The Modern Mathematics of Deep Learning

Kronecker delta

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.

Sets and notation · Describe domains, indices and membership.

Project vectors have two features; the training matrix has n rows and two columns. Mathematical tools may operate on these values when their assumptions apply.

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: 5.2 When parameters move only a little

The Kronecker delta is 1 when two indices are equal and 0 when they differ.

It selects matching indices.

Source formula example

\delta_{i,j}=0

illustration · This evaluates two index pairs; the rule applies to any pair of indices.

Symbols

$δᵢⱼ$: 1 if i = j and 0 otherwise.
$i, j$: Indices.

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

Calculated here

Result expression: { different: delta(1, 2), equal: delta(1, 1) }

Returns

Example result

different: 0
equal: 1

JavaScript

Read numbers from input and return the result. JavaScript basics.

Result

Enable JavaScript to run this example.

Technical statement

The numerical values are invented. This evaluates two index pairs; the rule applies to any pair of indices.

Source: Inline notation