The Deep Learning with PyTorch Workshop: Build deep neural networks and artificial intelligence applications with PyTorch
bGet a head start in the world of AI and deep learning by developing your skills with PyTorch/b h4Key Features/h4 ulliLearn how to define your own network architecture in deep learning /li liImplement helpful methods to create and train a model using PyTorch syntax /li liDiscover how intelligent app...
Gespeichert in:
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham
Packt Publishing Limited
2020
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| Ausgabe: | 1 |
| Schlagworte: | |
| Zusammenfassung: | bGet a head start in the world of AI and deep learning by developing your skills with PyTorch/b h4Key Features/h4 ulliLearn how to define your own network architecture in deep learning /li liImplement helpful methods to create and train a model using PyTorch syntax /li liDiscover how intelligent applications using features like image recognition and speech recognition really process your data/li/ul h4Book Description/h4 Want to get to grips with one of the most popular machine learning libraries for deep learning? The Deep Learning with PyTorch Workshop will help you do just that, jumpstarting your knowledge of using PyTorch for deep learning even if you're starting from scratch. It's no surprise that deep learning's popularity has risen steeply in the past few years, thanks to intelligent applications such as self-driving vehicles, chatbots, and voice-activated assistants that are making our lives easier. This book will take you inside the world of deep learning, where you'll use PyTorch to understand the complexity of neural network architectures. The Deep Learning with PyTorch Workshop starts with an introduction to deep learning and its applications. You'll explore the syntax of PyTorch and learn how to define a network architecture and train a model. Next, you'll learn about three main neural network architectures - convolutional, artificial, and recurrent - and even solve real-world data problems using these networks. Later chapters will show you how to create a style transfer model to develop a new image from two images, before finally taking you through how RNNs store memory to solve key data issues. By the end of this book, you'll have mastered the essential concepts, tools, and libraries of PyTorch to develop your own deep neural networks and intelligent apps. h4What you will learn/h4 ulliExplore the different applications of deep learning /li liUnderstand the PyTorch approach to building neural networks /li liCreate and train your very own perceptron using PyTorch /li liSolve regression problems using artificial neural networks (ANNs) /li liHandle computer vision problems with convolutional neural networks (CNNs) /li liPerform language translation tasks using recurrent neural networks (RNNs)/li/ul h4Who this book is for/h4 This deep learning book is ideal for anyone who wants to create and train deep learning models using PyTorch. A solid understanding of the Python programming language and its packages will help you grasp the topics covered in the book more quickly |
| Beschreibung: | 1 Online-Ressource (330 Seiten) |
| ISBN: | 9781838981846 |
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| 520 | |a bGet a head start in the world of AI and deep learning by developing your skills with PyTorch/b h4Key Features/h4 ulliLearn how to define your own network architecture in deep learning /li liImplement helpful methods to create and train a model using PyTorch syntax /li liDiscover how intelligent applications using features like image recognition and speech recognition really process your data/li/ul h4Book Description/h4 Want to get to grips with one of the most popular machine learning libraries for deep learning? The Deep Learning with PyTorch Workshop will help you do just that, jumpstarting your knowledge of using PyTorch for deep learning even if you're starting from scratch. It's no surprise that deep learning's popularity has risen steeply in the past few years, thanks to intelligent applications such as self-driving vehicles, chatbots, and voice-activated assistants that are making our lives easier. | ||
| 520 | |a This book will take you inside the world of deep learning, where you'll use PyTorch to understand the complexity of neural network architectures. The Deep Learning with PyTorch Workshop starts with an introduction to deep learning and its applications. You'll explore the syntax of PyTorch and learn how to define a network architecture and train a model. Next, you'll learn about three main neural network architectures - convolutional, artificial, and recurrent - and even solve real-world data problems using these networks. Later chapters will show you how to create a style transfer model to develop a new image from two images, before finally taking you through how RNNs store memory to solve key data issues. By the end of this book, you'll have mastered the essential concepts, tools, and libraries of PyTorch to develop your own deep neural networks and intelligent apps. | ||
