The Deep Learning with PyTorch Workshop

• Verfasst von: Saleh, Hyatt [VerfasserIn]
• Titel: The Deep Learning with PyTorch Workshop
• Institutionen: Safari, an O'Reilly Media Company. [MitwirkendeR]
• Verf.angabe: Saleh, Hyatt
• Ausgabe: 1st edition
• Verlagsort: [Erscheinungsort nicht ermittelbar]
• Verlag: Packt Publishing
• Jahr: 2020
• Umfang: 1 online resource (330 pages)
• Fussnoten: Online resource; Title from title page (viewed July 22, 2020)
• ISBN: 978-1-83898-921-7
• ComputerInfo: Mode of access: World Wide Web.
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Electronic books ; local
• K10plus-PPN: 1726626814
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext
• Lokale URL UB: Bibliothek der Medizinischen Fakultät Mannheim der Universität Heidelberg
• Bibliothek/Idn: UW / m3736230931
• Lokale URL Inst.: Zum Volltext

Abstract

Get a head start in the world of AI and deep learning by developing your skills with PyTorch Key Features Learn how to define your own network architecture in deep learning Implement helpful methods to create and train a model using PyTorch syntax Discover how intelligent applications using features like image recognition and speech recognition really process your data Book Description 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. What you will learn Explore the different applications of deep learning Understand the PyTorch approach to building neural networks Create and train your very own perceptron using PyTorch Solve regression problems using artificial neural networks (ANNs) Handle computer vision problems with convolutional neural networks (CNNs) Perform language translation tasks using recurrent neural networks (RNNs) Who this book is for 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.