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Gallery from IPython documentation: https://github.com/jupyter/jupyter/wiki/A-gallery-of-interesting-Jupyter-Notebooks
Gallery from IPython documentation:  
* https://github.com/jupyter/jupyter/wiki/A-gallery-of-interesting-Jupyter-Notebooks


News categorization using Naive Bayes via scikit-learn: https://nbviewer.jupyter.org/github/andressotov/News-Categorization-MNB/blob/master/News_Categorization_MNB%2010-oct-2017.ipynb
Statsmodels library - documentation notebooks:
* https://github.com/statsmodels/statsmodels/wiki/Examples


IPython notebooks for probabilistic methods/bayesian methods for hackers: https://nbviewer.jupyter.org/github/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/blob/master/Prologue/Prologue.ipynb
Scikit DSP and SDR:
* https://github.com/mwickert/SP-Comm-Tutorial-using-scikit-dsp-comm


Conway's game of life: https://nbviewer.jupyter.org/gist/jiffyclub/3778422
Statistical mechanics notebooks:
* http://pages.physics.cornell.edu/~sethna/StatMech/ComputerExercises.html
 
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:
* https://nbviewer.jupyter.org/github/iurisegtovich/PyTherm-applied-thermodynamics/blob/master/index.ipynb
 
Reaction simulation: theory and applications for numerical methods:
* https://nbviewer.jupyter.org/github/waltherg/notebooks/blob/master/2013-12-03-Crank_Nicolson.ipynb
 
Example Machine Learning notebook:
* https://nbviewer.jupyter.org/github/rhiever/Data-Analysis-and-Machine-Learning-Projects/blob/master/example-data-science-notebook/Example%20Machine%20Learning%20Notebook.ipynb
 
News categorization using Naive Bayes via scikit-learn:
* https://nbviewer.jupyter.org/github/andressotov/News-Categorization-MNB/blob/master/News_Categorization_MNB%2010-oct-2017.ipynb
 
IPython notebooks for probabilistic methods/bayesian methods for hackers:
* https://nbviewer.jupyter.org/github/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/blob/master/Prologue/Prologue.ipynb
 
Conway's game of life:
* https://nbviewer.jupyter.org/gist/jiffyclub/3778422


IPython recipes:
IPython recipes:
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* Longer list of notebooks here: https://ipython-books.github.io/cookbook/
* Longer list of notebooks here: https://ipython-books.github.io/cookbook/


IPython parallel pushing/executing/pulling (old):
* https://nbviewer.jupyter.org/gist/jtriley/3866987
Map Reduce and Python Spark API:
* https://nbviewer.jupyter.org/github/phelps-sg/python-bigdata/blob/master/src/main/ipynb/spark-mapreduce.ipynb
Visual PySpark notebook:
* https://nbviewer.jupyter.org/github/jkthompson/pyspark-pictures/blob/master/pyspark-pictures.ipynb


Pandas notebooks:
* https://github.com/jupyter/jupyter/wiki/A-gallery-of-interesting-Jupyter-Notebooks#pandas-for-data-analysis


GPFlow (Gaussian Process Modeling with TensorFlow): examples via notebooks:
* https://github.com/GPflow/GPflowOpt/tree/master/doc/source/notebooks


[[Category:IPython]]
[[Category:IPython]]

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:

tSNE visualization method:

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:

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: