Deep Learning.ipynb

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   "source": [
    "## What is Deep Learning?\n",
    "\n",
    "Deep learning (also known as deep structured learning or hierarchical learning) is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Learning can be supervised, semi-supervised or unsupervised.\n",
    "\n",
    "_https://en.wikipedia.org/wiki/Deep_learning_\n",
    "\n",
    "### Image\n",
    "\n",
    "An image (from Latin: imago) is an artifact that depicts visual perception, for example, a photo or a two-dimensional picture, that has a similar appearance to some subject—usually a physical object or a person, thus providing a depiction of it.\n",
    "\n",
    "_https://en.wikipedia.org/wiki/Image_\n",
    "\n",
    "![what-is-image](https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQiXIBxwSfJ58XeDpb7Rzvs20bIU2Vjr0hiSLYYLtqXbFBkmy18DA)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Our Task\n",
    "\n",
    "Kaggel - [Dogs vs Cats](https://www.kaggle.com/c/dogs-vs-cats)\n",
    "\n",
    "Again, we need good tools for images!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Numpy\n",
    "\n",
    "From [cs231n note](http://cs231n.github.io/python-numpy-tutorial/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n",
      "(3,)\n",
      "1 2 3\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "a = np.array([1, 2, 3])   # Create a rank 1 array\n",
    "print(type(a))            # Prints \"<class 'numpy.ndarray'>\"\n",
    "print(a.shape)            # Prints \"(3,)\"\n",
    "print(a[0], a[1], a[2])   # Prints \"1 2 3\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5 2 3]\n"
     ]
    }
   ],
   "source": [
    "a[0] = 5                  # Change an element of the array\n",
    "print(a)                  # Prints \"[5, 2, 3]\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2, 3)\n",
      "1 2 4\n"
     ]
    }
   ],
   "source": [
    "matrix = np.array([[1, 2, 3],[4, 5, 6]])    # Create a rank 2 array\n",
    "print(matrix.shape)                     # Prints \"(2, 3)\"\n",
    "print(matrix[0, 0], matrix[0, 1], matrix[1, 0])   # Prints \"1 2 4\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualize `matrix`\n",
    "```python\n",
    "matrix = np.array([[1, 2, 3],[4, 5, 6]])\n",
    "```\n",
    "$\\begin{bmatrix}1 & 2 &3 \\\\4 & 5 & 6\\end{bmatrix}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 0.]\n",
      " [0. 0.]]\n",
      "[[1. 1.]]\n"
     ]
    }
   ],
   "source": [