| 520 | |a h4What you will learn/h4 ulliExplore the different applications of deep learning /li liUnderstand the PyTorch approach to building neural networks /li liCreate and train your very own perceptron using PyTorch /li liSolve regression problems using artificial neural networks (ANNs) /li liHandle computer vision problems with convolutional neural networks (CNNs) /li liPerform language translation tasks using recurrent neural networks (RNNs)/li/ul h4Who this book is for/h4 This deep learning book is ideal for anyone who wants to create and train deep learning models using PyTorch. A solid understanding of the Python programming language and its packages will help you grasp the topics covered in the book more quickly | ||
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| spelling | Saleh, Hyatt Verfasser aut The Deep Learning with PyTorch Workshop Build deep neural networks and artificial intelligence applications with PyTorch Saleh, Hyatt 1 Birmingham Packt Publishing Limited 2020 1 Online-Ressource (330 Seiten) txt rdacontent c rdamedia cr rdacarrier bGet a head start in the world of AI and deep learning by developing your skills with PyTorch/b h4Key Features/h4 ulliLearn how to define your own network architecture in deep learning /li liImplement helpful methods to create and train a model using PyTorch syntax /li liDiscover how intelligent applications using features like image recognition and speech recognition really process your data/li/ul h4Book Description/h4 Want to get to grips with one of the most popular machine learning libraries for deep learning? The Deep Learning with PyTorch Workshop will help you do just that, jumpstarting your knowledge of using PyTorch for deep learning even if you're starting from scratch. It's no surprise that deep learning's popularity has risen steeply in the past few years, thanks to intelligent applications such as self-driving vehicles, chatbots, and voice-activated assistants that are making our lives easier. This book will take you inside the world of deep learning, where you'll use PyTorch to understand the complexity of neural network architectures. The Deep Learning with PyTorch Workshop starts with an introduction to deep learning and its applications. You'll explore the syntax of PyTorch and learn how to define a network architecture and train a model. Next, you'll learn about three main neural network architectures - convolutional, artificial, and recurrent - and even solve real-world data problems using these networks. Later chapters will show you how to create a style transfer model to develop a new image from two images, before finally taking you through how RNNs store memory to solve key data issues. By the end of this book, you'll have mastered the essential concepts, tools, and libraries of PyTorch to develop your own deep neural networks and intelligent apps. h4What you will learn/h4 ulliExplore the different applications of deep learning /li liUnderstand the PyTorch approach to building neural networks /li liCreate and train your very own perceptron using PyTorch /li liSolve regression problems using artificial neural networks (ANNs) /li liHandle computer vision problems with convolutional neural networks (CNNs) /li liPerform language translation tasks using recurrent neural networks (RNNs)/li/ul h4Who this book is for/h4 This deep learning book is ideal for anyone who wants to create and train deep learning models using PyTorch. A solid understanding of the Python programming language and its packages will help you grasp the topics covered in the book more quickly COMPUTERS / Intelligence (AI) & Semantics COMPUTERS / Neural Networks |
| spellingShingle | Saleh, Hyatt The Deep Learning with PyTorch Workshop Build deep neural networks and artificial intelligence applications with PyTorch COMPUTERS / Intelligence (AI) & Semantics COMPUTERS / Neural Networks |
| title | The Deep Learning with PyTorch Workshop Build deep neural networks and artificial intelligence applications with PyTorch |
| title_auth | The Deep Learning with PyTorch Workshop Build deep neural networks and artificial intelligence applications with PyTorch |
| title_exact_search | The Deep Learning with PyTorch Workshop Build deep neural networks and artificial intelligence applications with PyTorch |
| title_exact_search_txtP | The Deep Learning with PyTorch Workshop Build deep neural networks and artificial intelligence applications with PyTorch |
| title_full | The Deep Learning with PyTorch Workshop Build deep neural networks and artificial intelligence applications with PyTorch Saleh, Hyatt |
| title_fullStr | The Deep Learning with PyTorch Workshop Build deep neural networks and artificial intelligence applications with PyTorch Saleh, Hyatt |
| title_full_unstemmed | The Deep Learning with PyTorch Workshop Build deep neural networks and artificial intelligence applications with PyTorch Saleh, Hyatt |
| title_short | The Deep Learning with PyTorch Workshop |
| title_sort | the deep learning with pytorch workshop build deep neural networks and artificial intelligence applications with pytorch |
| title_sub | Build deep neural networks and artificial intelligence applications with PyTorch |
| topic | COMPUTERS / Intelligence (AI) & Semantics COMPUTERS / Neural Networks |
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