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Graphs and Computation
Neural Networks as Computation Graphs
A neural network can be viewed as a computation graph where data moves through connected computation steps.
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Neural Networks As Computation Graphs
Definition
A neural network can be viewed as a computation graph.
Data enters the graph, passes through connected computation steps, and produces an output.
Basic form:
Input -> Computation steps -> Output
Neural network as graph = connected computation flow.
Simple View
At a high level, a neural network does this:
input values -> intermediate values -> output values
As a graph:
Input -> Layer 1 -> Layer 2 -> Output
Each step transforms the values it receives.
The result of one step becomes input to the next step.
A neural network can be seen as layered data flow.
Nodes As Computation Steps
In this view, nodes can represent computation steps.
Example:
Input -> Step A -> Step B -> Output
Each step receives values and produces new values.
The graph shows the dependency between those steps.
Nodes show where computation happens.
Edges As Data Movement
Edges show how values move between steps.
Example:
Step A -> Step B
means:
Step B uses the output of Step A
The edge does not mean awareness or understanding.
It only means data dependency.
Edges show which computed values feed later computation.
Why This View Matters
Viewing a neural network as a computation graph keeps the idea grounded.
It is structured computation:
- values enter
- operations are applied
- intermediate values are produced
- later steps depend on earlier steps
- output is produced
The graph view makes neural computation a visible dependency structure.
Important Limitation
This graph view does not explain all details of neural networks.
It only gives the structural picture:
connected computations producing outputs
For now, the important point is that the computation has structure.
A graph can show the shape of computation before we study the details inside each step.
Final Summary
- A neural network can be viewed as a computation graph.
- Inputs flow through connected computation steps.
- Nodes can represent computation steps.
- Edges represent data dependency.
- The graph view shows structure, not awareness.
- This keeps neural networks connected to ordinary computation.
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