PyTorch recipes: A Problem-Solution Approach to Build, Train and Deploy Neural Network Models
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 st...
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Berkeley, CA
Apress L. P.
2022
|
| Ausgabe: | Second edition. |
| Schlagwörter: | |
| Links: | https://learning.oreilly.com/library/view/-/9781484289259/?ar |
| Zusammenfassung: | 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 Learn Utilize new code snippets and models to train machine learning models using PyTorch Train deep learning models with fewer and smarter implementations Explore the PyTorch framework for model explainability and to bring transparency to model interpretation Build, train, and deploy neural network models designed to scale with PyTorch Understand best practices for evaluating and fine-tuning models using PyTorch Use advanced torch features in training deep neural networks Explore various neural network models using PyTorch Discover functions compatible with sci-kit learn compatible models Perform distributed PyTorch training and execution Who This Book Is For Machine learning engineers, data scientists and Python programmers and software developers interested in learning the PyTorch framework. |
| Beschreibung: | Description based upon print version of record |
| Umfang: | 1 Online-Ressource (xxiv, 266 Seiten) illustrations |
| ISBN: | 9781484289259 1484289250 |
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| spelling | Mishra, Pradeepta VerfasserIn aut PyTorch recipes A Problem-Solution Approach to Build, Train and Deploy Neural Network Models Pradeepta Mishra Second edition. Berkeley, CA Apress L. P. 2022 1 Online-Ressource (xxiv, 266 Seiten) illustrations Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Description based upon print version of record 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 Learn Utilize new code snippets and models to train machine learning models using PyTorch Train deep learning models with fewer and smarter implementations Explore the PyTorch framework for model explainability and to bring transparency to model interpretation Build, train, and deploy neural network models designed to scale with PyTorch Understand best practices for evaluating and fine-tuning models using PyTorch Use advanced torch features in training deep neural networks Explore various neural network models using PyTorch Discover functions compatible with sci-kit learn compatible models Perform distributed PyTorch training and execution Who This Book Is For Machine learning engineers, data scientists and Python programmers and software developers interested in learning the PyTorch framework. Neural networks (Computer science) Machine learning Python (Computer program language) Réseaux neuronaux (Informatique) Apprentissage automatique Python (Langage de programmation) 9781484289242 Erscheint auch als Druck-Ausgabe 9781484289242 |
| spellingShingle | Mishra, Pradeepta PyTorch recipes A Problem-Solution Approach to Build, Train and Deploy Neural Network Models Neural networks (Computer science) Machine learning Python (Computer program language) Réseaux neuronaux (Informatique) Apprentissage automatique Python (Langage de programmation) |
| title | PyTorch recipes A Problem-Solution Approach to Build, Train and Deploy Neural Network Models |
| title_auth | PyTorch recipes A Problem-Solution Approach to Build, Train and Deploy Neural Network Models |
| title_exact_search | PyTorch recipes A Problem-Solution Approach to Build, Train and Deploy Neural Network Models |
| title_full | PyTorch recipes A Problem-Solution Approach to Build, Train and Deploy Neural Network Models Pradeepta Mishra |
| title_fullStr | PyTorch recipes A Problem-Solution Approach to Build, Train and Deploy Neural Network Models Pradeepta Mishra |
| title_full_unstemmed | PyTorch recipes A Problem-Solution Approach to Build, Train and Deploy Neural Network Models Pradeepta Mishra |
| title_short | PyTorch recipes |
| title_sort | pytorch recipes a problem solution approach to build train and deploy neural network models |
| title_sub | A Problem-Solution Approach to Build, Train and Deploy Neural Network Models |
| topic | Neural networks (Computer science) Machine learning Python (Computer program language) Réseaux neuronaux (Informatique) Apprentissage automatique Python (Langage de programmation) |
| topic_facet | Neural networks (Computer science) Machine learning Python (Computer program language) Réseaux neuronaux (Informatique) Apprentissage automatique Python (Langage de programmation) |
| work_keys_str_mv | AT mishrapradeepta pytorchrecipesaproblemsolutionapproachtobuildtrainanddeployneuralnetworkmodels |