grad descent - 17 Aug.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "m_ = m - eta * de/dm\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = []\n",
    "Y = []\n",
    "for x in range(1, 10):\n",
    "    y = 10*x + 10 + 5*random.random() - 5*random.random()\n",
    "    X.append(x)\n",
    "    Y.append(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fd3a4d566d8>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD4CAYAAAAXUaZHAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAUgklEQVR4nO3db2yd9X338fd3TlAP7I/540WJAwtSkbcKVNxa3OxmRR0pS+lQsaIKtbrXZR1aNg1tsN7ymtxPpkm3BJWndX1UKSLbMq2lMDAJ2qqmKNDt3oNlczBbgMyDMmjjBOJ1uH+tNWTf+8G5nDrGlBxfJ77sn98vyTrn/M51zvUBwcfHv+s61y8yE0lSWX6s6QCSpO6z3CWpQJa7JBXIcpekAlnuklSgdU0HALjiiityy5YtTceQpFXlyJEj/5GZfYs997blHhF/CtwOnMrMa6uxy4CHgC3Ay8Cdmfl6RATwWeBDwPeBX8vMp99uH1u2bGF8fPz8/mkkSQBExCtv9dz5TMv8OfDBBWO7gEOZeQ1wqHoMcBtwTfWzE/hcp2ElSfW9bbln5t8B/7lg+A5gX3V/HzA8b/wvsu0fgN6I2NilrJKk87TUA6obMvNkdf9VYEN1vx/4xrztjldjkqRlVPtsmWxfv6DjaxhExM6IGI+I8enp6boxJEnzLLXcX5ubbqluT1XjU8CV87bbXI29SWbuycyhzBzq61v0YK8kaYmWWu6PAzuq+zuAA/PGfzXabgS+NW/6RpJU2T8xxU33P8nVu/6Gm+5/kv0Ti34OXrLzORXyQeD9wBURcRz4A+B+4OGIuAt4Bbiz2vxLtE+DfJH2qZCf6GpaSSrA/okpdo8dZfb0GQCmZmbZPXYUgOHB7hymfNtyz8yPvcVTWxfZNoG764aSpJKNHpw8W+xzZk+fYfTgZNfK3csPSNIyOzEz29H4UljukrTMNvW2OhpfCstdkpbZyLYBWut7zhlrre9hZNtA1/axIi4cJklrydy8+ujBSU7MzLKpt8XItoGuzbeD5S5JjRge7O9qmS/ktIwkFchyl6QCWe6SVCDLXZIKZLlLUoEsd0kqkOUuSQWy3CWpQJa7JBXIb6hKKtr+iakL+jX/lcpyl1Ss5VgUY6VyWkZSsX7Uohilq1XuEXFPRDwbEc9FxL3V2GUR8UREvFDdXtqVpJLUoeVYFGOlWnK5R8S1wG8ANwDvBm6PiHcCu4BDmXkNcKh6LEnLbjkWxVip6nxy/zngcGZ+PzPfAP4W2A7cAeyrttkHDNdKKElLtByLYqxUdcr9WeB9EXF5RFwMfAi4EtiQmSerbV4FNiz24ojYGRHjETE+PT1dI4YkLW54sJ/7tl9Hf2+LAPp7W9y3/briD6YCRGYu/cURdwG/DXwPeA74L+DXMrN33javZ+aPnHcfGhrK8fHxJeeQpLUoIo5k5tBiz9U6oJqZezPzvZl5M/A68G/AaxGxsdrxRuBUnX1IkjpX92yZn65ur6I93/4F4HFgR7XJDuBAnX1IkjpX90tMj0bE5cBp4O7MnImI+4GHqymbV4A764aUJHWmVrln5vsWGfsmsLXO+0qS6vEbqpJUIMtdkgpkuUtSgSx3SSqQ5S5JBbLcJalAlrskFchyl6QCWe6SVCDLXZIKZLlLUoEsd0kqkOUuSQWy3CWpQJa7JBXIcpekAtVdZu/3IuK5iHg2Ih6MiHdExNURcTgiXoyIhyLiom6FlSSdnyWXe0T0A78LDGXmtUAP8FHg08BnMvOdtBfNvqsbQSWtbPsnprjp/ie5etffcNP9T7J/YqrpSGta3WmZdUArItYBFwMngVuAR6rn9wHDNfchaYXbPzHF7rGjTM3MksDUzCy7x45a8A1acrln5hTwR8DXaZf6t4AjwExmvlFtdhzoX+z1EbEzIsYjYnx6enqpMSStAKMHJ5k9feacsdnTZxg9ONlQItWZlrkUuAO4GtgEXAJ88Hxfn5l7MnMoM4f6+vqWGkPSCnBiZrajcV14daZlPgD8e2ZOZ+ZpYAy4CeitpmkANgP+XSYVblNvq6NxXXh1yv3rwI0RcXFEBLAVeB54CvhItc0O4EC9iJJWupFtA7TW95wz1lrfw8i2gYYSqc6c+2HaB06fBo5W77UH+BTwyYh4Ebgc2NuFnJJWsOHBfu7bfh39vS0C6O9tcd/26xgeXPSQm5ZBZGbTGRgaGsrx8fGmY0jSqhIRRzJzaLHn/IaqJBXIcpekAlnuklQgy12SCmS5S1KBLHdJKpDlLkkFstwlqUCWuyQVyHKXpAJZ7pJUIMtdkgpkuUtSgSx3SSqQ5S5JBbLcJalAdRbIHoiIZ+b9fDsi7o2IyyLiiYh4obq9tJuBJUlvr84ye5OZeX1mXg+8F/g+8BiwCziUmdcAh6rHkqRl1K1pma3A1zLzFeAOYF81vg8Y7tI+JEnnqVvl/lHgwer+hsw8Wd1/Fdiw2AsiYmdEjEfE+PT0dJdiSJIA1tV9g4i4CPgwsHvhc5mZEbHoCtyZuQfYA+0FsuvmkNaK/RNTjB6c5MTMLJt6W4xsG2B4sL/pWFphapc7cBvwdGa+Vj1+LSI2ZubJiNgInOrCPiTRLvbdY0eZPX0GgKmZWXaPHQWw4HWObkzLfIwfTskAPA7sqO7vAA50YR+SgNGDk2eLfc7s6TOMHpxsKJFWqlrlHhGXALcCY/OG7wdujYgXgA9UjyV1wYmZ2Y7GtXbVmpbJzO8Bly8Y+ybts2ckddmm3hZTixT5pt5WA2m0kvkNVWkVGdk2QGt9zzljrfU9jGwbaCiRVqpuHFCVtEzmDpp6tozejuUurTLDg/2Wud6W0zKSVCDLXZIKZLlLUoEsd0kqkOUuSQWy3CWpQJ4KKS3CKy9qtbPcpQW88qJK4LSMtIBXXlQJLHdpAa+8qBJY7tICb3WFRa+8qNXEcpcW8MqLKoEHVKUFvPKiSlCr3COiF3gAuBZI4NeBSeAhYAvwMnBnZr5eZz/ScvPKi1rt6k7LfBb4cmb+LPBu4BiwCziUmdcAh6rHkqRltORyj4ifAm4G9gJk5g8ycwa4A9hXbbYPGK4XUZLUqTqf3K8GpoE/i4iJiHigWjB7Q2aerLZ5Fdiw2IsjYmdEjEfE+PT0dI0YkqSF6pT7OuA9wOcycxD4HgumYDIzac/Fv0lm7snMocwc6uvrqxFDkrRQnXI/DhzPzMPV40dol/1rEbERoLo9VS+iJKlTSy73zHwV+EZEzJ38uxV4Hngc2FGN7QAO1EooSepY3fPcfwf4fERcBLwEfIL2L4yHI+Iu4BXgzpr7kCR1qFa5Z+YzwNAiT22t876SpHq8/IAkFchyl6QCWe6SVCDLXZIKZLlLUoEsd0kqkOUuSQWy3CWpQJa7JBXIcpekAlnuklQgy12SCmS5S1KBLHdJKpDlLkkFstwlqUC1FuuIiJeB7wBngDcycygiLgMeArYALwN3Zubr9WKqVPsnphg9OMmJmVk29bYY2TbA8GB/07GkVa8bn9x/MTOvz8y5FZl2AYcy8xrgUPVYepP9E1PsHjvK1MwsCUzNzLJ77Cj7J6aajiatehdiWuYOYF91fx8wfAH2oQKMHpxk9vSZc8ZmT59h9OBkQ4mkctQt9wS+EhFHImJnNbYhM09W918FNiz2wojYGRHjETE+PT1dM4ZWoxMzsx2NSzp/dcv9FzLzPcBtwN0RcfP8JzMzaf8CeJPM3JOZQ5k51NfXVzOGVqNNva2OxiWdv1rlnplT1e0p4DHgBuC1iNgIUN2eqhtSZRrZNkBrfc85Y631PYxsG2gokVSOJZd7RFwSET8xdx/4JeBZ4HFgR7XZDuBA3ZAq0/BgP/dtv47+3hYB9Pe2uG/7dZ4tI3VBnVMhNwCPRcTc+3whM78cEf8EPBwRdwGvAHfWj6lSDQ/2W+bSBbDkcs/Ml4B3LzL+TWBrnVCSpHr8hqokFchyl6QCWe6SVCDLXZIKZLlLUoEsd0kqkOUuSQWy3CWpQJa7JBWo1kpMWj1c8UhaWyz3NWBuxaO5hTHmVjwCLHipUE7LrAGueCStPZb7GuCKR9LaY7mvAa54JK09lvsa4IpH0trjAdU1YO6gqWfLSGtH7XKPiB5gHJjKzNsj4mrgi8DlwBHg45n5g7r7UT2ueCStLd2YlrkHODbv8aeBz2TmO4HXgbu6sA9JUgdqlXtEbAZ+GXigehzALcAj1Sb7gOE6+5Akda7uJ/c/AX4f+O/q8eXATGa+UT0+DjgXIEnLbMnlHhG3A6cy88gSX78zIsYjYnx6enqpMSRJi6jzyf0m4MMR8TLtA6i3AJ8FeiNi7kDtZmBqsRdn5p7MHMrMob6+vhoxJEkLLbncM3N3Zm7OzC3AR4EnM/N/AU8BH6k22wEcqJ1SktSRC/Elpk8Bn4yIF2nPwe+9APuQJP0IXfkSU2Z+Ffhqdf8l4IZuvK8kaWm8/IAkFchyl6QCWe6SVCDLXZIKZLlLUoEsd0kqkOUuSQWy3CWpQJa7JBXIcpekAlnuklQgy12SCmS5S1KBLHdJKlBXLvmrH9o/McXowUlOzMyyqbfFyLYBhgddRlbS8rLcu2j/xBS7x44ye/oMAFMzs+weOwpgwUtaVk7LdNHowcmzxT5n9vQZRg9ONpRI0lq15HKPiHdExD9GxD9HxHMR8YfV+NURcTgiXoyIhyLiou7FXdlOzMx2NC5JF0qdT+7/BdySme8Grgc+GBE3Ap8GPpOZ7wReB+6qnXKV2NTb6mhcki6UJZd7tn23eri++kngFuCRanwfMFwn4Goysm2A1vqec8Za63sY2TbQUCJJa1WtOfeI6ImIZ4BTwBPA14CZzHyj2uQ4sOiRxIjYGRHjETE+PT1dJ8aKMTzYz33br6O/t0UA/b0t7tt+nQdTJS27WmfLZOYZ4PqI6AUeA362g9fuAfYADA0NZZ0cK8nwYL9lLqlxXTlbJjNngKeAnwd