like to thank my wife, Prajna, for her continuous inspiration and support, and sacrificing her weekends just to sit alongside me to help me in completing the book; my daughter, Aarya, for being patient all through my writing time; my father, for his eagerness to know how many chapters I had completed.

A big thank you to Nikhil, Celestin, and Divya, for fast-tracking the whole process and helping me and guiding me in the right direction.

I would like to thank my bosses, Ashish and Saty, for always being supportive of my initiatives in the AI and ML journey, and their continuous motivation and inspiration in writing in the AI space.