diff --git a/.ipynb_checkpoints/slides-checkpoint.ipynb b/.ipynb_checkpoints/slides-checkpoint.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true,
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "<style type=\"text/css\">\n",
+    ".input_prompt, .input_area, .output_prompt {\n",
+    "display:none !important;\n",
+    "}\n",
+    ".reveal h1, .reveal h2 {\n",
+    "    font-family:\"League Gothic\"\n",
+    "}\n",
+    "</style>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "<center>Slides: https://doingmathwithpython.github.io/pycon-us-2016/ </center>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# <center> Doing Math with Python </center>\n",
+    "\n",
+    "\n",
+    "<center>\n",
+    "\n",
+    "\n",
+    "<p> <b>Amit Saha</b>\n",
+    "\n",
+    "<p>May 29, PyCon US 2016 Education Summit\n",
+    "\n",
+    "<p>Portland, Oregon\n",
+    "</center>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "## About me\n",
+    "\n",
+    "- Software Engineer at [Freelancer.com](https://www.freelancer.com) HQ in Sydney, Australia\n",
+    "\n",
+    "- Author of \"Doing Math with Python\" (No Starch Press, 2015)\n",
+    "\n",
+    "- Writes for Linux Voice, Linux Journal, etc.\n",
+    "\n",
+    "- [Blog](http://echorand.me), [GitHub](http://github.com/amitsaha)\n",
+    "\n",
+    "\n",
+    "#### Contact\n",
+    "\n",
+    "- [@echorand](http://twitter.com/echorand)\n",
+    "\n",
+    "- [Email](mailto:amitsaha.in@gmail.com)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "\n",
+    "### This talk - a proposal, a hypothesis, a statement\n",
+    "\n",
+    "*Python can lead to a more enriching learning and teaching experience in the classroom* \n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true,
+    "slideshow": {
+     "slide_type": "notes"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "As I will attempt to describe in the next slides, Python is an amazing way to lead to a more fun learning and teaching\n",
+    "experience.\n",
+    "\n",
+    "It can be a basic calculator, a fancy calculator and \n",
+    "\n",
+    "Math, Science, Geography..\n",
+    "\n",
+    "Tools that will help us in that quest are:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "###  (Main) Tools\n",
+    "\n",
+    "<img align=\"center\" src=\"images/logo_collage.png\"></img>\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Python - a scientific calculator\n",
+    "\n",
+    "- Python 3 is my favorite calculator (not Python 2 because 1/2 = 0)\n",
+    "\n",
+    "\n",
+    "- `fabs()`, `abs()`, `sin()`, `cos()`, `gcd()`, `log()` (See [math](https://docs.python.org/3/library/math.html))\n",
+    "\n",
+    "\n",
+    "- Descriptive statistics (See [statistics](https://docs.python.org/3/library/statistics.html#module-statistics))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "### Python - a scientific calculator\n",
+    "\n",
+    "- Develop your own functions: unit conversion, finding correlation, .., anything really\n",
+    "\n",
+    "- Use PYTHONSTARTUP to extend the battery of readily available mathematical functions\n",
+    "\n",
+    "```\n",
+    "$ PYTHONSTARTUP=~/work/dmwp/pycon-us-2016/startup_math.py idle3 -s\n",
+    "```\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "\n",
+    "### Unit conversion functions\n",
+    "\n",
+    "```\n",
+    ">>> unit_conversion()\n",
+    "1. Kilometers to Miles\n",
+    "2. Miles to Kilometers\n",
+    "3. Kilograms to Pounds\n",
+    "4. Pounds to Kilograms\n",
+    "5. Celsius to Fahrenheit\n",
+    "6. Fahrenheit to Celsius\n",
+    "Which conversion would you like to do? 6\n",
+    "Enter temperature in fahrenheit: 98\n",
+    "Temperature in celsius: 36.66666666666667\n",
+    ">>> \n",
+    "\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "### Finding linear correlation\n",
+    "\n",
+    "```\n",
+    ">>> \n",
+    ">>> x = [1, 2, 3, 4]\n",
+    ">>> y = [2, 4, 6.1, 7.9]\n",
+    ">>> find_corr_x_y(x, y)\n",
+    "0.9995411791453812\n",
+    "\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Python - a really fancy calculator\n",
+    "\n",
+    "SymPy - a pure Python symbolic math library\n",
+    "\n",
+    "*from sympy import awesomeness*  - don't try that :)\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true,
+    "slideshow": {
+     "slide_type": "notes"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "When you bring in SymPy to the picture, things really  get awesome. You are suddenly writing computer\n",
+    "programs which are capable of speaking algebra. You are no more limited to numbers.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
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fFUlF0qySEHrjDbc0PjtbOwtGItlnlWza5FojVau69sghjt2UYNOsklTQvr2b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\nCLwYM8wAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1113f94a8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create graphs from algebraic expressions\n",
+    "\n",
+    "from sympy import Symbol, plot\n",
+    "x = Symbol('x')\n",
+    "p = plot(2*x**2 + 2*x + 2)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[-1/2]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solve equations\n",
+    "\n",
+    "from sympy import solve, Symbol\n",
+    "x = Symbol('x')\n",
+    "solve(2*x + 1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Limits\n",
+    "\n",
+    "from sympy import Symbol, Limit, sin\n",
+    "x = Symbol('x')\n",
+    "Limit(sin(x)/x, x, 0).doit()\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAASUAAAAhCAYAAACfgb3VAAAABHNCSVQICAgIfAhkiAAAC7RJREFU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\n",
+      "text/latex": [
+       "$$\\left(\\frac{\\left(2 x + 1\\right) \\cos{\\left (x \\right )}}{\\sin{\\left (x \\right )}} + 2 \\log{\\left (\\sin{\\left (x \\right )} \\right )}\\right) \\sin^{2 x + 1}{\\left (x \\right )}$$"
+      ],
+      "text/plain": [
+       "⎛(2⋅x + 1)⋅cos(x)                ⎞    2⋅x + 1   \n",
+       "⎜──────────────── + 2⋅log(sin(x))⎟⋅sin       (x)\n",
+       "⎝     sin(x)                     ⎠              "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Derivative\n",
+    "\n",
+    "from sympy import Symbol, Derivative, sin, init_printing\n",
+    "x = Symbol('x')\n",
+    "init_printing()\n",
+    "Derivative(sin(x)**(2*x+1), x).doit()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAADQAAAAgCAYAAABdP1tmAAAABHNCSVQICAgIfAhkiAAAAzBJREFU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\nO/lIQVIcZphhhrkgzgGpUJkbjjeg3QAAAABJRU5ErkJggg==\n",
+      "text/latex": [
+       "$$\\frac{2 x^{\\frac{3}{2}}}{3}$$"
+      ],
+      "text/plain": [
+       "   3/2\n",
+       "2⋅x   \n",
+       "──────\n",
+       "  3   "
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Indefinite integral\n",
+    "\n",
+    "from sympy import Symbol, Integral, sqrt, sin, init_printing\n",
+    "x = Symbol('x')\n",
+    "init_printing()\n",
+    "Integral(sqrt(x)).doit()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false,
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAD8AAAAgCAYAAACl36CRAAAABHNCSVQICAgIfAhkiAAAAz1JREFU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\n",
+      "text/latex": [
+       "$$\\frac{4 \\sqrt{2}}{3}$$"
+      ],
+      "text/plain": [
+       "4⋅√2\n",
+       "────\n",
+       " 3  "
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Definite integral\n",
+    "\n",
+    "from sympy import Symbol, Integral, sqrt\n",
+    "x = Symbol('x')\n",
+    "Integral(sqrt(x), (x, 0, 2)).doit()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "\n",
+    "### Python - Making other subjects more lively\n",
+    "\n",
+    "<img align=\"center\" src=\"images/collage1.png\"></img>\n",
+    "\n",
+    "\n",
+    "- matplotlib\n",
+    "\n",
+    "- basemap\n",
+    "\n",
+    "- Interactive Jupyter Notebooks\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "\n",
+    "#### Bringing Science to life\n",
+    "\n",
+    "*Animation of a Projectile motion*\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"400\"\n",
+       "            height=\"300\"\n",
+       "            src=\"https://www.youtube.com/embed/30RlEnsMi5k\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.YouTubeVideo at 0x10450ce80>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import YouTubeVideo\n",
+    "YouTubeVideo(\"8uWRVh58KdQ\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "#### Drawing fractals\n",
+    "\n",
+    "*Interactively drawing a Barnsley Fern*\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "\n",
+    "#### The world is your graph paper\n",
+    "\n",
+    "*Showing places on a digital map*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Great base for the future\n",
+    "\n",
+    "*Statistics and Graphing data* -> *Data Science*\n",
+    "\n",
+    "*Differential Calculus* -> *Machine learning*\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "### Application of differentiation\n",
+    "\n",
+    "Use gradient descent to find a function's minimum value"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "### Predict the college admission score based on high school math score\n",
+    "\n",
+    "Use gradient descent as the optimizer for single variable linear regression model\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "### TODO: digit recognition using Neural networks\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "### Scikitlearn, pandas, scipy, statsmodel\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Book: Doing Math With Python\n",
+    "\n",
+    "<img align=\"center\" src=\"images/dmwp-cover.png\" href=\"https://doingmathwithpython.github.io\"></img>\n",
+    "\n",
+    "Published by No Starch Press, out in 2015.\n",
+    "\n",
+    "Early feedback very encouraging\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "#### Comments"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "\n",
+    "> Saha does an excellent job providing a clear link between Python and upper-level math concepts, and demonstrates how Python can be transformed into a mathematical stage."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "\n",
+    "> This book is highly recommended for the high school or college student and anyone who is looking for a more natural way of programming math and scientific functions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "> As a teacher I highly recommend this book as a way to work with someone in learning both math and programming\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Links\n",
+    "\n",
+    "- [Doing Math with Python](http://nostarch.com/doingmathwithpython)\n",
+    "\n",
+    "- [Doing Math with Python Blog](doingmathwithpython.github.io)\n",
+    "\n",
+    "- [Upcoming O'Reilly Webcast](http://www.oreilly.com/pub/e/3712)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### PyCon Special!\n",
+    "\n",
+    "*Use PYCONMATH code to get 30% off from No Starch Press*\n",
+    "\n",
+    "(Valid from  May 26th - June 8th) \n",
+    "\n",
+    "Book Signing - May 31st - 2.00 PM - No Starch Press booth"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Dialogue\n",
+    "\n",
+    "Questions, Thoughts, comments, discussions? \n",
+    "\n",
+    "Online: @echorand, amitsaha.in@gmail.com\n",
+    "    \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Acknowledgements\n",
+    "\n",
+    "PyCon US Education Summit team for inviting me\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "Thanks to PyCon US for reduced registration rates\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "\n",
+    "Massive thanks to my employer, Freelancer.com for sponsoring my travel and stay \n"
+   ]
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Slideshow",
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  },
+  "widgets": {
+   "state": {},
+   "version": "1.0.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
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+shoulders of giants: http://www.orphancaremovement.org/standing-on-the-shoulder-of-giants/
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+shoulders of giants: http://www.orphancaremovement.org/standing-on-the-shoulder-of-giants/
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@@ -11824,7 +11824,23 @@
 
 <div class="reveal">
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-<section><section>
+<aside class="notes">
+
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Hello everyone! My name is Amit Saha and today I am here to talk to about "Doing Math with Python".</p>
+<p>Thank you for coming to my talk - i know you could have chosen the other talk, so it's good to know that my talk's topic interests you.</p>
+<p>A bit about me - I am a software engineer at Freelancer.com in Sydney Australia.  I am a fairly regular writer for Linux Voice and other Linux magazines. And last, but not the least, I am the author of the book "Doing Math with Python" (not coincidentally titled the same as this talk - haha) published by No Starch Press in 2015.</p>
+<p>There is a link to my blog, GitHub, twitter, etc. so, if you want to learn more about my work or get in touch, those are the means to do so!</p>
+<p>Okay, so all that aside - let's start with the talk!</p>
+
+</div>
+</div>
+</div>
+</aside><section><section>
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@@ -11852,7 +11868,7 @@ <h2 id="About-me">About me<a class="anchor-link" href="#About-me">&#182;</a></h2
 </li>
 <li><p>Author of "Doing Math with Python" (No Starch Press, 2015)</p>
 </li>
-<li><p>Writes for Linux Voice, Linux Journal, etc.</p>
+<li><p>Writes for Linux Voice, Linux Journal, and others.</p>
 </li>
 <li><p><a href="http://echorand.me">Blog</a>, <a href="http://github.com/amitsaha">GitHub</a></p>
 </li>
@@ -11872,61 +11888,98 @@ <h4 id="Contact">Contact<a class="anchor-link" href="#Contact">&#182;</a></h4><u
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-<h3 id="This-talk---a-proposal,-a-hypothesis,-a-statement">This talk - a proposal, a hypothesis, a statement<a class="anchor-link" href="#This-talk---a-proposal,-a-hypothesis,-a-statement">&#182;</a></h3><p><em>Python can lead to a more enriching learning and teaching experience in the classroom</em></p>
-
+<center><font size="12"> Slides: https://doingmathwithpython.github.io/pycon-us-2016/</font> </center>
+<p>
+<p>
+<center><font size="12"> All presentation materials: https://github.com/doingmathwithpython</font></center>
 </div>
 </div>
 </div><aside class="notes">
 
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
 <div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">As</span> <span class="n">I</span> <span class="n">will</span> <span class="n">attempt</span> <span class="n">to</span> <span class="n">describe</span> <span class="ow">in</span> <span class="n">the</span> <span class="nb">next</span> <span class="n">slides</span><span class="p">,</span> <span class="n">Python</span> <span class="ow">is</span> <span class="n">an</span> <span class="n">amazing</span> <span class="n">way</span> <span class="n">to</span> <span class="n">lead</span> <span class="n">to</span> <span class="n">a</span> <span class="n">more</span> <span class="n">fun</span> <span class="n">learning</span> <span class="ow">and</span> <span class="n">teaching</span>
-<span class="n">experience</span><span class="o">.</span>
-
-<span class="n">It</span> <span class="n">can</span> <span class="n">be</span> <span class="n">a</span> <span class="n">basic</span> <span class="n">calculator</span><span class="p">,</span> <span class="n">a</span> <span class="n">fancy</span> <span class="n">calculator</span> <span class="ow">and</span> 
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>So, what am I selling to you today? Not my book (I am, but in a subtle way). I am presenting an idea, a hypothesis or even making a statement - Python can lead to a more enriching learning and teaching experience in the classroom.</p>
+<p>Let me explain where I am coming from. When I think back about when I was learning to program and learning all other subjects in standards 7-10. I think it's true today as well. Programming and other subjects such as Math, Science are taught in a disconnected fashion. Programming seems to be all about finding the sum of a series or generating fibonacci numbers. Make no mistake, these exercises are what builds up the programming logic. Some students get really excited about being able to do these, but a lot of them don't.  It's a lot like not everyone gets interested in solving puzzles - i don't, i never took to them.</p>
+<p>I think I know of a way we could excite more students! Show them how you can write programs to do your homework, or experiment without having to go the science lab or setup elaborate experimental setups. This is my goal for today - in the following slides and notebooks, I will hypothesise on a way of connecting Python programming and other subjects. That will show that programming is a way to get real work done, not something to learn for the sake of it.</p>
+<p>We need some tools to help us on our quest. The Python community has some giant shoulders we can stand upon - Python 3, SymPy and matplotlib.</p>
 
-<span class="n">Math</span><span class="p">,</span> <span class="n">Science</span><span class="p">,</span> <span class="n">Geography</span><span class="o">..</span>
+</div>
+</div>
+</div>
+</aside></section></section><section><section>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="This-talk---a-proposal,-a-hypothesis,-a-statement">This talk - a proposal, a hypothesis, a statement<a class="anchor-link" href="#This-talk---a-proposal,-a-hypothesis,-a-statement">&#182;</a></h3><p>What? <em>Python can lead to a more enriching learning and teaching experience in the classroom</em></p>
+<p>How? <em>Next slides</em></p>
 
-<span class="n">Tools</span> <span class="n">that</span> <span class="n">will</span> <span class="n">help</span> <span class="n">us</span> <span class="ow">in</span> <span class="n">that</span> <span class="n">quest</span> <span class="n">are</span><span class="p">:</span>
-</pre></div>
+</div>
+</div>
+</div></section><section>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Tools-(or,-Giant-shoulders-we-will-stand-on)">Tools (or, Giant shoulders we will stand on)<a class="anchor-link" href="#Tools-(or,-Giant-shoulders-we-will-stand-on)">&#182;</a></h3><p><img align="center" src="images/giant_shoulders_collage.jpg"></img></p>
+<p><em>Python 3</em>, <em>SymPy</em>, <em>matplotlib</em></p>
+<p>*Individual logos are copyright of the respective projects. <a href="http://www.orphancaremovement.org/standing-on-the-shoulder-of-giants/">Source</a> of the "giant shoulders" image.</p>
 
 </div>
 </div>
+</div><aside class="notes">
+
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
 </div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Whose calculator looks like this?</p>
+<p>Who uses Python as a calculator? Raise of hands please!</p>
+<p>I do! Specifically, I use Python 3 because of 1/2=0 messes up my monthly expenditure calculation.</p>
+<p>Besides the usual addition and subtraction, we have of course the math module and more recently the statistics module which makes Python a worthy scientific calculator.</p>
+<p>But then, there's more! You are not limited to the functions from those libraries, you can write your own custom functions and make them available whenever you start your Python interpreter. How?</p>
+<p>Use PYTHONSTARTUP!</p>
 
 </div>
-</aside></section><section>
+</div>
+</div>
+</aside></section></section><section><section>
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-<h3 id="(Main)-Tools">(Main) Tools<a class="anchor-link" href="#(Main)-Tools">&#182;</a></h3><p><img align="center" src="collage/logo_collage.png"></img></p>
+<h3 id="Python---a-scientific-calculator">Python - a scientific calculator<a class="anchor-link" href="#Python---a-scientific-calculator">&#182;</a></h3><p>Whose calculator looks like this?</p>
+<div class="highlight"><pre><span></span><span class="o">&gt;&gt;&gt;</span> <span class="p">(</span><span class="mi">131</span> <span class="o">+</span> <span class="mf">21.5</span> <span class="o">+</span> <span class="mf">100.2</span> <span class="o">+</span> <span class="mf">88.7</span> <span class="o">+</span> <span class="mf">99.5</span> <span class="o">+</span> <span class="mf">100.5</span> <span class="o">+</span> <span class="mf">200.5</span><span class="p">)</span><span class="o">/</span><span class="mi">4</span>
+<span class="mf">185.475</span>
+</pre></div>
+<p><em>Python 3 is my favorite calculator (not Python 2 because 1/2 = 0)</em></p>
+<p>Beyond basic operations:</p>
 