6I2Lul8ZmYKob+5Aknb86Z8v0VZ/YiYgWcCtwjHbJf6TabAdwoGZGSVKH6kzLbAT2RUQP7V8SD2fmX0fE88AXI+L/AhPA3i7klCR1YMnlnpn/AgwuMv4ScEOdUJKkevyGqiQVyHKXpAJZ7pJUIMtdkgpkuUtSgSx3SSqQ5S5JBbLcJalAlrskFchyl6QCWe6SVCDLXZIKZLlLUoEsd0kqkOUuSQWy3CWpQHWW2bsyIp6KiOcj4rmIuKcavywinoiIF6rbS7sXV5J0Pup8cn8D+N+Z+S7gRuDuiHgXsAs4lJnXAIeqx5KkZVRnmb2TwMnq/nci4hjQD9wBvL/abB/wVeBTtVIuYv/EFKMHJzkxM8um3hYj2wYYHuzv9m4kaVWqs0D2WRGxhfZ6qoeBDVXxA7wKbHiL1+wEdgJcddVVHe1v/8QUu8eOMnv6DABTM7PsHjsKYMFLEl04oBoRPw48Ctybmd+e/1xmJpCLvS4z92TmUGYO9fX1dbTP0YOTZ4t9zuzpM4wenOzofSSpVLXKPSLW0y72z2fmWDX8WkRsrJ7fCJyqF/HNTszMdjQuSWtNnbNlAtgLHMvMP5731OPAjur+DuDA0uMtblNvq6NxSVpr6nxyvwn4OHBLRDxT/XwIuB+4NSJeAD5QPe6qkW0DtNb3nDPWWt/DyLaBbu9KklalOmfL/D0Qb/H01qW+7/mYO2jq2TKStLiunC3ThOHBfstckt6Clx+QpAJZ7pJUIMtdkgpkuUtSgSx3SSpQtK8Q0HCIiGnglSW+/ArgP7oYp1vM1RlzdW6lZjNXZ+rk+pnMXPT6LSui3OuIiPHMHGo6x0Lm6oy5OrdSs5mrMxcql9MyklQgy12SClRCue9pOsBbMFdnzNW5lZrNXJ25ILlW/Zy7JOnNSvjkLklawHKXpAKt2nKPiD+NiFMR8WzTWeaLiCsj4qmIeD4inouIe5rOBBAR74iIf4yIf65y/WHTmeaLiJ6ImIiIv246y5yIeDkijlZrFYw3nWdORPRGxCMR8a8RcSwifn4FZBqYt67DMxHx7Yi4t+lcABHxe9V/889GxIMR8Y6mMwFExD1VpucuxL+rVTvnHhE3A98F/iIzr206z5xqacGNmfl0RPwEcAQYzsznG84VwCWZ+d1qecS/B+7JzH9oMteciPgkMAT8ZGbe3nQeaJc7MJSZK+qLLxGxD/h/mflARFwEXJyZMw3HOisieoAp4H9k5lK/nNitLP20/1t/V2bORsTDwJcy888bznUt8EXgBuAHwJeB38rMF7u1j1X7yT0z/w74z6ZzLJSZJzPz6er+d4BjQOMXns+271YP11c/K+I3e0RsBn4ZeKDpLCtdRPwUcDPtJS7JzB+spGKvbAW+1nSxz7MOaEXEOuBi4ETDeQB+Djicmd/PzDeAvwW2d3MHq7bcV4OI2AIMAocbjgKcnfp4hvai5U9k5orIBfwJ8PvAfzecY6EEvhIRRyJiZ9NhKlcD08CfVdNYD0TEJU2HWuCjwINNhwDIzCngj4CvAyeBb2XmV5pNBcCzwPsi4vKIuBj4EHBlN3dguV8gEfHjwKPAvZn57abzAGTmmcy8HtgM3FD9adioiLgdOJWZR5rOsohfyMz3ALcBd1dTgU1bB7wH+FxmDgLfA3Y1G+mHqmmiDwN/1XQWgIi4FLiD9i/FTcAlEfErzaaCzDwGfBr4Cu0pmWeAM93ch+V+AVRz2o8Cn8/MsabzLFT9Gf8U8MGGo0B7ofUPV/PbX6S94PpfNhuprfrUR2aeAh6jPT/atOPA8Xl/dT1Cu+xXituApzPztaaDVD4A/HtmTmfmaWAM+J8NZwIgM/dm5nsz82bgdeDfuvn+lnuXVQcu9wLHMvOPm84zJyL6IqK3ut8CbgX+tdFQQGbuzszNmbmF9p/zT2Zm45+sIuKS6oA41bTHL9H+U7pRmfkq8I2IGKiGtgKNHqxf4GOskCmZyteBGyPi4ur/za20j4M1LiJ+urq9ivZ8+xe6+f6rdoHsiHgQeD9wRUQcB/4gM/c2mwpofxL9OHC0mt8G+D+Z+aXmIgGwEdhXncnwY8DDmbliTjtcgTYAj7X7gHXAFzLzy81GOut3gM9XUyAvAZ9oOA9w9pfgrcBvNp1lTmYejohHgKeBN4AJVs5lCB6NiMuB08Dd3T4wvmpPhZQkvTWnZSSpQJa7JBXIcpekAlnuklQgy12SCmS5S1KBLHdJKtD/B6QaTLgiKQl/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X, Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sqloss(weights):\n",
    "    m, c = weights\n",
    "    totalsqerror = 0\n",
    "    for i, x in enumerate(X):\n",
    "        predictedy = x*m + c\n",
    "        actualy = Y[i]\n",
    "        error = actualy - predictedy\n",
    "        totalsqerror += error * error\n",
