ML Fundamentals
Perceptron
The simplest possible neural network, a single neuron that makes binary yes/no decisions based on weighted inputs.
Definition
The fundamental unit of neural networks: a linear classifier that computes a weighted sum of inputs and applies an activation function. First introduced by Rosenblatt in 1958. Multi-layer perceptrons form the basis of deep learning.
Why it matters
Understanding the perceptron is understanding the atom of deep learning, everything else builds from here.
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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