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-</div></section></section><section><section>
+</div><div class="fragment">
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-<h3 id="Python---a-scientific-calculator">Python - a scientific calculator<a class="anchor-link" href="#Python---a-scientific-calculator">&#182;</a></h3><ul>
-<li>Python 3 is my favorite calculator (not Python 2 because 1/2 = 0)</li>
-</ul>
-<ul>
-<li><code>fabs()</code>, <code>abs()</code>, <code>sin()</code>, <code>cos()</code>, <code>gcd()</code>, <code>log()</code> (See <a href="https://docs.python.org/3/library/math.html">math</a>)</li>
-</ul>
 <ul>
-<li>Descriptive statistics (See <a href="https://docs.python.org/3/library/statistics.html#module-statistics">statistics</a>)</li>
+<li><p><code>fabs()</code>, <code>abs()</code>, <code>sin()</code>, <code>cos()</code>, <code>gcd()</code>, <code>log()</code> and more (See <a href="https://docs.python.org/3/library/math.html">math</a>)</p>
+</li>
+<li><p>Descriptive statistics (See <a href="https://docs.python.org/3/library/statistics.html#module-statistics">statistics</a>)</p>
+</li>
 </ul>
 
 </div>
 </div>
-</div></section><section>
+</div></div></section><section>
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@@ -11938,8 +11991,8 @@ <h3 id="Python---a-scientific-calculator">Python - a scientific calculator<a cla
 <li><p>Use PYTHONSTARTUP to extend the battery of readily available mathematical functions</p>
 </li>
 </ul>
-
-<pre><code>$ PYTHONSTARTUP=~/work/dmwp/pycon-us-2016/startup_math.py idle3 -s</code></pre>
+<div class="highlight"><pre><span></span><span class="err">$</span> <span class="n">PYTHONSTARTUP</span><span class="o">=~/</span><span class="n">work</span><span class="o">/</span><span class="n">dmwp</span><span class="o">/</span><span class="n">pycon</span><span class="o">-</span><span class="n">us</span><span class="o">-</span><span class="mi">2016</span><span class="o">/</span><span class="n">startup_math</span><span class="o">.</span><span class="n">py</span> <span class="n">idle3</span> <span class="o">-</span><span class="n">s</span>
+</pre></div>
 
 </div>
 </div>
@@ -11949,64 +12002,64 @@ <h3 id="Python---a-scientific-calculator">Python - a scientific calculator<a cla
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-<h3 id="Unit-conversion-functions">Unit conversion functions<a class="anchor-link" href="#Unit-conversion-functions">&#182;</a></h3>
-<pre><code>&gt;&gt;&gt; unit_conversion()
-1. Kilometers to Miles
-2. Miles to Kilometers
-3. Kilograms to Pounds
-4. Pounds to Kilograms
-5. Celsius to Fahrenheit
-6. Fahrenheit to Celsius
-Which conversion would you like to do? 6
-Enter temperature in fahrenheit: 98
-Temperature in celsius: 36.66666666666667
-&gt;&gt;&gt;</code></pre>
+<h3 id="Unit-conversion-functions">Unit conversion functions<a class="anchor-link" href="#Unit-conversion-functions">&#182;</a></h3><div class="highlight"><pre><span></span><span class="o">&gt;&gt;&gt;</span> <span class="n">unit_conversion</span><span class="p">()</span>
+<span class="mf">1.</span> <span class="n">Kilometers</span> <span class="n">to</span> <span class="n">Miles</span>
+<span class="mf">2.</span> <span class="n">Miles</span> <span class="n">to</span> <span class="n">Kilometers</span>
+<span class="mf">3.</span> <span class="n">Kilograms</span> <span class="n">to</span> <span class="n">Pounds</span>
+<span class="mf">4.</span> <span class="n">Pounds</span> <span class="n">to</span> <span class="n">Kilograms</span>
+<span class="mf">5.</span> <span class="n">Celsius</span> <span class="n">to</span> <span class="n">Fahrenheit</span>
+<span class="mf">6.</span> <span class="n">Fahrenheit</span> <span class="n">to</span> <span class="n">Celsius</span>
+<span class="n">Which</span> <span class="n">conversion</span> <span class="n">would</span> <span class="n">you</span> <span class="n">like</span> <span class="n">to</span> <span class="n">do</span><span class="err">?</span> <span class="mi">6</span>
+<span class="n">Enter</span> <span class="n">temperature</span> <span class="ow">in</span> <span class="n">fahrenheit</span><span class="p">:</span> <span class="mi">98</span>
+<span class="n">Temperature</span> <span class="ow">in</span> <span class="n">celsius</span><span class="p">:</span> <span class="mf">36.66666666666667</span>
+<span class="o">&gt;&gt;&gt;</span>
+</pre></div>
 
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 </div>
-</div><div class="fragment">
+</div></section><section>
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-<h3 id="Finding-linear-correlation">Finding linear correlation<a class="anchor-link" href="#Finding-linear-correlation">&#182;</a></h3>
-<pre><code>&gt;&gt;&gt; 
-&gt;&gt;&gt; x = [1, 2, 3, 4]
-&gt;&gt;&gt; y = [2, 4, 6.1, 7.9]
-&gt;&gt;&gt; find_corr_x_y(x, y)
-0.9995411791453812</code></pre>
+<h3 id="Finding-linear-correlation">Finding linear correlation<a class="anchor-link" href="#Finding-linear-correlation">&#182;</a></h3><div class="highlight"><pre><span></span><span class="o">&gt;&gt;&gt;</span> 
+<span class="o">&gt;&gt;&gt;</span> <span class="n">x</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]</span>
+<span class="o">&gt;&gt;&gt;</span> <span class="n">y</span> <span class="o">=</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mf">6.1</span><span class="p">,</span> <span class="mf">7.9</span><span class="p">]</span>
+<span class="o">&gt;&gt;&gt;</span> <span class="n">find_corr_x_y</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+<span class="mf">0.9995411791453812</span>
+</pre></div>
 
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-</div></div></section></section><section><section>
+</div><aside class="notes">
+
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-<h3 id="Python---a-really-fancy-calculator">Python - a really fancy calculator<a class="anchor-link" href="#Python---a-really-fancy-calculator">&#182;</a></h3><p>SymPy - a pure Python symbolic math library</p>
-<p><em>from sympy import awesomeness</em>  - don't try that :)</p>
+<p>So, that was Python and it's standard libraries. When you bring in third party libraries to the mix, Python becomes a seriously fancy calculator.</p>
+<p>Who has heard about SymPy?</p>
+<p>You can give it algebraic expressions to a function and a graph will be created for you.</p>
+<p>You can give an equation and out comes the solutions for that equation.</p>
+<p>We can even solve calculus problems.</p>
 
 </div>
-</div>
-</div><aside class="notes">
-
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">When</span> <span class="n">you</span> <span class="n">bring</span> <span class="ow">in</span> <span class="n">SymPy</span> <span class="n">to</span> <span class="n">the</span> <span class="n">picture</span><span class="p">,</span> <span class="n">things</span> <span class="n">really</span>  <span class="n">get</span> <span class="n">awesome</span><span class="o">.</span> <span class="n">You</span> <span class="n">are</span> <span class="n">suddenly</span> <span class="n">writing</span> <span class="n">computer</span>
-<span class="n">programs</span> <span class="n">which</span> <span class="n">are</span> <span class="n">capable</span> <span class="n">of</span> <span class="n">speaking</span> <span class="n">algebra</span><span class="o">.</span> <span class="n">You</span> <span class="n">are</span> <span class="n">no</span> <span class="n">more</span> <span class="n">limited</span> <span class="n">to</span> <span class="n">numbers</span><span class="o">.</span>
-</pre></div>
-
 </div>
 </div>
+</aside></section></section><section><section>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
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+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Python---a-really-fancy-calculator">Python - a really fancy calculator<a class="anchor-link" href="#Python---a-really-fancy-calculator">&#182;</a></h3><p>SymPy - a pure Python symbolic math library</p>
+<p><em>from sympy import awesomeness</em>  - don't try that :)</p>
 
 </div>
-</aside></section><section>
+</div>
+</div></section><section>
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@@ -12379,13 +12432,50 @@ <h3 id="Python---a-really-fancy-calculator">Python - a really fancy calculator<a
 </div>
 </div>
 
-</div></div></section></section><section><section>
+</div><aside class="notes">
+
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>I will pause for a moment now. In the first two slides, we have seen how Python can be a super awesome calculator. What does that buy us? We have now been able to show that you can make computer programs literally do your homework. Write a program to do your work once and you will never have to make those lengthy calculations yourselves. Can we use Python to do more?</p>
+<p>Let's continue.</p>
+
+</div>
+</div>
+</div>
+</aside></div></section></section><section><section>
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-<h3 id="Python---Making-other-subjects-more-lively">Python - Making other subjects more lively<a class="anchor-link" href="#Python---Making-other-subjects-more-lively">&#182;</a></h3><p><img align="center" src="collage/collage1.png"></img></p>
+<center><h1>Can we do more than write smart calculators?</h1></center>
+</div>
+</div>
+</div><aside class="notes">
+
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Python can be more than a super powerful calculator. We can use it to enhance the learning experience of other subjects. Next, I have three examples including a demo. First up, a video of a projectile motion. This program uses matplotlib's animation API to create a basic animation of a projectile motion - a fairly common subject introduced in introductory Physics. The program which is linked asks for the angle of projection and speed and then draws the trajectory of the projectile. Just by running the program multiple times, we can see how the trajectory changes. We don't have to go outside and start throwing balls..</p>
+<p>Next, we will put Jupyter Notebook's interactive widgets to good effect by drawing a Barnsley Fern. Let's see how the demo goes.</p>
+<p>Next, with the help of basemap, we can draw places on a world map like we would draw points on a graph paper.</p>
+<p>I know I would be excited if someone was showing me all these cool things when I was learning these things!</p>
+
+</div>
+</div>
+</div>
+</aside></section></section><section><section>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Python---Making-other-subjects-more-lively">Python - Making other subjects more lively<a class="anchor-link" href="#Python---Making-other-subjects-more-lively">&#182;</a></h3><p><img align="center" src="images/collage1.png"></img></p>
 <ul>
 <li><p>matplotlib</p>
 </li>
@@ -12403,17 +12493,55 @@ <h3 id="Python---Making-other-subjects-more-lively">Python - Making other subjec
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-<h4 id="Bringing-Science-to-life">Bringing Science to life<a class="anchor-link" href="#Bringing-Science-to-life">&#182;</a></h4><p><em>Animation of a Projectile motion</em></p>
+<h4 id="Bringing-Science-to-life">Bringing Science to life<a class="anchor-link" href="#Bringing-Science-to-life">&#182;</a></h4><p><em>Animation of a Projectile motion</em> <a href="https://github.com/doingmathwithpython/pycon-us-2016/blob/master/py-files/projectile_animation.py">(Python Source)</a></p>
 
