ML Fundamentals
Active Learning
A technique where the AI asks humans to label only the most confusing examples, saving time and money on data labeling.
Definition
A machine learning approach where the model actively selects the most informative data points for labeling, reducing the need for large labeled datasets. It iteratively queries a human expert to label uncertain examples.
Why it matters
Drastically reduces data annotation costs (often by 50-80%) while maintaining model performance.
Related terms in ML Fundamentals
Activation Functions
The "switch" inside a neural network that decides whether a neuron should fire, allowing the AI to learn complex non-linear patterns.
Anomaly Detection
Finding the "weird" stuff in a dataset, like a credit card charge in a foreign country or a broken machine part.
Artificial General Intelligence (AGI)
A hypothetical "super-AI" that can learn and do any intellectual task a human can do, not just one specific thing.
Association Rules
Finding "what goes with what" patterns in data, like people who buy beer often buy diapers too.
From vocabulary to outcomes
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