Trained SVD algorithm instance
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-10.)
Trained SVD algorithm instance has 17 facts recorded in Dontopedia across 6 references, with 3 live disagreements.
Mostly:rdf:type(2), referenced in(2), input to(2)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (5)
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.
hasArgumentHas Argument(1)
- Pickle Dump
ex:pickle_dump
isInitializedIs Initialized(1)
- Svd Model
ex:svd_model
runsOnMetalRuns on Metal(1)
- Nanogpt
ex:nanogpt
serializesSerializes(1)
- Pickle Dump Call
ex:pickle_dump_call
usesUses(1)
- Model Update
ex:model_update
Other facts (16)
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 | Algorithm Instance | [5] |
| Rdf:type | Trained Model | [6] |
| Referenced in | Pickle Dump | [6] |
| Referenced in | Model Evaluation | [6] |
| Input to | Update Model With Feedback | [6] |
| Input to | Model Evaluation | [6] |
| Was Not | Converging | [1] |
| Was Messed Up Previously | null | [2] |
| Refers to Training Algo | Rul Head Training | [3] |
| Uses Memory | 6M | [3] |
| Uses Cpu | 1 | [3] |
| Is Not Nearly As Complex As | Some Earlier Stuff | [4] |
| Is Instance of | Svd | [5] |
| Method | fit | [5] |
| Trained on | Trainset | [5] |
| State | Trained State | [6] |
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 (6)
ctx:discord/blah/training-and-evals/part-41ctx:discord/blah/watt-activation/part-377ctx:discord/blah/watt-activation/part-511ctx:discord/blah/watt-activation/part-542ctx:claims/beam/51af00c3-127f-47f4-8b3a-d5d09a4ce3ae- full textbeam-chunktext/plain1 KB
doc:beam/51af00c3-127f-47f4-8b3a-d5d09a4ce3aeShow excerpt
# Use SVD for matrix factorization algo = SVD() trainset = surprise_data.build_full_trainset() algo.fit(trainset) predictions = [] for interaction in interactions: pred = algo.predict(interaction['user_id'], …
ctx:claims/beam/c1ca0898-d814-4ebd-a786-a3e5f69b8141- full textbeam-chunktext/plain1 KB
doc:beam/c1ca0898-d814-4ebd-a786-a3e5f69b8141Show excerpt
# Simulate collecting new feedback new_ratings = [ {'user_id': 1, 'item_id': 10, 'rating': 4}, {'user_id': 2, 'item_id': 11, 'rating': 3}, # Add more new ratings as needed ] return new_ratings # Coll…
See also
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