Posts with the #math tag
Perceptrons: Training (#2)
In the last post, I gave a relatively simple introduction and model for a perceptron. I feel like I didn’t explain the algebraic definition quite well, so here’s what I would like to add: The multiplication in the algebraic representation of a perceptron is the dot product. It is defined as the sum of the products of each element: $$ a \cdot b = \sum_i a_i b_i $$ Now that we’ve clarified that, here’s my question from the last post: “How do we actually obtain a useful weights vector \(w\) and a bias \(b\)?”
Perceptrons: Introduction (#1)
Hello! This is the first part of a relatively introductory series to Machine Learning/Artificial Intelligence, as well as the first part of the “Perceptron” subseries. Today, I’d like to cover how a very basic Perceptron, or an artificial neuron works. They’re the building blocks of neural networks and play a significant role in the development of AI.