Deep Learning at Scale

Part I. Foundational Concepts of Deep Learning

The following chapters delve into foundational concepts that are essential for understanding and implementing deep learning. It begins by introducing deep learning principles through computational graphs and data flow, offering clarity through Python exercises. The section further explores the computational aspects of deep learning, including electronic computations and hardware considerations crucial for achieving and scaling compute capabilities. Additionally, it provides insights into accelerated hardware options available today, aiding in informed hardware selection for deep learning projects. Finally, the section synthesizes this knowledge to provide practical guidance on building efficient and effective intelligent systems, emphasizing strategies for optimization and performance measurement through graph compilation and memory management techniques.