diff --git a/projects/3-mazes/Deitelzweig.ipynb b/projects/3-mazes/Deitelzweig.ipynb
new file mode 100644
index 0000000..1e19f19
--- /dev/null
+++ b/projects/3-mazes/Deitelzweig.ipynb
@@ -0,0 +1,805 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First, we load up some useful libraries for visualization and data structures."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "\n",
+    "import matplotlib\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.patches as patches\n",
+    "import matplotlib.axes as axes\n",
+    "import copy\n",
+    "import random"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next we need a Maze class for pathfinding. It will help to have read the RedBlobGames [A\\* tutorial](http://www.redblobgames.com/pathfinding/a-star/introduction.html) before continuing.\n",
+    "\n",
+    "These Mazes are defined in ASCII diagrams and can have walls (\"#\"), empty spaces (\".\"), switches (numbers), and doors (letters; closed are uppercase). The \"0\" switch toggles the open status of all the \"a\" doors, \"1\" goes to the \"b\" doors, etc. Mazes can also contain pits: \"?\" pits have a 30% chance of killing the player and \"!\" pits have a 60% chance. Every maze has one player start location \"@\" and one goal square \"X\". Walls and closed doors block movement."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[(1, 0), (0, -1), (0, 1), 'switch']\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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/kfRWYkjWC8tKzCdlWlsx3+sRsa/YPinpoM6/5iXLGkvOJeVd3+li82J1fsD0/r/N/fgN\nm0/KuD7bU5LWSvrGgCEzXl8VITHqi6/KzCdJNxenVzttL53lXLOpZxQXllWyNttL1DmL2d2zK/sa\nE3NJGddXnIq/KOl1SdMRcaBnSNa1lZhPyvv4fVXSFyUNerNxxuublIup9khaHBHL1Pkcyraa68mp\nkrXZni/pe5LuL37KV2bIXFnXFxHvRsQNkqYkfcz2qrkcL8N82dZn+1ZJx4qzMyvTGUoVIXFU0uKu\n21PFfb1jrhwyJtt8EXHy3GlfRDwl6X22L5vlfGXqybW2oapYm+0L1XnSPh4R2/sMybbGYXNV9dhF\nxC8l7ZT00Z5dlTx+g+bLvL6VktbZfkXSP0q6xfbWnjEzXl8VIfHexVe2L1Ln4qved1l3SFovvXdF\nZ9+Lr3LN1/2ay/ZydX71++Ys55PSKZ1zbUPnq2BtkvRNSQci4msD9udcY3KunOuzfbntBcX2pZI+\nrs4b3d2yra3MfDnXFxEPRMTiiPhddZ4Hz0bE+p5hM15fmSsuZ1roSC++KjOfpDts3yvprKQzku6a\n7Xy2vyNptaQP2P6FpIckXVTF2srMp4xrK+ZbKelzkvYXr6VD0gPq/PYo6xrLzJV5fR+U9G3bVuff\nyuMR8UxV/zbLzJd5fX3NdX1cTAUgaVLeuAQwS4QEgCRCAkASIQEgiZAAkERIAEgiJAAkERIAkv4f\nQGJXBTWJFV4AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x107339b70>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "class Maze:\n",
+    "    SwitchMap = {\"0\":\"a\", \"1\":\"b\", \"2\":\"c\", \"3\":\"d\", \"4\":\"e\", \"5\":\"f\", \"6\":\"g\", \"7\":\"h\", \"8\":\"i\", \"9\":\"j\"}\n",
+    "    Colors = {\"a\":\"coral\",\"b\":\"tan\",\"c\":\"palegreen\",\"d\":\"blue\",\"e\":\"cyan\",\"f\":\"magenta\",\"g\":\"yellow\",\"h\":\"olive\",\"i\":\"purple\",\"j\":\"darkgreen\",\n",
+    "              \"0\":\"coral\",\"1\":\"tan\",\"2\":\"palegreen\",\"3\":\"blue\",\"4\":\"cyan\",\"5\":\"magenta\",\"6\":\"yellow\",\"7\":\"olive\",\"8\":\"purple\",\"9\":\"darkgreen\",\n",
+    "             \"?\":\"orange\",\n",
+    "             \"!\":\"red\",\n",
+    "             \"x\":\"green\",\"@\":\"gray\",\n",
+    "             \"#\":\"sienna\",\".\":\"white\"}\n",
+    "    \n",
+    "    def __init__(self,rows):\n",
+    "        self.grid = [list(r) for r in rows]\n",
+    "        self.grid.reverse()\n",
+    "        height = len(self.grid)\n",
+    "        width = len(self.grid[0])\n",
+    "        self.exit_pos = None\n",
+    "        self.player_pos = None\n",
+    "        self.player_alive = True\n",
+    "        for y in range(0,height):\n",
+    "            assert len(self.grid[y]) == width, \"All rows must be equal length!\"\n",
+    "            for x in range(0,width):\n",
+    "                c = self.grid[y][x]\n",
+    "                assert c == \"#\" or c == \".\" or c == \"!\" or c == \"?\" or c == \"@\" or c.isalnum()\n",
+    "                if c.lower() == \"x\":\n",
+    "                    assert self.exit_pos == None\n",
+    "                    self.exit_pos = (x,y)\n",
+    "                if c == \"@\":\n",
+    "                    assert self.player_pos == None\n",
+    "                    self.player_pos = (x,y)\n",
+    "                    self.grid[y][x] = \".\"\n",
+    "    \n",
+    "    def clone(self):\n",
+    "        return copy.deepcopy(self)\n",
+    "    \n",
+    "    def toggle_cell(self,switchnum,c):\n",
+    "        if c.isalpha() and Maze.SwitchMap[switchnum] == c.lower():\n",
+    "            if c.islower():\n",
+    "                return c.upper()\n",
+    "            else:\n",
+    "                return c.lower()\n",
+    "        return c\n",
+    "    \n",
+    "    def toggle(self):\n",
+    "        assert self.player_alive\n",
+    "        height = len(self.grid)\n",
+    "        width = len(self.grid[0])\n",
+    "        (px,py) = self.player_pos\n",
+    "        switchnum = self.grid[py][px]\n",
+    "        assert switchnum.isnumeric()\n",
+    "        for y in range(0,height):\n",
+    "            for x in range(0,width):\n",
+    "                self.grid[y][x] = self.toggle_cell(switchnum,self.grid[y][x])\n",
+    "    \n",
+    "    def is_free(self,x,y):\n",
+    "        if y < 0 or y >= len(self.grid):\n",
+    "            return False\n",
+    "        if x < 0 or x >= len(self.grid[0]):\n",
+    "            return False\n",
+    "        cell = self.grid[y][x]\n",
+    "        return (\n",
+    "            cell == \".\" or\n",
+    "            cell == \"?\" or cell == \"!\" or cell == \"X\" or\n",
+    "            (cell.isalpha() and cell.islower()) or cell.isnumeric()\n",
+    "        )\n",
+    "    \n",
+    "    def move_player(self,dx,dy):\n",
+    "        assert self.player_alive\n",
+    "        assert abs(dx)+abs(dy) == 1\n",
+    "        (x,y) = self.player_pos\n",
+    "        (newx,newy) = (x+dx,y+dy)\n",
+    "        assert self.is_free(newx,newy)\n",
+    "        self.player_pos = (x+dx,y+dy)\n",
+    "        cell = self.grid[y+dy][x+dx]\n",
+    "        if cell == \"?\" and random.random() < 0.3:\n",
+    "            self.player_alive = False\n",
+    "        if cell == \"!\" and random.random() < 0.6:\n",
+    "            self.player_alive = False\n",
+    "    \n",
+    "    def available_moves(self):\n",
+    "        if not self.player_alive:\n",
+    "            return []\n",
+    "        (x,y) = self.player_pos\n",
+    "        can_switch = self.grid[y][x].isnumeric()\n",
+    "        return [(dx,dy) for (dx,dy) in [(-1,0),(1,0),(0,-1),(0,1)] if self.is_free(x+dx,y+dy)] + (\n",
+    "            [\"switch\"] if can_switch else []\n",
+    "        )\n",
+    "    \n",
+    "    def is_at_exit(self):\n",
+    "        return self.player_alive and self.player_pos == self.exit_pos\n",
+    "    \n",
+    "    def __hash__(self):\n",
+    "        return hash(str(self.grid)) % 1000007 + hash(self.player_pos) % 1000007 + hash(self.player_alive) % 1000007\n",
+    "    def __eq__(self,other):\n",
+    "        return self.grid == other.grid and self.player_pos == other.player_pos and self.player_alive == other.player_alive\n",
+    "    def __lt__(self,other):\n",
+    "        if self == other: return False\n",
+    "        return id(self) < id(other)\n",
+    "    def __gt(self, other):\n",
+    "        if self == other: return False\n",
+    "        return id(self) > id(other)\n",
+    "    \n",
+    "    def draw(self):\n",
+    "        fig1 = plt.figure()\n",
+    "        ax1 = fig1.add_subplot(1,1,1, aspect='equal')\n",
+    "        ax1.set_axis_bgcolor('sienna')\n",
+    "        height = len(self.grid)\n",
+    "        width = len(self.grid[0])\n",
+    "        ax1.set_xlim([0,width])\n",
+    "        ax1.set_ylim([0,height])\n",
