Will be doing all tests from Probabalistic Programming and Bayesian Thinking for Hackers by Cam Davidosn Pilon and updating packages and libraries accordingly. Most code is my interpretation of the excerises in the book or copied directlly where applicable. I am doing so to further my personal knowledge in Python. Will be updating this frequently
The book can be found here:- https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers
Tried to build a similar code to what was gone over in the classification lecture in Andrew Ng's Machine Learning class.
Using Welch labs YouTube videos, built an iPython notebook to learn basics of neural networks in Python.
- Data
- txtdata.csv
- challenger_data.csv
- Test
- PyMC-TextData.py
- PyMC-StochDeter.py
- PyMC-Challenger.py
- separation_plot.py
- MCMC.py
- Neural Networks.ipynb
[X] Chapter 1 - Introduction
[X] - PyMC-TextData.py
[X] Chapter 2 - More PyMC
[X] - PyMC-StochDeter.py
[X] - PyMC-Challenger.py
[I] Chapter 3 - MCMC
[ ] Chapter 4 - The Greatest Theorem Never Told
[ ] Chapter 5 - Loss Functions
[ ] Chapter 6 - Priorities
[ ] Chapter 7 - Bayesian Machine Learning