Jupyter/List: Difference between revisions
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Statsmodels library - documentation notebooks: | Statsmodels library - documentation notebooks: | ||
* https://github.com/statsmodels/statsmodels/wiki/Examples | * https://github.com/statsmodels/statsmodels/wiki/Examples | ||
Scikit DSP and SDR: | |||
* https://github.com/mwickert/SP-Comm-Tutorial-using-scikit-dsp-comm | |||
Statistical mechanics notebooks: | |||
* http://pages.physics.cornell.edu/~sethna/StatMech/ComputerExercises.html | |||
Security applications/analysis: | Security applications/analysis: | ||
Latest revision as of 11:57, 30 November 2017
Gallery from IPython documentation:
Statsmodels library - documentation notebooks:
Scikit DSP and SDR:
Statistical mechanics notebooks:
Security applications/analysis:
- PCAP exploration: https://nbviewer.jupyter.org/gist/jtriley/3866987
- Detecting algorithmically generated domain names: https://nbviewer.jupyter.org/github/ClickSecurity/data_hacking/blob/master/dga_detection/DGA_Domain_Detection.ipynb
- More here: https://clicksecurity.github.io/data_hacking/
- Hierarchical clustering of syslogs
- Data from malware domain list
- SQL injection
- Browser Agent Fingerprinting
- PCAP exploration
tSNE visualization method:
- iris dataset: https://nbviewer.jupyter.org/github/danielfrg/py_tsne/blob/master/examples/iris.ipynb
- mnist dataset: https://nbviewer.jupyter.org/github/danielfrg/py_tsne/blob/master/examples/mnist.ipynb
PyTherm - python and thermodynamics lecture notes:
Reaction simulation: theory and applications for numerical methods:
Example Machine Learning notebook:
News categorization using Naive Bayes via scikit-learn:
IPython notebooks for probabilistic methods/bayesian methods for hackers:
Conway's game of life:
IPython recipes:
- Github repository here: https://github.com/ipython-books/cookbook-code
- Longer list of notebooks here: https://ipython-books.github.io/cookbook/
IPython parallel pushing/executing/pulling (old):
Map Reduce and Python Spark API:
Visual PySpark notebook:
Pandas notebooks:
GPFlow (Gaussian Process Modeling with TensorFlow): examples via notebooks: