TOmographic MOdel-BAsed Reconstruction (ToMoBAR) software

The set of CPU/GPU optimised regularisation modules for iterative image reconstruction and other image processing tasks

In the context of Deep Learning: What is the right way to conduct example weighting? How do you understand loss functions and so-called theorems on them?

My project to recreate the results in "Understanding Deep Learning Requires Rethinking Generalization"

Python code to fit single-angle dynamic light scattering data using the Tikhonov Philips regularisation

Mixed Integer Piecewise Regression Algorithm with Regularisation

Horseshoe regression model fitted in PyMC.

Multivariate Polynomial Regression using gradient descent with regularisation

GEARS a toolbox for Global parameter Estimation with Automated Regularisation via Sampling by Jake Alan Pitt and Julio R. Banga

The use case is an application of the regularization technique such as Ridge and Lasso for building a linear regression model for housing price prediction.

a small machine learning theory practice.

The goal of this project is to develop and test two text classification systems: Task 1: sentiment analysis, in particular to predict the sentiment of movie review, i.e. positive or negative (binary classification). Task 2: topic classification, to predict whether a news article is about International issues, Sports or Business (multiclass class…

Regularisation and Cross-Validation of Determinants of Egalitarian Democracy: Demonstration for R

Repo for machine learning classes

Content: Classification, Sigmoid function, Decision Boundary, Cost function, Gradient descent, Overfitting, Regularisation

A series of Online Courses Offered by deeplearning.ai on Coursera.

The purpose of this study is to visualise and see how changing the regularisation constant affects svm classification. SVM with linear kernel has been used.

In this project we will build multiple CNN models for CIFAR-10 Image Classification

In this project, we use differents methods to transform our dataset (usually dimension modification) before making prediction thanks to machine learning and regressions.