An Introduction to Image Classification From Designed Models to End-to-End Learning The world we live in today is increasingly dependent on technology. Every day, we hear about new advancements in artificial intelligence, machine learning, and deep learning that have the potential to revolutionize our lives. One area where these technologies are making significant progress is image classification, which has far-reaching implications for industries such as healthcare, finance, transportation, and security. This book provides an introduction to image classification, from traditional designed models to end-to-end learning, and explores how these techniques can be used to solve real-world problems. The first part of the book covers the basics of image classification, including the different types of images, image processing techniques, and feature extraction methods. It also discusses the limitations of traditional designed models and their shortcomings when it comes to handling complex images. The second part of the book delves into the concept of end-to-end learning and its applications in image classification tasks. The authors explain how end-to-end learning can overcome the limitations of traditional designed models and provide better performance in certain tasks. The book also covers some of the most recent advances in image classification research, including transfer learning, data augmentation, and generative adversarial networks (GANs). It provides examples of how these techniques are being used in various industries and demonstrates their effectiveness in solving real-world problems. Finally, the book concludes with a discussion on the future of image classification and the potential impact of emerging technologies on society.

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