This video of the workbook covers activation functions which introduce non-linearity to neural networks. It explains how they determine whether a neuron should be activated based on its inputs, enabling networks to capture complex patterns and relationships in data. The video discusses the basics of common activation functions like ReLU, Sigmoid, and Tanh, explaining their roles in deep learning and how they impact a network’s performance, making neural networks powerful and effective.
The workbook pdf-
