Key CNN architectures from ILSVRC: AlexNet, VGGNet, GoogLeNet, and ResNet, with analysis of receptive fields and 1x1 convolutions.

Why weight initialization matters in deep learning and why zero initialization should be avoided.

Convolution fundamentals: stride, padding, parameter counting, and 1x1 convolutions.

Practical comparison of SGD, Momentum, and Adam optimizers on function approximation with noisy data in PyTorch.

Deep learning optimization fundamentals: generalization, overfitting, cross-validation, bias-variance tradeoff, bootstrapping, bagging, and boosting.

Data visualization concepts: data types, marks, channels, and pre-attentive attributes.