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Deep Learning in Computer Vision: From Principles to Real-World Applications
This book provides an in-depth exploration of deep learning techniques and their application in the field of computer vision. Beginning with the core principles of deep learning, it progresses to cover practical, real-world use cases in various industries. The book offers a comprehensive guide, starting with fundamental concepts such as neural networks, convolutional neural networks (CNNs), and backpropagation, before advancing to more complex topics like generative models, object detection, and image segmentation. It details the advancements in deep learning architectures, including the shift from traditional image processing methods to modern, AI-driven solutions.
As it progresses, the book emphasizes how deep learning techniques have become integral to industries ranging from healthcare to autonomous vehicles. Practical applications like medical image analysis, facial recognition, and automated inspection in manufacturing are discussed in depth. The author also highlights the importance of large datasets, transfer learning, and reinforcement learning in improving model accuracy and efficiency in real-world scenarios.
The book is designed not only for researchers and professionals in computer vision but also for engineers looking to implement deep learning solutions in their projects. It blends theoretical knowledge with practical advice on deploying AI models in production environments, addressing challenges such as data preprocessing, model optimization, and the ethical considerations of AI.
Real-world case studies illustrate the powerful impact of computer vision powered by deep learning, showcasing how these technologies are changing the way we interact with the world around us. By the end of the book, readers will have a clear understanding of both the foundational principles and the modern applications of deep learning in computer vision, preparing them to apply these methods in their own fields.
As it progresses, the book emphasizes how deep learning techniques have become integral to industries ranging from healthcare to autonomous vehicles. Practical applications like medical image analysis, facial recognition, and automated inspection in manufacturing are discussed in depth. The author also highlights the importance of large datasets, transfer learning, and reinforcement learning in improving model accuracy and efficiency in real-world scenarios.
The book is designed not only for researchers and professionals in computer vision but also for engineers looking to implement deep learning solutions in their projects. It blends theoretical knowledge with practical advice on deploying AI models in production environments, addressing challenges such as data preprocessing, model optimization, and the ethical considerations of AI.
Real-world case studies illustrate the powerful impact of computer vision powered by deep learning, showcasing how these technologies are changing the way we interact with the world around us. By the end of the book, readers will have a clear understanding of both the foundational principles and the modern applications of deep learning in computer vision, preparing them to apply these methods in their own fields.
104 pages, Hardcover
Published December 3, 2024
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