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+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[3]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="k">import</span> <span class="n">YouTubeVideo</span>
+<span class="n">YouTubeVideo</span><span class="p">(</span><span class="s2">&quot;8uWRVh58KdQ&quot;</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt output_prompt">Out[3]:</div>
+
+<div class="output_html rendered_html output_subarea output_execute_result">
+
+        <iframe
+            width="400"
+            height="300"
+            src="https://www.youtube.com/embed/8uWRVh58KdQ"
+            frameborder="0"
+            allowfullscreen
+        ></iframe>
+        
+</div>
+
+</div>
+
+</div>
+</div>
+
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-<h4 id="Drawing-fractals">Drawing fractals<a class="anchor-link" href="#Drawing-fractals">&#182;</a></h4><p><em>Interactively drawing a Barnsley Fern</em></p>
+<h4 id="Exploring-Fractals-in-Nature">Exploring Fractals in Nature<a class="anchor-link" href="#Exploring-Fractals-in-Nature">&#182;</a></h4><p><em>Interactively drawing a Barnsley Fern</em> <a href="https://github.com/doingmathwithpython/pycon-us-2016/blob/master/notebooks/Interactive%20Barnsley%20Fern.ipynb">(Notebook)</a></p>
+<p><img align="center" src="images/fern.jpg" width="400" ></img></p>
 
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@@ -12423,28 +12551,42 @@ <h4 id="Drawing-fractals">Drawing fractals<a class="anchor-link" href="#Drawing-
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-<h4 id="The-world-is-your-graph-paper">The world is your graph paper<a class="anchor-link" href="#The-world-is-your-graph-paper">&#182;</a></h4><p><em>Showing places on a digital map</em></p>
+<h4 id="The-world-is-your-graph-paper">The world is your graph paper<a class="anchor-link" href="#The-world-is-your-graph-paper">&#182;</a></h4><p><em>Showing places on a digital map</em> <a href="https://github.com/doingmathwithpython/pycon-us-2016/blob/master/notebooks/Maps%20using%20Basemap%20-%20demo.ipynb">(Notebook)</a></p>
 
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-</div></section></section><section><section>
+</div><aside class="notes">
+
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-<h3 id="Great-base-for-the-future">Great base for the future<a class="anchor-link" href="#Great-base-for-the-future">&#182;</a></h3><p><em>Statistics and Graphing data</em> -&gt; <em>Data Science</em></p>
-<p><em>Differential Calculus</em> -&gt; <em>Machine learning</em></p>
+<p>Next, I would like to talk about my book "Doing Math with Python". My idea was attractive enough to get it published by No Starch Press which makes me hope that I am probably onto something.</p>
+<p>Has anybody read my book? What do you think of it? You have read it and came to my talk? I am feeling better :)</p>
+<p>I discuss all of the topics I discuss today in my talk. In addition, I discuss sets, probability and random numbers and descriptive statistics.</p>
+<p>It's being translated into several non-English languages.</p>
+<p>The reviews/feedback so far has been really positive. I don't have any first hand involvement in teaching, so it's very appreciative of people to share their viewpoints with me.</p>
 
 </div>
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-</div></section><section>
+</div>
+</aside></section></section><section><section>
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-<h3 id="Application-of-differentiation">Application of differentiation<a class="anchor-link" href="#Application-of-differentiation">&#182;</a></h3><p>Use gradient descent to find a function's minimum value</p>
+<h3 id="Book:-Doing-Math-With-Python">Book: Doing Math With Python<a class="anchor-link" href="#Book:-Doing-Math-With-Python">&#182;</a></h3><p><img align="center" src="images/dmwp-cover.png" href="https://doingmathwithpython.github.io"></img></p>
+<p>Overview</p>
+<ul>
+<li><p>All of what I have discussed so far</p>
+</li>
+<li><p>In addition: Descriptive statistics, Sets and Probability, Random numbers</p>
+</li>
+</ul>
+<p>Published by <a href="https://www.nostarch.com/doingmathwithpython">No Starch Press</a> in August, 2015.</p>
+<p><em>Upcoming/In-progress translations</em>: Simplified Chinese, Japanese, French and Korean.</p>
 
 </div>
 </div>
@@ -12454,102 +12596,119 @@ <h3 id="Application-of-differentiation">Application of differentiation<a class="
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-<h3 id="Predict-the-college-admission-score-based-on-high-school-math-score">Predict the college admission score based on high school math score<a class="anchor-link" href="#Predict-the-college-admission-score-based-on-high-school-math-score">&#182;</a></h3><p>Use gradient descent as the optimizer for single variable linear regression model</p>
+<h4 id="Comments">Comments<a class="anchor-link" href="#Comments">&#182;</a></h4><blockquote><p>Saha does an excellent job providing a clear link between Python and upper-level math concepts, and demonstrates how Python can be transformed into a mathematical stage. This book deserves a spot on every geometry teacher’s bookshelf.</p>
+</blockquote>
+<p><a href="http://www.slj.com/2016/05/collection-development/read-watch-alikes/coding-lets-begin/#_">School Library Journal</a></p>
 
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 </div>
 </div></section><section>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
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-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">### TODO: digit recognition using Neural networks</span>
-</pre></div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<blockquote><p>Outstanding guide to using Python to do maths. Working back through my undergrad maths using Python.</p>
+</blockquote>
 
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-</div>
-
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-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
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-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">### Scikitlearn, pandas, scipy, statsmodel</span>
-</pre></div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<blockquote><p>Saha does an excellent job providing a clear link between Python and upper-level math concepts, and demonstrates how Python can be transformed into a mathematical stage.</p>
+</blockquote>
 
 </div>
 </div>
+</div><div class="fragment">
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
 </div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<blockquote><p>This book is highly recommended for the high school or college student and anyone who is looking for a more natural way of programming math and scientific functions</p>
+</blockquote>
 
-</div></section></section><section><section>
+</div>
+</div>
+</div></div><div class="fragment">
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-<h3 id="Book:-Doing-Math-With-Python">Book: Doing Math With Python<a class="anchor-link" href="#Book:-Doing-Math-With-Python">&#182;</a></h3><p><img align="center" src="dmwp-cover.png" href="https://doingmathwithpython.github.io"></img></p>
-<p>Published by No Starch Press, out in 2015.</p>
-<p>Early feedback very encouraging</p>
+<blockquote><p>As a teacher I highly recommend this book as a way to work with someone in learning both math and programming</p>
+</blockquote>
 
 </div>
 </div>
-</div></section><section>
+</div><aside class="notes">
+
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-<h4 id="Comments">Comments<a class="anchor-link" href="#Comments">&#182;</a></h4>
+<p>Okay, so that's great. We have successfully used Python to make the learning experience of young learners more fun and immediately applicable. Can we derive more benefit from doing that? Like something for the future? We all love doing things for the future, don't we?</p>
+<p>I think yes, i think if we teach young learners the things we have discussed today, it is a great base for someone wanting to go into data science or machine learning.</p>
+<p>Statistics and visualising data are two very key factors of data science.</p>
+<p>Differential calculus and specifically the gradient descent method is a simple but useful optimization method used in Machine Learning. Let's see a demo of using gradient descent to find the minimum value of a function.</p>
+<p>Now, let's apply gradient descent as an optimizer in a Linear Regression problem.</p>
+
 </div>
 </div>
-</div><div class="fragment">
+</div>
+</aside></div></section></section><section><section>
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-<blockquote><p>Saha does an excellent job providing a clear link between Python and upper-level math concepts, and demonstrates how Python can be transformed into a mathematical stage.</p>
-</blockquote>
+<h3 id="Great-base-for-the-future">Great base for the future<a class="anchor-link" href="#Great-base-for-the-future">&#182;</a></h3><p><em>Statistics and Graphing data</em> -&gt; <em>Data Science</em></p>
+<p><em>Differential Calculus</em> -&gt; <em>Machine learning</em></p>
 
 </div>
 </div>
-</div></div><div class="fragment">
+</div></section><section>
 <div class="cell border-box-sizing text_cell rendered">
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 </div>
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-<blockquote><p>This book is highly recommended for the high school or college student and anyone who is looking for a more natural way of programming math and scientific functions</p>
-</blockquote>
+<h3 id="Application-of-differentiation">Application of differentiation<a class="anchor-link" href="#Application-of-differentiation">&#182;</a></h3><p>Use gradient descent to find a function's minimum value <a href="https://github.com/doingmathwithpython/pycon-us-2016/blob/master/notebooks/Gradient%20Descent.ipynb">(Notebook)</a></p>
 
