Deep Learning Fundamentals

This lecture covers the basics of deep learning, starting with data representations, such as bag of words and visual words, and moving on to data pre-processing techniques like normalization and handling missing data. It also introduces the concept of artificial neural networks, explaining the structure and training process, including backpropagation and stochastic gradient descent. The lecture concludes with an overview of convolutional neural networks (CNNs) and their applications in image processing.