Dans ce répertoire, nous allons aborder les thèmes portant sur : introduction à l’apprentissage et la classification, régression, groupement (Clustering), réduction, de dimensionnalité et données massives, rétropropagation pour les grandes quantités de données, architectures et apprentissage profond, outils de programmation, applications.

Optimization methods for lasso penalized logistic regression.

in this project we'll Implement the basic functions of the Gradient-Descent algorithm to find the boundary in a small dataset.

Machine learning concepts using java

This repository includes discrete and independent code files in which various neural network algorithms and methods are implemented

An implementation of a logistic regression algo

This repository contains solved assignments of regression and classification algorithms.

The Neural Network is one of the most powerful learning algorithms (when a linear classifier doesn't work, this is what I usually turn to), and explaining the 'backpropagation' algorithm for training these models.

This repository will comprise primary optimization algorithms in Python language. Optimization is an extremely important part of machine learning.

A Logistic model for predicting divorce rates among couples, implemented using the sklearn library.

Collection of my notes from Udacity's Intro to Deep Learning--> Introduction to Neural Networks course.

Using python we have created a Linear Regression Machine Learning Model from Scratch. We have implemented Gradient Descent to find the best 'm' (Slope) and 'b' (Intercept).

Batch gradient algorithm implementation for linear regression

Perceptron Linear Classification Learning and Linear Regression Gradient Descent

In this example I show how Extreme-Gradient-Boosting can be used in a Survival Analysis database. At the end the importance of the variables is shown.

Explore a broad range of machine learning algorithms, including ML, RF, SVM, LR, NB, PCA, LogReg, DT, KMeans, SVMC, GD, HClust, DBSCAN, ICA, KNN, and more, within this repository. Gain practical insights and apply these diverse ML concepts effectively.

implementation of basic machine learning algorithm (GDA)

Supervised ML models

Implementation of the quasi Cauchy optimizer, an optimization method from the quasi Newton family. It uses a diagonal approximation of the Hessian and therefore has a small memory footprint.