🏆 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 comprehensive approach on recognizing emotion (sentiment) from a certain tweet. Supervised machine learning.

Implementing Artificial Neural Network training process in Python

Repo for my blogs explaining swish activation function

Neural Network with functions for forward propagation, error calculation and back propagation is built from scratch and is used to analyse the IRIS dataset.

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.

Simple multi layer perceptron application using feed forward back propagation algorithm

Avoiding the vanishing gradients problem by adding random noise and batch normalization

유전알고리즘과 인공신경망을 활용허여 마리오 학습

A classifier to differentiate between Cat and Non-Cat Images

building a deep neural network with as many layers as you want!

A simple neural network with backpropagation used to recognize ASCII coded characters

A neural network implementation in Ruby.

Implement Linear Regression, Logistic Regression, Neural networks and many other

binary linear classification from scratch with sigmoid function based gradient decente

Simple Tutorial to Explain the Pros and Cons of Sigmoid Activation Function

All pair approach to recognize handwritten digits based on the MNIST dataset.

This is an example machine learning programming using C++. I have created an example of Sigmoid function in C++.

Sigmodial decontrast interface with IM backend