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switch nbviewer links to github links

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Kevin Markham 2015-11-22 01:55:39 -05:00
parent 648e77b2c1
commit 6d2c9dc18f
4 changed files with 4 additions and 4 deletions
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2
02_machine_learning_setup.ipynb
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"\n",
"- [nbviewer](http://nbviewer.ipython.org/): view notebooks online as static documents\n",
"- [IPython documentation](http://ipython.org/ipython-doc/stable/index.html): focuses on the interpreter\n",
"- [IPython Notebook tutorials](http://nbviewer.ipython.org/github/ipython/ipython/blob/master/examples/Notebook/Index.ipynb): in-depth introduction\n",
"- [IPython Notebook tutorials](https://github.com/jupyter/notebook/blob/master/docs/source/examples/Notebook/Examples%20and%20Tutorials%20Index.ipynb): in-depth introduction\n",
"- [GitHub's Mastering Markdown](https://guides.github.com/features/mastering-markdown/): short guide with lots of examples"
]
},

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07_cross_validation.ipynb
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"- Section 5.1 of [An Introduction to Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/) (11 pages) and related videos: [K-fold and leave-one-out cross-validation](https://www.youtube.com/watch?v=nZAM5OXrktY) (14 minutes), [Cross-validation the right and wrong ways](https://www.youtube.com/watch?v=S06JpVoNaA0) (10 minutes)\n",
"- Scott Fortmann-Roe: [Accurately Measuring Model Prediction Error](http://scott.fortmann-roe.com/docs/MeasuringError.html)\n",
"- Machine Learning Mastery: [An Introduction to Feature Selection](http://machinelearningmastery.com/an-introduction-to-feature-selection/)\n",
"- Harvard CS109: [Cross-Validation: The Right and Wrong Way](http://nbviewer.ipython.org/github/cs109/content/blob/master/lec_10_cross_val.ipynb)\n",
"- Harvard CS109: [Cross-Validation: The Right and Wrong Way](https://github.com/cs109/content/blob/master/lec_10_cross_val.ipynb)\n",
"- Journal of Cheminformatics: [Cross-validation pitfalls when selecting and assessing regression and classification models](http://www.jcheminf.com/content/pdf/1758-2946-6-10.pdf)"
]
},

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08_grid_search.ipynb
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"\n",
"- scikit-learn documentation: [Grid search](http://scikit-learn.org/stable/modules/grid_search.html), [GridSearchCV](http://scikit-learn.org/stable/modules/generated/sklearn.grid_search.GridSearchCV.html), [RandomizedSearchCV](http://scikit-learn.org/stable/modules/generated/sklearn.grid_search.RandomizedSearchCV.html)\n",
"- Timed example: [Comparing randomized search and grid search](http://scikit-learn.org/stable/auto_examples/model_selection/randomized_search.html)\n",
"- scikit-learn workshop by Andreas Mueller: [Video segment on randomized search](https://youtu.be/0wUF_Ov8b0A?t=17m38s) (3 minutes), [related notebook](http://nbviewer.ipython.org/github/amueller/pydata-nyc-advanced-sklearn/blob/master/Chapter%203%20-%20Randomized%20Hyper%20Parameter%20Search.ipynb)\n",
"- scikit-learn workshop by Andreas Mueller: [Video segment on randomized search](https://youtu.be/0wUF_Ov8b0A?t=17m38s) (3 minutes), [related notebook](https://github.com/amueller/pydata-nyc-advanced-sklearn/blob/master/Chapter%203%20-%20Randomized%20Hyper%20Parameter%20Search.ipynb)\n",
"- Paper by Yoshua Bengio: [Random Search for Hyper-Parameter Optimization](http://www.jmlr.org/papers/volume13/bergstra12a/bergstra12a.pdf)"
]
},

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09_classification_metrics.ipynb
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"\n",
"- Blog post: [Simple guide to confusion matrix terminology](http://www.dataschool.io/simple-guide-to-confusion-matrix-terminology/) by me\n",
"- Videos: [Intuitive sensitivity and specificity](https://www.youtube.com/watch?v=U4_3fditnWg) (9 minutes) and [The tradeoff between sensitivity and specificity](https://www.youtube.com/watch?v=vtYDyGGeQyo) (13 minutes) by Rahul Patwari\n",
"- Notebook: [How to calculate \"expected value\"](http://nbviewer.ipython.org/github/podopie/DAT18NYC/blob/master/classes/13-expected_value_cost_benefit_analysis.ipynb) from a confusion matrix by treating it as a cost-benefit matrix (by Ed Podojil)\n",
"- Notebook: [How to calculate \"expected value\"](https://github.com/podopie/DAT18NYC/blob/master/classes/13-expected_value_cost_benefit_analysis.ipynb) from a confusion matrix by treating it as a cost-benefit matrix (by Ed Podojil)\n",
"- Graphic: How [classification threshold](https://media.amazonwebservices.com/blog/2015/ml_adjust_model_1.png) affects different evaluation metrics (from a [blog post](https://aws.amazon.com/blogs/aws/amazon-machine-learning-make-data-driven-decisions-at-scale/) about Amazon Machine Learning)\n",
"\n",
"\n",