Recording hyperparameters, metrics, and artifacts from training runs for comparison and reproducibility.
A collection of data examples used for training, validating, or testing machine learning models.
The process of fitting a model to data by repeatedly measuring how wrong its outputs are and adjusting its parameters to reduce that error.
Building systems that learn patterns from data instead of following hand-written rules, getting better at a task as they see more examples.
A computational model inspired by biological neural networks, consisting of interconnected nodes (neurons) organized in layers that process information through weighted connections.
Learnable values (weights and biases) in a neural network that are adjusted during training to minimize loss.
Configuration settings external to the model (learning rate, batch size) that must be set before training begins.
Recording hyperparameters, metrics, and artifacts from training runs for comparison and reproducibility.
This concept is essential for understanding practical deployment and forms a key part of modern AI systems.
Leads to
Nothing yet: a destination in its own right.