AI Foundations10
FoundationalThe basics before anything else - what AI and machine learning actually are, how neural networks learn, and the building blocks every other concept assumes you know.
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Today's concept
Artificial Intelligence
The field of building computer systems that can perform tasks normally requiring human intelligence - from recognising speech to writing code.
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Machine Learning
A way of teaching computers by showing them examples, rather than writing explicit rules - the engine behind almost everything labelled AI today.
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Neural Network
A computing architecture loosely inspired by the brain - layers of interconnected nodes that transform inputs into outputs through learned mathematical operations.
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Deep Learning
Machine learning using neural networks with many layers - the approach behind almost every significant AI breakthrough of the past decade.
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Parameters
The numbers inside a neural network that get adjusted during training and define everything the model knows and can do.
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Loss Function
The measure of how wrong a model's predictions are - the signal that training uses to decide how to improve.
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Gradient Descent
The algorithm that trains neural networks - iteratively adjusting parameters in the direction that reduces the model's error.
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Training
The process of teaching an AI model by adjusting its internal parameters until it gets better at its task - the computational work that creates intelligence.
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Inference
Using a trained AI model to make predictions or generate outputs - the fast, cheap counterpart to training's slow, expensive computation.
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Overfitting
When a model learns its training data too well and fails to generalise - the central challenge of machine learning.