PyTorch Artificial Intelligence Fundamentals: A recipe-based approach to design, build and deploy your own AI models with PyTorch 1.x Jibin Mathew Aut
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PyTorch Artificial Intelligence Fundamentals: A recipe-based approach to design, build and deploy your own AI models with PyTorch 1.x Jibin Mathew Aut
~EN NW EB DLISBN: 9781838558291 bzw. 1838558292, vermutlich in Englisch, Packt Publishing, neu, E-Book, elektronischer Download.
Lieferung aus: Vereinigte Staaten von Amerika, In Stock.
Use PyTorch to build end-to-end artificial intelligence systems using PythonKey FeaturesBuild smart AI systems to handle real-world problems using PyTorch 1.xBecome well-versed with concepts such as deep reinforcement learning (DRL) and genetic programmingCover PyTorch functionalities from tensor manipulation through to deploying in productionBook DescriptionArtificial Intelligence (AI) continues to grow in popularity and disrupt a wide range of domains, but it is a complex and daunting topic. In this book, you'll get to grips with building deep learning apps, and how you can use PyTorch for research and solving real-world problems. This book uses a recipe-based approach, starting with the basics of tensor manipulation, before covering Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) in PyTorch. Once you are well-versed with these basic networks, you'll build a medical image classifier using deep learning. Next, you'll use TensorBoard for visualizations. You'll also delve into Generative Adversarial Networks (GANs) and Deep Reinforcement Learning (DRL) before finally deploying your models to production at scale. You'll discover solutions to common problems faced in machine learning, deep learning, and reinforcement learning. You'll learn to implement AI tasks and tackle real-world problems in computer vision, natural language processing (NLP), and other real-world domains. By the end of this book, you'll have the foundations of the most important and widely used techniques in AI using the PyTorch framework.What you will learnPerform tensor manipulation using PyTorchTrain a fully connected neural networkAdvance from simple neural networks to convolutional neural networks (CNNs) and recurrent neural networks (RNNs)Implement transfer learning techniques to classify medical imagesGet to grips with generative adversarial networks (GANs), along with their implementationBuild deep reinforcement learning applications and learn how agents interact in the real environmentScale models to production using ONNX RuntimeDeploy AI models and perform distributed training on large datasetsWho this book is forThis PyTorch book is for AI engineers who are just getting started, machine learning engineers, data scientists and deep learning enthusiasts who are looking for a guide to help them solve AI problems effectively. Working knowledge of the Python programming language and a basic understanding of machine learning are expected.
Use PyTorch to build end-to-end artificial intelligence systems using PythonKey FeaturesBuild smart AI systems to handle real-world problems using PyTorch 1.xBecome well-versed with concepts such as deep reinforcement learning (DRL) and genetic programmingCover PyTorch functionalities from tensor manipulation through to deploying in productionBook DescriptionArtificial Intelligence (AI) continues to grow in popularity and disrupt a wide range of domains, but it is a complex and daunting topic. In this book, you'll get to grips with building deep learning apps, and how you can use PyTorch for research and solving real-world problems. This book uses a recipe-based approach, starting with the basics of tensor manipulation, before covering Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) in PyTorch. Once you are well-versed with these basic networks, you'll build a medical image classifier using deep learning. Next, you'll use TensorBoard for visualizations. You'll also delve into Generative Adversarial Networks (GANs) and Deep Reinforcement Learning (DRL) before finally deploying your models to production at scale. You'll discover solutions to common problems faced in machine learning, deep learning, and reinforcement learning. You'll learn to implement AI tasks and tackle real-world problems in computer vision, natural language processing (NLP), and other real-world domains. By the end of this book, you'll have the foundations of the most important and widely used techniques in AI using the PyTorch framework.What you will learnPerform tensor manipulation using PyTorchTrain a fully connected neural networkAdvance from simple neural networks to convolutional neural networks (CNNs) and recurrent neural networks (RNNs)Implement transfer learning techniques to classify medical imagesGet to grips with generative adversarial networks (GANs), along with their implementationBuild deep reinforcement learning applications and learn how agents interact in the real environmentScale models to production using ONNX RuntimeDeploy AI models and perform distributed training on large datasetsWho this book is forThis PyTorch book is for AI engineers who are just getting started, machine learning engineers, data scientists and deep learning enthusiasts who are looking for a guide to help them solve AI problems effectively. Working knowledge of the Python programming language and a basic understanding of machine learning are expected.
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Daten vom 12.04.2020 11:16h
ISBN (andere Schreibweisen): 1-83855-829-2, 978-1-83855-829-1
Daten vom 12.04.2020 11:16h
ISBN (andere Schreibweisen): 1-83855-829-2, 978-1-83855-829-1
Zuerst gefunden: 12.04.2020 11:16:49
Zuletzt gefunden: 12.04.2020 11:16:49
Kleinster Preis: € 21,02 (vom 12.04.2020 11:16:49)
Höchster Preis: € 21,02 (vom 12.04.2020 11:16:49)
Fundstellen insgesamt: 1
Zuletzt gefunden: 12.04.2020 11:16:49
Kleinster Preis: € 21,02 (vom 12.04.2020 11:16:49)
Höchster Preis: € 21,02 (vom 12.04.2020 11:16:49)
Fundstellen insgesamt: 1
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ISBN: 9781838558291
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