In this project, I used Hebbian, Perceptron, Adaline, MultiClassPerceptron and MultiClassAdaline neural networks to implement X and O character recognition. I used Qt5 for UI.
Hebbian Learning Rule, also known as Hebb Learning Rule, was proposed by Donald O Hebb. It is one of the first and also easiest learning rules in the neural network. It is used for pattern classification. It is a single layer neural network, i.e. it has one input layer and one output layer. The input layer can have many units, say n. The output layer only has one unit. Hebbian rule works by updating the weights between neurons in the neural network for each training sample.
In machine learning, the perceptron is an algorithm for supervised learning of binary classifiers. A binary classifier is a function which can decide whether or not an input, represented by a vector of numbers, belongs to some specific class. 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.
Adline stands for adaptive linear neuron. It makes use of linear activation function, and it uses the delta rule for training to minimize the mean squared errors between the actual output and the desired target output. The weights and bias are adjustable.
The multi-class perceptron algorithm is a supervised learning algorithm for classification of data into one of a series of classes. This algorithm is built in such a way that it can be generalized to any use-case, with details on how to format data in the sections below. It is meant to be easy to use and understand, without any significant performance issues.
It is a combination of Adaline algorithm and Multi class perceptron.
An example of running the program:
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent