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"Artificial Neural Networks" or more commonly known as "Artificial Intelligence" have become the most prominent research topic in modern computer science. As we are entering the golden age of artificial intelligence, machine learning & deep learning are used daily by consumers for practical purposes  

'''Artificial Neural Networks'' or more commonly known as '''Artificial Intelligence''' have become the most prominent research topic in modern computer science. As we are entering the golden age of artificial intelligence, machine learning & deep learning are used daily by consumers for practical purposes  

== Artificial Neural Networks ==  

== Artificial Neural Networks ==  

"Artificial Neural Networks" is a simple to complex series of data representation within a network that changes according the certain algorithms applied with the phases of the structure. The artificial neural network was initially inspired by biological neural networks and their functions of processing input and output data. Within A.I. the biological 'neurons' are represented as "nodes" within an A.I.  

'''Artificial Neural Networks''' are simple to complex series of data representation within a network that changes according the certain algorithms applied with the phases of the structure. The artificial neural network was initially inspired by biological neural networks and their functions of processing input and output data. Within A.I. the biological 'neurons' are represented as "nodes" within an A.I.  

which are systems of interconnected points exchanging messages between one another and have numeric weights/constraints applied to them that allow these neural nets to adapt/alter input data thus creating the Artificial Intelligence's ability to 'learn'.  

which are systems of interconnected points exchanging messages between one another and have numeric weights/constraints applied to them that allow these neural nets to adapt/alter input data thus creating the Artificial Intelligence's ability to 'learn'.  

Machine Learning Algorithms are the backbone of Artificial Neural Networks as they are what allow the neural network to created 'learned' material through the constraints placed on the data by the algorithms.

Machine Learning Algorithms are the backbone of Artificial Neural Networks as they are what allow the neural network to created 'learned' material through the constraints placed on the data by the algorithms.

There are various types of Machine Learning Algorithms which are used to comprise neural nets. The main include "Supervised Learning", "Unsupervised" and "Semisupervised".  

There are various types of Machine Learning Algorithms which are used to comprise neural nets. The main include '''Supervised Learning''', '''Unsupervised''' and '''Semi-supervised'''.  

"Supervised" machine learning algorithms consists of target/outcome variables (dependent variables) which is predicted from a given set of predictors (independent variables). This means that by using these sets of variables, we generate a function that maps inputs to desired outputs. In other words, we determine which output or end result data representation.  

'''Supervised''' machine learning algorithms consists of target/outcome variables (dependent variables) which is predicted from a given set of predictors (independent variables). This means that by using these sets of variables, we generate a function that maps inputs to desired outputs. In other words, we determine which output or end result data representation.  

The most common types of "Supervised Learning" algorithms include:  

The most common types of '''Supervised Learning''' algorithms include:  

Regression

Regression

Random Forest

Random Forest

"Semi-Supervised" or "Reinforcement Learning" Algorithms is when the machine is trained to make specific decisions and are exposed to environments where it trains itself continually by using trial and error. These machine learning algorithms learn from past experience and try to capture the best possible knowledge to make accurate business decisions.

'''Semi-Supervised''' or '''Reinforcement Learning''' Algorithms is when the machine is trained to make specific decisions and are exposed to environments where it trains itself continually by using trial and error. These machine learning algorithms learn from past experience and try to capture the best possible knowledge to make accurate business decisions.

An example of "Semi-Supervised" Learning includes:

An example of '''Semi-Supervised''' Learning includes:

Markov Decision Process

Markov Decision Process

"Unsupervised Learning" Algorithms are algorithms that are applied within a neural network when there is no target or outcome variable to predict/estimated. An example use of these algorithms could be used for clustering population in different groups.

"Unsupervised Learning" Algorithms are algorithms that are applied within a neural network when there is no target or outcome variable to predict/estimated. An example use of these algorithms could be used for clustering population in different groups.

Common Examples of "Unsupervised Learning" Algorithms include:  

Common Examples of '''Unsupervised Learning''' Algorithms include:  

"Apriori" algorithm

'''Apriori''' algorithm

"K-means" algorithms

'''K-means'''algorithms

== Deep Learning Neural Networks ==  

== Deep Learning Neural Networks ==