Adam: A Method for Stochastic Optimization

Diederik P. Kingma, Jimmy Ba · 2014

Adam. Adaptive first-order optimizer that became the default for almost every deep learning codebase — a must-implement from scratch.

What you'll get

Outline: a plain-English breakdown of the paper's core idea, prerequisites, and the concepts you'll need to implement it.
Exercises: five to ten hands-on tasks, each with a concept card, a prompt, a starter code stub, and a collapsible reference solution.
Runnable notebook: a single .ipynb you can download and open in Jupyter or VS Code to work through every exercise.
Extensions: suggested follow-up experiments so you don't stop at a faithful reimplementation.