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Neural Network Foundations
Input Layer
The input layer is the place where external data enters the network as values the model can process.
~/writing/math-foundations-for-reasoning-models $ cat topic.md
Input Layer
Definition
The input layer is where data enters a neural network.
It provides the first values that later layers transform.
Example:
input values -> network
The input layer is the network's starting representation.
Input Values
The input layer contains values the network can operate on.
Examples:
numbers
measurements
features
encoded symbols
The network does not process the original real-world object directly.
It processes the values given to it.
The input layer receives a representation of the data.
Input Layer vs Hidden Layers
The input layer provides data.
Hidden layers transform data.
input layer:
holds incoming values
hidden layers:
compute new representations
The input layer is not where most learned transformation happens.
The learned transformations happen in the layers after it.
The input layer supplies values; later layers transform them.
Shape of the Input
The input layer must match the structure expected by the network.
Example:
network expects 3 input values
input must provide 3 values
If the input shape does not match, the network cannot apply its parameters correctly.
The input must fit the network's expected structure.
Final Summary
- The input layer is where data enters the network.
- It contains values the network can process.
- The network processes representations, not raw reality.
- The input layer supplies values to later layers.
- The input shape must match what the network expects.
- Learning happens through how later layers transform the input.
~/writing/math-foundations-for-reasoning-models $ open
Continue the series.
Hidden Layer