Other awesome lists

The awesome-list pattern started with awesome on GitHub. Below are the lists most adjacent to Deepnote's audience — Python, data science, ML, visualization, and the broader awesome ecosystem.

General-purpose

• lists — A list of lists. The meta-index.
• awesome — The original. Worth a browse.

Python & data science

• awesome-python — The default reference for Python libraries by category.
• pytudes — Peter Norvig's Python études. Closer to a curriculum than a list.
• awesome-r — R-language counterpart, useful for cross-language teams.
• awesome-datascience — General data-science reference.
• awesome-datascience-ideas — Project ideas (rather than tools) for skill-building.

Machine learning

• awesome-machine-learning — The canonical ML list. Categorized by language.
• awesome-tensorflow — TensorFlow-specific.
• awesome-machine-learning-on-source-code — ML applied to code (program analysis, classification, generation).
• awesome-graph-classification — Graph classification papers + code.
• awesome-decision-tree-papers — Decision-tree literature.
• awesome-fraud-detection-papers — Fraud-detection literature.
• machine-learning-for-software-engineers — A top-down learning path.

Datasets & visualization

• awesome-public-datasets — Public datasets by domain.
• awesome-dataviz — Visualization libraries and references.

Cloud / infra

• awesome-aws — AWS-specific resources.

Reference

• Glossary of common statistics and ML terms — Quick reference when you hit unfamiliar jargon.

Find more

Search GitHub for #awesome or #awesome-lists for the full constellation.