ML Fundamentals
One-Shot Learning
An AI that can recognize a new concept from just a single example, like seeing one photo of a new face and remembering it.
Definition
A machine learning approach where a model can generalize from a single training example per class. Often uses metric learning or siamese networks to compare new inputs against stored examples.
Why it matters
Critical for applications where data is inherently scarce, face recognition, rare disease detection, security.
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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