What Is the Adam Optimizer? The Default Choice for Training Neural Networks
Nearly every modern neural network is trained using this specific optimization algorithm — here's what it actually does differently from plain gradient descent.
An adaptive version of gradient descent
Adam is an optimization algorithm that adjusts each individual parameter's update step size adaptively, based on that parameter's recent history of updates, rather than applying the exact same fixed step size to every parameter the way plain gradient descent does.
Why adapting per-parameter helps
Different parameters in a large network can need very different update sizes — some need large adjustments, others need to change very little. Adam tracks a running estimate of each parameter's typical update pattern and scales its step accordingly, converging faster and more reliably than a single fixed step size applied uniformly.
Why it became close to a universal default
Adam works well across an unusually wide range of network architectures and problems without needing extensive manual tuning, which is a big part of why it (and its variant, AdamW) became close to a default choice for training modern deep learning models, including nearly all large language models.
Frequently Asked Questions
What is the Adam optimizer?
An optimization algorithm that adaptively adjusts each parameter's update step size based on its recent history of updates, rather than applying one fixed step size uniformly across all parameters like plain gradient descent.
Why is Adam so widely used for training neural networks?
It converges reliably across a wide range of architectures and problems without needing extensive manual tuning, which made it close to a default choice for training modern deep learning models.