Source network parameter count
Penguin learning project
The project uses 342 complete Palmer penguin records: 240 training-pool and 102 held-out rows. The selected training rows determine feature scaling and model parameters. Source CSV
This project trains weights with softmax, unlike the homepage lab, which uses nearest species averages. Same penguin measurements; different classification methods. Predictions can differ.
Model classes and structure · Describe families of rules, their size and structural properties.
The project supplies finite observations and fitted parameters. A held-out sample does not establish population bounds or prove a theorem.
Consider this concept alongside the project; its source assumptions may differ. These observations do not prove a theorem.
Input: 12 × 2; Held-out evaluation: 102 · #58 · 40.6 mm, 193 mm. Known answer: Adélie; Prediction: Adélie · [≈0.7383, ≈0.2444, ≈0.01737].
Used in these source sections: 1.3 The questions ahead
The source describes a network with about 1.6 million parameters.
The count gives the scale of the cited training example.
Source formula example
illustration · This converts the stated approximate count; it does not reproduce the experiment.
Symbols
- The approximate parameter count, in millions.
Source formula example
This source example uses its own inputs and assumptions. Editing it does not change the project.
Edit a value or the code, then run the example. This is a finite demonstration, not a proof of the paper’s theorem.
This example’s data flow
Code changed. Reset to restore this example’s data flow.
Initial values in code
millions=1.6
Calculated here
Result expression: { millions, approximateParameters: millions * 1000000 }
Returns
millions- 1.6
approximateParameters- 1600000
Read numbers from input and return the result. JavaScript basics.
Result
Technical statement
The numerical values are invented unless a source value is specified. This converts the stated approximate count; it does not reproduce the experiment.
Source: Inline notation