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Sinopsis
Introduction to deep learning and PyTorch by building a convolutional neural network and recurrent neural network for real-world use cases such as image classification, transfer learning, and natural language processing.
Key Features
- Clear and concise explanations
- Gives important insights into deep learning models
- Practical demonstration of key concepts
Book 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.
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
Who this book is 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.
Table of Contents
- Introduction to PyTorch
- Deep Learning Fundamentals
- Computational Graphs and Linear Models
- Convolutional Networks
- Other NN Architectures
- Getting the Most out of PyTorch
"Sinopsis" puede pertenecer a otra edición de este libro.
Acerca del autor
David Julian is a freelance technology consultant and educator. He has worked as a consultant for government, private, and community organizations on a variety of projects, including using machine learning to detect insect outbreaks in controlled agricultural environments (Urban Ecological Systems Ltd., Bluesmart Farms), designing and implementing event management data systems (Sustainable Industry Expo, Lismore City Council), and designing multimedia interactive installations (Adelaide University). He has also written Designing Machine Learning Systems With Python for Packt Publishing and was a technical reviewer for Python Machine Learning and Hands-On Data Structures and Algorithms with Python - Second Edition, published by Packt.
"Sobre este título" puede pertenecer a otra edición de este libro.
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Packt Publishing Limited, 2018
ISBN 10: 1789534097
ISBN 13: 9781789534092
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Paperback. Condición: New. Introduction to deep learning and PyTorch by building a convolutional neural network and recurrent neural network for real-world use cases such as image classification, transfer learning, and natural language processing.Key FeaturesClear and concise explanationsGives important insights into deep learning modelsPractical demonstration of key conceptsBook DescriptionPyTorch 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.What you will learnSet up the deep learning environment using the PyTorch libraryLearn to build a deep learning model for image classificationUse a convolutional neural network for transfer learningUnderstand to use PyTorch for natural language processingUse a recurrent neural network to classify textUnderstand how to optimize PyTorch in multiprocessor and distributed environmentsTrain, optimize, and deploy your neural networks for maximum accuracy and performanceLearn to deploy production-ready modelsWho this book is forDevelopers 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. Nº de ref. del artículo: LU-9781789534092
Cantidad disponible: Más de 20 disponibles
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Deep Learning with PyTorch Quick Start Guide
Publicado por
Packt Publishing Limited, GB, 2018
ISBN 10: 1789534097
ISBN 13: 9781789534092
Nuevo
Paperback
Librería: Rarewaves.com UK, London, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Paperback. Condición: New. Introduction to deep learning and PyTorch by building a convolutional neural network and recurrent neural network for real-world use cases such as image classification, transfer learning, and natural language processing.Key FeaturesClear and concise explanationsGives important insights into deep learning modelsPractical demonstration of key conceptsBook DescriptionPyTorch 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.What you will learnSet up the deep learning environment using the PyTorch libraryLearn to build a deep learning model for image classificationUse a convolutional neural network for transfer learningUnderstand to use PyTorch for natural language processingUse a recurrent neural network to classify textUnderstand how to optimize PyTorch in multiprocessor and distributed environmentsTrain, optimize, and deploy your neural networks for maximum accuracy and performanceLearn to deploy production-ready modelsWho this book is forDevelopers 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. Nº de ref. del artículo: LU-9781789534092
Cantidad disponible: Más de 20 disponibles
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Deep Learning with PyTorch Quick Start Guide
Publicado por
Packt Publishing Limited, GB, 2018
ISBN 10: 1789534097
ISBN 13: 9781789534092
Nuevo
Paperback
Librería: Rarewaves USA United, OSWEGO, IL, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Paperback. Condición: New. Introduction to deep learning and PyTorch by building a convolutional neural network and recurrent neural network for real-world use cases such as image classification, transfer learning, and natural language processing.Key FeaturesClear and concise explanationsGives important insights into deep learning modelsPractical demonstration of key conceptsBook DescriptionPyTorch 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.What you will learnSet up the deep learning environment using the PyTorch libraryLearn to build a deep learning model for image classificationUse a convolutional neural network for transfer learningUnderstand to use PyTorch for natural language processingUse a recurrent neural network to classify textUnderstand how to optimize PyTorch in multiprocessor and distributed environmentsTrain, optimize, and deploy your neural networks for maximum accuracy and performanceLearn to deploy production-ready modelsWho this book is forDevelopers 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. Nº de ref. del artículo: LU-9781789534092
Cantidad disponible: Más de 20 disponibles
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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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ISBN 13: 9781789534092
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Deep Learning with PyTorch Quick Start Guide
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ISBN 10: 1789534097
ISBN 13: 9781789534092
Nuevo
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Librería: Rarewaves.com USA, London, LONDO, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Paperback. Condición: New. Introduction to deep learning and PyTorch by building a convolutional neural network and recurrent neural network for real-world use cases such as image classification, transfer learning, and natural language processing.Key FeaturesClear and concise explanationsGives important insights into deep learning modelsPractical demonstration of key conceptsBook DescriptionPyTorch 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.What you will learnSet up the deep learning environment using the PyTorch libraryLearn to build a deep learning model for image classificationUse a convolutional neural network for transfer learningUnderstand to use PyTorch for natural language processingUse a recurrent neural network to classify textUnderstand how to optimize PyTorch in multiprocessor and distributed environmentsTrain, optimize, and deploy your neural networks for maximum accuracy and performanceLearn to deploy production-ready modelsWho this book is forDevelopers 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. Nº de ref. del artículo: LU-9781789534092
Cantidad disponible: Más de 20 disponibles