+    "        for y in range(0,height):\n",
+    "            for x in range(0,width):\n",
+    "                cell = self.grid[y][x]\n",
+    "                if cell == \"#\": continue\n",
+    "                is_door = cell.isalpha() and cell.lower() != \"x\"\n",
+    "                is_pit = cell == \"?\" or cell == \"!\"\n",
+    "                is_open = is_door and cell.islower()\n",
+    "                is_switch = cell.isnumeric()\n",
+    "                ax1.add_patch(\n",
+    "                    patches.Rectangle((x, y),\n",
+    "                                      1,1,\n",
+    "                                      fill=True,\n",
+    "                                      facecolor=Maze.Colors[cell.lower()],\n",
+    "                                      edgecolor=\"black\",\n",
+    "                                      hatch=\"/\" if is_switch else (\"-\" if (is_door and not is_open) else None),\n",
+    "                                      label=cell)\n",
+    "                )\n",
+    "        ax1.add_patch(\n",
+    "            patches.Rectangle(self.player_pos,\n",
+    "                              1,1,\n",
+    "                              fill=True,\n",
+    "                              hatch=\"x\" if not self.player_alive else None,\n",
+    "                              facecolor=Maze.Colors[\"@\"] if self.player_alive else \"black\",\n",
+    "                              edgecolor=Maze.Colors[\"@\"] if self.player_alive else \"white\")\n",
+    "        )\n",
+    "        plt.show(fig1)\n",
+    "        \n",
+    "    def paint(self, path):\n",
+    "        for i in path:\n",
+    "            if i != \"switch\":\n",
+    "                (x,y) = i\n",
+    "                if (x, y) != self.exit_pos:\n",
+    "                    self.grid[y][x] = \"e\"\n",
+    "\n",
+    "sample=Maze([\n",
+    "        \"##X#\",\n",
+    "        \"#.A#\",\n",
+    "        \"#0?#\",\n",
+    "        \"a@##\"\n",
+    "    ])\n",
+    "sample.move_player(0,1)\n",
+    "print(sample.available_moves())\n",
+    "sample.move_player(1,0)\n",
+    "sample.draw()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Assignment 1\n",
+    "\n",
+    "Write a function to solve pathfinding and switch-and-door puzzles with one of the heuristic search algorithms described during the lecture. Try it on the provided sample puzzles; if a puzzle gives your algorithm trouble, try to explain why that happens. Make sure the path you're getting is the actual shortest path!\n",
+    "\n",
+    "Try to get this assignment done by Friday; the other two may take a little longer but the sooner you attempt them the earlier you can get feedback!\n",
+    "\n",
+    "You may also try visualizing paths through the maze, implementing several different heuristic searches, comparing against aheuristic search, etc.\n",
+    "\n",
+    "Generating mazes automatically would also be a great exercise!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from queue import PriorityQueue\n",
+    "\n",
+    "def dist(p1, p2):\n",
+    "    (x1, y1) = p1\n",
+    "    (x2, y2) = p2\n",
+    "    return abs(x1 - x2) + abs(y1 - y2)\n",
+    "\n",
+    "def heuristic(maze):\n",
+    "    start = maze\n",
+    "    goal = maze.exit_pos\n",
+    "    \n",
+    "    frontier = PriorityQueue()\n",
+    "    frontier.put((0, start))\n",
+    "    came_from = {}\n",
+    "    cost_so_far = {}\n",
+    "    came_from[start] = (None,None)\n",
+    "    cost_so_far[start] = 0\n",
+    "    \n",
+    "    while not frontier.empty():\n",
+    "        (key, current) = frontier.get()\n",
+    "        \n",
+    "        if current.is_at_exit():\n",
+    "            path = []\n",
+    "            while current:\n",
+    "                (current,move) = came_from[current]\n",
+    "                path.append(move)\n",
+    "            path.reverse()\n",
+    "            path.pop(0)\n",
+    "            return path\n",
+    "        \n",
+    "        for next in current.available_moves():\n",
+    "            succ = current.clone()\n",
+    "            if next == \"switch\":\n",
+    "                succ.toggle()\n",
+    "            else:\n",
+    "                succ.move_player(next[0],next[1])\n",
+    "            #update came_from and cost_so_far based on taking the move from current to succ via \"next\"\n",
+    "            new_cost = cost_so_far[current] + 1\n",
+    "            if succ not in cost_so_far or new_cost < cost_so_far[succ]:\n",
+    "                cost_so_far[succ] = new_cost\n",
+    "                priority = new_cost + dist(goal, succ.player_pos)\n",
+    "                frontier.put((priority, succ))\n",
+    "                if next != \"switch\":\n",
+    "                    (dx, dy) = next\n",
+    "                    (x, y) = current.player_pos\n",
+    "                    neighbor = (x+dx,y+dy)\n",
+    "                    came_from[succ] = (current,neighbor)\n",
+    "                else:\n",
+    "                    came_from[succ] = (current,next)\n",
+    "\n",
+    "    return \"Unsolvable\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10542ac50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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JoxFxT7eDLBYRd0TE2yLiUs3efgcj4mPdzjVfZxfzuO3LOidtV31vbh2TdLXtPtvWbMZa\n3tRavEfxiKSRzuGPS+r2E/qCfLZ3aPZlpOsj4n+7lmqhsxkj4j8j4uKIuDQitmj2yf3KiGjlSaiV\n8uw8w899gP6opG/W9gF629dI+qik99v+Uec1ux3dztWDdkp6yPYhzb7b/sUu51kgIg5LelDSk5IO\na/aBdm9XQ0my/bCk70u6zPZztm+RdJeka23P/eXeXZXl+xtJg5L+rfN4+ftu5Vsh43yhFnfb+ZA8\nACSs+TeMACCD8gSABMoTABIoTwBIoDwBIIHyBIAEyhMAEihPAEj4P6kmn+nlBYp5AAAAAElFTkSu\nQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x107301dd8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1053d8438>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def maze1():\n",
+    "    return Maze([\n",
+    "        \"########\",\n",
+    "        \"#X.#...#\",\n",
+    "        \"#.##.#.#\",\n",
+    "        \"#.#..#.#\",\n",
+    "        \"#....#.#\",\n",
+    "        \"#.##...#\",\n",
+    "        \"#..#..##\",\n",
+    "        \"##.#..@#\",\n",
+    "        \"########\"\n",
+    "    ])\n",
+    "\n",
+    "maze1().draw()\n",
+    "\n",
+    "def maze2():\n",
+    "    return Maze([\n",
+    "        \"###############\",\n",
+    "        \"#X#@.#.#...#..#\",\n",
+    "        \"#.##.....#....#\",\n",
+    "        \"#.#.#.#..#..#.#\",\n",
+    "        \"#...#.#....#..#\",\n",
+    "        \"#.#.#.........#\",\n",
+    "        \"#.#.##.#..#...#\",\n",
+    "        \"#.....#..#..#.#\",\n",
+    "        \"#####...#.....#\",\n",
+    "        \"########...####\",\n",
+    "        \"###############\"\n",
+    "    ])\n",
+    "\n",
+    "maze2().draw()\n",
+    "\n",
+    "def mazeUnsolvable1():\n",
+    "    return Maze([\n",
+    "        \"###############\",\n",
+    "        \"#X#@.#.#...#..#\",\n",
+    "        \"#.##.....#....#\",\n",
+    "        \"#.#.#.#..#..#.#\",\n",
+    "        \"#.#.#.#....#..#\",\n",
+    "        \"#..##...#.....#\",\n",
+    "        \"#.#.##....#...#\",\n",
+    "        \"#.#...#..#..#.#\",\n",
+    "        \"#####...#.....#\",\n",
+    "        \"########...####\",\n",
+    "        \"###############\"\n",
+    "    ])\n",
+    "\n",
+    "mazeUnsolvable1().draw()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Maze 1:\n",
+      "[(5, 1), (5, 2), (5, 3), (4, 3), (4, 4), (3, 4), (2, 4), (1, 4), (1, 5), (1, 6), (1, 7)]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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dx+eAxi+OK2mQzoGaP2967IjYVe2NbANOAjuAe89ZT3V41cxeRz5YY5aAg2iWgINoloCD\naJaAg2iWgINoloCDaJaAg2iWwP8BoEdmpOhzTLEAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1076a3f98>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Maze 2:\n",