    "a = np.zeros((2,2))   # Create an array of all zeros\n",
    "print(a)              # Prints \"[[ 0.  0.]\n",
    "                      #          [ 0.  0.]]\"\n",
    "\n",
    "b = np.ones((1,2))    # Create an array of all ones\n",
    "print(b)              # Prints \"[[ 1.  1.]]\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[7 7]\n",
      " [7 7]]\n",
      "[[1. 0.]\n",
      " [0. 1.]]\n",
      "[[0.53938814 0.63525057]\n",
      " [0.57450637 0.36722064]]\n"
     ]
    }
   ],
   "source": [
    "c = np.full((2,2), 7)  # Create a constant array\n",
    "print(c)               # Prints \"[[ 7.  7.]\n",
    "                       #          [ 7.  7.]]\"\n",
    "\n",
    "d = np.eye(2)         # Create a 2x2 identity matrix\n",
    "print(d)              # Prints \"[[ 1.  0.]\n",
    "                      #          [ 0.  1.]]\"\n",
    "\n",
    "e = np.random.random((2,2))  # Create an array filled with random values\n",
    "print(e)                     # Might print \"[[ 0.91940167  0.08143941]\n",
    "                             #               [ 0.68744134  0.87236687]]\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 3],\n",
       "       [6, 7]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the following rank 2 array with shape (3, 4)\n",
    "# [[ 1  2  3  4]\n",
    "#  [ 5  6  7  8]\n",
    "#  [ 9 10 11 12]]\n",
    "a = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])\n",
    "\n",
    "# Use slicing to pull out the subarray consisting of the first 2 rows\n",
    "# and columns 1 and 2; b is the following array of shape (2, 2):\n",
    "# [[2 3]\n",
    "#  [6 7]]\n",
    "b = a[:2, 1:3]\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "77\n"
     ]
    }
   ],
   "source": [
    "# A slice of an array is a view into the same data, so modifying it\n",
    "# will modify the original array.\n",
    "\n",
    "print(a[0, 1])   # Prints \"2\"\n",
    "b[0, 0] = 77     # b[0, 0] is the same piece of data as a[0, 1]\n",
    "print(a[0, 1])   # Prints \"77\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array([[1,2],[3,4]], dtype=np.float64)\n",
    "y = np.array([[5,6],[7,8]], dtype=np.float64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "x = $\\begin{bmatrix}1 & 2 \\\\3 & 4\\end{bmatrix}$\n",
    "y = $\\begin{bmatrix}5 & 6 \\\\7 & 8\\end{bmatrix}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 6.  8.]\n",
      " [10. 12.]]\n",
      "[[ 6.  8.]\n",
      " [10. 12.]]\n"
     ]
    }
   ],
   "source": [
    "# Elementwise sum; both produce the same array\n",
