LDAPWiki: Machine Learning Taxonomy

Machine Learning Taxonomy

Overview#

Machine Learning Taxonomy, 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
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#

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	Wl	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#

Artificial Intelligence

More Information#

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

Machine Learning

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