This repository aggregates resources on Multi-Task Learning from a machine learning perspective, organizing content into categories covering survey papers, benchmarks, codebases, model architectures (hard/soft parameter sharing, adapters, MoE), and optimization strategies (loss functions, gradient methods, task interference). It serves as a reference collection for practitioners and researchers working on multi-task deep learning systems.