You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
🏆 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.
This program implements logistic regression from scratch using the gradient descent algorithm in Python to predict whether customers will purchase a new car based on their age and salary.
Neural Network with functions for forward propagation, error calculation and back propagation is built from scratch and is used to analyse the IRIS dataset.
Three layer perceptron. Generates varied character images from scratch so does not depend on an existing dataset, like MNIST. Can train for an arbitrarily long time due to the open ended nature of the dataset. Contains a basic user interface.
• Trained the network for MNIST dataset • Implemented neural network on MNIST dataset by using Sigmoid, ReLU, ELU as the activation function. • Analyzed network’s running time, error rate, efficiency and accuracy.