tab_glossary.md

title	layout	tab	order	tags
Glossary		true	3	related-tag

0 1 2 3 4 5 6 7 8 9 A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

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0 {#zero}

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4 {#four}

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A {#a}

Adversarial attack
Type of attack which seeks to trick machine learning models into misclassifying inputs by maliciously tampering with input data

B {#b}

C {#c}

Classification
Process of arranging things in groups which are distinct from each other, and are separated by clearly determined lines of demarcation

D {#d}

Data labeling
Process of assigning tags or categories to each data point in a dataset

Data poisoning
Type of attack that inject poisoning samples into the data

Deep learning
Family of machine learning methods based on artificial neural networks with long chains of learnable causal links between actions and effects

E {#e}

Ensemble
See: Model Ensemble

F {#f}

G {#g}

H {#h}

I {#i}

Input Validation
Input validation is a technique for checking potentially dangerous inputs in order to ensure that the inputs are safe for processing within the code, or when communicating with other components

Intrusion Detection Systems (IDS)
Security service that monitors and analyzes network or system events for the purpose of finding, and providing real-time or near real-time warning of, attempts to access system resources in an unauthorized manner

Intrusion Prevention System (IPS)
System that can detect an intrusive activity and can also attempt to stop the activity, ideally before it reaches its targets

J {#j}

K {#k}

L {#l}

M {#m}

MLOps
The selection, application, interpretation, deployment, and maintenance of machine learning models within an AI-enabled system

Model
Detailed description or scaled representation of one component of a larger system that can be created, operated, and analyzed to predict actual operational characteristics of the final produced component

Model ensemble
Art of combining a diverse set of learners (individual models) together to improvise on the stability and predictive power of the model

N {#n}

O {#o}

Obfuscation
Defense mechanism in which details of the model or training data are kept secret by adding a large amount of valid but useless information to a data store

Overfitting
Overfitting is when a statistical model begins to describe the random error in the data rather than the relationships between variables. This occurs when the model is too complex

P {#p}

Perturbation
Noise added to an input sample

Q {#q}

R {#r}

Regularisation
Controlling model complexity by adding information in order to solve ill-posed problems or to prevent overfitting

S {#s}

Spam
The abuse of electronic messaging systems to indiscriminately send unsolicited bulk messages

T {#t}

U {#u}

Underfitting
Underfitting is when a data model is unable to capture the relationship between the input and output variables accurately, generating a high error rate on both the training set and unseen data

V {#v}

W {#w}

X {#x}

Y {#y}

Z {#z}