Machine learning concepts using java
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Updated
Sep 25, 2018 - Jupyter Notebook
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.
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.
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
A simplified explanation of gradient descent for linear regression in python using numpy
A deep learning library developed from scratch in Java.
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
Ball, Obstacle, and Target Simulation
Perceptron Linear Classification Learning and Linear Regression Gradient Descent
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