Practical CNN implementation in PyTorch: add_module, training loops, and batch normalization.

A curated list of reference blogs for machine learning and engineering study.

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.