Deep Learning: Data Representations and Neural Networks

This lecture covers data representations in deep learning, including data heterogeneity, size, and noisiness, as well as attributes and the bag of words model. It also delves into deep learning concepts such as histograms, data pre-processing, missing data, noisy data, data normalization, and imbalanced vs balanced data. The lecture progresses to discuss lessons learned from linear models and kernel methods, transitioning from handcrafted to learned representations, and the workings of artificial neural networks with hidden layers and activation functions.