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Unfolding the Mystery of Loss Functions
Immerse yourself in the world of most overlooked concept, Loss function in Deep Learning, with this comprehensive guide to understanding and implementing them. This book is a vital resource for both beginners and experienced readers in the field, providing a clear and in-depth explanation of key concepts and their mathematical foundations. This book delves into a wide array of Loss Functions used in Machine Learning, detailing their mathematical principles, visualizations, and real-world applications.
This book offers Python and PyTorch code snippets for various loss functions, enabling readers to grasp how these theoretical concepts come alive in practical implementations. Whether you are a beginner starting your journey in Deep Learning, a Data Scientist aiming to broaden your understanding of the field, or an AI enthusiast looking to gain in-depth knowledge, this book is a must-read. This book also offers an understanding of loss functions from some of the conference papers. It offers why and when a specific loss function shall be used. It gives an understanding of loss landscape and most interestingly its initial chapter covers all concepts that are being used in subsequent chapters making it easier to grasp for both a beginner and of course the professional audience. It is one of the first book of its kind which is totally dedicated to the concept of loss functions. Hope you enjoy reading it!
This book offers Python and PyTorch code snippets for various loss functions, enabling readers to grasp how these theoretical concepts come alive in practical implementations. Whether you are a beginner starting your journey in Deep Learning, a Data Scientist aiming to broaden your understanding of the field, or an AI enthusiast looking to gain in-depth knowledge, this book is a must-read. This book also offers an understanding of loss functions from some of the conference papers. It offers why and when a specific loss function shall be used. It gives an understanding of loss landscape and most interestingly its initial chapter covers all concepts that are being used in subsequent chapters making it easier to grasp for both a beginner and of course the professional audience. It is one of the first book of its kind which is totally dedicated to the concept of loss functions. Hope you enjoy reading it!
Kindle Edition
Published July 31, 2023
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Displaying 1 - 2 of 2 reviews
August 6, 2023
"Unfolding the Mystery of Loss Functions" by Aarti Balana is a comprehensive and ambitious book that delves deep into the intricate world of loss functions and their applications in various fields, particularly machine learning, and natural language processing. The text covers a wide range of topics, from foundational mathematics, including sets, functions, and relations, to advanced concepts like generative adversarial networks and natural language processing.
Balana has taken great care to structure each section and chapter logically, making it easy for readers to follow the progression of concepts. The book's coverage of different loss functions is extensive, and the explanations are clear.
The book shines in its exploration of loss functions in machine learning and their relevance across diverse tasks. She skillfully navigates through various loss functions used in regression, classification, and image segmentation tasks, among others. Also, the book's discussions on generative adversarial network (GAN) loss and its variations offer valuable insights into state-of-the-art techniques. The explanations of Wasserstein GAN (WGAN) loss and Least Squares GAN (LSGAN) loss are particularly clear and comprehensive.
This is a valuable resource for researchers, machine learning practitioners, and students interested in understanding loss functions and their applications in various fields. The book provides a solid foundation and offers a wealth of knowledge on loss functions, making it a worthy addition to the literature in the domain.
Balana has taken great care to structure each section and chapter logically, making it easy for readers to follow the progression of concepts. The book's coverage of different loss functions is extensive, and the explanations are clear.
The book shines in its exploration of loss functions in machine learning and their relevance across diverse tasks. She skillfully navigates through various loss functions used in regression, classification, and image segmentation tasks, among others. Also, the book's discussions on generative adversarial network (GAN) loss and its variations offer valuable insights into state-of-the-art techniques. The explanations of Wasserstein GAN (WGAN) loss and Least Squares GAN (LSGAN) loss are particularly clear and comprehensive.
This is a valuable resource for researchers, machine learning practitioners, and students interested in understanding loss functions and their applications in various fields. The book provides a solid foundation and offers a wealth of knowledge on loss functions, making it a worthy addition to the literature in the domain.
August 7, 2023
It nice book with Very simple language... Really Good one!
Displaying 1 - 2 of 2 reviews