The torch.optim package contains a number of optimization algorithms, and each of these algorithms has several parameters that we can use to fine-tune deep learning models. Optimization is a critical component in deep learning, so it is no surprise that different optimization techniques can be key to a model's performance. Remember, its role is to store and update the parameter state based on the calculated gradients of the loss function.
Deep Learning with PyTorch Quick Start Guide
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Deep Learning with PyTorch Quick Start Guide
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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)
Preface
Free Chapter
Introduction to PyTorch
Deep Learning Fundamentals
Computational Graphs and Linear Models
Convolutional Networks
Other NN Architectures
Getting the Most out of PyTorch
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