Changes

'''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 ANN 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".  

'''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 ANN 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".  

These nodes within the network can be thought of as phases within a neural net where data can be processed, stored or gathered. Information can flow readily through out these nodes and can be modified by the algorithms the architect chooses to weigh on them.  

These nodes within the network can be thought of as phases within a neural net where data can be processed, stored or gathered. Information can flow readily throughout these nodes and can be modified by the algorithms the architect chooses to weigh on them.  

There are a variety of algorithms available for use within neural networks and these can be interchangeably placed depending on the structure of the ANN and what the intended use/purpose is.  

There are a variety of algorithms available for use within neural networks and these can be interchangeably placed depending on the structure of the ANN and what the intended use/purpose is.  

'''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 are 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 predictable output 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

KNN

*KNN

Logistic Regression  

*Logistic Regression  

Random Forest Algorithms

==='''Regression Algorithms'''===

KNN Algorithms

==='''Random Forest Algorithms'''===

Logistic Regression Algorithms'''  

==='''KNN Algorithms'''===