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

Jupyter Notebook tutorials on solving real-world problems with Machine Learning & Deep Learning using PyTorch. Topics: Face detection with Detectron 2, Time Series anomaly detection with LSTM Autoencoders, Object Detection with YOLO v5, Build your first Neural Network, Time Series forecasting for Coronavirus daily cases, Sentiment Analysis with BER

Repository: https://github.com/curiousily/Getting-Things-Done-with-Pytorch
Canonical: https://ross.abutalabs.com/products/getting-things-done-with-pytorch
Homepage: https://mlexpert.io
Language: Jupyter Notebook
License: Apache-2.0
License Family: permissive
Topics: pytorch, deep-learning, machine-learning, computer-vision, object-detection, face-detection, face-recognition, time-series, time-series-forecasting, coronavirus, tutorial, lstm, anomaly-detection, time-series-anomaly-detection, bert, sentiment-analysis, transformer, nlp, transfer-learning, yolo
Last push: 2024-06-30T16:28:31+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2406, "days_push": 794, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2502, forks 642 (observed 2026-08-28T04:06:56.997106+00:00)

## What it is
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
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, deep-learning, nlp, computer-vision, data-science
- domain: deep-learning, machine-learning, computer-vision, tutorials
- platform: python, cross-platform
- tags: 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
- curiousily/Getting-Things-Done-with-Pytorch (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:56.997106+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:26:49.385855+00:00, confidence not recorded.
  - readme: https://github.com/curiousily/Getting-Things-Done-with-Pytorch (fetched 2026-08-28T04:06:56.997106+00:00, sha 9481ccc02dcc)
  - homepage: https://mlexpert.io (fetched 2026-08-29T10:09:24.169814+00:00, sha 36849e3fc4f4)
  - site_page: https://www.mlexpert.io/academy/v1/setup-and-toolkit/local-ai-quickstart (fetched 2026-08-29T10:09:24.181633+00:00, sha 2b295caeb746)
  - site_page: https://www.mlexpert.io/academy/v1/setup-and-toolkit/langchain-quickstart (fetched 2026-08-29T10:09:24.184790+00:00, sha e7bd62e18b03)
  - site_page: https://www.mlexpert.io/changelog (fetched 2026-08-29T10:09:24.178980+00:00, sha 69be5f79923d)
- Data as of 2026-08-30T08:39:29.467469+00:00.