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Deep Learning Fundamentals

Deep learning is generally considered a subset of machine learning, involving the training of artificial neural networks (ANNs). ANNs are at the forefront of machine learning. They have the ability to solve complex problems involving massive amounts of data. Many of the principles of machine learning generally are also important in deep learning specifically, so we will spend some time reviewing these here.

In this chapter, we will discuss the following topics:

Approaches to machine learning
Learning tasks
Features
Models
Artificial neural networks

Key benefits

Clear and concise explanations

Gives important insights into deep learning models

Practical demonstration of key concepts

Description

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.

Who is this book for?

Developers and Data Scientist familiar with Machine Learning but new to deep learning, or existing practitioners of deep learning who would like to use PyTorch to train their deep learning models will find this book to be useful. Having knowledge of Python programming will be an added advantage, while previous exposure to PyTorch is not needed.

What you will learn

Set up the deep learning environment using the PyTorch library

Learn to build a deep learning model for image classification

Use a convolutional neural network for transfer learning

Understand to use PyTorch for natural language processing

Use a recurrent neural network to classify text

Understand how to optimize PyTorch in multiprocessor and distributed environments

Train, optimize, and deploy your neural networks for maximum accuracy and performance

Learn to deploy production-ready models

What do you get with Print?

Instant access to your digital copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Redeem a companion digital copy on all Print orders

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

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Deep Learning with PyTorch Quick Start Guide

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Total € 90.97

杉原優 Jan 25, 2019

Although too many typos inside of this book, this book is very helpful for beginners.

Amazon Verified review

Benny Prijono Aug 08, 2019

This book is compact (only 158 printed pages) but yet it’s rather comprehensive introductory book to PyTorch. The way it lays out the progression of the projects is quite nice. It starts out with logistic regression, linear regression, multi-class logistic regression, and then NN, NN with some hidden layers, then CNN, and then RNN and LSTM etc. For each topic the discussion starts with some theories and then the explanation of the parts of the code. The theory part can be very thin though (for example only 1-2 pages for RNN!), so I won't try to understand the theories from this book.This book is very good when treated as introductory book to PyTorch. I would give it five stars, but unfortunately the RNN chapter is quite hard to understand.

Tae S. Shin Feb 06, 2019

With Kindle version, example code is in figures and its font size is so small and can't be controlled. Just returned for refund.

Deep Learning with PyTorch Quick Start Guide: Learn to train and deploy neural network models in Python

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Deep Learning with PyTorch Quick Start Guide

Deep Learning Fundamentals

Approaches to machine learning

Learning tasks

Unsupervised learning

Features

Models

Linear algebra review

Artificial neural networks

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Deep Learning with PyTorch Quick Start Guide: Learn to train and deploy neural network models in Python

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