Convolutional Neural Networks: Basics and Techniques

This lecture covers the fundamentals of Convolutional Neural Networks (CNNs), including the concepts of convolution, pooling, and weight sharing. It explains the importance of non-linearity in CNNs and the role of normalization layers such as Batch Normalization. The lecture also delves into data augmentation techniques and their significance in training deep learning models. Additionally, it discusses weight decay as a regularization method and the use of dropout to prevent overfitting in neural networks.