Machine Learning for PyTorch: A Hands-On Guide to Building Scalable and Flexible AI Models with Ease

Neal, Gilberto

/ ISBN 13: 9798312089103

Published by Independently published, 2025

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Synopsis:

PyTorch has become one of the most powerful and widely adopted frameworks for deep learning. Whether you're a beginner looking to understand the fundamentals or an experienced developer aiming to optimize models and deploy them efficiently, this book is your ultimate guide.

This comprehensive resource takes you through every stage of deep learning with PyTorch, covering everything from basic tensor operations to advanced topics such as generative models, reinforcement learning, and distributed training. With detailed explanations, practical code examples, and real-world applications, you’ll gain a deep understanding of PyTorch’s capabilities and how to apply them to cutting-edge AI projects.

What You Will Learn:

• Core PyTorch Concepts – Grasp the essentials of tensors, autograd, and model building.
• Deep Learning Architectures – Implement CNNs, RNNs, Transformers, GANs, and VAEs.
• Training Optimization – Improve model efficiency with quantization, pruning, and mixed-precision training.
• Handling Large Datasets – Leverage PyTorch’s DataLoader API and distributed training techniques.
• Model Deployment – Deploy models with Flask, FastAPI, TorchServe, and even on mobile and edge devices.
• Ethical AI Development – Understand bias, responsible AI practices, and debugging techniques.

With hands-on coding exercises, step-by-step implementations, and industry insights, this book empowers you to build and deploy AI models confidently.

Whether you’re a researcher, engineer, or AI enthusiast, this book provides the knowledge and tools to excel in deep learning with PyTorch. Start building powerful AI models today—your journey to mastering PyTorch begins here!

Bibliographic Details

Title: Machine Learning for PyTorch: A Hands-On ...
Publisher: Independently published
Publication Date: 2025
Binding: Soft cover
Condition: New