Learn more about computerscience with this collection
Understanding machine learning models
Improving data analysis and decision-making
How Google uses logic in machine learning
Conclusion
AI is ubiquitous. The term has flooded our lives, and it has lost its flavor. But, as a data scientist / ML engineer / AI engineer (whatever you call yourself), we can hold the community to a higher standard. We can be specific with our algorithms, so showcase our work is more than a series of pre-defined if-then statements. Granted, I know there are intelligent algorithms outside of this framework, but this is a realistic way to discuss our work and highlight the uniqueness of our methods (if you’re using these). If you’re new to CI, I challenge you to find applications, extend the theory.
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MORE IDEAS ON THIS
Evolutionary Computation
Inspiration: “Using the biological evolution as a source of inspiration, evolutionary computation (EC) solves optimization problems by generating, evaluating and modifying a population of possible solutions.” [1]
Genetic Algorithms (GAs) are likely the most popular algorithm belongi...
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Neural Networks
Inspiration: “Using the human brain as a source of inspiration, artificial neural networks (NNs) are massively parallel distributed networks that have the ability to learn and generalize from examples.” [1]
Each NN is composed of neurons, and their organization defines their archite...
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Fuzzy Systems
Inspiration: “Using the human language as a source of inspiration, fuzzy systems (FS) model linguistic imprecision and solve uncertain problems based on a generalization of traditional logic, which enables us to perform approximate reasoning.” [1]
Full disclosure —I’m biased towards Fuzzy S...
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Artificial Intelligence
What is Artificial Intelligence? Who knows. It’ s an ever-moving target to define what is or isn’t AI. So, I’d like to dive into a science that’s a little more concrete — Computational Intelligence (CI). CI is a three-branched set of theories along with their design a...
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CI Theory
One of the most common questions I’ve received when talking about CI is, “what problems does each branch solve?” While I can appreciate this question, the branches are not segmented by which problems they solve.
The inspiration of the theories segments the branches. So, it...
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