Deep Convolutional Neural Network for Image Classification

This scientific teaching module covers the architecture and performance of large, deep convolutional neural networks, including their success in the ImageNet LSVRC contests. It explains key components like convolutional and fully-connected layers, and techniques such as non-saturating neurons, dropout regularization, and data augmentation. The module also explores real-world applications, training details, and qualitative evaluations, culminating in an interactive quiz to reinforce understan...