!!! Overview
[{$pagename}], Well there is almost no common [Taxonomy] as the field covers several different disciplines.

Recommendations: 
* __[lowercase]__, bold-face letters to refer to [vectors]
* __[UPPERCASE]__, bold-face letters to refer to [matrices|Matrix]
* ''italics'' to refer to single elements in a [vector] or [matrix]
* transpose - "superscript T" an operation that transforms a column [vector] into a row [vector] and vice versa:
**  transpose applied to a [Matrix] to reflect the [Linear transformation] a [Matrix] without changing the [Basis vector]

Generally some poorly used terms are:
* [features] 
* labels which we will call [Classification] 
* [hyperparameters]

!! Table of Symbols
%%zebra-table
%%sortable
%%table-filter
||Code Symbol||Math Symbol||Definition||Dimensions
|__X__|__X__|the input [matrix] ([Independent variables])|(numExamples, inputLayerSize)
|__Y__|__Y__|is the label ([Classification]) [matrix]| 
|''y''(i)|''y''(i)|[Predictor variable] ([Dependent variable]) for the ''i''(th) entry|(numExamples, outputLayerSize)
|W|W[l]|[weights] [Vector] for the l(th) layer|(inputLayerSize, hiddenLayerSize)
|z2|z[2]|Layer 2 [activation Function]|(numExamples, hiddenLayerSize)
|a2|a[2]|Layer 2 activity|(numExamples, hiddenLayerSize)
|z3|z[3]|Layer 3 [activation Function]|(numExamples, outputLayerSize)
|__J__|__J__|[Cost function]|(1, outputLayerSize)
|__W__|__W__|the [weight] [Matrix] for the ''l''(th) layer|[weight] 
|__b__(l)|__b__(l)|the [bias] [vector] for the ''l''(th) layer |[bias]
|__L__|the [Loss function]|[Loss function]
|m|m|number of examples in the [Training dataset]| 
|n(subx)|n(subx)|input size| 
|n(suby)|n(suby)|number of [Classifications] (Output Size)|   
|𝑦̂|𝑦̂|y-Hat is the [Predictor variable] [vector] ([Dependent variables]) which can also be denoted as a(number of layers)| 
/%
/%
/%

!! Category
%%category [Artificial Intelligence]%%


!! More Information
There might be more information for this subject on one of the following:
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