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)

Working with text data

Text is one of the commonly used sequential data types. Text data can be seen as either a sequence of characters or a sequence of words. It is common to see text as a sequence of words for most problems. Deep learning sequential models such as RNN and its variants are able to learn important patterns from text data that can solve problems in areas such as:

  • Natural language understanding
  • Document classification
  • Sentiment classification

These sequential models also act as important building blocks for various systems, such as question and answering (QA) systems.

Though these models are highly useful in building these applications, they do not have an understanding of human language, due to its inherent complexities. These sequential models are able to successfully find useful patterns that are then used for performing different tasks. Applying deep learning...