https://github.com/gtraines/perceptron_classification

*Background*

One of the fundamental concepts in artificial intelligence and machine learning is the perceptron learning algorithm which gives life to the abstract data structure known as the perceptron. The perceptron is a data structure created to resemble the functioning of a neuron in the brain. The perceptron has a set of inputs (variable values) which each has an excitatory (positive) or inhibitory (negative) weight associated with it. During the training phase, the perceptron receives a set of values corresponding to its inputs along with an expected target outcome. If the sum of the weights multiplied by their corresponding input values is greater than a threshold value, the perceptron will emit a positive response; if the sum is lower than the threshold value, the perceptron will emit a negative response.

Continue reading Introducing the Perceptron →

I’ve posted a project providing a visual step-through explanation of Dijkstra’s shortest-path algorithm implemented on a randomly-generated digraph, as well as a short report on the algorithm and my project, available for download on the “projects” page.

Multithreading and Parallel Breadth-First Search: An implementation in C++11

So, if you read the accompanying lab report, you’ll see this was an attempt at using the C++11 multithreading library (essentially the same as the Boost multithreading library) to implement a parallel breadth-first search algorithm. The results weren’t spectacular, but I don’t feel too bad; of the two computer scientists who developed the more successful approach, one of them literally wrote the book on algorithms.

Anyway, if this is helpful to anyone, please feel free to add to, change, or take away from my work here. I’d really appreciate any feedback, though. Thanks!

## always choosing the local optimum