    "    return totalsqerror"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Coding differentiation / derivative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sq(x):\n",
    "    return x*x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def diff(f, x):\n",
    "    delta = 0.000000001\n",
    "    y = f(x)\n",
    "    y1 = f(x+delta)\n",
    "    return (y1 - y)/delta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20.00000165480742"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff(sq, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cube(x):\n",
    "    return x*x*x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "431.9999789004214"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff(cube, 12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "def diff(lossf, weights, idx):\n",
    "    weights = weights.copy()\n",
    "    delta = 0.000000001\n",
    "    y = lossf(weights)\n",
    "    weights[idx] += delta \n",
    "    y1 = lossf(weights)\n",
    "    return (y1 - y)/delta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17.618582148770656"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff(sqloss, [10, 10], 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-2.4303226098254527"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff(sqloss, [10, 10], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-5112.386133987457"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff(sqloss, [1, 10], 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "m = random.random()\n",
    "c = random.random()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "eta = .002 #Learning rate scheduling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 Current Loss:  100.07328557115078 [10.759624553760233, 5.195374221067066]\n",
      "1 Current Loss:  98.93922829360298 [10.752135717542416, 5.24249571000011]\n",
      "2 Current Loss:  97.82187739891481 [10.744702388923116, 5.289268862462375]\n",
      "3 Current Loss:  96.72098728495996 [10.737323828937889, 5.335696179564291]\n",
      "4 Current Loss:  95.6363157330075 [10.729999895478187, 5.381780247681416]\n",
      "5 Current Loss:  94.56762425119913 [10.72273007695324, 5.42752356792418]\n",
      "6 Current Loss:  93.51467705621953 [10.715514117567665, 5.472928698246431]\n",
      "7 Current Loss:  92.47724190929851 [10.708351363622143, 5.51799811133689]\n",
      "8 Current Loss:  91.45508948856953 [10.701241673008127, 5.562734365149407]\n",
      "9 Current Loss:  90.44799591193879 [10.694184534134848, 5.607139847107572]\n",
      "10 Current Loss:  89.45573807663091 [10.687179549098374, 5.651217058321816]\n",
