🏆 A Comparative Study on Handwritten Digits Recognition using Classifiers like K-Nearest Neighbours (K-NN), Multiclass Perceptron/Artificial Neural Network (ANN) and Support Vector Machine (SVM) discussing the pros and cons of each algorithm and providing the comparison results in terms of accuracy and efficiecy of each algorithm.
A program that trains a perceptron to classify a flower as one of three categories. Trained using data from the Iris data set. Epochs currently set to 500000 but can be tinkered with. Currently classifying for Iris-setosa based on sepal-length and sepal-width. Current accuracy: 98.7%, also generates a map of error for different weight values (look at map generating implementation to read map). The website for this project is a desmos link linear, quadratic, and cubic separation generated by model (sepal-length vs sepal-width).
Some recognized algorithms[Decision Tree, Adaboost,Perceptron,Clustering, Neural network etc. ] of machine learning and pattern recognition are implemented from scratch using python. Data sets are also included to test the algorithms.