What is Backpropagation?
The algorithm that calculates how to adjust each part of a neural network to reduce errors.
Why It Matters
Backpropagation is what allows neural networks to learn from their mistakes and improve over time.
Real-World Example
After making a wrong prediction, backpropagation traces the error back through the network to adjust the responsible connections.
“Understanding terms like Backpropagation matters because it helps you have better conversations with developers and make smarter decisions about your software. You do not need to be technical. You just need to know enough to ask the right questions.”
Related Terms
Gradient Descent
The mathematical process AI models use to gradually reduce errors and improve their predictions.
Neural Network
A computing system inspired by the human brain, made up of layers of connected nodes that learn patterns from data.
Deep Learning
A type of machine learning that uses neural networks with many layers to learn complex patterns.
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Related Terms
Neural Network
A computing system inspired by the human brain, made up of layers of connected nodes that learn patterns from data.
Deep Learning
A type of machine learning that uses neural networks with many layers to learn complex patterns.
Gradient Descent
The mathematical process AI models use to gradually reduce errors and improve their predictions.
Large Language Model (LLM)
An AI system trained on massive amounts of text that can understand and generate human language.
Transformer
A type of AI architecture that processes text by paying attention to relationships between all words at once, rather...
Attention Mechanism
A technique that lets AI models focus on the most relevant parts of the input when generating output.