In this new episode we explore Feedforward Neural Networks (FNNs), the simplest form of artificial neural networks. We explore how data flows in one direction, passing through input, hidden, and output layers. Learn about the two key phases: forward propagation where weighted inputs are combined and activated, and prediction, where outputs become continuous values or probabilities. FNNs are versatile, useful for classification and regression tasks, but they struggle with sequential data, need good feature engineering, have difficulty with high-dimensional inputs, are prone to overfitting, and can be inefficient in large networks. Tune in to understand the foundations of machine learning.