Book Image

Deep Learning with PyTorch Quick Start Guide

By : David Julian
Book Image

Deep Learning with PyTorch Quick Start Guide

By: David Julian

Overview of this book

PyTorch is extremely powerful and yet easy to learn. It provides advanced features, such as supporting multiprocessor, distributed, and parallel computation. This book is an excellent entry point for those wanting to explore deep learning with PyTorch to harness its power. This book will introduce you to the PyTorch deep learning library and teach you how to train deep learning models without any hassle. We will set up the deep learning environment using PyTorch, and then train and deploy different types of deep learning models, such as CNN, RNN, and autoencoders. You will learn how to optimize models by tuning hyperparameters and how to use PyTorch in multiprocessor and distributed environments. We will discuss long short-term memory network (LSTMs) and build a language model to predict text. By the end of this book, you will be familiar with PyTorch's capabilities and be able to utilize the library to train your neural networks with relative ease.
Table of Contents (8 chapters)

Learning tasks

There are several distinct types of learning tasks that are partially defined by the type of data that they work on. Based on this, we can divide learning tasks into two broad categories:

  • Unsupervised learning: Data is unlabeled so the algorithm must infer a relationship between variables or by finding clusters of similar variables
  • Supervised learning: Uses a labeled dataset to build an inferred function that can be used to predict the label of an unlabeled sample

Whether the data is labeled or not has a predetermining effect on the way a learning algorithm is built.

Unsupervised learning

One of the main drawbacks to supervised learning is that it requires data that is accurately labeled. Most real-world...