\[\newcommand{\ud}{\mathop{}\negthinspace\mathrm{d}} \newcommand{\pfrac}[2][x]{\frac{\partial #2}{\partial #1}}\]

Tutorials

The easiest way to get familiar with Matilda is to follow along with our tutorials. Tutorials by default work with the latest installable version of Matilda. To view older tutorials, change the documentation version using the tab at the bottom of the left sidebar.

Here are some tutorials to get you started:

• Introduction to the Matilda (Designment)
  • Multi-task learning architecture
  • Loss function
  • Joint feature selection from multiple modalities
  • Matilda model training
  • Reference
• Workflow
  • 1. Data Preperation
  • 2. Data Simulation and aggregation
  • 3. Data augmentaion and balancing
  • 4. Dimension Reduction
  • 5. Classification
  • 6. Feature selection
  • 7. Training continue
  • Reference
• Implementing Matilda
  • Preparing intput for Matilda
  • Running Matilda with the example dataset.
  • Perform multiple tasks using trained Matilda model.
  • Reference
  • License
• Hyperparameter Tuning
  • Main Functions for Matilda
  • Tunable hyperparameters
  • Functional hyperparameters
  • Tuning strategies and methods