Convolutional Neural Networks

This lecture covers the fundamentals of Convolutional Neural Networks (CNNs) for autonomous vehicles. It explains the structure of CNNs, including convolution layers, pooling layers, and fully connected layers. The instructor discusses popular CNN architectures like LeNet-5 and AlexNet, as well as the applications of CNNs in semantic segmentation, object detection, and more. The lecture also delves into regularization techniques such as dropout and data augmentation to prevent overfitting and improve model performance.