ML Fundamentals
Graph Neural Networks (GNNs)
AI designed to understand data that comes in networks and connections, like social networks, molecules, or supply chains.
Definition
Neural networks designed to operate on graph-structured data, where entities are nodes and relationships are edges. They learn by aggregating information from neighboring nodes.
Why it matters
Powerful for fraud detection, drug discovery, social network analysis, and recommendation systems.
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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