Deep Learning with PyTorch Playbook: Practical Techniques for Building, Training, and Optimizing Deep Learning Models (PyTorch Programming Series) - Brossura

Libro 2 di 3: PyTorch Programming Series

Marsh, Alvin

9798171180928: Deep Learning with PyTorch Playbook: Practical Techniques for Building, Training, and Optimizing Deep Learning Models (PyTorch Programming Series)

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ISBN 13: 9798171180928

Casa editrice: Independently published, 2026

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Da: EUR 22,44

Deep Learning with PyTorch Playbook takes the fundamentals of PyTorch and moves into more advanced techniques for developing, training, evaluating, and optimizing deep learning models.
This book focuses on the practical challenges that arise when moving from simple neural networks to more capable deep learning systems. Readers explore deeper architectures, convolutional neural networks, sequence models, modern training strategies, transfer learning, regularization, performance optimization, and techniques for building more reliable models.
The emphasis is on understanding the complete deep learning workflow—from preparing data and designing architectures to training models efficiently, diagnosing problems, evaluating results, and improving performance.
What You Will Learn

Design and implement advanced neural network architectures
Build convolutional neural networks for computer vision
Work with sequential and structured data
Apply regularization and techniques for improving generalization
Select and configure loss functions and optimizers
Use learning-rate strategies and training techniques
Apply transfer learning and pretrained models
Build more efficient and scalable training workflows
Diagnose overfitting, underfitting, and training instability
Improve model performance and computational efficiency
Evaluate deep learning models effectively
Structure practical PyTorch deep learning projects

This book is intended for readers who have a basic understanding of PyTorch and want to develop stronger practical skills for building and optimizing modern deep learning models.

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