Architecture
Neural Network
A computing architecture loosely inspired by biological neurons, made up of layers of interconnected nodes that transform input data through learned weights and activation functions.
In depth
A computing architecture loosely inspired by biological neurons, made up of layers of interconnected nodes that transform input data through learned weights and activation functions. Neural networks are the foundation of modern AI. Stacking many layers creates "deep" neural networks, which is where the term deep learning comes from.
Example
In practice, developers reach for Neural Network when they need the capability described above as part of an AI feature or workflow.
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FAQ
Common questions
What is Neural Network?
A computing architecture loosely inspired by biological neurons, made up of layers of interconnected nodes that transform input data through learned weights and activation functions.
Why does Neural Network matter for AI developers?
Neural Network sits in the Architecture part of the AI stack. Understanding it helps you make better decisions when building, debugging, and shipping AI features.
Where can I learn more about Neural Network?
Developers Digest publishes tutorials and videos that cover Architecture topics including Neural Network. Check the blog and YouTube channel for hands-on walkthroughs.
Related
Related terms
Architecture
Backpropagation
The algorithm that computes how much each weight in a neural network contributed to the error, so gradient descent knows how to adjust it.
Architecture
Parameter Count
The number of learned weights in a model, usually given in billions (7B, 70B, 405B).
Architecture
SwiGLU
SwiGLU is a gated activation used in the transformer feed-forward block: the input goes through two linear projections, one passed through the Swish function, and the two results are multiplied element-wise before the output projection.
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