Comparison of performance between our next reaction implementation and the Python library from Ref. [3]. by Samuel Cure (22250922)

Segmentation module training details. by Khelina Fedorchuk (19130089)

Cost functions implemented in Neuroptimus. by Máté Mohácsi (20469514)

“…In recent years, manual model tuning has been gradually replaced by automated parameter search using a variety of different tools and methods. …”

Advancing Solar Magnetic Field Modeling by Carlos António (21257432)

“…<br><br>We developed a significantly faster Python code built upon a functional optimization framework previously proposed and implemented by our team. …”

MCCN Case Study 3 - Select optimal survey locality by Donald Hobern (21435904)

Code by Baoqiang Chen (21099509)

“…We divided the dataset into training and test sets, using 70% of the genes for training and 30% for testing. We implemented machine learning algorithms using the following R packages: rpart for Decision Trees, gbm for Gradient Boosting Machines (GBM), ranger for Random Forests, the glm function for Generalized Linear Models (GLM), and xgboost for Extreme Gradient Boosting (XGB). …”

Core data by Baoqiang Chen (21099509)

“…We divided the dataset into training and test sets, using 70% of the genes for training and 30% for testing. We implemented machine learning algorithms using the following R packages: rpart for Decision Trees, gbm for Gradient Boosting Machines (GBM), ranger for Random Forests, the glm function for Generalized Linear Models (GLM), and xgboost for Extreme Gradient Boosting (XGB). …”