Learning Rate Parameter Training Results Over Three Trial Runs

M. Rifqi Rafsanjani created training results for a learning rate parameter across validation and training datasets. The dataset includes results from three trial runs with standard deviation calculations based on an optimum model architecture parameter. It was last updated on 2026-05-26.

Use Cases

• Compare training and validation loss curves based on the described learning rate trials.
• Analyze the stability of model training based on the standard deviation results mentioned.
• Select an optimal learning rate based on the comparative results for validation and training datasets.

Strengths

• Results are based on three trial runs, which may provide a measure of variance.
• The dataset is focused on a specific hyperparameter (learning rate) for a defined optimum model architecture.

Limitations

• The dataset is very small at 5.5 KB, indicating extremely limited scope.
• Row count and column-level documentation are unknown, which limits suitability assessment.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

M. Rifqi Rafsanjani via figshare.

Collection Method

Likely generated from machine learning training experiments.

Freshness

Last updated 2026-05-26 17:41:10; freshness should be verified.