AutoML
Automated Machine Learning - automating the process of model selection, architecture search, and hyperparameter tuning.
Your route here
9 stops · basics first
- Dataset ✓ understood
A collection of data examples used for training, validating, or testing machine learning models.
- Train-Test Split ✓ understood
Dividing a dataset into separate portions for training the model and evaluating its performance on unseen data.
- Validation Set ✓ understood
A portion of data held out from training, used to tune hyperparameters and monitor overfitting.
- Training ✓ understood
The process of fitting a model to data by repeatedly measuring how wrong its outputs are and adjusting its parameters to reduce that error.
- Machine Learning ✓ understood
Building systems that learn patterns from data instead of following hand-written rules, getting better at a task as they see more examples.
- Neural Network ✓ understood
A computational model inspired by biological neural networks, consisting of interconnected nodes (neurons) organized in layers that process information through weighted connections.
- Parameter ✓ understood
Learnable values (weights and biases) in a neural network that are adjusted during training to minimize loss.
- Hyperparameter ✓ understood
Configuration settings external to the model (learning rate, batch size) that must be set before training begins.
- Hyperparameter Tuning ✓ understood
The process of finding optimal hyperparameter values through techniques like grid search, random search, or Bayesian optimization.
- AutoML · you are here ✓ understood