Machine-learning-Data-science-Tutorials
A list of machine learning and data science tutorials with full implementation code, covering topics such as time-series forecasting, LoRA fine-tuning, GPU kernel design, document parsing pipelines and vector search.
This repository lists machine learning and data science tutorials, each linking to a write-up and its accompanying code. Topics include forecasting with a foundation time-series model, fine-tuning Qwen3 with LoRA using NVIDIA NeMo AutoModel, GPU kernel design with TileLang, document intelligence parsing with Docling, memory-efficient transformer construction with xFormers, and building a semantic search system with pgvector.
Other entries cover building forecasting and anomaly-detection pipelines, spatial graph neural networks for urban analysis, 3D medical image segmentation with MONAI, federated learning comparisons, and dataset pipelines built from large pretraining corpora.
Each listed tutorial includes runnable code alongside the write-up.