    "print(x + y)\n",
    "print(np.add(x, y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-4. -4.]\n",
      " [-4. -4.]]\n",
      "[[-4. -4.]\n",
      " [-4. -4.]]\n"
     ]
    }
   ],
   "source": [
    "# Elementwise difference; both produce the array\n",
    "print(x - y)\n",
    "print(np.subtract(x, y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 5. 12.]\n",
      " [21. 32.]]\n",
      "[[ 5. 12.]\n",
      " [21. 32.]]\n"
     ]
    }
   ],
   "source": [
    "# Elementwise product; both produce the array\n",
    "print(x * y)\n",
    "print(np.multiply(x, y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.2        0.33333333]\n",
      " [0.42857143 0.5       ]]\n",
      "[[0.2        0.33333333]\n",
      " [0.42857143 0.5       ]]\n"
     ]
    }
   ],
   "source": [
    "# Elementwise division; both produce the array\n",
    "print(x / y)\n",
    "print(np.divide(x, y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.         1.41421356]\n",
      " [1.73205081 2.        ]]\n"
     ]
    }
   ],
   "source": [
    "# Elementwise square root; produces the array\n",
    "print(np.sqrt(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array([[1,2],[3,4]])\n",
    "y = np.array([[5,6],[7,8]])\n",
    "\n",
    "v = np.array([9,10])\n",
    "w = np.array([11,12])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "x = $\\begin{bmatrix}1 & 2 \\\\3 & 4\\end{bmatrix}$\n",
    "y = $\\begin{bmatrix}5 & 6 \\\\7 & 8\\end{bmatrix}$\n",
    "\n",
    "v = $\\begin{bmatrix}9 & 10\\end{bmatrix}$\n",
    "w = $\\begin{bmatrix}11 & 12\\end{bmatrix}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "219\n",
      "219\n"
     ]
    }
   ],
   "source": [
    "# Inner product of vectors;\n",
    "print(v.dot(w))\n",
    "print(np.dot(v, w))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[29 67]\n",
      "[29 67]\n"
     ]
    }
   ],
   "source": [
    "# Matrix / vector product;\n",
    "print(x.dot(v))\n",
    "print(np.dot(x, v))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[19 22]\n",
      " [43 50]]\n",
      "[[19 22]\n",
      " [43 50]]\n"
     ]
    }
   ],
   "source": [
    "# Matrix / matrix product; both produce the rank 2 array\n",
    "print(x.dot(y))\n",
    "print(np.dot(x, y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "[4 6]\n",
      "[3 7]\n"
     ]
    }
   ],
   "source": [
    "x = np.array([[1,2],[3,4]])\n",
    "\n",
    "print(np.sum(x))  # Compute sum of all elements; prints \"10\"\n",
