A jupyter notebook to show how numpy dot product is faster compare to for loop based dot product. Taken from Andrew Ng deeplearning course

numpy dot product vs loop based dot product.ipynb

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      "250189.622136\n",
      "Vectorized Version:2.13003158569\n",
      "250189.622136\n",
      "For Loop Version:676.230192184\n"
     ]
    }
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   "source": [
    "import numpy as np\n",
    "import time\n",
    "\n",
    "a = np.random.rand(1000000)\n",
    "b = np.random.rand(1000000)\n",
    "\n",
    "tic = time.time()\n",
    "c = np.dot(a,b)\n",
    "toc = time.time()\n",
    "\n",
    "print (c)\n",
    "print (\"Vectorized Version:\" + str(1000*(toc-tic)))\n",
    "\n",
    "c = 0\n",
    "tic = time.time()\n",
    "for i in range(1000000):\n",
    "    c += a[i]*b[i]\n",
    "toc = time.time()\n",
    "\n",
    "print(c)\n",
    "print(\"For Loop Version:\" + str(1000*(toc-tic)))\n"
   ]
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