Book Image

Deep Learning with PyTorch Lightning

By : Kunal Sawarkar
3.5 (2)
Book Image

Deep Learning with PyTorch Lightning

3.5 (2)
By: Kunal Sawarkar

Overview of this book

Building and implementing deep learning (DL) is becoming a key skill for those who want to be at the forefront of progress.But with so much information and complex study materials out there, getting started with DL can feel quite overwhelming. Written by an AI thought leader, Deep Learning with PyTorch Lightning helps researchers build their first DL models quickly and easily without getting stuck on the complexities. With its help, you’ll be able to maximize productivity for DL projects while ensuring full flexibility – from model formulation to implementation. Throughout this book, you’ll learn how to configure PyTorch Lightning on a cloud platform, understand the architectural components, and explore how they are configured to build various industry solutions. You’ll build a neural network architecture, deploy an application from scratch, and see how you can expand it based on your specific needs, beyond what the framework can provide. In the later chapters, you’ll also learn how to implement capabilities to build and train various models like Convolutional Neural Nets (CNN), Natural Language Processing (NLP), Time Series, Self-Supervised Learning, Semi-Supervised Learning, Generative Adversarial Network (GAN) using PyTorch Lightning. By the end of this book, you’ll be able to build and deploy DL models with confidence.
Table of Contents (15 chapters)
1
Section 1: Kickstarting with PyTorch Lightning
6
Section 2: Solving using PyTorch Lightning
11
Section 3: Advanced Topics

Introduction to time series

In a typical machine learning use case, a dataset is a collection of features (x) and target variables (y). The model uses features to learn and predict the target variable.

Take the following example. To predict house prices, the features could be the number of bedrooms, the number of baths, and square footage, and the target variable is the price of the house. Here, the goal can be to use all the features (x) to train the model and predict the price (y) of the house. One thing we observe in such a use case is that all the records in the dataset are treated equally when predicting target variables, which is the price of the house in our example, and the order of the data doesn't matter much. The outcome (y) depends only on the values of x.

On the other hand, in time series prediction, the order of the data plays an important role in capturing some of the features, such as trends and seasons. Time series datasets are typically datasets where...