Beginner · Systems
Configuration
Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.
TL;DR. Setting initial property values for model training, including layers, data location, and hyperparameters like learning rate.
Technical Definition
Setting initial property values for model training, including layers, data location, and hyperparameters like learning rate.
How it works
Configuration involves defining all the initial settings required to train a machine learning model. This includes the model's architecture (like layers), data sources, and tuning parameters known as hyperparameters (e.g., learning rate, number of iterations, optimizer, loss function). These settings are often managed through configuration files or specialized libraries.
Related Concepts
- Learning Rate — A hyperparameter that controls how large each parameter update step is during gradient descent optimization.
- Hyperparameter — A configuration variable set before the training process begins, controlling aspects of the learning algorithm itself.