Deep Learning Fundamentals

This lecture covers the basics of deep learning, starting from logistic regression to neural networks. It explains the limitations of logistic regression and the need for deep learning to handle non-linearly separable data. The lecture delves into neural networks, activation functions, and the process of forward and backward passes. It also discusses the challenges in training neural networks and the use of deep learning frameworks like PyTorch and TensorFlow.