This repository contains comprehensive tutorials for implementing deep learning models from scratch using PyTorch. Each tutorial focuses on a specific application or architecture by reproducing results from a research paper. Topics include image captioning with attention mechanisms, sequence labeling with CRFs, single-shot object detection (SSD), super-resolution, and transformer architectures. The tutorials also teach foundational concepts such as encoder-decoder architectures, transfer learning, and multi-task learning.