Introduction

Building upon our foundational understanding of predictive modeling, we now dive into the dynamic world of Multilayer Perceptron (MLP) models. In this chapter, we embark on a journey to construct an MLP model from scratch, leveraging the versatility and power of neural networks for predictive analytics.

Our exploration of MLPs represents a significant leap into the realm of complex modeling techniques. While linear regression provided valuable insights into modeling relationships within data, MLPs offer a rich framework for capturing intricate patterns and nonlinear dependencies, making them well suited for a wide range of predictive tasks.

Through hands-on experimentation and iterative refinement, we will unravel the intricacies of MLP architecture and optimization. From designing the initial network structure to fine-tuning hyperparameters and incorporating advanced techniques such as batch normalization and dropout, we aim to equip you with the knowledge and...