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Pytorch Recipes Problem Solution Approach by Mishra Pradeepta (18 results)
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Condition: New. Brand New! Not Overstocks or Low Quality Book Club Editions! Direct From the Publisher! We're not a giant, faceless warehouse organization! We're a small town bookstore that loves books and loves it's customers! Buy from Lakeside Books!
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Paperback or Softback. Condition: New. Pytorch Recipes: A Problem-Solution Approach to Build, Train and Deploy Neural Network Models. Book.
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PyTorch Recipes: A Problem-Solution Approach to Build, Train and Deploy Neural Network Models
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PyTorch Recipes: A Problem-Solution Approach to Build, Train and Deploy Neural Network Models
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Condition: New. 2022. 2nd Edition. paperback. . . . . .
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PyTorch Recipes: A Problem-Solution Approach to Build, Train and Deploy Neural Network Models
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Paperback. Condition: Brand New. 2nd edition. 290 pages. 10.00x7.01x1.02 inches. In Stock.
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Pytorch Recipes: A Problem-solution Approach to Build, Train and Deploy Neural Network Models
Language: English
Published by Apress, Incorporated, 2022
ISBN 10: 1484289242 ISBN 13: 9781484289242
Condition: New.
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Taschenbuch. Condition: Neu. PyTorch Recipes | A Problem-Solution Approach to Build, Train and Deploy Neural Network Models | Pradeepta Mishra | Taschenbuch | xxiv | Englisch | 2022 | Apress | EAN 9781484289242 | Verantwortliche Person für die EU: APress in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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PyTorch Recipes: A Problem-Solution Approach to Build, Train and Deploy Neural Network Models
Seller: THE SAINT BOOKSTORE, Southport, United Kingdom
Seller rating 5 out of 5 stars
Paperback / softback. Condition: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.
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Pytorch Recipes: A Problem-solution Approach to Build, Train and Deploy Neural Network Models
Language: English
Published by Apress, Incorporated, 2022
ISBN 10: 1484289242 ISBN 13: 9781484289242
Condition: New. Print on Demand.
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Pytorch Recipes: A Problem-solution Approach to Build, Train and Deploy Neural Network Models
Language: English
Published by Apress, Incorporated, 2022
ISBN 10: 1484289242 ISBN 13: 9781484289242
Condition: New. PRINT ON DEMAND.
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Learn how to use PyTorch to build neural network models using code snippets updated for this second edition. This book includes new chapters covering topics such as distributed PyTorch modeling, deploying PyTorch models in production, and developments around PyTorch with updated code.You'll start by learning how to use tensors to develop and fine-tune neural network models and implement deep learning models such as LSTMs, and RNNs. Next, you'll explore probability distribution concepts using PyTorch, as well as supervised and unsupervised algorithms with PyTorch. This is followed by a deep dive on building models with convolutional neural networks, deep neural networks, and recurrent neural networks using PyTorch. This new edition covers also topics such as Scorch, a compatible module equivalent to the Scikit machine learning library, model quantization to reduce parameter size, and preparing a model for deployment within a production system. Distributed parallel processing for balancing PyTorch workloads, using PyTorch for image processing, audio analysis, and model interpretation are also covered in detail. Each chapter includes recipe code snippets to perform specific activities.By the end of this book, you will be able to confidently build neural network models using PyTorch.What You Will LearnUtilize new code snippets and models to train machine learning models using PyTorchTrain deep learning models with fewer and smarter implementationsExplore the PyTorch framework for model explainability and to bring transparency to model interpretationBuild, train, and deploy neural network models designed to scale with PyTorchUnderstand best practices for evaluating and fine-tuning models using PyTorchUse advanced torch features in training deep neural networksExplore various neural network models using PyTorchDiscover functions compatible with sci-kit learn compatible modelsPerform distributed PyTorch training and executionWho This Book Is ForMachine learning engineers, data scientists and Python programmers and software developers interested in learning the PyTorch framework.
