Deep Learning for Computer Vision: Advanced Techniques and Applications

The chapters of this book cover a broad spectrum, beginning with the fundamentals of convolutional neural networks (CNNs), recurrent models, and transformers, before moving toward advanced topics such as self-supervised learning, generative models, and multimodal integration. Alongside technical discussions, we highlight real-world case studies that demonstrate how these methods are applied in diverse domains such as healthcare, security, agriculture, and robotics.

Special emphasis is placed on challenges such as data scarcity, explainability, and robustness, offering readers a nuanced perspective that balances the promise of deep learning with its practical limitations.

This book is intended for students, researchers, and industry practitioners who wish to deepen their knowledge of computer vision through the lens of deep learning. By combining theoretical insights with hands-on approaches, it aims to serve as both a learning resource and a reference guide. Our hope is that readers will not only gain technical expertise but also be inspired to explore innovative solutions that push the boundaries of what machines can see, understand, and create.