Machine learning techniques, such as Linear Regression, Logistic Regression, Neural Networks (feedforward propagation, backpropagation algorithms), Diagnosing Bias/Variance, Evaluating a Hypothesis, Learning Curves, Error Analysis, Support Vector Machines, K-Means Clustering, PCA, Anomaly Detection System, and Recommender System.

Impacts of data anonymization on model prediction for diabetes

Real-world application sized Neural Network. Implemented back-propagation algorithm with momentum, auto-encoder network, dropout during learning, least mean squares algorithm.

Neural networks is a model inspired by how the brain works. It is widely used today in many applications: when your phone interprets and understand your voice commands, it is likely that a neural network is helping to understand your speech; when you cash a check, the machines that automatically read the digits also use neural networks.

A MATLAB script for visualizing a feedforward neural network's understanding of a math function under different parameters

Content-based image retrieval (CBIR) is a technique that helps in searching a user desired information from a huge set of image files and interpret user intentions for the desired information. The retrieval of information is based on features of image like colour, shape, texture, annotation etc. In addition, the system with neural network can le…

This repository consists of Neural Network Projects

This repository stores my personal projects related to data science studies.

Multivariate Classification Using a Feed-Forward Neural Network and Backpropagation.

Optimierungsroutine für rechenaufwendige Systeme

Handwritten Digit Recognition - Neural Network - minimizing the cost function (Backpropagation) ---- OCTAVE ----- the exercise details are in ex4.pdf in the repo.

Letters and numbers classifier for chess games using computer vision and neural networks.

Wrangling on MNIST dataset based on Andrew Ng's course Machine Learning and Geoffrey Hinton's course Neural Networks for Machine Learning from Coursera.

There are two models for predicting the future stock prices of companies using feed forward network and SVR

This is a simple feed-forward neural network using MATLAB with Alarm and Warning situations. This implementation is to simplify the basic concept of a neural network.

Machine learning algorithms for the calibration of epidemiological compartmental models: application to the Italian COVID-19 outbreaks in Italy and to the newly developed SUIHTER model.