 </div>
 </div>
-</div></div><div class="fragment">
+</div></section><section>
 <div class="cell border-box-sizing text_cell rendered">
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-<blockquote><p>As a teacher I highly recommend this book as a way to work with someone in learning both math and programming</p>
-</blockquote>
+<h3 id="Predict-the-college-admission-score-based-on-high-school-math-score">Predict the college admission score based on high school math score<a class="anchor-link" href="#Predict-the-college-admission-score-based-on-high-school-math-score">&#182;</a></h3><p>Use gradient descent as the optimizer for single variable linear regression model <a href="https://github.com/doingmathwithpython/pycon-us-2016/blob/master/notebooks/Simple%20Linear%20Regression.ipynb">(Notebook)</a></p>
 
 </div>
 </div>
-</div></div></section></section><section><section>
+</div></section><section>
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-<h3 id="Links">Links<a class="anchor-link" href="#Links">&#182;</a></h3><ul>
-<li><p><a href="http://nostarch.com/doingmathwithpython">Doing Math with Python</a></p>
+<h3 id="Advanced-libraries">Advanced libraries<a class="anchor-link" href="#Advanced-libraries">&#182;</a></h3><ul>
+<li><p><a href="https://scipy.org">scipy</a></p>
 </li>
-<li><p><a href="doingmathwithpython.github.io">Doing Math with Python Blog</a></p>
+<li><p><a href="http://www.numpy.org/">numpy</a></p>
 </li>
-<li><p><a href="http://www.oreilly.com/pub/e/3712">Upcoming O'Reilly Webcast</a></p>
+<li><p><a href="http://scikit-learn.org/stable/">scikit-learn</a></p>
+</li>
+<li><p><a href="http://pandas.pydata.org/">pandas</a></p>
+</li>
+<li><p><a href="http://statsmodels.sourceforge.net/">Statsmodels</a></p>
 </li>
 </ul>
 
@@ -12561,9 +12720,13 @@ <h3 id="Links">Links<a class="anchor-link" href="#Links">&#182;</a></h3><ul>
 </div>
 <div class="inner_cell">
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-<h3 id="PyCon-Special!">PyCon Special!<a class="anchor-link" href="#PyCon-Special!">&#182;</a></h3><p><em>Use PYCONMATH code to get 30% off from No Starch Press</em></p>
-<p>(Valid from  May 26th - June 8th)</p>
-<p>Book Signing - May 31st - 2.00 PM - No Starch Press booth</p>
+<h3 id="Dialogue">Dialogue<a class="anchor-link" href="#Dialogue">&#182;</a></h3><p>Questions, Thoughts, comments, discussions?</p>
+<h4 id="Online">Online<a class="anchor-link" href="#Online">&#182;</a></h4><ul>
+<li><p>Twitter: @echorand</p>
+</li>
+<li><p>Email: amitsaha.in@gmail.com</p>
+</li>
+</ul>
 
 </div>
 </div>
@@ -12573,8 +12736,10 @@ <h3 id="PyCon-Special!">PyCon Special!<a class="anchor-link" href="#PyCon-Specia
 </div>
 <div class="inner_cell">
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-<h3 id="Dialogue">Dialogue<a class="anchor-link" href="#Dialogue">&#182;</a></h3><p>Questions, Thoughts, comments, discussions?</p>
-<p>Online: @echorand, amitsaha.in@gmail.com</p>
+<h3 id="PyCon-Special!">PyCon Special!<a class="anchor-link" href="#PyCon-Special!">&#182;</a></h3><p><em>Use PYCONMATH code to get 30% off "Doing Math with Python" from <a href="https://www.nostarch.com/doingmathwithpython">No Starch Press</a></em></p>
+<p><img align="center" src="images/dmwp-cover.png" href="https://doingmathwithpython.github.io"></img></p>
+<p>(Valid from  May 26th - June 8th)</p>
+<p>Book Signing - May 31st - 2.00 PM - No Starch Press booth</p>
 
 </div>
 </div>
@@ -12608,7 +12773,26 @@ <h3 id="Acknowledgements">Acknowledgements<a class="anchor-link" href="#Acknowle
 
 </div>
 </div>
-</div></div></section></section>
+</div></div></section></section><section><section>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Links">Links<a class="anchor-link" href="#Links">&#182;</a></h3><ul>
+<li><p><a href="http://www.oreilly.com/pub/e/3712">Upcoming O'Reilly Webcast</a></p>
+</li>
+<li><p><a href="https://nostarch.com/doingmathwithpython">Doing Math with Python</a></p>
+</li>
+<li><p><a href="https://doingmathwithpython.github.io">Doing Math with Python Blog</a></p>
+</li>
+<li><p><a href="https://github.com/doingmathwithpython">Doing Math with Python on GitHub</a></p>
+</li>
+</ul>
+
+</div>
+</div>
+</div></section></section>
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diff --git a/slides.html b/lightning.html
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-<title>slides slides</title>
+<title>lightning-talk-slides slides</title>
 
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@@ -11835,7 +11835,7 @@ <h1 id="-Doing-Math-with-Python-"><center> Doing Math with Python </center><a cl
 
 <p> <b>Amit Saha</b>
 
-<p>May 29, PyCon US 2016 Education Summit
+<p>May 30, PyCon US 2016
 
 <p>Portland, Oregon
 </center>
@@ -11852,7 +11852,7 @@ <h2 id="About-me">About me<a class="anchor-link" href="#About-me">&#182;</a></h2
 </li>
 <li><p>Author of "Doing Math with Python" (No Starch Press, 2015)</p>
 </li>
-<li><p>Writes for Linux Voice, Linux Journal, etc.</p>
+<li><p>Writes for Linux Voice, Linux Journal, and others.</p>
 </li>
 <li><p><a href="http://echorand.me">Blog</a>, <a href="http://github.com/amitsaha">GitHub</a></p>
 </li>
@@ -11872,61 +11872,67 @@ <h4 id="Contact">Contact<a class="anchor-link" href="#Contact">&#182;</a></h4><u
 </div>
 <div class="inner_cell">
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-<h3 id="This-talk---a-proposal,-a-hypothesis,-a-statement">This talk - a proposal, a hypothesis, a statement<a class="anchor-link" href="#This-talk---a-proposal,-a-hypothesis,-a-statement">&#182;</a></h3><p><em>Python can lead to a more enriching learning and teaching experience in the classroom</em></p>
+<h3 id="Book:-Doing-Math-with-Python">Book: Doing Math with Python<a class="anchor-link" href="#Book:-Doing-Math-with-Python">&#182;</a></h3><p><em>Use PYCONMATH code to get 30% off "Doing Math with Python" from <a href="https://www.nostarch.com/doingmathwithpython">No Starch Press</a></em></p>
+<p><img align="center" src="images/dmwp-cover.png" href="https://doingmathwithpython.github.io"></img></p>
+<p>(Valid from  May 26th - June 8th)</p>
+<p>Book Signing - May 31st - 2.00 PM - No Starch Press booth</p>
 
 </div>
 </div>
-</div><aside class="notes">
-
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div>
+</div></section></section><section><section>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
 <div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">As</span> <span class="n">I</span> <span class="n">will</span> <span class="n">attempt</span> <span class="n">to</span> <span class="n">describe</span> <span class="ow">in</span> <span class="n">the</span> <span class="nb">next</span> <span class="n">slides</span><span class="p">,</span> <span class="n">Python</span> <span class="ow">is</span> <span class="n">an</span> <span class="n">amazing</span> <span class="n">way</span> <span class="n">to</span> <span class="n">lead</span> <span class="n">to</span> <span class="n">a</span> <span class="n">more</span> <span class="n">fun</span> <span class="n">learning</span> <span class="ow">and</span> <span class="n">teaching</span>
-<span class="n">experience</span><span class="o">.</span>
-
-<span class="n">It</span> <span class="n">can</span> <span class="n">be</span> <span class="n">a</span> <span class="n">basic</span> <span class="n">calculator</span><span class="p">,</span> <span class="n">a</span> <span class="n">fancy</span> <span class="n">calculator</span> <span class="ow">and</span> 
-
-<span class="n">Math</span><span class="p">,</span> <span class="n">Science</span><span class="p">,</span> <span class="n">Geography</span><span class="o">..</span>
-
-<span class="n">Tools</span> <span class="n">that</span> <span class="n">will</span> <span class="n">help</span> <span class="n">us</span> <span class="ow">in</span> <span class="n">that</span> <span class="n">quest</span> <span class="n">are</span><span class="p">:</span>
-</pre></div>
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="What?">What?<a class="anchor-link" href="#What?">&#182;</a></h3><p><em>Python can lead to a more enriching learning and teaching experience in the classroom</em></p>
+<p>How? <em>Next slides</em></p>
 
 </div>
 </div>
+</div></section><section>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
 </div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Tools-(or,-Giant-shoulders-we-will-stand-on)">Tools (or, Giant shoulders we will stand on)<a class="anchor-link" href="#Tools-(or,-Giant-shoulders-we-will-stand-on)">&#182;</a></h3><p><img align="center" src="images/giant_shoulders_collage.jpg"></img></p>
+<p><em>Python 3</em>, <em>SymPy</em>, <em>matplotlib</em></p>
+<p>*Individual logos are copyright of the respective projects. <a href="http://www.orphancaremovement.org/standing-on-the-shoulder-of-giants/">Source</a> of the "giant shoulders" image.</p>
 
 </div>
-</aside></section><section>
+</div>
+</div></section></section><section><section>
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-<h3 id="(Main)-Tools">(Main) Tools<a class="anchor-link" href="#(Main)-Tools">&#182;</a></h3><p><img align="center" src="collage/logo_collage.png"></img></p>
+<h3 id="Python---a-scientific-calculator">Python - a scientific calculator<a class="anchor-link" href="#Python---a-scientific-calculator">&#182;</a></h3><p>Whose calculator looks like this?</p>
+<div class="highlight"><pre><span></span><span class="o">&gt;&gt;&gt;</span> <span class="p">(</span><span class="mi">131</span> <span class="o">+</span> <span class="mf">21.5</span> <span class="o">+</span> <span class="mf">100.2</span> <span class="o">+</span> <span class="mf">88.7</span> <span class="o">+</span> <span class="mf">99.5</span> <span class="o">+</span> <span class="mf">100.5</span> <span class="o">+</span> <span class="mf">200.5</span><span class="p">)</span><span class="o">/</span><span class="mi">4</span>
+<span class="mf">185.475</span>
+</pre></div>
+<p><em>Python 3 is my favorite calculator (not Python 2 because 1/2 = 0)</em></p>
+<p>Beyond basic operations:</p>
 
 </div>
 </div>
-</div></section></section><section><section>
+</div><div class="fragment">
 <div class="cell border-box-sizing text_cell rendered">
 <div class="prompt input_prompt">
 </div>
 <div class="inner_cell">
 <div class="text_cell_render border-box-sizing rendered_html">
-<h3 id="Python---a-scientific-calculator">Python - a scientific calculator<a class="anchor-link" href="#Python---a-scientific-calculator">&#182;</a></h3><ul>
-<li>Python 3 is my favorite calculator (not Python 2 because 1/2 = 0)</li>
-</ul>
 <ul>
-<li><code>fabs()</code>, <code>abs()</code>, <code>sin()</code>, <code>cos()</code>, <code>gcd()</code>, <code>log()</code> (See <a href="https://docs.python.org/3/library/math.html">math</a>)</li>
-</ul>
-<ul>
-<li>Descriptive statistics (See <a href="https://docs.python.org/3/library/statistics.html#module-statistics">statistics</a>)</li>
+<li><p><code>fabs()</code>, <code>abs()</code>, <code>sin()</code>, <code>cos()</code>, <code>gcd()</code>, <code>log()</code> and more (See <a href="https://docs.python.org/3/library/math.html">math</a>)</p>
+</li>
+<li><p>Descriptive statistics (See <a href="https://docs.python.org/3/library/statistics.html#module-statistics">statistics</a>)</p>
+</li>
 </ul>
 
 </div>
 </div>
-</div></section><section>
+</div></div></section><section>
 <div class="cell border-box-sizing text_cell rendered">
 <div class="prompt input_prompt">
 </div>
@@ -11938,8 +11944,8 @@ <h3 id="Python---a-scientific-calculator">Python - a scientific calculator<a cla
 <li><p>Use PYTHONSTARTUP to extend the battery of readily available mathematical functions</p>
 </li>
 </ul>
-
-<pre><code>$ PYTHONSTARTUP=~/work/dmwp/pycon-us-2016/startup_math.py idle3 -s</code></pre>
+<div class="highlight"><pre><span></span><span class="err">$</span> <span class="n">PYTHONSTARTUP</span><span class="o">=~/</span><span class="n">work</span><span class="o">/</span><span class="n">dmwp</span><span class="o">/</span><span class="n">pycon</span><span class="o">-</span><span class="n">us</span><span class="o">-</span><span class="mi">2016</span><span class="o">/</span><span class="n">startup_math</span><span class="o">.</span><span class="n">py</span> <span class="n">idle3</span> <span class="o">-</span><span class="n">s</span>
+</pre></div>
 
 </div>
 </div>
@@ -11949,37 +11955,37 @@ <h3 id="Python---a-scientific-calculator">Python - a scientific calculator<a cla
 </div>
 <div class="inner_cell">
 <div class="text_cell_render border-box-sizing rendered_html">
-<h3 id="Unit-conversion-functions">Unit conversion functions<a class="anchor-link" href="#Unit-conversion-functions">&#182;</a></h3>
-<pre><code>&gt;&gt;&gt; unit_conversion()
-1. Kilometers to Miles
-2. Miles to Kilometers
-3. Kilograms to Pounds
-4. Pounds to Kilograms
-5. Celsius to Fahrenheit
-6. Fahrenheit to Celsius
-Which conversion would you like to do? 6
-Enter temperature in fahrenheit: 98
-Temperature in celsius: 36.66666666666667
-&gt;&gt;&gt;</code></pre>
+<h3 id="Unit-conversion-functions">Unit conversion functions<a class="anchor-link" href="#Unit-conversion-functions">&#182;</a></h3><div class="highlight"><pre><span></span><span class="o">&gt;&gt;&gt;</span> <span class="n">unit_conversion</span><span class="p">()</span>
+<span class="mf">1.</span> <span class="n">Kilometers</span> <span class="n">to</span> <span class="n">Miles</span>
+<span class="mf">2.</span> <span class="n">Miles</span> <span class="n">to</span> <span class="n">Kilometers</span>
+<span class="mf">3.</span> <span class="n">Kilograms</span> <span class="n">to</span> <span class="n">Pounds</span>
+<span class="mf">4.</span> <span class="n">Pounds</span> <span class="n">to</span> <span class="n">Kilograms</span>
+<span class="mf">5.</span> <span class="n">Celsius</span> <span class="n">to</span> <span class="n">Fahrenheit</span>
+<span class="mf">6.</span> <span class="n">Fahrenheit</span> <span class="n">to</span> <span class="n">Celsius</span>
+<span class="n">Which</span> <span class="n">conversion</span> <span class="n">would</span> <span class="n">you</span> <span class="n">like</span> <span class="n">to</span> <span class="n">do</span><span class="err">?</span> <span class="mi">6</span>
+<span class="n">Enter</span> <span class="n">temperature</span> <span class="ow">in</span> <span class="n">fahrenheit</span><span class="p">:</span> <span class="mi">98</span>
+<span class="n">Temperature</span> <span class="ow">in</span> <span class="n">celsius</span><span class="p">:</span> <span class="mf">36.66666666666667</span>
+<span class="o">&gt;&gt;&gt;</span>
+</pre></div>
 
 </div>
 </div>
-</div><div class="fragment">
+</div></section><section>
 <div class="cell border-box-sizing text_cell rendered">
 <div class="prompt input_prompt">
 </div>
 <div class="inner_cell">
 <div class="text_cell_render border-box-sizing rendered_html">
-<h3 id="Finding-linear-correlation">Finding linear correlation<a class="anchor-link" href="#Finding-linear-correlation">&#182;</a></h3>
-<pre><code>&gt;&gt;&gt; 
-&gt;&gt;&gt; x = [1, 2, 3, 4]
-&gt;&gt;&gt; y = [2, 4, 6.1, 7.9]
-&gt;&gt;&gt; find_corr_x_y(x, y)
-0.9995411791453812</code></pre>
+<h3 id="Finding-linear-correlation">Finding linear correlation<a class="anchor-link" href="#Finding-linear-correlation">&#182;</a></h3><div class="highlight"><pre><span></span><span class="o">&gt;&gt;&gt;</span> 
+<span class="o">&gt;&gt;&gt;</span> <span class="n">x</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]</span>
+<span class="o">&gt;&gt;&gt;</span> <span class="n">y</span> <span class="o">=</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mf">6.1</span><span class="p">,</span> <span class="mf">7.9</span><span class="p">]</span>
+<span class="o">&gt;&gt;&gt;</span> <span class="n">find_corr_x_y</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+<span class="mf">0.9995411791453812</span>
+</pre></div>
 
 </div>
 </div>
-</div></div></section></section><section><section>
+</div></section></section><section><section>
 <div class="cell border-box-sizing text_cell rendered">
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 </div>
@@ -11990,23 +11996,7 @@ <h3 id="Python---a-really-fancy-calculator">Python - a really fancy calculator<a
 
 </div>
 </div>
-</div><aside class="notes">
-
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">When</span> <span class="n">you</span> <span class="n">bring</span> <span class="ow">in</span> <span class="n">SymPy</span> <span class="n">to</span> <span class="n">the</span> <span class="n">picture</span><span class="p">,</span> <span class="n">things</span> <span class="n">really</span>  <span class="n">get</span> <span class="n">awesome</span><span class="o">.</span> <span class="n">You</span> <span class="n">are</span> <span class="n">suddenly</span> <span class="n">writing</span> <span class="n">computer</span>
-<span class="n">programs</span> <span class="n">which</span> <span class="n">are</span> <span class="n">capable</span> <span class="n">of</span> <span class="n">speaking</span> <span class="n">algebra</span><span class="o">.</span> <span class="n">You</span> <span class="n">are</span> <span class="n">no</span> <span class="n">more</span> <span class="n">limited</span> <span class="n">to</span> <span class="n">numbers</span><span class="o">.</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-</div>
-</aside></section><section>
+</div></section><section>
 <div class="cell border-box-sizing code_cell rendered">
 <div class="input">
 <div class="prompt input_prompt">In&nbsp;[5]:</div>
@@ -12385,46 +12375,7 @@ <h3 id="Python---a-really-fancy-calculator">Python - a really fancy calculator<a
 </div>
 <div class="inner_cell">
 <div class="text_cell_render border-box-sizing rendered_html">
-<h3 id="Python---Making-other-subjects-more-lively">Python - Making other subjects more lively<a class="anchor-link" href="#Python---Making-other-subjects-more-lively">&#182;</a></h3><p><img align="center" src="collage/collage1.png"></img></p>
-<ul>
-<li><p>matplotlib</p>
-</li>
-<li><p>basemap</p>
-</li>
-<li><p>Interactive Jupyter Notebooks</p>
-</li>
-</ul>
-
-</div>
-</div>
-</div></section><section>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h4 id="Bringing-Science-to-life">Bringing Science to life<a class="anchor-link" href="#Bringing-Science-to-life">&#182;</a></h4><p><em>Animation of a Projectile motion</em></p>
-
-</div>
-</div>
-</div></section><section>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h4 id="Drawing-fractals">Drawing fractals<a class="anchor-link" href="#Drawing-fractals">&#182;</a></h4><p><em>Interactively drawing a Barnsley Fern</em></p>
-
-</div>
-</div>
-</div></section><section>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h4 id="The-world-is-your-graph-paper">The world is your graph paper<a class="anchor-link" href="#The-world-is-your-graph-paper">&#182;</a></h4><p><em>Showing places on a digital map</em></p>
-
+<center><h1>Can we do more than write smart calculators?</h1></center>
 </div>
 </div>
 </div></section></section><section><section>
@@ -12433,8 +12384,15 @@ <h4 id="The-world-is-your-graph-paper">The world is your graph paper<a class="an
 </div>
 <div class="inner_cell">
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-<h3 id="Great-base-for-the-future">Great base for the future<a class="anchor-link" href="#Great-base-for-the-future">&#182;</a></h3><p><em>Statistics and Graphing data</em> -&gt; <em>Data Science</em></p>
-<p><em>Differential Calculus</em> -&gt; <em>Machine learning</em></p>
+<h3 id="Python---Making-other-subjects-more-lively">Python - Making other subjects more lively<a class="anchor-link" href="#Python---Making-other-subjects-more-lively">&#182;</a></h3><p><img align="center" src="images/collage1.png"></img></p>
+<ul>
+<li><p>matplotlib</p>
+</li>
+<li><p>basemap</p>
+</li>
+<li><p>Interactive Jupyter Notebooks</p>
+</li>
+</ul>
 
