Building Blocks

This chapter focuses on the architecture and components of multi-layer perceptrons (MLPs). It covers the historical context and applications of MLPs, detailing the structure of input, hidden, and output layers. Various activation functions, training methods, optimization techniques, and regularization methods are discussed. The chapter provides practical examples using TensorFlow and PyTorch, along with case studies to demonstrate building and training MLPs effectively.