Probablistic Programming Data Science with PyMC3 (Thomas Wiecki)
https://www.youtube.com/watch?v=LlzVlqVzeD8
https://docs.google.com/presentation/d/1QNxSjDHJbFL7vFwQHHheeGmBHEJAo39j28xdObFY6Eo/edit
https://gist.github.com/anonymous/9287a213fe188a79d7d7774eef79ad4d
prob_diff = np.mean(trace['mean_in_sample'] < trace['mean_out_of_sample])ppc = pm.sample_ppc(trace, model=model_name, samples=500); pred = ppc['out'].mean(axis=0)- Example of Bayesian Neural Network in the above gist
- Example of mapping out the entire classifier space, including the uncertainty.
- Example of using ADVI, and mini-batch ADVI!
- ADVI must be continuous.
- "Puppy Book" code ported to PyMC3
- https://github.com/aloctavodia/Doing_bayesian_data_analysis
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Deep learning tutorial - advanced techniques (Geoffrey French)
https://www.youtube.com/watch?v=DlNR1MrK4qE
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