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)

Diving Deep into Neural Networks

In this chapter, we will explore the different modules of deep learning architectures that are used to solve real-world problems. In the previous chapter, we used low-level operations of PyTorch to build modules such as a network architecture, a loss function, and an optimizer. In this chapter, we will explore some of the important components of neural networks required to solve real-world problems, along with how PyTorch abstracts away a lot of complexity by providing a lot of high-level functions. Towards the end of the chapter, we will build algorithms that solve real-world problems such as regression, binary classification, and multi-class classification.

In this chapter, we will go through following topics:

  • Deep dive into the various building blocks of neural networks
  • Exploring higher-level functionalities in PyTorch to build deep learning...