Neural Networks Recap: Activation Functions

This lecture provides a recap on artificial neural networks, focusing on activation functions for hidden layers and the final output layer. It covers the basics of training a multilayer perceptron, working with images, and the concept of convolutional layers in Convolutional Neural Networks (CNN). The instructor explains the importance of regularization techniques to prevent overfitting and discusses the transition from supervised learning to data transformation for further analysis, including dimensionality reduction methods like PCA and Fisher Linear Discriminant Analysis. The lecture concludes with a brief overview of t-distributed stochastic neighbor embedding (t-SNE) for data visualization.