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.

Demonstrate gradient descent

Optimization methods for lasso penalized logistic regression.

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.

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

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

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.

A simplified explanation of gradient descent for linear regression in python using numpy

Gradient Descent is the process of minimizing a function by following the gradients of the cost function. This involves knowing the form of the cost as well as the derivative so that from a given point you know the gradient and can move in that direction, e.g. downhill towards the minimum value.

Stanford talks by Andrew Ng

Create a Deep Neural Network from Scratch using Python3.

Ball, Obstacle, and Target Simulation

implementation of basic machine learning algorithm (GDA)

Supervised ML models

A simple perceptron based artificial neural network using python and numpy package only.

Implemented machine learning algorithms.