├── README.md ├── bayesianinference-1 ├── Introduction to Bayesian Inference with Linear Regression (complete).ipynb └── Introduction to Bayesian Inference with Linear Regression.ipynb ├── bayesianinference-2 ├── mcmc-and-outliers-filled.ipynb ├── mcmc-and-outliers.ipynb └── slides.pdf ├── dataviz-1 ├── 01_types_of_plots.ipynb ├── 02_simplify_and_beautify.ipynb ├── 03_challenge.ipynb ├── ft-lockdowns.png └── ft-seaborn.png ├── docs ├── data_collection_disclosure.md ├── img │ ├── home_directories.png │ ├── hub_spawn.png │ ├── hub_spawn_progress.png │ ├── lab_interface.png │ └── notebook_interface.png └── jupyterhub_guide.md ├── hacks ├── README.md └── smoothflows │ ├── .ipynb_checkpoints │ ├── dummy-checkpoint.py │ └── visualize_flow-checkpoint.py │ ├── README.md │ ├── __pycache__ │ ├── spectral_norm.cpython-37.pyc │ ├── toy_data.cpython-37.pyc │ ├── utils.cpython-37.pyc │ └── visualize_flow.cpython-37.pyc │ ├── args.pkl │ ├── cmd_ffjord.ipynb │ ├── cmd_ffjord_save.ipynb │ ├── demo_model_ode_flow_v9.pt │ ├── isochrone_nflows.ipynb │ ├── lib │ ├── .ipynb_checkpoints │ │ ├── dummy-checkpoint.py │ │ └── visualize_flow-checkpoint.py │ ├── __pycache__ │ │ ├── spectral_norm.cpython-37.pyc │ │ ├── toy_data.cpython-37.pyc │ │ ├── utils.cpython-37.pyc │ │ └── visualize_flow.cpython-37.pyc │ ├── custom_optimizers.py │ ├── datasets.py │ ├── layers │ │ ├── __init__.py │ │ ├── __pycache__ │ │ │ ├── __init__.cpython-37.pyc │ │ │ ├── cnf.cpython-37.pyc │ │ │ ├── container.cpython-37.pyc │ │ │ ├── coupling.cpython-37.pyc │ │ │ ├── elemwise.cpython-37.pyc │ │ │ ├── glow.cpython-37.pyc │ │ │ ├── norm_flows.cpython-37.pyc │ │ │ ├── normalization.cpython-37.pyc │ │ │ ├── odefunc.cpython-37.pyc │ │ │ └── squeeze.cpython-37.pyc │ │ ├── cnf.py │ │ ├── container.py │ │ ├── coupling.py │ │ ├── diffeq_layers │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-37.pyc │ │ │ │ ├── basic.cpython-37.pyc │ │ │ │ ├── container.cpython-37.pyc │ │ │ │ ├── resnet.cpython-37.pyc │ │ │ │ └── wrappers.cpython-37.pyc │ │ │ ├── basic.py │ │ │ ├── container.py │ │ │ ├── resnet.py │ │ │ └── wrappers.py │ │ ├── elemwise.py │ │ ├── glow.py │ │ ├── norm_flows.py │ │ ├── normalization.py │ │ ├── odefunc.py │ │ ├── resnet.py │ │ ├── squeeze.py │ │ └── wrappers │ │ │ ├── __pycache__ │ │ │ └── cnf_regularization.cpython-37.pyc │ │ │ └── cnf_regularization.py │ ├── multiscale_parallel.py │ ├── odenvp.py │ ├── priors.py │ ├── spectral_norm.py │ ├── toy_data.py │ ├── utils.py │ └── visualize_flow.py │ ├── nflows_gpu.ipynb │ ├── quick_isochrone.npy │ └── train_misc.py ├── machinelearning-1 ├── Decision Trees.ipynb ├── decisiontree.png └── randomforest.png └── machinelearning-2 ├── astrohackweek_2020_neural_networks.ipynb ├── astrohackweek_2020_neural_networks_completed.ipynb └── images ├── activation.png ├── augmentation.png ├── backprop.png ├── cnn.png ├── deep_nn.png ├── dropout.png ├── gradient_descent_arrow.png ├── linear.png ├── maxpool.png ├── 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