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)

Training word embedding by building a sentiment classifier

In the last section, we briefly learned about word embedding without implementing it. In this section, we will download a dataset called IMDB, which contains reviews, and build a sentiment classifier which calculates whether a review's sentiment is positive, negative, or unknown. In the process of building, we will also train word embedding for the words present in the IMDB dataset. We will use a library called torchtext that makes a lot of processes such as downloading, text vectorization, and batching much easier. Training a sentiment classifier will involve the following steps:

  1. Downloading IMDB data and performing text tokenization
  2. Building a vocabulary
  3. Generating batches of vectors
  4. Creating a network model with embeddings
  5. Training the model