 </div>
 </div>
@@ -12444,137 +12402,65 @@ <h3 id="Great-base-for-the-future">Great base for the future<a class="anchor-lin
 </div>
 <div class="inner_cell">
 <div class="text_cell_render border-box-sizing rendered_html">
-<h3 id="Application-of-differentiation">Application of differentiation<a class="anchor-link" href="#Application-of-differentiation">&#182;</a></h3><p>Use gradient descent to find a function's minimum value</p>
+<h4 id="Bringing-Science-to-life">Bringing Science to life<a class="anchor-link" href="#Bringing-Science-to-life">&#182;</a></h4><p><em>Animation of a Projectile motion</em> <a href="https://github.com/doingmathwithpython/pycon-us-2016/blob/master/py-files/projectile_animation.py">(Python Source)</a></p>
 
 </div>
-</div>
-</div></section><section>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h3 id="Predict-the-college-admission-score-based-on-high-school-math-score">Predict the college admission score based on high school math score<a class="anchor-link" href="#Predict-the-college-admission-score-based-on-high-school-math-score">&#182;</a></h3><p>Use gradient descent as the optimizer for single variable linear regression model</p>
-
 </div>
 </div>
-</div></section><section>
 <div class="cell border-box-sizing code_cell rendered">
 <div class="input">
-<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div>
+<div class="prompt input_prompt">In&nbsp;[3]:</div>
 <div class="inner_cell">
     <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">### TODO: digit recognition using Neural networks</span>
+<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="k">import</span> <span class="n">YouTubeVideo</span>
+<span class="n">YouTubeVideo</span><span class="p">(</span><span class="s2">&quot;8uWRVh58KdQ&quot;</span><span class="p">)</span>
 </pre></div>
 
 </div>
 </div>
 </div>
 
-</div></section><section>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">### Scikitlearn, pandas, scipy, statsmodel</span>
-</pre></div>
+<div class="output_wrapper">
+<div class="output">
 
-</div>
-</div>
-</div>
 
-</div></section></section><section><section>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h3 id="Book:-Doing-Math-With-Python">Book: Doing Math With Python<a class="anchor-link" href="#Book:-Doing-Math-With-Python">&#182;</a></h3><p><img align="center" src="dmwp-cover.png" href="https://doingmathwithpython.github.io"></img></p>
-<p>Published by No Starch Press, out in 2015.</p>
-<p>Early feedback very encouraging</p>
+<div class="output_area"><div class="prompt output_prompt">Out[3]:</div>
 
-</div>
-</div>
-</div></section><section>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h4 id="Comments">Comments<a class="anchor-link" href="#Comments">&#182;</a></h4>
-</div>
-</div>
-</div><div class="fragment">
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<blockquote><p>Saha does an excellent job providing a clear link between Python and upper-level math concepts, and demonstrates how Python can be transformed into a mathematical stage.</p>
-</blockquote>
+<div class="output_html rendered_html output_subarea output_execute_result">
 
+        <iframe
+            width="400"
+            height="300"
+            src="https://www.youtube.com/embed/8uWRVh58KdQ"
+            frameborder="0"
+            allowfullscreen
+        ></iframe>
+        
 </div>
-</div>
-</div></div><div class="fragment">
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<blockquote><p>This book is highly recommended for the high school or college student and anyone who is looking for a more natural way of programming math and scientific functions</p>
-</blockquote>
 
 </div>
-</div>
-</div></div><div class="fragment">
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<blockquote><p>As a teacher I highly recommend this book as a way to work with someone in learning both math and programming</p>
-</blockquote>
 
 </div>
 </div>
-</div></div></section></section><section><section>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h3 id="Links">Links<a class="anchor-link" href="#Links">&#182;</a></h3><ul>
-<li><p><a href="http://nostarch.com/doingmathwithpython">Doing Math with Python</a></p>
-</li>
-<li><p><a href="doingmathwithpython.github.io">Doing Math with Python Blog</a></p>
-</li>
-<li><p><a href="http://www.oreilly.com/pub/e/3712">Upcoming O'Reilly Webcast</a></p>
-</li>
-</ul>
 
-</div>
-</div>
-</div></section></section><section><section>
+</div></section><section>
 <div class="cell border-box-sizing text_cell rendered">
 <div class="prompt input_prompt">
 </div>
 <div class="inner_cell">
 <div class="text_cell_render border-box-sizing rendered_html">
-<h3 id="PyCon-Special!">PyCon Special!<a class="anchor-link" href="#PyCon-Special!">&#182;</a></h3><p><em>Use PYCONMATH code to get 30% off from No Starch Press</em></p>
-<p>(Valid from  May 26th - June 8th)</p>
-<p>Book Signing - May 31st - 2.00 PM - No Starch Press booth</p>
+<h4 id="Exploring-Fractals-in-Nature">Exploring Fractals in Nature<a class="anchor-link" href="#Exploring-Fractals-in-Nature">&#182;</a></h4><p><em>Interactively drawing a Barnsley Fern</em> <a href="https://github.com/doingmathwithpython/pycon-us-2016/blob/master/notebooks/Interactive%20Barnsley%20Fern.ipynb">(Notebook)</a></p>
+<p><img align="center" src="images/fern.jpg" width="400" ></img></p>
 
 </div>
 </div>
-</div></section></section><section><section>
+</div></section><section>
 <div class="cell border-box-sizing text_cell rendered">
 <div class="prompt input_prompt">
 </div>
 <div class="inner_cell">
 <div class="text_cell_render border-box-sizing rendered_html">
-<h3 id="Dialogue">Dialogue<a class="anchor-link" href="#Dialogue">&#182;</a></h3><p>Questions, Thoughts, comments, discussions?</p>
-<p>Online: @echorand, amitsaha.in@gmail.com</p>
+<h4 id="The-world-is-your-graph-paper">The world is your graph paper<a class="anchor-link" href="#The-world-is-your-graph-paper">&#182;</a></h4><p><em>Showing places on a digital map</em> <a href="https://github.com/doingmathwithpython/pycon-us-2016/blob/master/notebooks/Maps%20using%20Basemap%20-%20demo.ipynb">(Notebook)</a></p>
 
 </div>
 </div>
@@ -12584,31 +12470,12 @@ <h3 id="Dialogue">Dialogue<a class="anchor-link" href="#Dialogue">&#182;</a></h3
 </div>
 <div class="inner_cell">
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-<h3 id="Acknowledgements">Acknowledgements<a class="anchor-link" href="#Acknowledgements">&#182;</a></h3><p>PyCon US Education Summit team for inviting me</p>
-
-</div>
-</div>
-</div><div class="fragment">
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>Thanks to PyCon US for reduced registration rates</p>
-
-</div>
-</div>
-</div></div><div class="fragment">
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>Massive thanks to my employer, Freelancer.com for sponsoring my travel and stay</p>
+<h3 id="Great-base-for-the-future">Great base for the future<a class="anchor-link" href="#Great-base-for-the-future">&#182;</a></h3><p><em>Statistics and Graphing data</em> -&gt; <em>Data Science</em></p>
+<p><em>Differential Calculus</em> -&gt; <em>Machine learning</em></p>
 
