firstNeuralNetwork.ipynb

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   "cell_type": "markdown",
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   "source": [
    "# Simple Neural Network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define weights "
   ]
  },
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   "cell_type": "code",
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   "source": [
    "weights = [0.1, 0.2, 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define input-data"
   ]
  },
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   "cell_type": "code",
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   "source": [
    "toes = [8.5, 9.5, 9.9, 9.0]\n",
    "wlrec = [0.65, 0.8, 0.8, 0.9]\n",
    "nfans = [1.2, 1.3, 0.5, 1.0]"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Functions to predict "
   ]
  },
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   "execution_count": 11,
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   "outputs": [],
   "source": [
    "def neural_network(input, weights):\n",
    "    prediction = w_sum(input, weights)\n",
    "    return prediction\n",
    "\n",
    "def w_sum(a, b):\n",
    "    assert(len(a) == len(b))\n",
    "    output = 0\n",
    "    for i in range(len(a)):\n",
    "        output += a[i] * b[i]\n",
    "    return output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predict one input datapoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "input = [toes[0], wlrec[0], nfans[0]]\n",
    "pred = neural_network(input, weights)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9800000000000001\n"
     ]
    }
   ],
   "source": [
    "print(pred)"
   ]
  }
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