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Artificial Neural Networks (view source)
Revision as of 03:12, 7 December 2016
, 03:12, 7 December 2016Created page with ""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 t..."
"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" 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.
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'.
There are a variety of
== Machine Learning 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".
"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:
== Deep Learning Neural Networks ==
== Developing Data Sets ==
== Crawling the Internet ==
== Writing your own code for your neural network ==
The building blocks of Artificial Neural Networks are "Machine Learning Algorithms" which are algorithms that are explicitly developed to allow computers/hardware to learn without specifically being 'programmed'. The ability of these algorithms to train data is altered by the developer who trains the algorithms based on specified input and output values. The alteration in the input data and output data within the neural network is affected by its' 'constraint'.
Here is a arbitrary example of a input-output node to node alteration within a neural network:
== 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.
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'.
There are a variety of
== Machine Learning 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".
"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:
== Deep Learning Neural Networks ==
== Developing Data Sets ==
== Crawling the Internet ==
== Writing your own code for your neural network ==
The building blocks of Artificial Neural Networks are "Machine Learning Algorithms" which are algorithms that are explicitly developed to allow computers/hardware to learn without specifically being 'programmed'. The ability of these algorithms to train data is altered by the developer who trains the algorithms based on specified input and output values. The alteration in the input data and output data within the neural network is affected by its' 'constraint'.
Here is a arbitrary example of a input-output node to node alteration within a neural network:
