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Found 5 applets
| Name of the applet | Description |
|---|---|
| Understanding Machine Learning algorithms and the impact of hyperparameters on their performance: SVM | The applet has been created to allow the students to test and became more familiar with Machine Learning fundamentals and basic algorithms. In particular, you could test Support Vector Machine (SVM) algorithm on a classification task. You can choose between three different datasets (Moons, Circles, linearly separable) and set noise, number of samples and distance between classes, and you can tune the main model’s hyperparameters: kernel type, kernel parameter γ, regularization parameter C and the approach to adopt in muti-class case (one-vs-one, one-vs-rest). The applet has been realized using Dash and Plotly. |
| Understanding Machine Learning algorithms and the impact of hyperparameters on their performance: Random Forest | The applet has been created to allow the students to test and became more familiar with Machine Learning fundamentals and basic algorithms.In particular, you could test Random Forest (RF) on a classification task. You can choose between three different datasets (Moons, Circles, linearly separable) and set noise, number of samples and distance between classes, but most of all you can tune the main model’s hyperparameters: number of trees in the forest and number of features to consider deciding the best split.The applet has been realized using Dash and Plotly. |
| Understanding Machine Learning algorithms and the impact of hyperparameters on their performance: error’s double descent | The applet has been created to allow the students to test and became more familiar with Machine Learning fundamentals and basic algorithms.In particular, you could study the error double descent phenomenon when approximating a function with a polynomial regressor: test error first decreases, then increases, and then decreases again, increasing model's polynomial degree. The phenomenon occurs under specific conditions, but the user will be able to modify regularization's parameter and polynomial degree and change the training dataset to play with the regressor and see what happens on the error’s curve.The applet has been realized using Dash and Plotly. |
| Concept Map Processing | This applet aims to provide a visual tool for examining different critical valuesand provide insights into the evaluation of a concept map’s quality and itsvisual representation. We focus on salience metrics to assess and help usersenhance the effectiveness of their concept maps with an evaluation of itsquality. |
| k-means Method | K-means is a clustering method widely recognized in the literature and used to subdivide a set of data into distinct groups called clusters. The purpose of this algorithm is to assign each element of the data set to clusters so that elements within the same cluster are more similar to each other than to elements in other clusters. |
| Concept Map Processing | The applet is a web-based concept map editor and requires no local installation. The export is compatible with OptLearn algorithm proposed by University of Ljubljana. |
| Image Recognition Web Application with TensorFlow and Streamlit | This web application enables image recognition using neural networks for various datasets such as Fashion MNIST, CIFAR-10, and MNIST. It provides various actions, including retraining the model, testing images, and guidelines for usage. It is based on TensorFlow for the neural network and Streamlit for the user interface. |