DEEP LEARNING SOLVER¶
Trains a deep learning model using the SOLVERAI engine on a supplied tabular dataset. Specify independent (input) and dependent (target) columns to define the supervised learning task; the worker outputs a serialised model checkpoint for downstream inference or deployment.
When to use¶
Tagged: checkpoint, deep_learning, neural_network, simulation, solverai, supervised, tabular, training.
Inputs¶
| Label | ID | Type | Default | Required | Description |
|---|---|---|---|---|---|
| Input | input | dataset | — | ✓ | Tabular training dataset (any format resolvable by the platform); must contain both the feature columns and target columns as named headers. |
| Input Columns | input_cols | scalar | — | Comma-separated list of column names to use as independent (feature) variables; leave blank to let SOLVERAI infer them automatically from the dataset. | |
| Target Columns | target_cols | scalar | — | Comma-separated list of column names to use as dependent (target/label) variables; leave blank to let SOLVERAI infer them automatically from the dataset. |
Outputs¶
| Label | ID | Type | Description |
|---|---|---|---|
| Checkpoint | checkpoint | text | Serialised model checkpoint path/reference (text) produced after training completes; pass this to a SOLVERAI inference worker to generate predictions on new data. |
Disciplines¶
- ai_ml.supervised.classification
- ai_ml.supervised.regression
- ai_ml.surrogate
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