Adam Optimizer

Définition

Adam combines the ideas of mean gradient accumulation and mean squared gradient. This helps the model learn faster and more consistently on many deep learning tasks. It is often used as a base choice for experiments with neural networks, although quality and settings are still checked for a specific task.

Exemple

When training a language model, Adam helps you update millions of parameters so that the training isn't too abrupt or too slow.

Pourquoi c'est important

The term appears in almost any conversation about training neural networks. It is important for developers, researchers, and those choosing model training tools.

Fonctionnement

At each step, Adam estimates the direction of change in parameters and scales it taking into account past gradients. Due to this, different model parameters can be updated at different speeds.

Où c'est utilisé

• neural network training
• fine tuning of models
• research experiments

Limites

Adam is convenient, but does not always provide the best generalization ability. Sometimes after it other optimizers are compared or the learning speed is further adjusted.

FAQ