      "11 Current Loss:  88.47810010277044 [10.680226319994773, 5.6949683009506025]\n",
      "12 Current Loss:  87.5148637307067 [10.67332456260695, 5.738396132947781]\n",
      "13 Current Loss:  86.56581906878012 [10.666473822187555, 5.781502828050105]\n",
      "14 Current Loss:  85.63075486577476 [10.659673700832656, 5.824290858946295]\n",
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      "914 Current Loss:  23.094707188132492 [9.747867660383719, 11.561610974830199]\n"
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      "915 Current Loss:  23.094705729418568 [9.747859162292599, 11.561664365011364]\n",
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      "994 Current Loss:  23.09463833007879 [9.747353746139225, 11.564844669031498]\n",
      "995 Current Loss:  23.094637884495874 [9.747349049451742, 11.564874156555032]\n",
      "996 Current Loss:  23.094637445327418 [9.74734440250225, 11.564903438021172]\n",
      "997 Current Loss:  23.094637012730374 [9.747339769763613, 11.564932492113638]\n",
      "998 Current Loss:  23.09463658661002 [9.747335186762967, 11.564961325937855]\n",
      "999 Current Loss:  23.09463616660572 [9.74733066060574, 11.564989960810106]\n"
     ]
    }
   ],
   "source": [
    "for epoch in range(1000):\n",
    "    weights = [m, c]\n",
    "    print(epoch, \"Current Loss: \", sqloss(weights), weights)\n",
    "    de_dm = diff(sqloss, weights, 0)\n",
    "    de_dc = diff(sqloss, weights, 1)\n",
    "    m1 = m - eta * de_dm\n",
    "    c1 = c - eta * de_dc\n",
    "    m, c = m1, c1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Linear Regression -> Optimizer - Gradient\n",
    "# - Normal Equation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Logistic Regression -> Optimizer - GD with a different loss function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "# NN -> Optimizer GD -> Backpropagation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See https://cloudxlab.com/blog/backpropagation-from-scratch/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Few columns into multiple columns\n",
    "# Netflix challenge solution .\n",
    "# https://en.wikipedia.org/wiki/Netflix_Prize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Promptsize === No of Features\n",
    "# The data crawled from internet ~ rows\n",
    "\n",
    "Too many rows\n",
    "- Do the batchwise training. Avoid the models that need all rows in memory - example SVM/ Decision Trees\n",
    "- XGBoost..\n",
    "- is a nice problem to have\n",
    "- Sample the data...\n",
    "- Deduplicate the data"
   ]
  }
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