PyTorch Deep Learning Hands-On

This hands-on guide to PyTorch takes you directly into building real-world deep learning models. Through practical examples, you'll explore CNNs, RNNs, GANs, reinforcement learning, and more. You'll streamline your workflows and experiment effectively, gaining the skills to bring your models into production.

What this Book will help me do

• Build and train neural networks efficiently using PyTorch.
• Create advanced computer vision models like CNNs to solve image-related tasks.
• Develop sequential models using RNNs to handle text and time-series data.
• Understand and construct GANs for generative task applications.
• Deploy deep learning models to production using PyTorch's tools and best practices.

Author(s)

The authors, Sherin Thomas and Sudhanshu Passi, bring a wealth of experience in deep learning and PyTorch development. Sherin, a machine learning engineer with a passion for optimizing workflows, pairs perfectly with Sudhanshu, who excels in designing deployable AI systems. Together, they deliver a pragmatic and actionable approach.

Who is it for?

This book is designed for machine learning engineers and practitioners eager to incorporate PyTorch into their workflow. If you have a basic understanding of Python and machine learning concepts but wish to build and deploy deep learning models using PyTorch, this book is an excellent choice. It is perfect for those who value hands-on, implementation-focused learning. Whether you're looking to upscale your AI projects or dive deeper into PyTorch capabilities, this book will guide you.