Weiler M Equivariant and Coordinate Independent Convolutional Networks 2026 | 32.73 MB
Title: Equivariant and Coordinate Independent Convolutional Networks: A Gauge Field Theory of Neural Networks (591 Pages)
Author: Maurice Weiler
Description:
Audience: This book is designed for anyone who has a keen interest in Machine Learning, Deep Learning, and Artificial Intelligence, particularly in the realm of image processing. Whether you’re a student, a professional, or an enthusiast, this book will serve as your gateway to understanding the complex world of CNNs. While prior experience with C++ programming can be beneficial, it’s not a requirement. The book covers the basics and gradually progresses to more advanced topics, making it suitable for readers of all levels.
Content: The book is divided into several chapters, each focusing on different aspects of CNNs and their implementation using LibTorch. From understanding the basics of Machine Learning and CNNs, to setting up your development environment, to coding your first CNN model, to optimizing and fine-tuning your models – this book covers it all. Real-world examples and case studies are provided throughout the book to help you understand the practical applications of the concepts discussed.
Conclusion: By the end of this book, you will have a solid understanding of Convolutional Neural Networks and how to implement them using LibTorch. You will be equipped with the knowledge and skills to apply these powerful algorithms to real-world problems, paving the way for your journey in the exciting field of Machine Learning.
We hope you enjoy the journey as much as we enjoyed putting it together for you!
Audience: This book is designed for anyone who has a keen interest in Machine Learning, Deep Learning, and Artificial Intelligence, particularly in the realm of image processing. Whether you’re a student, a professional, or an enthusiast, this book will serve as your gateway to understanding the complex world of CNNs. While prior experience with C++ programming can be beneficial, it’s not a requirement. The book covers the basics and gradually progresses to more advanced topics, making it suitable for readers of all levels.
Content: The book is divided into several chapters, each focusing on different aspects of CNNs and their implementation using LibTorch. From understanding the basics of Machine Learning and CNNs, to setting up your development environment, to coding your first CNN model, to optimizing and fine-tuning your models – this book covers it all. Real-world examples and case studies are provided throughout the book to help you understand the practical applications of the concepts discussed.
Conclusion: By the end of this book, you will have a solid understanding of Convolutional Neural Networks and how to implement them using LibTorch. You will be equipped with the knowledge and skills to apply these powerful algorithms to real-world problems, paving the way for your journey in the exciting field of Machine Learning.
We hope you enjoy the journey as much as we enjoyed putting it together for you!
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