curiousily/Getting-Things-Done-with-Pytorch resource

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

2502 stars · 642 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A collection of Jupyter Notebook tutorials (with a free companion book) teaching how to solve real-world machine learning and deep learning problems with PyTorch. Topics span NLP, computer vision, and time series, including BERT sentiment analysis, YOLOv5 object detection, Detectron2 face detection, and LSTM forecasting and anomaly detection.

Use cases

• learn pytorch by building real projects
• sentiment analysis with bert tutorial
• time series anomaly detection with lstm autoencoder
• object detection with yolo v5 on custom dataset
• face detection with detectron2
• time series forecasting for coronavirus cases
• deploy bert model as rest api with fastapi
• transfer learning for image classification

When to choose

• you want hands-on, notebook-based PyTorch tutorials covering NLP, vision, and time series
• you prefer learning through end-to-end projects including deployment with FastAPI
• you want free, readable book-style content runnable in Google Colab

When to avoid

• you need a production library or maintained codebase rather than educational notebooks
• you need up-to-date coverage of the latest model architectures, as content dates from 2020-2021
• you want structured courses on LLMs, RAG, or agents, which are outside this repo's scope

Facets

learning-resource · maturity maintenance

machine-learning deep-learning nlp computer-vision data-science deep-learning machine-learning computer-vision tutorials python cross-platform pytorch jupyter-notebooks bert lstm yolo detectron2 time-series-forecasting anomaly-detection sentiment-analysis transfer-learning object-detection face-detection natural-language-processing

Member repositories

For agents

markdown · JSON · MCP: product_card(name="curiousily/Getting-Things-Done-with-Pytorch")