Streamline slides

09_mappings/02_content.ipynb

@@ -614,7 +614,11 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "outputs": [],
    "source": [
     "def fibonacci(i):\n",
@@ -636,7 +640,11 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -655,14 +663,22 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "source": [
     "#### Efficiency of Algorithms"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
    "source": [
     "Timing the code cells below with the `%%timeit` magic shows how doubling the input (i.e., `12` becomes `24`) more than doubles how long it takes `fibonacci()` to calculate the solution. This is actually an understatement as we see the time go up by roughly a factor of $1000$ (i.e., from nano-seconds to milli-seconds). That is an example of **exponential growth**."
    ]
@@ -670,7 +686,11 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -688,7 +708,11 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -729,7 +753,11 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "source": [
     "### \"Easy at second Glance\" Example: [Fibonacci Numbers <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Fibonacci_number) (revisited)"
    ]
@@ -797,7 +825,11 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
    "source": [
     "When we follow the flow of execution closely, we realize that the intermediate results represented by the left-most path in the graph above are calculated first. `fibonacci(1)`, the left-most leaf node $F(1)$, is the first base case reached, followed immediately by `fibonacci(0)`. From that moment onwards, the flow of execution moves back up the left-most path while adding together the two corresponding child nodes. Effectively, this mirrors the *iterative* implementation in that the order of all computational steps are *identical* (cf., the \"*Hard at first Glance*\" example in [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/02_content.ipynb#\"Hard-at-first-Glance\"-Example:-Fibonacci-Numbers--(revisited))).\n",
     "\n",
@@ -1000,7 +1032,11 @@
   {
    "cell_type": "code",
    "execution_count": 32,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
    "outputs": [
     {
      "ename": "RecursionError",

09_mappings/04_content.ipynb

@@ -526,7 +526,11 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
    "outputs": [],
    "source": [
     "import collections.abc as abc"