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abonady

Config files for my GitHub profile.

binary_classification_from_scratch

A Binary Classification From Scratch, very simple algorithm to classify simple data

fmnist_cnn

This project is a convolutional neural network (CNN) built using PyTorch that classifies images from the Fashion-MNIST dataset. The network consists of several layers including convolutional layers, pooling layers, and fully connected layers. The model achieved an accuracy of 92.1%

house_prediction

A Kaggle's competition - The main task is to predict the prices of the houses.

k_means_from_scratch

An implementation of the basic idea of K-Means from scratch.

multiple_linear_regression_from_scratch

A very simple Multiple Linear Regression (MLR) algorithm from Scratch. I did not use Scikit-Learn or any similar libraries

neural_network_from_scratch

This is the code of a Neural Network built from scratch without any libraries other than NumPy to deal with matrices and to handle the Linear Algebra part.

oskundocs

simple_linear_regression_from_scratch

I have applied the fundamental idea of Linear Regression with Single Variable input. I implemented the Gradian Descent algorithm simply from scratch with no libraries such as Scikit-Learn. I just used NumPy.

svm_spam_classification

This a very simple SVM Spam Emails Classification problem, I used Sklearn library to train the model and get better results

titanic_classification

In this challenge, I build a predictive model that answers the question: “what sorts of people were more likely to survive?” using passenger data (ie name, age, gender, socio-economic class, etc).