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Mathematics - Data Science - Computer Science

Overview
Calculus
Computing Systems
Data
Graphs
Linear Algebra
Models and Modeling
Organization
Probability
Programming Constructs
- Programming Constructs Overview
- Abstraction
- Array
- Attribute
- Best-first Search
- Binary Search
- Block
- Branch
- Callback
- Class
- Conditional
- Constructor
- Container/Collection
- Dynamic Array
- Encapsulation
- Exception
- Expression
- Function
- Garbage Collection
- Greedy Algorithms
- Hash
- HTTP Request
- Identifier
- Inheritance
- Inner Class
- Instance
- Iterator
- Keyword
- Lambda
- Libraries
- List
- Linked List
- Literal
- Metaclass
- Method
- Mixin
- Object
- Operator
- Overloading
- Overriding
- Package
- Parameter
- Polymorphism
- Primitive
- Programming Process
- Recursion
- Reflection
- Regular Expression
- Reserved Word
- Return
- Sort
- Statement
- Switch
- Table
- This/Self
- Token
- Type
- Variable
Statistics
Trigonometry

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Mathematical Notation Powered by CodeCogs

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Masking

Masking is a method of indicating which elements of a matrix or vector should and should not be used.

In the example below, the masking matrix indicates:

0 - masking is not applied to the corresponding value in the input matrix
1 - masking is applied to the corresponding value in the input matrix

$\\ \\M_{i}=\begin{bmatrix}1 &2 \\ 3 &4 \end{bmatrix} \\\\M_{k}=\begin{bmatrix}0 &1 \\ 1 &0 \end{bmatrix} \\\\M_{o}=K\left ( M_{i},M_{k} \right ) \\\\M_{o}=\begin{bmatrix}1 &-- \\ -- &4 \end{bmatrix} \\\\\mathrm{\textup{where:}} \\\\M_{i}\,\,\,\,\,\mathrm{\textup{input matrix}} \\M_{k}\,\,\,\,\mathrm{\textup{masking matrix}} \\M_{o}\,\,\,\,\mathrm{\textup{output matrix}} \\K\,\,\,\,\,\,\,\mathrm{\textup{masking function}}$

References

Python NumPy Masking Module
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