---
title: "Adam: A Method for Stochastic Optimization"
description: "Adam is the optimizer paper learners meet constantly in code before they understand it. The first pass is approachable: it combines momentum-like gradient averages with squared-gradient adaptation."
canonical_url: "https://fanout.sh/daily/2026-07-06-adam"
md_url: "https://fanout.sh/daily/2026-07-06-adam.md"
last_updated: "2026-07-06"
access: "public"
---

# Adam: A Method for Stochastic Optimization

Adam is the optimizer paper learners meet constantly in code before they understand it. The first pass is approachable: it combines momentum-like gradient averages with squared-gradient adaptation.

## Paper details

- Authors: Kingma and Ba

- Venue: ICLR 2015

- Track: ML Math

- Difficulty: Very Easy

- Reading time: 30 min

- Original paper: https://arxiv.org/abs/1412.6980

## What you will learn

- How first and second moment estimates change parameter updates.

- Why bias correction matters early in training.

- What tradeoffs make Adam convenient but not automatically best.

## Continue exploring

[Explore Adam Optimizer](https://fanout.sh/knowledge-graph?node=ai-research%3Amodule%3Aneural-network-from-scratch&detail=ai-research%3Aconcept%3Aadam-optimizer): Continue into Fanout's optimizer material around Adam, gradients, learning rates, and practical training loops.

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