Book Image

Mastering PyTorch - Second Edition

By : Ashish Ranjan Jha
4 (1)
Book Image

Mastering PyTorch - Second Edition

4 (1)
By: Ashish Ranjan Jha

Overview of this book

PyTorch is making it easier than ever before for anyone to build deep learning applications. This PyTorch deep learning book will help you uncover expert techniques to get the most out of your data and build complex neural network models. You’ll build convolutional neural networks for image classification and recurrent neural networks and transformers for sentiment analysis. As you advance, you'll apply deep learning across different domains, such as music, text, and image generation, using generative models, including diffusion models. You'll not only build and train your own deep reinforcement learning models in PyTorch but also learn to optimize model training using multiple CPUs, GPUs, and mixed-precision training. You’ll deploy PyTorch models to production, including mobile devices. Finally, you’ll discover the PyTorch ecosystem and its rich set of libraries. These libraries will add another set of tools to your deep learning toolbelt, teaching you how to use fastai to prototype models and PyTorch Lightning to train models. You’ll discover libraries for AutoML and explainable AI (XAI), create recommendation systems, and build language and vision transformers with Hugging Face. By the end of this book, you'll be able to perform complex deep learning tasks using PyTorch to build smart artificial intelligence models.
Table of Contents (21 chapters)
20
Index

Building a bidirectional LSTM

So far, we have trained and tested a simple RNN model on the sentiment analysis task, which is a binary classification task based on textual data. In this section, we will try to improve our performance on the same task by using a more advanced recurrent architecture – LSTMs.

LSTMs, as we know, are more capable of handling longer sequences due to their memory cell gates, which help retain important information from several time steps before and forget irrelevant information even if it was recent. With the exploding and vanishing gradients problem in check, LSTMs should be able to perform well when processing long movie reviews.

Moreover, we will be using a bidirectional model as it broadens the context window at any time step for the model to make a more informed decision about the sentiment of the movie review. The RNN model we looked at in the previous exercise overfitted the dataset during training, so to tackle that, we will be using...