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.
Definition
Mathematical functions applied to the output of a neuron in a neural network to introduce non-linearity. Common examples include ReLU, Sigmoid, and Tanh. Without them, neural networks would behave like simple linear regression models.
Why it matters
Essential for deep learning models to handle real-world data like images and language that aren't linearly separable.
Related terms in 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.
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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