Reference Sheet
Key Points
- DeepPumas augments Pumas models with neural networks
- This workshop focuses both on DeepPumas and on machine learning concepts
- Fundamental concepts of machine learning and neural networks
- supervised learning
- empirical risk minimization
- multilayer perceptron
- bias-variance tradeoff
- training, underfitting, overfitting
- generalization
- regularization
- model selection
- hyperparameter optimization
- DeepPumas basic functionalities to work with neural networks
preprocessMLPDomainfitoptim_optionshyperopt
Summary of Basic Commands
| Action | Command | Observations |
|---|---|---|
| Get a supervised machine learning dataset ready for further use with DeepPumas | preprocess |
Expects data as matrices X, Y, with samples stored as columns, and returns a FitTarget for further use with fit and hyperopt |
| Construct a multilayer perceptron | MLPDomain |
The constructor accepts parameters to specify the number of layers, the number of units in each layer, the activation functions, and the type of regularization |
| Fit a multilayer perceptron to a preprocessed supervised machine learning dataset | fit |
It can also fit other machine learning models |
| Pass options to the optimizer | optim_options |
A NamedTuple of options to be passed on to the optimizer. Here, we experiment with the number of iterations |
| Automate fitting and hyperparameter tuning | hyperopt |
Optimize the parameters and hyperparameters of a machine learning model, in particular, of a multilayer perceptron |
Get in touch
If you have any suggestions or want to get in touch with our education team, please send an email to training@pumas.ai.
License
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