Book Image

Deep Learning with PyTorch

By : Vishnu Subramanian
Book Image

Deep Learning with PyTorch

By: Vishnu Subramanian

Overview of this book

Deep learning powers the most intelligent systems in the world, such as Google Voice, Siri, and Alexa. Advancements in powerful hardware, such as GPUs, software frameworks such as PyTorch, Keras, TensorFlow, and CNTK along with the availability of big data have made it easier to implement solutions to 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 libraries—PyTorch. PyTorch is grabbing the attention of deep learning researchers and data science professionals due to its accessibility, efficiency and being more native to Python way of development. You'll start off by installing PyTorch, then quickly move on to learn various fundamental blocks that power modern deep learning. You will also learn how to use CNN, RNN, LSTM and other networks to solve real-world problems. This book explains the concepts of various state-of-the-art deep learning architectures, such as ResNet, DenseNet, Inception, and Seq2Seq, without diving deep into the math behind them. You will also learn about GPU computing during the course of the book. You will see how to train a model with PyTorch and dive into complex neural networks such as generative networks for producing text and images. By the end of the book, you'll be able to implement deep learning applications in PyTorch with ease.
Table of Contents (11 chapters)

Generative Networks

All the examples that we have seen in the previous chapters were focused on solving problems such as classification or regression. This chapter is very interesting and important for understanding how deep learning is being evolved to solve problems in unsupervised learning.

In this chapter, we will train networks that learn how to create:

  • Images based on content and a particular artistic style, popularly called style transfer
  • Generating faces of new persons using a particular type of generative adversarial network (GAN)
  • Generating new text using language modeling

These techniques form the basis of most of the advanced research that is happening in the deep learning space. Going into the exact specifics of each of the subfields, such as GANs and language modeling is out of the scope of this book, as they deserve a separate book for themselves. We will learn...