+      "[(4, 9), (4, 8), (5, 8), (6, 8), (7, 8), (7, 7), (8, 7), (8, 6), (8, 5), (8, 4), (8, 3), (7, 3), (7, 2), (6, 2), (5, 2), (5, 3), (4, 3), (3, 3), (3, 4), (3, 5), (3, 6), (2, 6), (1, 6), (1, 7), (1, 8), (1, 9)]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1076bf7b8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Unsolvable maze:\n",
+      "Unsolvable\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Maze 1:\")\n",
+    "print(heuristic(maze1()))\n",
+    "m1 = maze1()\n",
+    "m1.paint(heuristic(m1))\n",
+    "m1.draw()\n",
+    "print(\"Maze 2:\")\n",
+    "print(heuristic(maze2()))\n",
+    "m2 = maze2()\n",
+    "m2.paint(heuristic(m2))\n",
+    "m2.draw()\n",
+    "print(\"Unsolvable maze:\")\n",
+    "print(heuristic(mazeUnsolvable1()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You also have to be able to handle switch and door puzzles:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x107321ef0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x107321f98>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def maze3():\n",
+    "    return Maze([\n",
+    "        \"###############\",\n",
+    "        \"#X#@.#.#...#.1#\",\n",
+    "        \"#.##.....#....#\",\n",
+    "        \"#A#.#.#..#..#.#\",\n",
+    "        \"#.a.#C#....#..#\",\n",
+    "        \"#.#.#0C.......#\",\n",
+    "        \"#.#.##.#..#...#\",\n",
+    "        \"#.B...#.2#..#.#\",\n",
+    "        \"#####...#.....#\",\n",
+    "        \"########...####\",\n",
+    "        \"###############\"\n",
+    "    ])\n",
+    "\n",
+    "maze3().draw()\n",
+    "\n",
+    "def maze4():\n",
+    "    return Maze([\n",
+    "        \"########\",\n",
+    "        \"#@0#.01#\",\n",
+    "        \"#A1#C#a#\",\n",
+    "        \"#0#..#.#\",\n",
+    "        \"#aBc2#.#\",\n",
+    "        \"#B##c..#\",\n",
+    "        \"#..#bb##\",\n",
+    "        \"##1#..X#\",\n",
+    "        \"########\"\n",
+    "    ])\n",
+    "\n",
+    "maze4().draw()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Maze 3:\n",
+      "[(4, 9), (4, 8), (5, 8), (6, 8), (7, 8), (8, 8), (8, 9), (9, 9), (10, 9), (10, 8), (11, 8), (12, 8), (12, 9), (13, 9), 'switch', (12, 9), (12, 8), (11, 8), (10, 8), (10, 7), (10, 6), (9, 6), (8, 6), (8, 5), (8, 4), (8, 3), 'switch', (8, 4), (8, 5), (7, 5), (6, 5), (5, 5), 'switch', (6, 5), (7, 5), (8, 5), (8, 4), (8, 3), (7, 3), (7, 2), (6, 2), (5, 2), (5, 3), (4, 3), (3, 3), (2, 3), (1, 3), (1, 4), (1, 5), (1, 6), (1, 7), (1, 8), (1, 9)]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1079a8748>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Maze 4:\n",
+      "[(2, 7), 'switch', (2, 6), 'switch', (1, 6), (1, 5), 'switch', (1, 4), (2, 4), (3, 4), (4, 4), 'switch', (4, 5), (4, 6), (4, 7), (5, 7), (6, 7), 'switch', (6, 6), (6, 5), (6, 4), (6, 3), (5, 3), (5, 2), (5, 1), (6, 1)]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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4js7BrUZExNqIuCgiLqHzOjwWEbc2NT6ApNnVXgOS5gAfAH7c5BwRcRg4JGlR9aflQNO7\n8GespOHd0spTwNWS+tW5hvpyOscdRjWhi4hHD072VxdKfS/w25KeBT5/5o18g3MsA24BBqv3cAGs\njYh/bXCaNwHfrF6YPjpb3kcbHL8XLgS2VB9nnAZsjIitCfOsBjZWu47PAI1fHFfSbDoHav646bEj\nYm+1N7ITOAXsBu49Zz3V4VUzO498sMasAA6iWQEcRLMCOIhmBXAQzQrgIJoVwEE0K4CDaFaA/wOp\nS/Q+GxnJYQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1077a0b38>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "print(\"Maze 3:\")\n",
+    "print(heuristic(maze3()))\n",
+    "m3 = maze3()\n",
+    "m3.paint(heuristic(m3))\n",
+    "m3.draw()\n",
+    "print(\"Maze 4:\")\n",
+    "print(heuristic(maze4()))\n",
+    "m4 = maze4()\n",
+    "m4.paint(heuristic(m4))\n",
+    "m4.draw()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Assignment 2\n",
+    "\n",
+    "Once you have this working, write an agent which finds a policy for a \"blind\" puzzle using MCTS. \"Blind\" puzzles are just like the puzzles above, only (a) you don't get to see the whole puzzle or know the goal states in advance, and (b) some nodes are trap doors with a chance of dropping the player into a bottomless pit! Try different policies for deciding between exploit/explore and for doing rollouts and compare them.  Plot graphs on how learning improves with  more rollouts.\n",
+    "\n",
+    "Of course, it should also be able to solve the earlier maze puzzles!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "class TreeNode:\n",
+    "    def __init__(self, maze):\n",
+    "        self.maze = maze\n",
+    "        self.reward = None\n",
+    "        self.moves = 0\n",
+    "        self.action = (None,None)\n",
+    "        self.children = []\n",
+    "        self.parent = None\n",
+    "        self.untried_actions =  self.maze.available_moves()\n",
+    "        \n",
+    "    def add_child(self, tn):\n",
+    "        self.children.append(tn)\n",
+    "        tn.parent = self\n",
+    "        \n",
+    "def expand(tn):\n",
+    "    action = random.choice(tn.untried_actions)\n",
+    "    tn.untried_actions.remove(action)\n",
+    "    m = tn.maze.clone()\n",
+    "    if action == \"switch\":\n",
+    "        m.toggle()\n",
+    "    else:\n",
+    "        m.move_player(action[0],action[1])\n",
+    "    return m\n",
+    "        \n",
+    "def tree_policy(tn):\n",
+    "    if tn.untried_actions:\n",
+    "        child = TreeNode(expand(tn))\n",
+    "        tn.add_child(child)\n",
+    "        return child\n",
+    "    else:\n",
+    "        #implement best_child here and fix the code below to work with it\n",
+    "        child.maze = expand(tn)\n",
+    "        for c in tn.children:\n",
+    "            if c.maze == m:\n",
+    "                return tree_policy(c)\n",
+    "    \n",
+    "        \n",
+    "def mcts(maze,iterations):\n",
+    "    # Return the expected value (a number between 0 for \"player dead\", 1 for \"made it to the end\")\n",
+    "    # for a budget of `iterations` rollouts.\n",
+    "    # Should also return the best found path (the one most likely to lead to success).\n",
+    "    # Here, don't look at maze.exit_pos or maze.grid:\n",
+    "    # you're only allowed to query `maze.available_moves()`, `maze.player_alive`, and `maze.is_at_exit`.\n",
+    "\n",
+    "    # After training for `iterations` rollouts, run an agent through the maze using that learned policy \n",
+    "    # for a large number of times and return the average reward:\n",
+    "    # (best_path, expected_reward, test_reward)\n",
+    "\n",
+    "    \n",
+    "    \n",
+    "    #return ([], 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x107513940>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVQAAAEACAYAAADsjY5UAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nASAJQQWAJAQVAJIQVABIQlABIMn/AKye19Tip1+4AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1075134e0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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2CzDrZYtXjxMRLd8V826aivTd6M36Vp6PL74L/CdwrKRnJV2cviyz/pKnwfBnulGIWT/z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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1075c12b0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def maze5():\n",
+    "    return Maze([\n",
+    "        \"#######\",\n",
+    "        \"#@???X#\",\n",
+    "        \"#.....#\",\n",
+    "        \"#######\"\n",
+    "    ])\n",
+    "\n",
+    "maze5().draw()\n",
+    "\n",
+    "def maze6():\n",
+    "    return Maze([\n",
+    "        \"#######\",\n",
+    "        \"#@?!?X#\",\n",
+    "        \"#.???.#\",\n",
+    "        \"#.....#\",\n",
+    "        \"#######\"\n",
+    "    ])\n",
+    "\n",
+    "maze6().draw()\n",
+    "\n",
+    "def maze7():\n",
+    "    return Maze([\n",
+    "        \"########\",\n",
+    "        \"#@0#?01#\",\n",
+    "        \"#A1#C#a#\",\n",
+    "        \"#0#.?#!#\",\n",
+    "        \"#aBc2#.#\",\n",
+    "        \"#B##c.?#\",\n",
+    "        \"#.!#bb##\",\n",
+    "        \"##1#.?X#\",\n",
+    "        \"########\"\n",
+    "    ])\n",
+    "\n",
+    "maze7().draw()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Maze 5:\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Maze 5:\")\n",
+    "print(mcts(maze5(),500))\n",
+    "#print(\"Maze 6:\")\n",
+    "#print(mcts(maze6(),500))\n",
+    "#print(\"Maze 7:\")\n",
+    "#print(mcts(maze7(),1000000))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Assignment 3\n",
+    "\n",
+    "Do assignment 2 again, but with reinforcement learning! Compare various approaches and parameters (e.g.\\ different discounting rates, Sarsa vs Q-learning, etc) against your MCTS agents in terms of iterations required to reach certain levels of performance. Plot graphs showing how learning improves with more iterations. Print or draw out (at least some of) the state-value or action-value matrix.\n",
+    "\n",
+    "Read as much as you care to of Sutton & Barto---[section 2](https://webdocs.cs.ualberta.ca/~sutton/book/ebook/node39.html) is especially useful.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def rl(maze,iterations):\n",
+    "    # Return the best path (most likely to lead to success) along with its expected value and a validated value\n",
+    "    # for a budget of `iterations` experiments.\n",
+    "    # As above, don't look at maze.exit_pos or maze.grid:\n",
+    "    # you're only allowed to query `maze.available_moves()`, `maze.player_alive`, and `maze.is_at_exit`.\n",
+    "\n",
+    "    # After training for `iterations` experiments, run an agent through the maze using that learned policy \n",
+    "    # for a large number of times and return the average reward:\n",
+    "    # (best_path, expected_reward, test_reward)\n",
+    "\n",
+    "    return ([],0,0)"
+   ]
+  }
+ ],
+ "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": "3.5.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/projects/3-mazes/Maze Solvers.ipynb b/projects/3-mazes/Maze Solvers.ipynb
index 96f4aee..bab35bc 100644
--- a/projects/3-mazes/Maze Solvers.ipynb	
+++ b/projects/3-mazes/Maze Solvers.ipynb	
@@ -9,7 +9,11 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+   "execution_count": 3,
+=======
    "execution_count": null,
+>>>>>>> cdd4ae825a1a875051c03b2e0291b67e78280b4d
    "metadata": {
     "collapsed": false
    },
@@ -39,11 +43,37 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[(1, 0), (0, -1), (0, 1), 'switch']\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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/kfRWYkjWC8tKzCdlWlsx3+sRsa/YPinpoM6/5iXLGkvOJeVd3+li82J1fsD0/r/N/fgN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\nQGJXBTWJFV4AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x107242438>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+=======
    "execution_count": null,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
+>>>>>>> cdd4ae825a1a875051c03b2e0291b67e78280b4d
    "source": [
     "@functools.total_ordering\n",
     "class Maze:\n",
@@ -229,16 +259,56 @@
     "def heuristic(maze):\n",
     "    # Return a path which solves the maze: a sequence of elements like (dx,dy) or \"switch\".\n",
     "    # You can use maze.exit_pos and query maze.grid[row][column] to investigate the maze.\n",
+    "    \n",
     "    return []"
    ]
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10725a748>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10751edd8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAU8AAAD7CAYAAADq4RYlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nngCQQHkCQALlCQAJlCcAJFCeAJDwf/33eWSmMrQpAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x107497080>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+=======
    "execution_count": null,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
+>>>>>>> cdd4ae825a1a875051c03b2e0291b67e78280b4d
    "source": [
     "def maze1():\n",
     "    return Maze([\n",
@@ -292,11 +362,31 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'heuristic' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-9-7fa836bbfb31>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mheuristic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmaze1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mheuristic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmaze2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mheuristic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmazeUnsolvable1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'heuristic' is not defined"
+     ]
+    }
+   ],
+=======
    "execution_count": null,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
+>>>>>>> cdd4ae825a1a875051c03b2e0291b67e78280b4d
    "source": [
     "print(heuristic(maze1()))\n",
     "print(heuristic(maze2()))\n",
@@ -312,11 +402,40 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10788f588>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAOIAAAEACAYAAACu66rqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10788fe48>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+=======
    "execution_count": null,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
+>>>>>>> cdd4ae825a1a875051c03b2e0291b67e78280b4d
    "source": [
     "def maze3():\n",
     "    return Maze([\n",
@@ -353,11 +472,31 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'heuristic' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-8-88decc01e22f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mheuristic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmaze3\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mheuristic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmaze4\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'heuristic' is not defined"
+     ]
+    }
+   ],
+=======
    "execution_count": null,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
+>>>>>>> cdd4ae825a1a875051c03b2e0291b67e78280b4d
    "source": [
     "print(heuristic(maze3()))\n",
     "print(heuristic(maze4()))"
@@ -499,7 +638,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.5.2"
   }
  },
  "nbformat": 4,
diff --git a/projects/5-emojiworld/Deitelzweig.json b/projects/5-emojiworld/Deitelzweig.json
new file mode 100644
index 0000000..e6e8eb1
--- /dev/null
+++ b/projects/5-emojiworld/Deitelzweig.json
@@ -0,0 +1 @@
+ {"meta":{"description":"The hungry man wants some grapes. Click to place him and watch him eat.","draw":1,"fps":60,"play":true},"states":[{"actions":[{"actions":[{"actions":[{"stateID":"2","type":"go_to_state","actions":[]}],"num":1,"sign":">=","stateID":"2","type":"if_neighbor"}],"probability":0.0025,"type":"if_random"}],"description":"","icon":"","id":0,"name":"empty spot"},{"actions":[{"leaveStateID":"3","space":0,"spotStateID":0,"type":"move_to","actions":[]}],"description":"He eats","icon":"😀","id":1,"name":"Hungry Man"},{"actions":[{"actions":[{"stateID":0,"type":"go_to_state","actions":[]}],"num":1,"sign":">=","stateID":"1","type":"if_neighbor"}],"description":"Gets eaten","icon":"🍇","id":2,"name":"Food"},{"actions":[{"actions":[{"stateID":0,"type":"go_to_state","actions":[]}],"probability":0.2,"type":"if_random"}],"description":"Ew","icon":"💩","id":3,"name":"Poop"}],"world":{"neighborhood":"moore","proportions":[{"stateID":0,"parts":0},{"stateID":1,"parts":0},{"stateID":2,"parts":100},{"stateID":3,"parts":0}],"size":{"height":33,"width":40},"update":"simultaneous"}}
\ No newline at end of file
