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Deep Learning with PyTorch Quick Start Guide

Deep Learning with PyTorch Quick Start Guide

By : David Julian
3.3 (3)
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Deep Learning with PyTorch Quick Start Guide

Deep Learning with PyTorch Quick Start Guide

3.3 (3)
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)
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Introduction to recurrent networks

Recurrent networks have been shown to be very powerful in predicting time series data. This is something fundamental to biological brains that enables us to do things such as safely drive a car, play a musical instrument, evade predators, understand language, and interact with a dynamic world. This sense of the flow of time and the understanding of how things change over time is fundamental to intelligent life, so it is no surprise that in artificial systems this ability is important.

The ability to understand time series data is also important in creative endeavors, and recurrent networks have shown some ability in things such as composing a melody, constructing grammatically correct sentences, and creating visually pleasing images.

Feedforward and convolutional networks achieve very good results, as we have seen, in tasks such as the classification...

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