Deep Learning: Convolutional Neural Networks and Training Techniques

This lecture covers advanced concepts in deep learning, focusing on convolutional neural networks (CNNs) and their architecture. It begins with a recap of parametric feature expansion and activation functions, emphasizing the importance of nonlinearity in neural networks. The instructor explains the structure of simple artificial neural networks and the significance of multilayer perceptrons in modeling complex functions. The lecture then transitions to CNNs, detailing their layers, including convolutional and pooling layers, and the advantages of shared parameters across spatial locations. The discussion includes techniques for training CNNs, such as stochastic gradient descent, backpropagation, and regularization methods to prevent overfitting. The instructor also introduces fully convolutional networks and transposed convolutions for tasks like semantic segmentation, highlighting the need for pixel-wise predictions. Finally, the lecture addresses the challenges of adversarial examples in deep learning, illustrating the sensitivity of models to small perturbations in input data, and concludes with insights on the importance of large datasets and computational resources in training effective deep learning models.