diff --git a/projects/6-perceptron/Deitelzweig.ipynb b/projects/6-perceptron/Deitelzweig.ipynb
new file mode 100644
index 0000000..c43c568
--- /dev/null
+++ b/projects/6-perceptron/Deitelzweig.ipynb
@@ -0,0 +1,306 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from random import uniform, choice\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def plot_xys(xy_l_tuples,sz=15):\n",
+    "    t_yes = [a[0] for a in xy_l_tuples if a[1] == 1]\n",
+    "    t_no = [a[0] for a in xy_l_tuples if a[1] == 0]\n",
+    "    plt.plot([x for (x,y) in t_yes],[y for (x,y) in t_yes],'go',markersize=sz)\n",
+    "    plt.plot([x for (x,y) in t_no],[y for (x,y) in t_no],'ro',markersize=sz)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def tuple_append(tpl,elt):\n",
+    "    return tuple(list(tpl)+[elt])\n",
+    "\n",
+    "def perceptron(training, epochs):\n",
+    "    # some parameters, maybe interesting to make these arguments\n",
+    "    rate = 1.0\n",
+    "    # how many weights do we need?\n",
+    "    feature_count = len(training[0][0])\n",
+    "    # pad each training example with a dummy 1 input\n",
+    "    training = [(tuple_append(ins,1), label) for (ins,label) in training]\n",
+    "    # initialize weights to random values\n",
+    "    weights = [uniform(0,0.05) for _r in range(0,feature_count+1)]\n",
+    "    # let's store the errors found during training\n",
+    "    errors = []\n",
+    "    for i in range(0,epochs):\n",
+    "        # in each epoch, pick a subset of the training set (just one for now)\n",
+    "        (example,actual) = choice(training)\n",
+    "        # calculate the perceptron activation and the predicted category\n",
+    "        # Note: You can use dot(vec1, vec2) to get the dot product between two sequential collections.\n",
+    "        # calculate the error between predicted and actual and add it to errors\n",
+    "        # update each weight according to the perceptron update rule\n",
+    "        activation = np.dot(weights, example)\n",
+    "        predicted = 1 if activation >= 0 else 0\n",
+    "        error = actual - predicted\n",
+    "        for i in range(0, len(weights)):\n",
+    "            weights[i] += example[i] * error\n",
+    "        errors.append(error)\n",
+    "    return (errors,weights)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x10b1aaac8>]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10b47edd8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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R3r3AffdVL4uQWDjtNODMM9s7Xhti0Do7duzAN76RBJVzz53H/Pz8us83bkwCRJb77gOu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\nSUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10b482518>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "and_set = [((0,0),0), ((0,1),0), ((1,0),0), ((1,1),1)]\n",
+    "plot_xys(and_set)\n",
+    "\n",
+    "(errs,_) = perceptron(and_set,100)\n",
+    "plt.figure()\n",
+    "plt.plot(errs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x10ab966a0>]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10b47e3c8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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h8Dbg9yLiduC5wFsBIuLhEfHJ3pj9wE0R8XXgy8D1mXnDhPvdsq50E5UyH0ndNNH7DDLz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\ng+rvjtqeegAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10ad6f0f0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "or_set = [((0,0),0), ((0,1),1), ((1,0),1), ((1,1),1)]\n",
+    "plot_xys(or_set)\n",
+    "\n",
+    "(errs,_) = perceptron(or_set,100)\n",
+    "plt.figure()\n",
+    "plt.plot(errs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x10b37a438>]"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x107b46b70>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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EdJ3m/y8nosNEdN/Wz7ts9YWoiVS1QNuZgUvFE+vYLsjNMebm8qqJVD60jqHSRKriw6Qm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\n+8V2OClBh5wAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10b177c18>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "xor_set = [((0,0),0), ((0,1),1), ((1,0),1), ((1,1),0)]\n",
+    "plot_xys(xor_set)\n",
+    "\n",
+    "(errs,_) = perceptron(xor_set,100)\n",
+    "plt.figure()\n",
+    "plt.plot(errs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation error: 0.04\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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tqrmoDyRKqvoFVd2gqt0ovyaeUdVboj6uKFQupYsi8u7KTdcivhPFLwLYIiIX\niYigfC7iOEFce9X6CIDbKt/fCmDZwaDvhUVL4aKjOSJyDcq9sH4kIuMoXzZ9QVWfiPbIyACfATAs\nIm8CMAXg9oiPJxKq+sPKldoPAJwHMA5gKNqjCpeIfANAGsDvi8hLAA4C+CKAb4rIXwEoAPjYso/D\nhUVERG7gpCgRkSMY0ImIHMGATkTkCAZ0IiJHMKATETmCAZ2IyBEM6EREjmBAJyJyxP8DVFinIbdh\nrIQAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10c07d4a8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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A+iZsMER8iN6o7aIOJbURKIQW0E/hgyHCPlQdJupr8ZkaA9BP4YMhwoWxrruBCMcCANP0\nPhgmbV/0V0mj2pznoSQA/RQ2GAaqXuCqDHcUGSaapZ0q+9DWdgAQNhgoPpffpi2R9w1AeeGDIcI+\nUHwejeIz0E/hgyHChZHiM4B5EjYYBiIMJaU4wY0QAlBW2GCIcMdQV42hi6EkJrgBKCt8MDSp7B1D\n0XVWrltE5IsvNQagn8IHQ4QLIzUGAPMkbDAMRBhKqlpj6GIeAwCUFTYYmMdQfpsq20XrA0D7wgdD\nhH1gHsNo1BiAfgofDBEujNQYAMyT3gcDD9EDgGLCBsMAD9FrdzsACBsMFJ/LbwMAVYQPhgiqXvQj\n1UsAYJrwwdBkjaHsPqRQYwCAssIGw0CTNYY6H4nRFGoMANq2usrGZrZO0pclHSbp+5J+392fGLHe\n9yU9IekZSU+7+6bpbS//uws8RK/7PgC0r+odw1mSrnP3V0v6uqQPj1nvGUkL7v6rs4SCFOOiw0P0\nJmOCG9BPVYPhZEkX568vlvSuMetZ0b4i1BjGtTGqzZQmuEXZDwAxVQ2Gg9x9pyS5+6OSDhqznku6\n1sxuNbM/maXhuoKhrRpDSncMfNMHMMnUGoOZXStp/fAiZRf6j4xYfdwl503u/oiZ/bKygNjq7jeN\n6/Occ87Rz36Wvf7OdxZ0/PEL03azlDrnMZRpZxaRawwAurG4uKjFxcXG2p8aDO7+lnGfmdlOM1vv\n7jvN7GBJu8a08Uj+9w/N7FJJmyRNDIbdu6VPflI6+uipx9AYHqIHIKKFhQUtLCw8+/7cc8+ttf2q\nQ0mbJZ2av36/pMtWrmBmzzezF+SvD5D0VknfndZwhF8lDfAQPQDzpGowfELSW8zsPknHS/q4JJnZ\nBjO7PF9nvaSbzOx2STdL2uLu10xrOELxOeUJboQQgLIqzWNw9x9L+p0Ryx+R9I789f9KKj0glErx\neZZ2yoh05wRgPoSd+cxD9Iq3X7UvAJASCIYIIjxEL9L5ANBv4YOBGkP5bQGgjLDBMNCHGkMVDCUB\naFvYYIjwTTnSQ/S40ANoS/hgiCDCPIZI5wNAv4UPBmoM2d+rCv6X6nL4C0Daeh8Mk7T1EL1oQ0k8\nRA/AJGGDYSBi8bnoQ/SqoPgMoG1hgyHCha3I8FBKdwwAMEn4YOiyxjCujVFtplR8JmQATNL7YGh6\nH1IsPlNjADBJ2GAY6EONgYfoAUhJ2GCIcCGsq8ZQpL06tqmyHQCEDwZqDJmiQ0lN7QeA/ut9MDS9\nD/wqCUDf9D4YqhRaIxWf65zHQPEZwCRhg2Egwh3DShSfAfRZ2GCIdCHkIXoA5kn4YIhQfJ7UZorz\nGAgZAJP0PhialmLxmRoDgEnCBkNdxl0Eq1xoeYgegD7rfTC0pak7hlnaB4A69T4Y+lJj4CF6ANrS\n+2AYp66LY4rFZwCYpPfB0EShdVSNgeIzgL7ofTCMU/cdQ1N4iB6AtvU+GJquMRTph4foAUhB74Oh\naSkOJQHAJL0PhibmMYxqJ9qvkniIHoCyeh8MTeMhegD6pvfB0NYFNaWH6BEyACbpfTCM08XFkXkM\nAFIwt8HQpmjFZ2oMACaZ22BI5Rs1D9ED0La5DYYuUGMAkAKCoQXRhpIAYJK5DYZUis8p9AWgX3of\nDE0/RG8W0b71U3wGMEnvgwEAUEzvg2Hct/QuCsFt9jnLs5sAYJTeB0ME/K89AaSk98HQ9EP0iohS\nfKbGAGCS3gdDBHzbB5CS3gdDhIvy4Bt6lKGkCOcEQFy9D4Zx+n5x7PvxAWhOpWAws3eb2XfNbK+Z\nHTNhvRPN7F4zu9/MzqzSZ4rqGNPnQg+gLVXvGO6S9LuSbhi3gpmtkvQpSSdIeq2k95rZURX7rSyF\nC+3i4mLpbVM4vllVOQ99w7lYwrloTqVgcPf73H2bpEmXoU2Strn7A+7+tKRLJJ1cpd95wT/8DOdh\nCediCeeiOW3UGA6R9NDQ+4fzZXODoSQAKVk9bQUzu1bS+uFFklzSX7v7lqZ2bGC//WZb73nPe+6y\nDRukffcdvf7GjdKjj05uc//9Ry9fu3bp9bj2h73kJdPXmWbNmmLrb9gwevkBB0gvfWn1/ZGydnbv\nrqctAHGY1/B11syul/QX7n7biM+OlXSOu5+Yvz9Lkrv7J8a0xfQrACjI3WsbV5h6x1DAuJ26VdIR\nZnaYpEcknSLpveMaqfPgAADFVf256rvM7CFJx0q63MyuzJdvMLPLJcnd90o6Q9I1ku6WdIm7b622\n2wCAptQylAQA6I8wM5/nbRKcmW00s6+b2d1mdpeZfTBfvs7MrjGz+8zsajNbO7TNh81sm5ltNbO3\ndrf39TOzVWZ2m5ltzt/P5XmQJDNba2ZfyY/vbjP79Xk8H/lx3W1md5rZv5rZmnk6D2Z2oZntNLM7\nh5YVPn4zOyY/h/eb2d/P1Lm7d/5HWUB9T9JhkvaVdIeko7rer4aP+WBJR+evXyDpPklHSfqEpA/l\ny8+U9PH89Wsk3a6sLvTy/HxZ18dR4/n4c0n/Imlz/n4uz0N+jP8s6bT89WpJa+ftfOTXgu2S1uTv\nvyzp/fN0HiT9pqSjJd05tKzw8Uu6RdIb8tdXSDphWt9R7hjmbhKcuz/q7nfkr5+UtFXSRmXHfXG+\n2sWS3pW/fqey+swv3P37krYpO2/JM7ONkt4m6R+HFs/deZAkM/slSb/l7hdJUn6cT2j+zsdPJP2f\npAPMbLWk50naoTk6D+5+k6THViwudPxmdrCkF7r7rfl6XxjaZqwowTDXk+DM7OXKvhncLGm9u++U\nsvCQdFC+2spztEP9OUd/J+kvlc2PGZjH8yBJh0v6kZldlA+tXWBmz9ecnQ93f0zS30p6UNkxPeHu\n12nOzsMIBxU8/kOUXU8HZrq2RgmGuWVmL5D0VUl/lt85rPw1QK9/HWBmb5e0M797mvRT5V6fhyGr\nJR0j6dPufoykn0o6S/P37+IVyoYXD5P0UmV3Dn+oOTsPM2jk+KMEww5Jhw6935gv67X8Fvmrkr7o\n7pfli3ea2fr884Ml7cqX75D0sqHN+3KO3iTpnWa2XdK/SfptM/uipEfn7DwMPCzpIXf/dv7+35UF\nxbz9u/g1Sf/t7j/27Cfvl0p6o+bvPKxU9PhLnZcowfDsJDgzW6NsEtzmjvepDf8k6R53P29o2WZJ\np+av3y/psqHlp+S/zDhc0hGSvtXWjjbF3f/K3Q9191co++/+dXf/Y0lbNEfnYSAfJnjIzF6VLzpe\n2fyfufp3oezHGMea2f5mZsrOwz2av/NgWn4nXej48+GmJ8xsU34e3ze0zXhdV96Hqu0nKvvHsE3S\nWV3vTwvH+yZJe5X9Aut2Sbfl5+DFkq7Lz8U1kl40tM2Hlf3aYKukt3Z9DA2ckzdr6VdJ83wefkXZ\nl6U7JP2Hsl8lzd35UFZ3ulvSncoKrfvO03mQ9CVJP5D0lLJay2mS1hU9fkmvV/a/SNgm6bxZ+maC\nGwBgmShDSQCAIAgGAMAyBAMAYBmCAQCwDMEAAFiGYAAALEMwAACWIRgAAMv8P06ubixUB3noAAAA\nAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10c07d898>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "noise_chance = 0.0\n",
