Deep Learning: Convolutional Neural Networks

This lecture covers the fundamentals of Convolutional Neural Networks (CNNs), starting from the basics of artificial neural networks to the advanced concepts of deep learning. It explains the architecture of CNNs, including convolutional layers, pooling layers, and fully connected layers. The lecture also delves into training CNNs using stochastic gradient descent and explores different activation functions and regularization techniques. Additionally, it discusses the challenges faced in semantic segmentation tasks and presents solutions using transposed convolutions. The lecture concludes with an overview of standard CNN architectures and practical demonstrations of semantic segmentation.