DeCart2019 Machine Learning Resources.ipynb

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   "source": [
    "## DeCART 2019 Machine Learning for HealthCare: Resources"
   ]
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   "source": [
    "### General Textbooks\n",
    "\n",
    "Goodfellow, I, Bengio, Y. & Courville, A. __Deep Learning__. Cambridge MA: MIT Press, 2016 [ISBN-978-0262035613]\n",
    "\n",
    "Russell, S. & Norvig, P. __Artificial Intelligence:  A Modern Approach__. 3rd Ed. Pearson Education India, 2015 (paperback, 8th impression)[ISBN-978-9332543515]\n",
    "\n"
   ]
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   "source": [
    "### Python-Specific Books\n",
    "\n",
    "Géron, A. 2017. Hands-On Machine Learning with Scikit-Learn & TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. Sebastopol, Calif.: O'Reilly. [ISBN-978-1491962299] Source code available at https://github.com/ageron/handson-ml \n",
    "\n",
    "Müller, A. C. and Guido, S. 2017. Introduction to Machine Lea\n",
    "rning with Python: A Guide for Data Scientists. Sebastopol, Calif.: O’Reilly. [ISBN-978-1449369415] Code examples at https://github.com/amueller/introduction_to_ml_with_python "
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    "### An R Book That's Worth Having Around\n",
    "\n",
    "James, Garth, Witten, Daniel,Hastie, Trevor, and Tibshirani, Robert. 2017. An Introduction to Statistical Learning: with Applications in R. Available online as a pdf file at: http://www-bcf.usc.edu/~gareth/ISL/ "
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    "\n",
    "\n",
    "### Various blog posts, articles, web sites\n",
    "\n",
    "Netflix Tech Blog [Jupyter Notebook Use 2018](https://medium.com/netflix-techblog/notebook-innovation-591ee3221233)"
   ]
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