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‎DataSets.ipynb

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‎DecisionTree.ipynb

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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"metadata": {
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"scrolled": false
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{
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‎GradientDescent.ipynb

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‎KNN.ipynb

+4-4
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‎Kmeans.ipynb

+10-49
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‎Logistic Matrix Factorization.ipynb

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‎LogisticRegression.ipynb

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‎Matrix Calculus.ipynb

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"\n",
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"\\begin{eqnarray}\n",
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"E^\\top E &= & (X - M C)^\\top (X - M C) \\\\\n",
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"& = & X^\\top X + C^\\top M^\\top M C - 2 X^\\top M C \\\\\n",
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"& = & X^\\top X + C^\\top M^\\top M C - X^\\top M C - C^\\top M^\\top X \\\\\n",
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"\\|E\\|_F^2 & = & \\trace \\left( X^\\top X + C^\\top M^\\top M C - 2 X^\\top M C \\right)\n",
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"\\end{eqnarray}\n",
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"\n",

‎MatrixFactorization.ipynb

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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"deletable": true,
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"metadata": {},
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"source": [
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"## Matrix Factorization Models\n",
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"\n"
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{
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"cell_type": "code",
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"execution_count": 22,
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"data": {
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"deletable": true,
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"metadata": {},
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"source": [
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"## Singular Value Decomposition"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {
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"metadata": {},
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"outputs": [
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{
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"data": {
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {
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"scrolled": true
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"outputs": [
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"deletable": true,
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"editable": true
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"metadata": {},
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"source": [
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"## Recommendation Systems\n",
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"\n"
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {
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},
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"outputs": [
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"collapsed": true
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"outputs": [],
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"source": [
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"collapsed": false,
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"scrolled": false
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"outputs": [
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{
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"cell_type": "code",
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"execution_count": 17,
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"metadata": {},
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"data": {
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"collapsed": true
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"cell_type": "code",
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"execution_count": 8,
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{
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"cell_type": "code",
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"execution_count": 2,
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Create ListeningCount(user, artist) data\n",
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"cell_type": "code",
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"execution_count": 4,
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"cell_type": "code",
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"execution_count": 100,
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"outputs": [
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"cell_type": "code",
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"outputs": [
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"cell_type": "code",
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"execution_count": 74,
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"execution_count": 8,
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"data": {
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"execution_count": 122,
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"execution_count": 76,
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"cell_type": "code",
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"execution_count": 12,
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"cell_type": "code",
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"execution_count": 150,
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"outputs": [
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"data": {
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython2",
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"version": "2.7.12"
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"version": "2.7.13"
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},
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"toc": {
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"nav_menu": {},
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"number_sections": true,
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"sideBar": true,
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"skip_h1_title": false,
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"toc_cell": false,
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"toc_position": {},
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"toc_section_display": "block",
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"toc_window_display": false
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}
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},
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"nbformat": 4,
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"nbformat_minor": 1
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}

‎ModelingExamples.ipynb

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},
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"cell_type": "code",
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"outputs": [
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"<TABLE border=\"1\" style=\"border: 1px solid #000000; border-collapse: collapse;\" cellpadding=\"4\">\n",
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" <TR>\n",
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" <TD width=\"65px\">&nbsp;</TD>\n",
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" <TD width=\"65px\">$x_2=1$</TD>\n",
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" <TD width=\"65px\">$x_2=j$</TD>\n",
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" <TD width=\"80px\">$x_2=S_2$</TD>\n",
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" </TR>\n",
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" <TR>\n",
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" <TD>$x_1=1$</TD>\n",
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" <TD>$C(1,1)$</TD>\n",
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" <TD>&nbsp;</TD>\n",
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" <TD>$C(1,S_2)$</TD>\n",
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" </TR>\n",
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" <TR>\n",
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" <TD>$x_1=i$</TD>\n",
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" <TD>&nbsp;</TD>\n",
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" <TD>$C(i,j)$</TD>\n",
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" <TD>&nbsp;</TD>\n",
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" </TR>\n",
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" <TR>\n",
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" <TD>$x_1=S_1$</TD>\n",
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" <TD>$C(S_1,1)$</TD>\n",
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" <TD>&nbsp;</TD>\n",
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" <TD>$C(S_1,S_2)$</TD>\n",
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" </TR>\n",
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"</TABLE>\n"
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]
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}
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],
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"source": [
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"from IPython.display import display, Math, Latex, HTML\n",
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"import html_utils as htm\n",

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