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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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Graph Recurrent Data Flow

In a recurrent graph, data is passed from one node back to another. These data flows form the basis for recurrent neural networks. Below is a graph recurrent data flow segment example:

$\\\mathrm{\textup{}}\: \: \: \: \: \: \: \: \: \, \,\: \: \: \: \: \: \mathrm{\textup{\_\_\_\_\_\_\_\_\_}} \: \: \: \: \: \: \: \: \: \: \: \: \: \: \: \: \: \: \, \,\: \: \: \: \: \mathrm{\textup{\_\_\_}} \\\mathrm{\textup{}}\: \: \: \: \: \: \: \: \: \,\,\,\, \: \: \; \mid \mathrm{\textup{}}\: \: \: \, \; \;\:\, \;\, \mid \: \: \: \: \: \: \: \: \: \: \: \: \: \: \: \: \:\,\,\,\, \: \: \; \mid \,\,\,\,\, \mid \\\mathrm{\textup{}}\: \: \: \: \: \: \: \: \:\: \:\:\,\,\, \bigtriangledown \: \; \, \: \:\;\: \, \, \bigtriangleup \: \: \: \: \: \: \: \: \: \: \: \: \: \: \: \:\: \:\,\, \bigtriangledown \bigtriangleup \\\bigoplus \rightarrow \bigoplus \rightarrow \bigoplus\rightarrow \bigoplus \rightarrow \bigoplus \rightarrow \bigoplus$

where:

$\\\bigoplus \, \, \, \mathrm{\textup{node performing functions}} \\ \\\rightarrow \: \: \: \: \mathrm{\textup{forward data flow}} \\ \\\bigtriangleup \: \: \: \:\: \mathrm{\textup{data out of a node for backward flow}} \\ \\\bigtriangledown \: \: \: \:\: \mathrm{\textup{data into a node for backward flow}}$

References

Recurrent Neural Networks
Web Search
Wikipedia Article
YouTube Search