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PyTorch recipesa problem-solution approach to build, train and deploy neural network models /

Record Type:	Electronic resources : Monograph/item
Title/Author:	PyTorch recipesby Pradeepta Mishra.
Reminder of title:	a problem-solution approach to build, train and deploy neural network models /
Author:	Mishra, Pradeepta.
Published:	Berkeley, CA :Apress :2023.
Description:	xxiv, 266 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Neural networks (Computer science)
Online resource:	https://doi.org/10.1007/978-1-4842-8925-9
ISBN:	9781484289259$q(electronic bk.)

PyTorch recipesa problem-solution approach to build, train and deploy neural network models /
Mishra, Pradeepta.

PyTorch recipesa problem-solution approach to build, train and deploy neural network models /[electronic resource] :by Pradeepta Mishra. - Second edition. - Berkeley, CA :Apress :2023. - xxiv, 266 p. :ill., digital ;24 cm.

Chapter 1: Introduction to PyTorch, Tensors, and Tensor Operations -- Chapter 2: Probability Distributions Using PyTorch -- Chapter 3: CNN and RNN Using PyTorch -- Chapter 4: Introduction to Neural Networks Using PyTorch -- Chapter 5: Supervised Learning Using PyTorch -- Chapter 6: Fine-Tuning Deep Learning Models Using PyTorch -- Chapter 7: Natural Language Processing Using PyTorch -- Chapter 8: Distributed PyTorch Modelling, Model Optimization and Deployment -- Chapter 9: Data Augmentation, Feature Engineering and Extractions for Image and Audio -- Chapter 10: PyTorch Model Interpretability and Interface to Sklearn.

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. You will: 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.

ISBN: 9781484289259$q(electronic bk.)

Standard No.: 10.1007/978-1-4842-8925-9doiSubjects--Topical Terms:

181982
Neural networks (Computer science)

LC Class. No.: QA76.73.P98 / M57 2023

Dewey Class. No.: 006.32

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505 0 $a Chapter 1: Introduction to PyTorch, Tensors, and Tensor Operations -- Chapter 2: Probability Distributions Using PyTorch -- Chapter 3: CNN and RNN Using PyTorch -- Chapter 4: Introduction to Neural Networks Using PyTorch -- Chapter 5: Supervised Learning Using PyTorch -- Chapter 6: Fine-Tuning Deep Learning Models Using PyTorch -- Chapter 7: Natural Language Processing Using PyTorch -- Chapter 8: Distributed PyTorch Modelling, Model Optimization and Deployment -- Chapter 9: Data Augmentation, Feature Engineering and Extractions for Image and Audio -- Chapter 10: PyTorch Model Interpretability and Interface to Sklearn.
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