Hyperparameters

Overview

Hyperparameters are the parameters you set before training a model. They govern the learning process itself—such as the learning rate, batch size, number of layers, or choice of activation functions—and are distinct from the internal parameters (weights and biases) learned during training.

Core Concepts

• Pre-Training Configuration
  Defined by data scientists or engineers prior to model training.
• Control Learning Process
  Determines how quickly or deeply a model learns patterns.
• Experimentation & Tuning
  Often tuned via systematic search or optimization methods.
• Performance Impact
  Significantly affects model accuracy, generalization, and training stability.

Implementation

Hyperparameters require careful tuning and experimentation to achieve optimal model performance, and are often determined through methods like:

• Grid Search
  Exhaustive search over a defined parameter grid.
• Random Search
  Randomly picking values within a range for faster experimentation.
• Bayesian Optimization
  Iteratively refining search based on prior results.

Key Applications

• Learning Rate Configuration
  Controls the step size in gradient-based optimization.
• Batch Size Determination
  Balances computation efficiency and gradient stability.
• Neural Network Architecture
  Specifies number of layers and hidden units per layer.
• Choice of Activation Functions
  E.g., ReLU, Sigmoid, Tanh—each has different impacts on learning.

Benefits

• Fine-Tuned Model Performance
  Unlocks higher accuracy through careful parameter adjustment.
• Control Over Learning Dynamics
  Allows balancing speed vs. stability in training.
• Avoid Overfitting/Underfitting
  Proper hyperparameters can mitigate common training pitfalls.
• Tailored Optimization
  Specific hyperparameter choices can be optimized for certain tasks or data types.

Related Concepts

• Model Training
• Model Evaluation
• Regularization
• Overfitting
• Training Data