Artificial Neural Networks: Difference between revisions
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Created 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..." |
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The most common types of "Supervised Learning" algorithms include: | 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 | |||
== Deep Learning Neural Networks == | == Deep Learning Neural Networks == | ||
