The Algebra and Geometry of Deep Learning

This chapter explores the algebraic and geometric underpinnings of deep learning through the lens of tensor algebra. It covers fundamental tensor operations, geometric interpretations, and their applications in neural networks. The chapter discusses transformations, tensor fields, and manifolds, providing practical examples of tensor representations and operations in neural networks. Visualization techniques for tensors and geometric structures are also covered, along with practical applications and exercises to reinforce the concepts.