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    "Predict citations, accidents, illnesses\n",

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    "## Model Representation \n",
    "\n",
    "Let's examine how we will represent a hypothesis function using neural networks. At a very simple level, neurons are basically computational units that take inputs (dendrites) as electrical inputs (called \"spikes\") that are channeled to outputs (axons). In our model, our dendrites are like the input features $x_1⋯x_n$, and the output is the result of our hypothesis function. In this model our $x_0$ input node is sometimes called the \"bias unit.\" It is always equal to $1$. In neural networks, we use the same logistic function as in classification, $\\frac{1}{1+e^{-\\theta^Tx}}$, yet we sometimes call it a sigmoid (logistic) activation function. In this situation, our \"theta\" parameters are sometimes called \"weights\".\n",
    "\n",

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    "# Introduction to LDA\n",
    "In this tutorial, we will learn about the text-mining aspect of an algorithm called Latent Dirichlet Allocation, or LDA for short. We have already spent a decent amount of time learning about text mining in this class, so I won't go over those basics now, but have a look at this wikipedia page if you would like a refresher: [Text Mining](https://en.wikipedia.org/wiki/Text_mining \"link to wikipedia page\")\n",
    "\n",
    "Latent Dirichlet Allocation was developed by David Blei, Andrew Ng, and Michael I. Jordan, and was published in their 2003 paper about topic discovery called [Latent Dirichlet Allocation](http://jmlr.csail.mit.edu/papers/v3/blei03a.html). \n",

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    "# Amazon.com Product Reviews Unsupervised Analysis and Recommendations\n",
    "Authors: Julianne Friend, Nathan Miller, Sara Reisler\n"
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