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

TOmographic MOdel-BAsed Reconstruction (ToMoBAR) software

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?

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

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

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.

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

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

Predicting whether two persons meeting in a speed dating event get matched using deep learning methods

Repo for machine learning classes

Various regularized regression models for predicting car prices, using web scraped data

A model to classify images of waste products as Organic or Recyclable. Applied Image Augmentation to images and used basic CNN to classify images using Keras. Analysed the performance using Tensorboard. Detected over fitting using metric curves (accuracy) and addressed it using Dropout Regularization.

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.