source-code

#| Source code

This is source code that is either used in the presentation, or was developed to create it. There is some material not covered in the presentation as well.

Requirements

• Python version: at least 3.7
• Packages (names listed that can be used with pip or conda to install):
  • pandas
  • xlrd
  • seaborn
  • matplotlib
  • scipy
  • jupyter
  • scikit-learn
  • keras
  • hyperopt

What is it?

• keras: illustration of using Keras for machine learning.
• parameter-optimization: example of parameter optimization kusing hyperopt, although the examples do not optimize hyperparameters in machine learning, that would be very similar.
• scikit-learn: examples of scikit-learn for machine learning, examples are provided forsupervised (regression and classification) and unsupervised (clustering) learnign, as well as dimensionality reduction for visualization of high-demensional data.
• [kullback_leibler_divergence.ipynb}(kullback_leibler_divergence.ipynb): illustrating the Kullback-Leibler divergence for probability distributions.
• curse_of_dimensionality.ipynb: illustration of the curse of dimensionality.