The Modern Mathematics of Deep Learningexplained for non-mathematicians

Random variables and their laws

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.

Probability and expectation · Describe random variables and their averages under a law.

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: 1.1 A note on symbols

A random variable assigns a value to each outcome. Its law gives the probability that its value lies in a set A.

A distribution describes possible values and their probabilities.

Source formula example

P{\mathbbm{P}}

illustration · This invented finite example illustrates the notation; it does not establish a general theorem.

Symbols

XX
A random variable taking values in 𝒳.
AA
A measurable subset of 𝒳.
PXℙX
The probability law of X.

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

Initial values in code

  • values = [0, 1, 2]
  • probabilities = [0.25, 0.5, 0.25]
  • A = [1, 2]

Calculated here

Result expression: { values, probabilities, A, probability: probabilities.reduce( (s, p, i) => s + (A.includes(values[i]) ? p : 0), 0, ), }

Returns

Example result
values
array (3 items): [0, 1, 2]
probabilities
array (3 items): [0.25, 0.5, 0.25]
A
array (2 items): [1, 2]
probability
0.75

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

Result

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Technical statement

The numerical values in this example are invented. This invented finite example illustrates the notation; it does not establish a general theorem.

Source: Inline notation