+    "N = 100\n",
+    "xys = [(uniform(0,10),uniform(0,10)) for _r in range(0,N)]\n",
+    "secret_f = lambda x: x*2 - 1\n",
+    "dataset = [(xy,(1 if (secret_f(xy[0]) <= xy[1] or uniform(0,1)<noise_chance) else 0)) for xy in xys]\n",
+    "plot_xys(dataset,5)\n",
+    "\n",
+    "trials = 1000\n",
+    "(errs,weights) = perceptron(dataset,trials)\n",
+    "plt.figure()\n",
+    "plt.plot(errs)\n",
+    "\n",
+    "validation_N = 100\n",
+    "# note the bounds here are a bit different, but we hope to generalize a bit from the training set\n",
+    "val_xys = [(uniform(5,20),uniform(5,20)) for _r in range(0,validation_N)]\n",
+    "validation = [(tuple_append(xy,1),(1 if secret_f(xy[0]) <= xy[1] else 0)) for xy in val_xys]\n",
+    "val_net_error = 0\n",
+    "for (example,actual) in validation:\n",
+    "    # get the prediction based on the weights, calculate the (absolute) error and add it to the net error\n",
+    "    activation = np.dot(weights, example)\n",
+    "    predicted = 1 if activation >= 0 else 0\n",
+    "    error = actual - predicted\n",
+    "    val_net_error += abs(error)\n",
+    "    \n",
+    "val_error = val_net_error/len(validation)\n",
+    "print(\"Validation error:\",val_error)"
+   ]
+  },
+  {
+   "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": "3.5.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/projects/7-dnns/Deitelzweig/Deitelzweig Keras.ipynb b/projects/7-dnns/Deitelzweig/Deitelzweig Keras.ipynb
new file mode 100644
index 0000000..b18a027
--- /dev/null
+++ b/projects/7-dnns/Deitelzweig/Deitelzweig Keras.ipynb	
@@ -0,0 +1,441 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using Theano backend.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from keras.models import Sequential\n",
+    "from keras.layers import Dense\n",
+    "import numpy\n",
+    "# fix random seed for reproducibility\n",
+    "seed = 7\n",
+    "numpy.random.seed(seed)\n",
+    "\n",
+    "# load pima indians dataset\n",
+    "dataset = numpy.loadtxt(\"pima-indians-diabetes.csv\", delimiter=\",\")\n",
+    "# split into input (X) and output (Y) variables\n",
+    "X = dataset[:,0:8]\n",
+    "Y = dataset[:,8]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# create model\n",
+    "model = Sequential()\n",
+    "model.add(Dense(12, input_dim=8, init='uniform', activation='relu'))\n",
+    "model.add(Dense(8, init='uniform', activation='relu'))\n",
+    "model.add(Dense(1, init='uniform', activation='sigmoid'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Compile model\n",
+    "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/150\n",
+      "768/768 [==============================] - 0s - loss: 0.6826 - acc: 0.6328     \n",
+      "Epoch 2/150\n",
+      "768/768 [==============================] - 0s - loss: 0.6590 - acc: 0.6510     \n",
+      "Epoch 3/150\n",
+      "768/768 [==============================] - 0s - loss: 0.6475 - acc: 0.6549     \n",
+      "Epoch 4/150\n",
+      "768/768 [==============================] - 0s - loss: 0.6416 - acc: 0.6615     \n",
+      "Epoch 5/150\n",
+      "768/768 [==============================] - 0s - loss: 0.6216 - acc: 0.6745     \n",
+      "Epoch 6/150\n",
+      "768/768 [==============================] - 0s - loss: 0.6128 - acc: 0.6680     \n",
+      "Epoch 7/150\n",
+      "768/768 [==============================] - 0s - loss: 0.6018 - acc: 0.6927     \n",
+      "Epoch 8/150\n",
+      "768/768 [==============================] - 0s - loss: 0.5962 - acc: 0.6927     \n",
+      "Epoch 9/150\n",
+      "768/768 [==============================] - 0s - loss: 0.5991 - acc: 0.6953     \n",
+      "Epoch 10/150\n",
+      "768/768 [==============================] - 0s - loss: 0.5920 - acc: 0.6927     \n",
+      "Epoch 11/150\n",
+      "768/768 [==============================] - 0s - loss: 0.5905 - acc: 0.6979     \n",
+      "Epoch 12/150\n",
+      "768/768 [==============================] - 0s - loss: 0.5883 - acc: 0.6901     \n",
+      "Epoch 13/150\n",
+      "768/768 [==============================] - 0s - loss: 0.5870 - acc: 0.6953     \n",
+      "Epoch 14/150\n",
+      "768/768 [==============================] - 0s - loss: 0.5869 - acc: 0.6836     \n",
+      "Epoch 15/150\n",
+      "768/768 [==============================] - 0s - loss: 0.5815 - acc: 0.6953     \n",
+      "Epoch 16/150\n",
+      "768/768 [==============================] - 0s - loss: 0.5779 - acc: 0.6966     \n",
+      "Epoch 17/150\n",
+      "768/768 [==============================] - 0s - loss: 0.5809 - acc: 0.6849     \n",
+      "Epoch 18/150\n",
+      "768/768 [==============================] - 0s - loss: 0.5818 - acc: 0.6953     \n",
+      "Epoch 19/150\n",
+      "768/768 [==============================] - 0s - loss: 0.5814 - acc: 0.6901     \n",
+      "Epoch 20/150\n",
+      "768/768 [==============================] - 0s - loss: 0.5748 - acc: 0.7096     \n",
+      "Epoch 21/150\n",
+      "768/768 [==============================] - 0s - loss: 0.5758 - acc: 0.7005     \n",
+      "Epoch 22/150\n",
+      "768/768 [==============================] - 0s - loss: 0.5739 - acc: 0.7135     \n",
+      "Epoch 23/150\n",
+      "768/768 [==============================] - 0s - loss: 0.5736 - acc: 0.6927     \n",
+      "Epoch 24/150\n",
+      "768/768 [==============================] - 0s - loss: 0.5750 - acc: 0.6940     \n",
+      "Epoch 25/150\n",
+      "768/768 [==============================] - 0s - loss: 0.5734 - acc: 0.7031     \n",
+      "Epoch 26/150\n",
+      "768/768 [==============================] - 0s - loss: 0.5683 - acc: 0.7083     \n",
+      "Epoch 27/150\n",
+      "768/768 [==============================] - 0s - loss: 0.5688 - acc: 0.7018     \n",
+      "Epoch 28/150\n",
+      "768/768 [==============================] - 0s - loss: 0.5714 - acc: 0.7070     \n",
+      "Epoch 29/150\n",
+      "768/768 [==============================] - 0s - loss: 0.5621 - acc: 0.7188     \n",
+      "Epoch 30/150\n",
+      "768/768 [==============================] - 0s - loss: 0.5647 - acc: 0.7122     \n",
+      "Epoch 31/150\n",
+      "768/768 [==============================] - 0s - loss: 0.5630 - acc: 0.7135     \n",
+      "Epoch 32/150\n",
+      "768/768 [==============================] - 0s - loss: 0.5613 - acc: 0.7214     \n",
+      "Epoch 33/150\n",
+      "768/768 [==============================] - 0s - loss: 0.5594 - acc: 0.7188     \n",
+      "Epoch 34/150\n",
+      "768/768 [==============================] - 0s - loss: 0.5598 - acc: 0.7187     \n",
+      "Epoch 35/150\n",
+      "768/768 [==============================] - 0s - loss: 0.5624 - acc: 0.7187     \n",
+      "Epoch 36/150\n",
