Deep learning, due to the diversity of its application domains, is today attracting growing interest in research and innovation, as well as in many sectors of activity. But what do these neural networks actually contain, capable of translating, diagnosing, generating content, or even assisting in decision-making?

This book is intended for students, researchers, and professionals who wish to gain a deep understanding of this key area of artificial intelligence and master its main conceptual and practical aspects.

From introductory concepts to advanced methodologies, this book offers a rigorous and structured pedagogical progression, enriched with concrete examples, applied case studies, and operational recommendations. It thus serves as a reference guide for applying deep learning principles in diverse fields such as healthcare, computer vision, and finance.

Key features of this book:

• A structured pedagogical progression, suitable for an audience with varied profiles and levels.
• Clear and accessible explanations of theoretical foundations, systematically illustrated with practical and contextualized examples (implemented in Python).
• Detailed implementations of the main architectures and machine learning algorithms, fostering an understanding of the underlying mechanisms and their concrete application.