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Modeling text topics with Latent Dirichlet Allocation
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...ustering_and_retrieval/assigment/week5/.ipynb_checkpoints/5_lda_graphlab-checkpoint.ipynb
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| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Modeling text topics with Latent Dirichlet Allocation" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Question 1\n", | ||
| "\n", | ||
| "<img src=\"images/Screen Shot 2016-07-31 at 7.12.04 PM.png\">\n", | ||
| "\n", | ||
| "*Screenshot taken from [Coursera](https://www.coursera.org/learn/ml-clustering-and-retrieval/exam/LXSnc/modeling-text-topics-with-latent-dirichlet-allocation)*\n", | ||
| "\n", | ||
| "<!--TEASER_END-->" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Question 2\n", | ||
| "\n", | ||
| "<img src=\"images/Screen Shot 2016-07-31 at 7.12.11 PM.png\">\n", | ||
| "\n", | ||
| "*Screenshot taken from [Coursera](https://www.coursera.org/learn/ml-clustering-and-retrieval/exam/LXSnc/modeling-text-topics-with-latent-dirichlet-allocation)*\n", | ||
| "\n", | ||
| "<!--TEASER_END-->" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Question 3\n", | ||
| "\n", | ||
| "<img src=\"images/Screen Shot 2016-07-31 at 7.12.16 PM.png\">\n", | ||
| "\n", | ||
| "*Screenshot taken from [Coursera](https://www.coursera.org/learn/ml-clustering-and-retrieval/exam/LXSnc/modeling-text-topics-with-latent-dirichlet-allocation)*\n", | ||
| "\n", | ||
| "<!--TEASER_END-->" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Question 4\n", | ||
| "\n", | ||
| "<img src=\"images/Screen Shot 2016-07-31 at 7.12.20 PM.png\">\n", | ||
| "\n", | ||
| "*Screenshot taken from [Coursera](https://www.coursera.org/learn/ml-clustering-and-retrieval/exam/LXSnc/modeling-text-topics-with-latent-dirichlet-allocation)*\n", | ||
| "\n", | ||
| "<!--TEASER_END-->" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Question 5\n", | ||
| "\n", | ||
| "<img src=\"images/Screen Shot 2016-07-31 at 7.12.26 PM.png\">\n", | ||
| "\n", | ||
| "*Screenshot taken from [Coursera](https://www.coursera.org/learn/ml-clustering-and-retrieval/exam/LXSnc/modeling-text-topics-with-latent-dirichlet-allocation)*\n", | ||
| "\n", | ||
| "<!--TEASER_END-->" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Question 6\n", | ||
| "\n", | ||
| "<img src=\"images/Screen Shot 2016-07-31 at 7.12.30 PM.png\">\n", | ||
| "\n", | ||
| "*Screenshot taken from [Coursera](https://www.coursera.org/learn/ml-clustering-and-retrieval/exam/LXSnc/modeling-text-topics-with-latent-dirichlet-allocation)*\n", | ||
| "\n", | ||
| "<!--TEASER_END-->" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Question 7\n", | ||
| "\n", | ||
| "<img src=\"images/Screen Shot 2016-07-31 at 7.12.34 PM.png\">\n", | ||
| "\n", | ||
| "*Screenshot taken from [Coursera](https://www.coursera.org/learn/ml-clustering-and-retrieval/exam/LXSnc/modeling-text-topics-with-latent-dirichlet-allocation)*\n", | ||
| "\n", | ||
| "<!--TEASER_END-->" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Question 8\n", | ||
| "\n", | ||
| "<img src=\"images/Screen Shot 2016-07-31 at 7.12.39 PM.png\">\n", | ||
| "\n", | ||
| "*Screenshot taken from [Coursera](https://www.coursera.org/learn/ml-clustering-and-retrieval/exam/LXSnc/modeling-text-topics-with-latent-dirichlet-allocation)*\n", | ||
| "\n", | ||
| "<!--TEASER_END-->" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Question 9\n", | ||
| "\n", | ||
| "<img src=\"images/Screen Shot 2016-07-31 at 7.12.44 PM.png\">\n", | ||
| "\n", | ||
| "*Screenshot taken from [Coursera](https://www.coursera.org/learn/ml-clustering-and-retrieval/exam/LXSnc/modeling-text-topics-with-latent-dirichlet-allocation)*\n", | ||
| "\n", | ||
| "<!--TEASER_END-->" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Question 10\n", | ||
| "\n", | ||
| "<img src=\"images/Screen Shot 2016-07-31 at 7.12.49 PM.png\">\n", | ||
| "\n", | ||
| "*Screenshot taken from [Coursera](https://www.coursera.org/learn/ml-clustering-and-retrieval/exam/LXSnc/modeling-text-topics-with-latent-dirichlet-allocation)*\n", | ||
| "\n", | ||
| "<!--TEASER_END-->" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Question 11\n", | ||
| "\n", | ||
| "<img src=\"images/Screen Shot 2016-07-31 at 7.12.53 PM.png\">\n", | ||
| "\n", | ||
| "*Screenshot taken from [Coursera](https://www.coursera.org/learn/ml-clustering-and-retrieval/exam/LXSnc/modeling-text-topics-with-latent-dirichlet-allocation)*\n", | ||
| "\n", | ||
| "<!--TEASER_END-->" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Question 12\n", | ||
| "\n", | ||
| "<img src=\"images/Screen Shot 2016-07-31 at 7.12.57 PM.png\">\n", | ||
| "\n", | ||
| "*Screenshot taken from [Coursera](https://www.coursera.org/learn/ml-clustering-and-retrieval/exam/LXSnc/modeling-text-topics-with-latent-dirichlet-allocation)*\n", | ||
| "\n", | ||
| "<!--TEASER_END-->" | ||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "Python 2", | ||
| "language": "python", | ||
| "name": "python2" | ||
| }, | ||
| "language_info": { | ||
| "codemirror_mode": { | ||
| "name": "ipython", | ||
| "version": 2 | ||
| }, | ||
| "file_extension": ".py", | ||
| "mimetype": "text/x-python", | ||
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython2", | ||
| "version": "2.7.12" | ||
| }, | ||
| "toc": { | ||
| "toc_cell": false, | ||
| "toc_number_sections": false, | ||
| "toc_threshold": "8", | ||
| "toc_window_display": false | ||
| } | ||
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| "nbformat": 4, | ||
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| } |
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