ML Fundamentals
Semi-Supervised Learning
Training an AI with a small amount of labeled data and a large amount of unlabeled data, getting more from less.
Definition
A learning paradigm that combines a small amount of labeled data with a large amount of unlabeled data during training. Techniques include self-training, co-training, and label propagation.
Why it matters
A practical middle ground when labeling data is expensive, gets most of the benefit of supervised learning at a fraction of the labeling cost.
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.
Active Learning
A technique where the AI asks humans to label only the most confusing examples, saving time and money on data labeling.
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.
From vocabulary to outcomes
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