+      "768/768 [==============================] - 0s - loss: 0.5615 - acc: 0.7201     \n",
+      "Epoch 37/150\n",
+      "768/768 [==============================] - 0s - loss: 0.5544 - acc: 0.7214     \n",
+      "Epoch 38/150\n",
+      "768/768 [==============================] - 0s - loss: 0.5529 - acc: 0.7135     \n",
+      "Epoch 39/150\n",
+      "768/768 [==============================] - 0s - loss: 0.5550 - acc: 0.7227     \n",
+      "Epoch 40/150\n",
+      "768/768 [==============================] - 0s - loss: 0.5574 - acc: 0.7331     \n",
+      "Epoch 41/150\n",
+      "768/768 [==============================] - 0s - loss: 0.5562 - acc: 0.7357     \n",
+      "Epoch 42/150\n",
+      "768/768 [==============================] - 0s - loss: 0.5458 - acc: 0.7370     \n",
+      "Epoch 43/150\n",
+      "768/768 [==============================] - 0s - loss: 0.5487 - acc: 0.7253     \n",
+      "Epoch 44/150\n",
+      "768/768 [==============================] - 0s - loss: 0.5409 - acc: 0.7344     \n",
+      "Epoch 45/150\n",
+      "768/768 [==============================] - 0s - loss: 0.5445 - acc: 0.7435     \n",
+      "Epoch 46/150\n",
+      "768/768 [==============================] - 0s - loss: 0.5362 - acc: 0.7357     \n",
+      "Epoch 47/150\n",
+      "768/768 [==============================] - 0s - loss: 0.5400 - acc: 0.7357     \n",
+      "Epoch 48/150\n",
+      "768/768 [==============================] - 0s - loss: 0.5354 - acc: 0.7409     \n",
+      "Epoch 49/150\n",
+      "768/768 [==============================] - 0s - loss: 0.5406 - acc: 0.7357     \n",
+      "Epoch 50/150\n",
+      "768/768 [==============================] - 0s - loss: 0.5476 - acc: 0.7357     \n",
+      "Epoch 51/150\n",
+      "768/768 [==============================] - 0s - loss: 0.5288 - acc: 0.7461     \n",
+      "Epoch 52/150\n",
+      "768/768 [==============================] - 0s - loss: 0.5284 - acc: 0.7474     \n",
+      "Epoch 53/150\n",
+      "768/768 [==============================] - 0s - loss: 0.5306 - acc: 0.7370     \n",
+      "Epoch 54/150\n",
+      "768/768 [==============================] - 0s - loss: 0.5283 - acc: 0.7487     \n",
+      "Epoch 55/150\n",
+      "768/768 [==============================] - 0s - loss: 0.5252 - acc: 0.7539     \n",
+      "Epoch 56/150\n",
+      "768/768 [==============================] - 0s - loss: 0.5257 - acc: 0.7552     \n",
+      "Epoch 57/150\n",
+      "768/768 [==============================] - 0s - loss: 0.5282 - acc: 0.7422     \n",
+      "Epoch 58/150\n",
+      "768/768 [==============================] - 0s - loss: 0.5269 - acc: 0.7513     \n",
+      "Epoch 59/150\n",
+      "768/768 [==============================] - 0s - loss: 0.5248 - acc: 0.7487     \n",
+      "Epoch 60/150\n",
+      "768/768 [==============================] - 0s - loss: 0.5202 - acc: 0.7500     \n",
+      "Epoch 61/150\n",
+      "768/768 [==============================] - 0s - loss: 0.5160 - acc: 0.7552     \n",
+      "Epoch 62/150\n",
+      "768/768 [==============================] - 0s - loss: 0.5161 - acc: 0.7461     \n",
+      "Epoch 63/150\n",
+      "768/768 [==============================] - 0s - loss: 0.5100 - acc: 0.7591     \n",
+      "Epoch 64/150\n",
+      "768/768 [==============================] - 0s - loss: 0.5145 - acc: 0.7526     \n",
+      "Epoch 65/150\n",
+      "768/768 [==============================] - 0s - loss: 0.5125 - acc: 0.7474     \n",
+      "Epoch 66/150\n",
+      "768/768 [==============================] - 0s - loss: 0.5148 - acc: 0.7617     \n",
+      "Epoch 67/150\n",
+      "768/768 [==============================] - 0s - loss: 0.5088 - acc: 0.7539     \n",
+      "Epoch 68/150\n",
+      "768/768 [==============================] - 0s - loss: 0.5124 - acc: 0.7721     \n",
+      "Epoch 69/150\n",
+      "768/768 [==============================] - 0s - loss: 0.5152 - acc: 0.7487     \n",
+      "Epoch 70/150\n",
+      "768/768 [==============================] - 0s - loss: 0.5163 - acc: 0.7552     \n",
+      "Epoch 71/150\n",
+      "768/768 [==============================] - 0s - loss: 0.5195 - acc: 0.7422     \n",
+      "Epoch 72/150\n",
+      "768/768 [==============================] - 0s - loss: 0.5094 - acc: 0.7461     \n",
+      "Epoch 73/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4984 - acc: 0.7617     \n",
+      "Epoch 74/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4979 - acc: 0.7617     \n",
+      "Epoch 75/150\n",
+      "768/768 [==============================] - 0s - loss: 0.5073 - acc: 0.7591     \n",
+      "Epoch 76/150\n",
+      "768/768 [==============================] - 0s - loss: 0.5028 - acc: 0.7513     \n",
+      "Epoch 77/150\n",
+      "768/768 [==============================] - 0s - loss: 0.5018 - acc: 0.7552     \n",
+      "Epoch 78/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4929 - acc: 0.7578     \n",
+      "Epoch 79/150\n",
+      "768/768 [==============================] - 0s - loss: 0.5011 - acc: 0.7630     \n",
+      "Epoch 80/150\n",
+      "768/768 [==============================] - 0s - loss: 0.5018 - acc: 0.7474     \n",
+      "Epoch 81/150\n",
+      "768/768 [==============================] - 0s - loss: 0.5026 - acc: 0.7578     \n",
+      "Epoch 82/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4986 - acc: 0.7604     \n",
+      "Epoch 83/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4948 - acc: 0.7578     \n",
+      "Epoch 84/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4915 - acc: 0.7747     \n",
+      "Epoch 85/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4982 - acc: 0.7578     \n",
+      "Epoch 86/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4906 - acc: 0.7721     \n",
+      "Epoch 87/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4972 - acc: 0.7578     \n",
+      "Epoch 88/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4921 - acc: 0.7695     \n",
+      "Epoch 89/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4900 - acc: 0.7630     \n",
+      "Epoch 90/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4870 - acc: 0.7669     \n",
+      "Epoch 91/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4979 - acc: 0.7591     \n",
+      "Epoch 92/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4943 - acc: 0.7682     \n",
+      "Epoch 93/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4907 - acc: 0.7630     \n",
+      "Epoch 94/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4859 - acc: 0.7617     \n",
+      "Epoch 95/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4833 - acc: 0.7695     \n",
+      "Epoch 96/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4842 - acc: 0.7695     \n",
+      "Epoch 97/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4753 - acc: 0.7786     \n",
+      "Epoch 98/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4990 - acc: 0.7422     \n",
+      "Epoch 99/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4856 - acc: 0.7695     \n",
+      "Epoch 100/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4814 - acc: 0.7721     \n",
+      "Epoch 101/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4857 - acc: 0.7695     \n",
+      "Epoch 102/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4769 - acc: 0.7878     \n",
+      "Epoch 103/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4844 - acc: 0.7552     \n",
+      "Epoch 104/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4817 - acc: 0.7760     \n",
+      "Epoch 105/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4803 - acc: 0.7760     \n",
+      "Epoch 106/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4811 - acc: 0.7812     \n",
