Courses are available in three formats:
-
Jupyter notebooks
*/*.ipynbfiles. -
Python files using sphinx-gallery
*/*.pyfiles. -
ReStructuredText files.
All notebooks and python files are converted into rst format and then assembled together using sphinx.
Directories and main files:
introduction/
├── machine_learning.rst
└── python_ecosystem.rst
python_lang/ # (Python language)
├── python_lang.py # (main file)
└── python_lang_solutions.py
scientific_python/
├── matplotlib.ipynb
├── scipy_numpy.py
├── scipy_numpy_solutions.py
├── scipy_pandas.py
└── scipy_pandas_solutions.py
statistics/ # (Statistics)
├── stat_multiv.ipynb # (multivariate statistics)
├── stat_univ.ipynb # (univariate statistics)
├── stat_univ_solutions.ipynb
├── stat_univ_lab01_brain-volume.py # (lab)
├── stat_univ_solutions.ipynb
└── time_series.ipynb
machine_learning/ # (Machine learning)
├── clustering.ipynb
├── decomposition.ipynb
├── decomposition_solutions.ipynb
├── linear_classification.ipynb
├── linear_regression.ipynb
├── manifold.ipynb
├── non_linear_prediction.ipynb
├── resampling.ipynb
├── resampling_solution.py
└── sklearn.ipynb
optimization/
├── optim_gradient_descent.ipynb
└── optim_gradient_descent_lab.ipynb
deep_learning/
├── dl_backprop_numpy-pytorch-sklearn.ipynb
├── dl_cnn_cifar10_pytorch.ipynb
├── dl_mlp_mnist_pytorch.ipynb
└── dl_transfer-learning_cifar10-ants-
Install Anaconda at https://www.anaconda.com/ with python >= 3.
Standard user (student) should install the required data analysis packages.
Create and activate the pystatsml_student environment:
conda env create -f environment_student.yml
conda activate pystatsml_student
Expert users (teachers) who need to build (pdf, html, etc.) the course should install additional packages including:
- pandoc
- sphinx-gallery
- nbstripout
Create and activate the pystatsml_teacher environment:
conda env create -f environment_teacher.yml
conda activate pystatsml_teacher
Build the documents.
Configure your git repository with nbstripout: a pre-commit hook for users who don't want to track notebooks' outputs in git.
nbstripout --install
Optional: install LaTeX to generate pdf. For Linux debian like:
sudo apt-get install latexmk texlive-latex-extra
After pulling the repository execute Jupyter notebooks (outputs are expected to be removed before git submission):
make exe
Build the pdf file (requires LaTeX):
make pdf
Build the html files:
make html
Clean everything:
make clean
Optional to generate Microsoft docx. Use docxbuilder:
make docx
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