awesome-deepnote — curated notebooks, integrations & resources

Multi-chapter curricula and reference texts, all runnable in Deepnote. These are the long reads — pick one, work through it project by project.

Courses

Intro to Deep Learning by Ryan Holbrook @ Kaggle — Beginner-friendly path through Keras + tabular DL.
Datascience IPython Notebooks by Donne Martin — A large curated index covering pandas, NumPy, scikit-learn, TensorFlow, AWS, and command-line data tools.

Maths: Form and Function with Python by James G. Hill — Notebooks accompanying the Mathematics: Form and Function text. Mathematical foundations rendered as runnable code.
Probabilistic Programming and Bayesian Methods for Hackers — The Bayesian-methods-for-hackers book, every chapter as a notebook. PyMC3-based. → Launch prologue
Python for Probability, Statistics, and Machine Learning 2E — Companion notebooks. Heavier on stats theory than most ML books. → Launch ML intro chapter
Mining the Social Web by Mikhail Klassen — Twitter, Facebook, LinkedIn, Instagram, GitHub, web scraping. The third edition is current with modern APIs. → Launch preface