Horseshoe regression model fitted in PyMC.

Repo for machine learning classes

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…

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

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

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

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.

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

Jacobian regularisation for neural networks (PyTorch) and hyperparameter tuning with Skorch

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

Mixed Integer Piecewise Regression Algorithm with Regularisation

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.

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

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.

Multivariate Polynomial Regression using gradient descent with regularisation

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