Neural Network Lexicon

Optimizers

Short Definition

Optimizers are algorithms that update model parameters to minimize the loss function.

Definition

Optimizers define the specific rules used to adjust a neural network’s parameters (weights and biases) during training. They determine how gradients are transformed into parameter updates and how learning progresses over time.

While optimization describes the overall goal of minimizing loss, optimizers specify how that minimization is carried out in practice.

Why It Matters

The choice of optimizer strongly influences:

convergence speed
training stability
sensitivity to hyperparameters
ability to escape poor solutions

An inappropriate optimizer can prevent a model from learning effectively, even if the architecture and data are well chosen.

How It Works (Conceptually)

Gradients are computed via backpropagation
The optimizer processes gradients (e.g. scaling, smoothing, accumulating)
Parameters are updated according to optimizer-specific rules
Hyperparameters control update behavior

Different optimizers embody different assumptions about noise, curvature, and learning dynamics.

Minimal Python Example

			
# Generic optimizer step
param = param - learning_rate * processed_gradient

Common Pitfalls

Treating optimizers as interchangeable
Using default hyperparameters blindly
Assuming faster convergence implies better generalization
Ignoring optimizer-specific failure modes
Over-tuning optimizers instead of fixing data or model issues