Artificial Neural Networks

"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: |- Regression |- Random Forest |- KNN |- Logistic Regression

"Regression" Algorithms

"Random Forest" Algorithms

"KNN" Algorithms

"Logistic Regression" Algorithms

"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: |- 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.

Common Examples of "Unsupervised Learning" Algorithms include: |- "Apriori" algorithm |- "K-means" algorithms

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: