Deep Learning with PyTorch

Build neural network models in text, vision and advanced analytics using PyTorch

Deep Learning is powering the most intelligent systems in the world such as Google Voice, Siri, and Alexa. Advancements in powerful hardware such as GPU, software frameworks like PyTorch, Keras, Tensorflow, CNTK, etc and availability of big data have made it easier to implement solutions for various problems in the areas of Text, Vision, and advanced analytics.

This book will get you up and running with one of the most cutting-edge deep learning library—PyToch. Written in Python, PyTorch is grabbing the attention of all the data science professionals due to its accessibility and efficiency. You will start off with installing PyTorch, then quickly move on to the various statistical operations with it. Next you'll learn about Neural networks with PyTorch and we'll explore CNN, RNN, LSTM, and autoencoders.

This book provides the intuition behind the various state of the art Deep Learning architectures such as ResNet, DenseNet, Inception, and Seq2Seq without diving deep into the math of it. Then you will also learn about GPU computing during the course of the book. You will learn how you can train a model with PyTorch and also dive into complex neural networks such as generative networks to produce text and images.

By the end of the book, you'll be able to implement PyTorch in deep learning applications with ease. You’ll also know everything it takes to get up and running with PyTorch.

This book is for data analysts, data scientists, and anyone who is familiar with machine learning and is looking for the best options to perform Deep Learning in their systems. Those who are looking for high speed and flexibility in their deep learning models will find this book very useful. Knowledge of Python programming is expected

Vishnu Subramanian has been responsible for architecting and implementing various Big data analytical projects. His interests include distributed computing, Machine Learning, Deep Learning.