Unsupervised learning
A type of machine learning where a model is given unlabeled data to find patterns on its own.
Detailed Definition
Unsupervised learning is when the model is given data without any labels or answers. Its job is to discover patterns or structure on its own, like grouping similar news articles together or detecting unusual patterns in a dataset. This method is often used for tasks like anomaly detection, clustering, and topic modeling, where the goal is to explore and organize information rather than make specific predictions.
Learning MethodsMore in this Category
Few-Shot Learning
The ability of AI models to learn new tasks with only a small number of training examples.
Fine-tuning
A post-training technique to specialize a trained model on specific data for a particular task.
Post-training
Additional steps taken after a model's initial training is complete to make it more useful.
RLHF (reinforcement learning from human feedback)
A post-training technique to align AI models with human preferences using feedback.
Training/Pre-training
The process by which an AI model learns by analyzing massive amounts of data.
Supervised learning
A type of machine learning where a model is trained on labeled data with correct answers provided.