SGD
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-10.)
SGD has 13 facts recorded in Dontopedia across 4 references, with 2 live disagreements.
Mostly:rdf:type(4), optimizes(2), learning rate(1)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (9)
Other subjects in dontopedia point AT this entity as a value. These are inverse relationships — e.g. "X motherOf this subject" — and answer questions the forward facts can't. Grouped by predicate.
replacesReplaces(3)
- Optimizer Modification
ex:optimizer-modification - Optimizer Substitution
ex:optimizer-substitution - Python Code Example
ex:python-code-example
usesOptimizerUses Optimizer(3)
- Training Loop
ex:training-loop - Training Loop
ex:training-loop - Training Process
ex:training-process
hasOptimizerHas Optimizer(1)
- Session 2026 03 21
ex:session-2026-03-21
replacesInCodeReplaces in Code(1)
- Adam Optimizer
ex:adam-optimizer
usesUses(1)
- Existing Code
ex:existing-code
Other facts (10)
The long tail: predicates that appear too rarely to warrant their own section. Filter or scroll to find a specific one. Each row links to its source.
| Predicate | Value | Ref |
|---|---|---|
| Rdf:type | Stochastic Gradient Descent | [1] |
| Rdf:type | Optimizer | [2] |
| Rdf:type | Optimizer | [3] |
| Rdf:type | Optimizer | [4] |
| Optimizes | Model Parameters | [1] |
| Optimizes | Model Parameters | [3] |
| Learning Rate | 0.01 | [1] |
| Used by | Training Loop | [1] |
| Has Parameter | Learning Rate | [2] |
| Has Learning Rate | 0.01 | [3] |
Timeline
Timeline axis is valid_time — when each source says the fact was true in the world, not when Dontopedia learned about it. Retracted rows are kept for provenance; coloured stripes indicate the context kind.
References (4)
ctx:claims/beam/11a08133-821e-4ec4-b8c6-b06571f6e244- full textbeam-chunktext/plain1 KB
doc:beam/11a08133-821e-4ec4-b8c6-b06571f6e244Show excerpt
x = self.fc2(x) return x model = SecureTuningModel() criterion = nn.CrossEntropyLoss() optimizer = optim.SGD(model.parameters(), lr=0.01) for epoch in range(100): for x, y in dataset: x = x.view(-1, 512) …
ctx:claims/beam/bb661926-a23e-4f89-b0a0-8fd1c07034c4- full textbeam-chunktext/plain1 KB
doc:beam/bb661926-a23e-4f89-b0a0-8fd1c07034c4Show excerpt
1. **Data Loading and Preprocessing**: - Use `DataLoader` with `num_workers` to enable multi-threaded data loading. - Ensure data is moved to the GPU using `.to(device)`. 2. **Model and Optimizer Initialization**: - Move the model…
ctx:claims/beam/e0132e2b-72f6-4f78-accb-ecb30e4872dfctx:claims/beam/6edc4c3a-4a2d-408e-9bf1-1f44cdcdbb84
See also
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