In this video we'll introduce the Single-Layer Perceptron (aka "Neuron" or simply "Perceptron"), the most fundamental element of nearly all modern neural network and machine learning models. We'll begin by covering the history and main idea, then open up a coding editor and actually implement the element from scratch.
Machine Learning textbook (mentioned in video): [ Ссылка ]
Github repo: [ Ссылка ]
If you'd like to learn about Python data structures, check out my video series starting with: [ Ссылка ]
Video series covering GUI development in Python: [ Ссылка ]
References:
[1] - [ Ссылка ]
In machine learning, the perceptron is an algorithm for supervised learning of binary classifiers (functions that can decide whether an input, represented by a vector of numbers, belongs to some specific class or not). It is a type of linear classifier, i.e. a classification algorithm that makes its predictions based on a linear predictor function combining a set of weights with the feature vector. The algorithm allows for online learning, in that it processes elements in the training set one at a time.
