Step-by-step guides on building deep learning models from scratch.
A hands-on guide to building Graph Neural Networks from scratch, covering message passing, graph convolution, and practical applications to molecular data.
A step-by-step guide to understanding and building transformer models, including self-attention mechanisms, multi-head attention, and the full encoder-decoder architecture.