 </div>
 </div>
-</div></div></section></section>
+</div></section></section>
 </div>
 </div>
 
diff --git a/notebooks/Drawing Maps.ipynb b/notebooks/Drawing Maps.ipynb
deleted file mode 100644
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--- a/notebooks/Drawing Maps.ipynb	
+++ /dev/null
@@ -1,186 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
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-   },
-   "outputs": [
-    {
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8qc5aJC1Gxo8fjxEjRkjdzypVqmDdunVYsmQJDh06lK/ZWEBAAJYvXy7z2JMn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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x1037486a0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "%matplotlib inline\n",
-    "\n",
-    "# With h\n",
-    "\n",
-    "from mpl_toolkits.basemap import Basemap\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "map = Basemap()\n",
-    "map.drawcoastlines()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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T1oZdZfTW84ge3dHN+Ul27dzEbIaoqJzylTRsCAkJKBfMx6y2gaAgyMhAu+Zv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\nMpeAaQAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x10aa59828>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from mpl_toolkits.basemap import Basemap\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "map = Basemap(projection='ortho', \n",
-    "              lat_0=0, lon_0=0)\n",
-    "\n",
-    "#Fill the globe with a blue color \n",
-    "map.drawmapboundary(fill_color='aqua')\n",
-    "#Fill the continents with the land color\n",
-    "map.fillcontinents(color='coral',lake_color='aqua')\n",
-    "\n",
-    "map.drawcoastlines()\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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oStJkpqdFX3drJdG6KIoVoj28rYy5mZzJ5kHN6VPGAVqCkmdooqNJ+qw+NRr3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\nugwMDAxGhBFdBgYGBiPy/wGChTxZfxEUnQAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x111def5f8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# http://introtopython.org/visualization_earthquakes.html#Making-a-simple-map\n",
-    "from mpl_toolkits.basemap import Basemap\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    " \n",
-    "# make sure the value of resolution is a lowercase L,\n",
-    "#  for 'low', not a numeral 1\n",
-    "map = Basemap(projection='robin', lat_0=50, lon_0=-100,\n",
-    "              resolution='l', area_thresh=1000.0)\n",
-    " \n",
-    "map.drawcoastlines()\n",
-    "map.drawcountries()\n",
-    "map.fillcontinents(color='coral')\n",
-    " \n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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DPojlWelwy4Moisw5dpuVt9M5f/mKybwZWq2WhIQEQ6kbgBf69WXrzj9Z1rcR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\nYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x113a549b0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from mpl_toolkits.basemap import Basemap\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    " \n",
-    "# make sure the value of resolution is a lowercase L,\n",
-    "#  for 'low', not a numeral 1\n",
-    "map = Basemap(projection='robin', lat_0=-30, lon_0=100,\n",
-    "              resolution='l', area_thresh=1000.0)\n",
-    " \n",
-    "map.drawcoastlines()\n",
-    "map.drawcountries()\n",
-    "map.fillcontinents(color='coral')\n",
-    "\n",
-    "# Sydney, Australia\n",
-    "lon =  151.216666667\n",
-    "lat = -33.8830555556\n",
-    "x,y = map(lon, lat)\n",
-    "map.plot(x, y, 'bo', markersize=12)\n",
-    "\n",
-    " \n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Resources\n",
-    "- # http://introtopython.org/visualization_earthquakes.html#Making-a-simple-map\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.5.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/notebooks/ProjectileMotionAnimation.ipynb b/notebooks/ProjectileMotionAnimation.ipynb
deleted file mode 100644
index f961044..0000000
--- a/notebooks/ProjectileMotionAnimation.ipynb
+++ /dev/null
@@ -1,135 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Enter the initial velocity (m/s): 25\n",
-      "Enter the angle of projection (degrees): 60\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW0AAAD7CAYAAAChScXIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x10a92d898>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "%matplotlib inline\n",
-    "\n",
-    "#P156: Animating a projectile's projectory\n",
-    "'''\n",
-    "Animate the trajectory of an object in projectile motion\n",
-    "'''\n",
-    "from matplotlib import pyplot as plt\n",
-    "from matplotlib import animation, rc\n",
-    "\n",
-    "from IPython.display import HTML\n",
-    "\n",
-    "import math\n",
-    "g = 9.8\n",
-    "\n",
-    "def get_intervals(u, theta):\n",
-    "    t_flight = 2*u*math.sin(theta)/g\n",
-    "    intervals = []\n",
-    "    start = 0\n",
-    "    interval = 0.005\n",
-    "    while start < t_flight:\n",
-    "        intervals.append(start)\n",
-    "        start = start + interval\n",
-    "    return intervals\n",
-    "\n",
-    "def update_position(i, circle, intervals, u, theta):\n",
-    "    t = intervals[i]\n",
-    "    x = u*math.cos(theta)*t\n",
-    "    y = u*math.sin(theta)*t - 0.5*g*t*t\n",
-    "    circle.center = x, y\n",
-    "    return circle,\n",
-    "\n",
-    "def create_animation(u, theta):\n",
-    "    intervals = get_intervals(u, theta)\n",
-    "    xmin = 0\n",
-    "    xmax = u*math.cos(theta)*intervals[-1]\n",
-    "    ymin = 0\n",
-    "    t_max = u*math.sin(theta)/g\n",
-    "    ymax = u*math.sin(theta)*t_max - 0.5*g*t_max**2\n",
-    "    fig = plt.gcf()\n",
-    "    ax = plt.axes(xlim=(xmin, xmax), ylim=(ymin, ymax))\n",
-    "    circle = plt.Circle((xmin, ymin), 1.0)\n",
-    "    ax.add_patch(circle)\n",
-    "    anim = animation.FuncAnimation(fig, update_position,\n",
-    "                        fargs=(circle, intervals, u, theta),\n",
-    "                        frames=len(intervals), interval=1,\n",
-    "                        repeat=False)\n",
-    "    #plt.title('Projectile Motion')\n",
-    "    #plt.xlabel('X')\n",
-    "    #plt.ylabel('Y')\n",
-    "    #plt.show()\n",
-    "    HTML(anim.to_html5_video())\n",
-    "    rc('animation', html='html5')\n",
-    "    anim\n",
-    "\n",
-    "if __name__ == '__main__':\n",
-    "    try:\n",
-    "        u = float(input('Enter the initial velocity (m/s): '))\n",
-    "        theta = float(input('Enter the angle of projection (degrees): '))\n",
-    "    except ValueError:\n",
-    "        print('You entered an invalid input')\n",
-    "    else:\n",
-    "        theta = math.radians(theta)\n",
-    "        create_animation(u, theta)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.5.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/py-files/projectile_animation.py b/py-files/projectile_animation.py
new file mode 100644
index 0000000..819d17f
--- /dev/null
+++ b/py-files/projectile_animation.py
@@ -0,0 +1,52 @@
+from matplotlib import pyplot as plt
+from matplotlib import animation
+import math
+g = 9.8
+
+def get_intervals(u, theta):
+    t_flight = 2*u*math.sin(theta)/g
+    intervals = []
+    start = 0
+    interval = 0.001
+    while start < t_flight:
+        intervals.append(start)
+        start = start + interval
+    return intervals
+
+def update_position(i, circle, intervals, u, theta):
+    t = intervals[i]
+    x = u*math.cos(theta)*t
+    y = u*math.sin(theta)*t - 0.5*g*t*t
+    circle.center = x, y
+    return circle,
+
+def create_animation(u, theta):
+    intervals = get_intervals(u, theta)
+    xmin = 0
+    xmax = u*math.cos(theta)*intervals[-1]
+    ymin = 0
+    t_max = u*math.sin(theta)/g
+    ymax = u*math.sin(theta)*t_max - 0.5*g*t_max**2
+    fig = plt.gcf()
+    ax = plt.axes(xlim=(xmin, xmax), ylim=(ymin, ymax))
+    circle = plt.Circle((xmin, ymin), 1.0)
+    ax.add_patch(circle)
+    anim = animation.FuncAnimation(fig, update_position,
+                        fargs=(circle, intervals, u, theta),
+                        frames=len(intervals), interval=1,
+                        repeat=False)
+    plt.title('Projectile Motion')
+    plt.xlabel('X')
+    plt.ylabel('Y')
+    #plt.show()
+    anim.save('projectile_motion.mp4')
+
+if __name__ == '__main__':
+    try:
+        u = float(input('Enter the initial velocity (m/s): '))
+        theta = float(input('Enter the angle of projection (degrees): '))
+    except ValueError:
+        print('You entered an invalid input')
+    else:
+        theta = math.radians(theta)
+        create_animation(u, theta)
diff --git a/py-files/projectile_motion.mp4 b/py-files/projectile_motion.mp4
new file mode 100644
index 0000000..caf7d7b
Binary files /dev/null and b/py-files/projectile_motion.mp4 differ
diff --git a/startup_math.py b/py-files/startup_math.py
similarity index 100%
rename from startup_math.py
rename to py-files/startup_math.py
diff --git a/slides.ipynb b/slides.ipynb
index f969a7a..6a2ea26 100644
--- a/slides.ipynb
+++ b/slides.ipynb
@@ -32,6 +32,17 @@
     "</style>"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "<center>Slides: https://doingmathwithpython.github.io/pycon-us-2016/ </center>"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -131,7 +142,7 @@
    "source": [
     "###  (Main) Tools\n",
     "\n",
-    "<img align=\"center\" src=\"collage/logo_collage.png\"></img>\n",
+    "<img align=\"center\" src=\"images/logo_collage.png\"></img>\n",
     "\n"
    ]
   },
@@ -205,7 +216,7 @@
    "cell_type": "markdown",
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "subslide"
     }
    },
    "source": [
@@ -461,7 +472,7 @@
     "\n",
     "### Python - Making other subjects more lively\n",
     "\n",
-    "<img align=\"center\" src=\"collage/collage1.png\"></img>\n",
+    "<img align=\"center\" src=\"images/collage1.png\"></img>\n",
     "\n",
     "\n",
     "- matplotlib\n",
@@ -484,9 +495,45 @@
     "#### Bringing Science to life\n",
     "\n",
     "*Animation of a Projectile motion*\n",
+    "\n",
     "\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"400\"\n",
+       "            height=\"300\"\n",
+       "            src=\"https://www.youtube.com/embed/8uWRVh58KdQ\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.YouTubeVideo at 0x10450c9e8>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import YouTubeVideo\n",
+    "YouTubeVideo(\"8uWRVh58KdQ\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -596,7 +643,7 @@
    "source": [
     "### Book: Doing Math With Python\n",
     "\n",
-    "<img align=\"center\" src=\"dmwp-cover.png\" href=\"https://doingmathwithpython.github.io\"></img>\n",
+    "<img align=\"center\" src=\"images/dmwp-cover.png\" href=\"https://doingmathwithpython.github.io\"></img>\n",
     "\n",
     "Published by No Starch Press, out in 2015.\n",
     "\n",