+      "Epoch 107/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4836 - acc: 0.7773     \n",
+      "Epoch 108/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4774 - acc: 0.7826     \n",
+      "Epoch 109/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4775 - acc: 0.7786     \n",
+      "Epoch 110/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4795 - acc: 0.7695     \n",
+      "Epoch 111/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4713 - acc: 0.7747     \n",
+      "Epoch 112/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4745 - acc: 0.7826     \n",
+      "Epoch 113/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4751 - acc: 0.7773     \n",
+      "Epoch 114/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4808 - acc: 0.7617     \n",
+      "Epoch 115/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4790 - acc: 0.7643     \n",
+      "Epoch 116/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4723 - acc: 0.7786     \n",
+      "Epoch 117/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4668 - acc: 0.7799     \n",
+      "Epoch 118/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4644 - acc: 0.7839     \n",
+      "Epoch 119/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4711 - acc: 0.7839     \n",
+      "Epoch 120/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4694 - acc: 0.7839     \n",
+      "Epoch 121/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4760 - acc: 0.7839     \n",
+      "Epoch 122/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4725 - acc: 0.7747     \n",
+      "Epoch 123/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4699 - acc: 0.7826     \n",
+      "Epoch 124/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4701 - acc: 0.7799     \n",
+      "Epoch 125/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4695 - acc: 0.7747     \n",
+      "Epoch 126/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4642 - acc: 0.7799     \n",
+      "Epoch 127/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4719 - acc: 0.7656     \n",
+      "Epoch 128/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4579 - acc: 0.7865     \n",
+      "Epoch 129/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4644 - acc: 0.7865     \n",
+      "Epoch 130/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4735 - acc: 0.7760     \n",
+      "Epoch 131/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4606 - acc: 0.7773     \n",
+      "Epoch 132/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4639 - acc: 0.7760     \n",
+      "Epoch 133/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4751 - acc: 0.7865     \n",
+      "Epoch 134/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4729 - acc: 0.7773     \n",
+      "Epoch 135/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4601 - acc: 0.7917     \n",
+      "Epoch 136/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4564 - acc: 0.7852     \n",
+      "Epoch 137/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4625 - acc: 0.7852     \n",
+      "Epoch 138/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4684 - acc: 0.7760     \n",
+      "Epoch 139/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4605 - acc: 0.7734     \n",
+      "Epoch 140/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4590 - acc: 0.7839     \n",
+      "Epoch 141/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4602 - acc: 0.7734     \n",
+      "Epoch 142/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4593 - acc: 0.7760     \n",
+      "Epoch 143/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4614 - acc: 0.7878     \n",
+      "Epoch 144/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4508 - acc: 0.7969     \n",
+      "Epoch 145/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4580 - acc: 0.7747     \n",
+      "Epoch 146/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4627 - acc: 0.7812     \n",
+      "Epoch 147/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4531 - acc: 0.7943     \n",
+      "Epoch 148/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4656 - acc: 0.7734     \n",
+      "Epoch 149/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4566 - acc: 0.7839     \n",
+      "Epoch 150/150\n",
+      "768/768 [==============================] - 0s - loss: 0.4593 - acc: 0.7839     \n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.callbacks.History at 0x109496b70>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Fit the model\n",
+    "model.fit(X, Y, nb_epoch=150, batch_size=10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " 32/768 [>.............................] - ETA: 0sacc: 79.56%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# evaluate the model\n",
+    "scores = model.evaluate(X, Y)\n",
+    "print(\"%s: %.2f%%\" % (model.metrics_names[1], scores[1]*100))"
+   ]
+  },
+  {
+   "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": "3.5.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/projects/7-dnns/Deitelzweig/pima-indians-diabetes.csv b/projects/7-dnns/Deitelzweig/pima-indians-diabetes.csv
new file mode 100644
index 0000000..982aab8
--- /dev/null
+++ b/projects/7-dnns/Deitelzweig/pima-indians-diabetes.csv
@@ -0,0 +1,768 @@
+6,148,72,35,0,33.6,0.627,50,1
+1,85,66,29,0,26.6,0.351,31,0
+8,183,64,0,0,23.3,0.672,32,1
+1,89,66,23,94,28.1,0.167,21,0
+0,137,40,35,168,43.1,2.288,33,1
+5,116,74,0,0,25.6,0.201,30,0
+3,78,50,32,88,31.0,0.248,26,1
+10,115,0,0,0,35.3,0.134,29,0
+2,197,70,45,543,30.5,0.158,53,1
+8,125,96,0,0,0.0,0.232,54,1
+4,110,92,0,0,37.6,0.191,30,0
+10,168,74,0,0,38.0,0.537,34,1
+10,139,80,0,0,27.1,1.441,57,0
+1,189,60,23,846,30.1,0.398,59,1
+5,166,72,19,175,25.8,0.587,51,1
+7,100,0,0,0,30.0,0.484,32,1
+0,118,84,47,230,45.8,0.551,31,1
+7,107,74,0,0,29.6,0.254,31,1
+1,103,30,38,83,43.3,0.183,33,0
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diff --git a/projects/8-chm-writeup/Deitelzweig.md b/projects/8-chm-writeup/Deitelzweig.md
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+Recently, many companies have started to develop self-driving cars that they hope will occupy the roads in a few years. In the forefront of this development is Google. They have been working for years on accomplishing this, and in the last year or so, they have started to test drive these on the road. Google is not the only company that has this feature in cars. Tesla and many other cars have self-driving functions; however, many of them are just used for parking. Eventually, these companies hope that self-driving cars will be the only cars on the road, and they hope that it will make driving safer overall. If these cars are finished in the future, they will be able to be used by all people. These people also want to be safe when they drive on the road, so their goals are aligned with the creators. It will likely learn the rules of the road and also be trained statistically to learn new environments. They will have sensors that allow them to see their environment and therefore be able to react to stimuli. It interacts with humans because humans are the ones that give it controls of where to go. It can also be stopped in the event of failure. A hostile user could theoretically sabotage the system by intentionally turning it off in a dangerous situation or by jumping in front of the car. In this instance, the car may not know what to do. It also presents important moral dilemmas since the car must likely at some point decide which is more valuable in terms of lives, similar to the trolley problem. However, it still would likely present a much safer driving environment on the road since here is not room for human error as much.
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