    "print(np.sum(x, axis=0))  # Compute sum of each column; prints \"[4 6]\"\n",
    "print(np.sum(x, axis=1))  # Compute sum of each row; prints \"[3 7]\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Optional: Advanced Numpy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check http://cs231n.github.io/python-numpy-tutorial/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112a607b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "# Compute the x and y coordinates for points on a sine curve\n",
    "x = np.arange(0, 3 * np.pi, 0.1)\n",
    "y = np.sin(x)\n",
    "\n",
    "# Plot the points using matplotlib\n",
    "plt.plot(x, y)\n",
    "plt.show()  # You must call plt.show() to make graphics appear."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113723cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute the x and y coordinates for points on sine and cosine curves\n",
    "x = np.arange(0, 3 * np.pi, 0.1)\n",
    "y_sin = np.sin(x)\n",
    "y_cos = np.cos(x)\n",
    "\n",
    "# Plot the points using matplotlib\n",
    "plt.plot(x, y_sin)\n",
    "plt.plot(x, y_cos)\n",
    "plt.xlabel('x axis label')\n",
    "plt.ylabel('y axis label')\n",
    "plt.title('Sine and Cosine')\n",
    "plt.legend(['Sine', 'Cosine'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nb14p74yKpZWfh9FxbFJEoBevjehG2qEi/rVql9FxADsr9uraeu5ZnALAzLHxuDk77pWlF8vZ5MSMMXF4uJi4e9FWm1kjQ5y7L7bmsDQph3uu6ED/jra7DaQ1GNS1FRP7RjB3fbZVrOFuV8X+2qpdpB0q4vUR3WjT3NPoODavpa87b4+KZU/eSZ77Kt3oOMKMMvNO8tTydHq2bS6nAZvJE9dG0TXEj4c/SzP8rDK7KfYfdx5jzvpsJvQJZ2BMK6Pj2I3+HYO4+/IOLE3KYUXqYaPjCDOorKnj7kUpeLiamD46Ti5CMhM3ZxMzx8ajNdy7OIWaOuPm2+3iJ5pbVMHDn6fRpbUvT8i7+mY39apIEsOb8dTydPYXlBkdR1ykF1buZPexk7x1U3eC/dyNjmNXwgI8efXGbqQeKuLN743b78Dmi72uXjN1SSo1tfW8OzbeoVdsbCrOJiemjYnD5KS4d7H1vPMvzt9324/wyaaD3NG/HZd3amF0HLs0uFsrxvQM4/1f9vHrnnxDMth8sc/4aS+b9xfy4rAY2gZ6GR3HboX4e/DaiG5sP1xsNe/8i/OTc6Kcx5Zto3uoHw9d08noOHbt2SGd6djSmweXphmy85JNF/vm7EKmr9nL8LgQhseHGh3H7v2tSzAT+oQzd302a63sSjvx12rr6pm6JJV6DTPGyPnqTc3D1cSMMfGcrKzhwaWWX0/GZn+6ReXVTF2SQlhzT14YFmN0HIfxxLXRRAX78PDSNPJOVhodR5yj6Wv2knTgBC/fEENYgJwxZgmdgn149rrOrNtbwJz1WRZ9bJssdq01jy/bTn5pFTPGxOPt5mx0JIfh7mJixpg4yqpreWhpmlWubCf+aGPWcd5dm8nIhFCGxsqV2JY0tmcYA7sE8/rq3WzPKbbY49pksS/efIhVO47y6N+i6BrqZ3QchxPZ0odnhhgzEhHnp6i8mgc+TSU8wIvnr+9idByHo5Ti1Ru7Eujtxn1LGhYltASzFLtSaqBSardSKlMp9bg57vNM9h47yQsrd3BpZCCT+rVtyocSf+HUSOS1VbvZllNkdBzxJ7TWPLZsGwWlVUwfHYeXHNkawt/TlbdHxbL/eBnPWWhhvYsudqWUCZgJDAI6A2OUUk2ySEtlTR33Lk7By9WZN2+S9dWNdGokEuTjxn2LU2S/VCv0yeaDrN5xjEf+1kmObA3Wu10A91zRgc+Tc1htgSUHzDFi7wlkaq2ztNbVwBJgqBnu93+8tmo3u46e5I2R3WnhIxdWGO3USORAYbks8Wtl9h47yYsrd3JpZCC392tndBwB3D8gkscGRnFpZGCTP5Y5ij0EOHTaxzmNn/sDpdRkpVSSUiopP//CTtq/tmswj/ytE1dEyYUV1uL0kchXablGxxH8/5Gtp6szb46UI1tr4WxyYsrl7fF0bfopMXMU+5/9q/mfUyW01rO01ola68SgoAtbSS4xojl3X9Hhgr5XNJ37BkQSF+bPU19sN3zxIwH/WrWr8ci2Gy185cjWEZmj2HOANqd9HArI0M2BuJicmD46DoD7l6RQa+DiR45u7a48PvxtPxP7RnBlVEuj4wiDmKPYtwCRSqm2SilXYDTwlRnuV9iQNs09eemGGLYeLGLaGuvb3NcR5JVU8vBnaUQF+/D4oCij4wgDXXSxa61rgXuA1UAGsFRrLe+kOaChsSGMSAjl3bWZbNh33Og4DqW+XvPg0jTKqmuZMSZOFsNzcGY5j11r/a3WuqPWur3W+mVz3KewTf+4vgsRAV488GkqJ8qqjY7jMD74NYv1mQU8d10XImXzdodnk1eeCuvl5ebMjDFxHC+r4tFl29BalhxoaikHT/Dm97sZ3LUVo3u0Ofs3CLsnxS7MLibEj8cGRvHDzmN8tOGA0XHsWkllDfctSaGlrzuvDO+KUnJqo5BiF01kUr+2XBnVgpe/ySD9sOUWP3IkWmue+GI7uUWVTB8Ti5+Hi9GRhJWQYhdNQinFGyO708zLhXtlyYEm8cnmg3yz7QgPXdORhPDmRscRVkSKXTSZ5l6uTBsdx4HjZTzzZbrMt5tRxpESXvi6YcmAO/u3NzqOsDJS7KJJ9W4XwH0DIlmecpjPknKMjmMXyqtrueeTrfh6uPD2qFhZMkD8Dyl20eTuvTKSSzoE8MyKdDKOlBgdx6ZprXlqeTpZBWVMGxVLoLeb0ZGEFZJiF03O5KR4Z1Qcfh4u3L1oq8y3X4TFmw+xPOUwUwd0pG+Hpl8lUNgmKXZhEUE+bkwfE8f+42U88cV2mW+/AOmHi3n+qx307xjEvVfKYnjizKTYhcX0bhfAQ9d04uu0XBb8vt/oODaluKKGKYuSCfB25R2ZVxdnIcUuLGrKZe25KroFL32TwZb9hUbHsQn19ZqHlqZypKiSd8fG09zL1ehIwspJsQuLcnJSvHlTLKHNPLhr0VbySiqNjmT1ZvyUyY8ZeTw9OJqE8GZGxxE2QIpdWJyfhwvvj0+gtLKWuz/ZSo2s335GazKO8faPexgeH8KEvhFGxxE2QopdGCIq2JdXb+zKlv0neHHlTqPjWKXsgjKmfppKTIgvr9wg68CIc9f0m+8JcQZDY0NIP1zM7HXZdAr24eZe4UZHshollTVM/igJZyfF++MSZH11cV5kxC4M9figaC7vFMRzK3bI5hyNauvqufeTFLILynjv5gRCm3kaHUnYGCl2YSiTk2L6mDjCAzy5a1EyB4/LZtgvf5vBL3vyeWlYDH3aBxgdR9ggKXZhOF93F+ZM6EG9htsWbKG4vMboSIZZtOkAH/62n0n92jK6Z5jRcYSNkmIXVqFtoBfvj0vg4PFyJi9Moqq2zuhIFrd2dx7PrtjBFZ2CePLaaKPjCBsmxS6sRp/2Abw+shubsgt5+LNt1Nc7zrIDaYeKuOvjrUQF+zB9TBwmubJUXAQ5K0ZYlaGxIeQWVfKvVbto7efOEw4wct1fUMZt87cQ6OPKh7f2wMdddkISF0eKXVidOy9rR25RBR/8moW/pytTLrffjSTyTlZyy7zNaGDBrT1p4eNudCRhB6TYhdVRSvH89V0oqazhX6t24eVm4pY+EUbHMrvjpVXcPHsTBaVVLLq9F+2CvI2OJOyEFLuwSianhj1Ty6vreHbFDjxdnRmREGp0LLMpKq9m3NzNHCwsZ/6tPYkLkzVghPnIm6fCarmYnJgxJo5+HQJ59PM0lqfYx9Z6JZU13DJvM/vySpl9S6Kcqy7MTopdWDV3FxOzbkmgd7sAHlyaxiebDhod6aIcL61i7OyNZBwp4d/j4unfMcjoSMIOSbELq+fp6sy8iT24vGMQTy7fztz12UZHuiBHiiu46YMN7D1WygfjExgQ3dLoSMJOSbELm+DuYuKD8YkMignmxZU7eWP1bps6zz27oIwR/95AXkkVCyf14sooKXXRdKTYhc1wdW6Ycx+V2IZ312Zy35IUKmus/wrVDfuOM/y936ioqWPx5N70bNvc6EjCzslZMcKmOJucePXGrrQN8uLV73ZxuKiC2bckEujtZnS0P7Vo0wGeW7GD8ABP5k7oQUSgl9GRhAOQEbuwOUop7rysPf++OZ6MIyUMmb6eTVnWteRvZU0dz3yZzlPL0+kXGcjyuy+RUhcWI8UubNagrq34/M6+eLiaGDN7IzPW7KXOCubddx89ybCZv7Fw4wEm92/H3Ak98JVlAoQFSbELmxYT4sfX9/bjuu6tefOHPdw8ZyNZ+aWGZKmv18z/LZvr3l1PQWkVH07swZPXRsuCXsLilNaWH+EkJibqpKQkiz+usF9aaz5LzuHFlTupqqlnyuXtmXJ5e4ttKZd6qIjnvtpB2qEirugUxGsjuhPkY53z/sJ2KaWStdaJZ7udvHkq7IJSipsS23B5pyBeWpnBtDV7WZF6mKlXdWRIt1Y4m5rm4PRIcQVvfb+Hz5JzCPJx482R3RkeHyIbTwtDyYhd2KV1e/N5aWUGu4+dpG2gF3dd3p7rY1vj5myeEfyuoyXM+jWLr1JzUQpuu6Qt9w6IxNtNxkqi6ZzriP2iil0pNRJ4HogGemqtz6mtpdiFJdTXa77feYzpa/ay80gJvu7ODO7WmuHxISSENcPpPOe+j5VUsir9KN9uP8Km7EI8XEyM6tGGSf3a0qa5bDgtmp6lij0aqAc+AB6WYhfWSGvNur0FLE85zKr0o1TU1OHn4UJcmD8JYc2IbuVLoI8bAV6u+Lq7UF5TS1lVLUXlNew6epKdR0pIP1zMtpxiACJbeDM0tjU39wqnmZerwc9OOBKLzLFrrTMaH+xi7kaIJqWUon/HIPp3DOKlYbX8mHGMDfuOk3zgBD/vzj/r9/u6O9O5tS8PXt2RQTHBRLb0sUBqIS6cxSYElVKTgckAYWGy+7owhpebM0NjQxgaGwJAcXkN2cfLKCyroqC0mpOVtXi6mvByc8bH3ZkOQd6ENvOQwYuwKWctdqXUj0Dwn3zpKa31inN9IK31LGAWNEzFnHNCIZqQn6cLsZ7+RscQwqzOWuxa66ssEUQIIYR5yJWnQghhZy72rJgbgBlAEFAEpGqt/3YO35cPHLjAhw0ECi7we+2FvAbyGjj68wfHfA3CtdZn3XbLkAuULoZSKulcTvexZ/IayGvg6M8f5DX4KzIVI4QQdkaKXQgh7IwtFvssowNYAXkN5DVw9OcP8hqckc3NsQthKUqpHcDdWuufjc4ixPmwxRG7EH9KKTVWKZWklCpVSh1RSn2nlOp3ofente4ipS5skRS7sAtKqQeBd4BXgJZAGPAeMNTIXEIYwaaKXSk1UCm1WymVqZR63Og8lqSUaqOUWquUylBK7VBK3W90JqMopUxKqRSl1MrGj/2AF2iYNvlCa12mta7RWn+ttX5EKeWmlHpHKZXb+OcdpZRb4/cGKqVWKqWKlFKFSql1Simnxq/tV0pd1fj355VSS5VSHymlTjb+DBJPy9RaKbVMKZWvlMpWSt3XhM/fXyn1uVJqV+O/hz5N9VjWSin1QOPPIF0ptVgp5W50JmtiM8WulDIBM4FBQGdgjFKqs7GpLKoWeEhrHQ30Bu52sOd/uvuBjNM+7gO4A8vPcPunaHjNYoHuQE/g6cavPQTk0HCRXUvgSeBMbzxdDywB/IGvgHcBGv8j+BpIA0KAAcBUpdRZL9a7QNOAVVrrKBqeT8ZZbm9XlFIhwH1AotY6BjABo41NZV1spthp+GXM1Fpnaa2rafgFc5jDbK31Ea311sa/n6ThlznE2FSWp5QKBQYDc077dABQoLWuPcO33Qy8oLXO01rnA/8Axjd+rQZoRcMVfTVa63X6zGcUrNdaf6u1rgMW0lCqAD2AIK31C1rraq11FjCbJigbpZQv0B+YC9D4eEXmfhwb4Ax4KKWcAU8g1+A8VsWWij0EOHTaxzk4YLEBKKUigDhgk7FJDPEO8CgNG7ycchwIbPwl/zOt+eMSFgcaPwfwOpAJfK+UyjrLFN/R0/5eDrg3PmY40LpxOqdIKVVEw8i/5bk+qfPQDsgHPmycjpqjlPJqgsexWlrrw8AbwEHgCFCstf7e2FTWxZaK/c8WxHa4czWVUt7AMmCq1rrE6DyWpJQaAuRprZP/60sbgEpg2Bm+NZeG8j0lrPFzaK1Paq0f0lq3A64DHlRKDTjPaIeAbK21/2l/fLTW157n/ZwLZyAe+LfWOg4oAxzt/aZmNBytt6XhP2gvpdQ4Y1NZF1sq9hygzWkfh+Jgh19KKRcaSn2R1voLo/MY4BLgeqXUfhqm4q5USn2stS4GngVmKqWGKaU8lVIuSqlBSqnXgMXA00qpIKVUYONtP4aG/yyUUh1Uw04aJUBd45/zsRkoUUo9ppTyaHxzN0Yp1cMsz/qPcoAcrfWpo7XPaSh6R3IVDf+R5muta4AvgL4GZ7IqtlTsW4BIpVRbpZQrDfOXXxmcyWIai2cukKG1fsvoPEbQWj+htQ7VWkfQ8PP/SWs9rvFrbwEP0vCmaD4No+h7gC+Bl4AkYBuwHdja+DmASOBHoJSGkf9753vueuOc+3U0vDmbTcOKg3MAvwt8qn/1WEeBQ0qpTo2fGgDsNPfjWLmDQO/G/8AVDa+BQ72BfDY2deWpUupaGuZYTcA8rfXLBkeymMYLbdbRUEyn5pc+jX+WAAAcQUlEQVSf1Fp/a1wq4yilLqdhA/UhRmexNKVULA3/cbgCWcCtWusTxqayLKXUP4BRNJwtlgLcrrWuMjaV9bCpYhdCCHF2tjQVI4QQ4hxIsQshhJ2RYhdCCDtzpgs6mlRgYKCOiIgw4qGFEMJmJScnF5zLnqdmKXal1Dzg1MUjMWe7fUREBElJSeZ4aCGEcBhKqQNnv5X5pmLmAwPNdF9CCCEugllG7FrrXxvXL2lSGUdKyD9Zhb+nC34eLjT3csXH3aWpH1YIm3GirJr80irKqmqpqG64gDbQx40gbzf8PFxwcvqzlTmEvbHYHLtSajIwGSAsLOyC7uPjjQdYtOngHz4X1tyTbqF+xLbx54qoFrQP8r7orELYAq012w8X8+POY2w7XEzGkRKOlZz5Gh03Zye6h/qTENGMHhHN6Ns+EHcXkwUTC0sx2wVKjSP2lecyx56YmKgvZI49t6iC3KIKisprKK6o4WhJJdtzitmWU0RucSUAXVr7cl331twQF0JLX1l7X9ifzLyTLNp0kNXpR8ktrsTkpIhs4U10K1+iW/nQys8DbzdnPF1N1GsoKK2ioLSKg4XlbD1wgh25JdTWa3zcnLm2aytuiA+hZ0RzGc3bAKVUstY68ay3s6Vi/yu5RRV8l36Ur9NyST1UhKvJiRsTQrijf3siAh1qVVNhh7TWbMouZPavWazZlYersxP9I4MYGBPMgKgWNPNyPef7qqiuY8v+Qlak5vJd+hHKq+vo2NKb+wZEcm1MKyl4K+ZwxX66/QVlzFmfxdKkHGrr6hkWG8Ljg6JoISN4YYMyjpTwwtc72ZB1nOZertzSJ5zxvcMJ8Ha76Psur67lu+1H+fcv+8jMKyWyhTcPXt2RgTHBNKyvJayJRYtdKbUYuBwIBI4Bz2mt557p9k1d7KfklVQyZ30283/fj6vJiQeu7siEPuE4m+S6LGH9TpRV89YPe1i06QC+Hi5MHRDJ6J5hTTIvXlev+Xb7Eaav2cvevFIu6xjES8NiaNPc0+yPJS6cxUfs58NSxX7K/oIynvtqB7/syScq2Id3RscSFexrsccX4nyt3ZXHI5+ncaK8hnG9wnjg6o74e577dMuFqqvXfLRhP2+s3k2d1tw/oCOT+7fDJNMzVkGK/b9orVm94xjPrEinuKKGpwdHM753uBxuCqtSUV3HK99msHDjAaKCfXh7VCzRrSw/CDlSXMFzK3bw/c5j9G7XnGmj4+RkBCsgxX4GBaVVPPxZGj/vzueq6Ja8MbKbRUZCQpxNVn4pkxcmk5lXyu392vLIwE64ORt3OqLWms+Tc3h2xQ48XU28PSqW/h3PejW7aELnWuwON9kc6O3GvAk9eGZIZ37Zk8cN7/1OVn6p0bGEg/tlTz5DZ/5GYVk1H0/qxdNDOhta6gBKKUYmtuGrey4hwNuVCR9uZubaTGQPB+vncMUO4OSkmNSvLZ/8vTfFFTXc8N7v/J5ZYHQs4YC01sxZl8WtH24mxN+DFXdfQr/IQKNj/UFkSx9W3N2P67q15vXVu3ls2TZq6urP/o3CMA5Z7Kf0iGjOirsvoaWvG7fM28zSLYeMjiQcSH295h9f7+SlbzK4pnMwy6b0tdqzUDxcTUwbHct9V3ZgaVIOEz/cTHFFjdGxxBk4dLEDtGnuybIpfenTPoBHl21j7vpsoyMJB1BTV89Dn6Ux//f9TOrXlvdujsfLzZBVtM+ZUooHr+nEGyO7szm7kNGzNnK8VLYZtUYOX+wAPu4uzJmQyKCYYF5cuZPpa/bKPKJoMpU1dUz5OJnlKYd5+JqOPD042qau9hyREMqcCT3Iyi9l1KyN5JVUGh1J/Bcp9kZuziZmjInjxvhQ3vphD69+t0vKXZhdZU0df/8oiTW78nhxWAz3XBlpk6fcXtYxiPm39iS3qIKbPtjA4aIKoyOJ00ixn8bZ5MTrI7oxvnc4H/yaxVs/7DE6krAj1bX13LVoK+v2FvCvGxv+ndmyPu0DWDipF8dLqxk9awNHi2Xkbi2k2P+Lk5PiH9d3YVRiG2b8lMnMtZlGRxJ2oKaunns+2cpPu/J45Yau3JTYxuhIZpEQ3oyFt/eisLSacXM3yZy7lZBi/xNOTopXhndlaGzD6V3z5A1VcRHq6zUPLU3j+53H+Mf1XRjb68L2I7BWsW38mTuxB4cKy7ll3mZKKuVsGaNJsZ+ByUnx5sjuDOwSzAsrd7Ii9bDRkYQN0lrz4jc7+Sotl8cGRjGhb4TRkZpE73YBvD8+gT3HTjJp/hYqa+qMjuTQpNj/grPJiWljYundrjkPf5bGb3IRkzhPs37N4sPf9nPbJW2587J2RsdpUld0asE7o+JIOnCCqUtSqauXkw+MIsV+Fm7OJj4Yn0i7QG/uXJhMxpESoyMJG7E8JYd/freLId1a8fTgaJs8++V8De7WimcGd2bVjqO8/E2G0XEclhT7OfDzcOHDW3vg5ebMxA83kyundomz2Jh1nEc+20afdgG8eVN3mzpP/WLd1q8tt13Slnm/ZcsFfwaRYj9Hrf09mH9bD8qq6rh9QRLl1bVGRxJWan9BGXd+nEx4gCfvj08wfDEvIzw1OJqBXYJ56ZudfL/jqNFxHI4U+3mICvZl+phYMo6W8NDSNOplDlH8l+KKGiYt2ALA3Ak98PNwMTiRMUxOindGx9It1J+pn6ay66hMYVqSFPt5ujKqJU8Oiua79KO886NcwCT+X23jueoHC8t5f1yCw2+i7u5iYtb4BLzdnLl9QZKc425BUuwX4PZL2zIyIZTpP2XydVqu0XGElXj1u12s21vAS8Ni6N0uwOg4VqGlrzuzbkkk72QVUxZtpbpWlvu1BCn2C6CU4uUbupIY3oxHP98mh5mCFamHmbM+mwl9whnVw74uQLpYsW38ee3GbmzOLuTFlTuNjuMQpNgvkKuzE+/dHI+PuzN3LEyWtakd2M7cEh5bto2eEc15ekhno+NYpWFxIUzu346FGw+wLDnH6Dh2T4r9IrTwdeff4+LJLarggU9T5c1UB1RUXs0dHyfh7+HKzJvjcTHJr9SZPPq3TvRpF8CTy7ezI7fY6Dh2Tf4VXqSE8OY8O6QzP+3KY9qavUbHERZUX6+Z+mkqx4qr+Pe4eIJ83IyOZNWcTU7MGBtHM09X7vw4maLyaqMj2S0pdjMY1zuc4fEhTP9pL7/uyTc6jrCQ937O5Ofd+TxzXWfiwpoZHccmBHq78d64eI4WV8pRbhOSYjcDpRQvD+tKxxY+TP00lSPFcmWqvfs9s4C3ftjD0NjWjLOz1RqbWnxYM569rgtrd+fz/q/7jI5jl6TYzcTD1cTMm+Opqqnj3k9SZBd3O3aspJL7lqTQLsibV27o6hBrwJjbuF5hDOnWijdW72ZT1nGj49gdKXYz6tDCm1eGdyXpwAleX73b6DiiCdTW1XPv4hTKqur4tw1sQG2tlFL8c3hXwgO8uHdxCgVy8ZJZSbGb2dDYEMb1DmPWr1n8tOuY0XGEmU1fs5fN2YW8fEMMkS19jI5j03zcXZg5Np7iihqZbzczKfYm8PTgznRu5ctDS9Nkvt2O/JZZwIy1mYxMCGV4fKjRcexC59a+/OP6LqzbW8C/f5H5dnORYm8C7i4m3h0bR1VtPfcvTqVW5tttXv7JKu5fkkr7IG/+MbSL0XHsyqgebbiue2ve+mEPSfsLjY5jF6TYm0i7IG9eGhbD5v2FTJfz221afb3mwaWpnKys4d2xcXi6yry6OSmleOWGGEL8PbhvcYqc324GUuxNaHh8KCMSQpmxNpPf98m2erZq1ros1u0t4LnruhAV7Gt0HLvk4+7Cu2PjyC+t4tHPt6G1zLdfDCn2JvbC0C60DfTigU9TKSyTkYitSTl4gjdW72Zw11aM6dnG6Dh2rVuoP48NjOL7ncdYuPGA0XFsmhR7E/N0dWb66DhOlNXw6OdpMhKxISWVNdy3JIWWvu68MlzOV7eE2y5py+WdgnjpmwxZNfUiSLFbQEyIH48PiuLHjDw+2iAjEVugtebp5enkFlUyfUysw+6EZGlOToo3RnbH192F+xanUFFdZ3QkmyTFbiG3XhLBlVEtePnbDDKOyEjE2i3bepiv0nK5f0AkCeHNjY7jUAK93Xjrpu7sOVbKS9/I+u0XwizFrpQaqJTarZTKVEo9bo77tDdKKV4f0Q0/DxfulZGIVcsuKOPZFen0atucu6/oYHQch9S/YxB39G/Hok0HWZUum2Gfr4sudqWUCZgJDAI6A2OUUrLbwJ8IaByJZObJSMRaVdfWc/+SFFxMTrw9KhaTk8yrG+WhazrRLdSPx7/YJhf6nSdzjNh7Apla6yytdTWwBBhqhvu1S5dGykjEmr35w2625RTzrxu70drfw+g4Ds3V2Ylpo+Oorq1n6pJU6mTJgXNmjmIPAQ6d9nFO4+f+QCk1WSmVpJRKys937DXLH7qmE11DZCRibdbvLeCDX7IY2yuMgTHBRscRQNtAL/5xfRc2ZRfyviw5cM7MUex/dqz6P/+1aq1naa0TtdaJQUFBZnhY2+Xq7MT0MQ0jkQc+lZGINTheWsUDS1Pp0MKbZwbLTKI1GZEQ+p8lB7YePGF0HJtgjmLPAU6/ciMUyDXD/dq1UyORjVkyEjGa1ppHPt9GcUUNM8bE4eFqMjqSOI1SipeGxRDs6879S1IoqZSN48/GHMW+BYhUSrVVSrkCo4GvzHC/dk9GItZhwe/7+WlXHk8MiiK6lSwZYI38PFyYPiaW3KJKnl6eLhf6ncVFF7vWuha4B1gNZABLtdY7LvZ+HYFSipdviKGVnzv3LZaRiBEyjpTwyne7uDKqBRP7RhgdR/yFhPDmTB0QyVdpuSzbetjoOFbNLOexa62/1Vp31Fq311q/bI77dBS+7i5MGx3HkWIZiVhaeXUt9y5Owc/DhddHdJMlA2zAXVd0oFfb5jy7Ip2s/FKj41gtufLUCiSEN/vPSOSz5Byj4ziMF77eyb78Ut6+KZYAbzej44hzYHJSvD0qFheTE/ctSaG6VvY6+DNS7Fbiris60Ltdc55bsYPMPBmJNLWv03JZsuUQd17Wnn6RgUbHEeehtb8Hr43oRvrhEl5btcvoOFZJit1KmJwU00Y3nJFxzydbqayRJQeayqHCcp78YjtxYf48eHVHo+OIC/C3LsHc0iecOeuzZW/hPyHFbkVa+rrzxshu7Dp6kn9+m2F0HLtUU1fPvYtTQMH00XG4mORXwFY9eW000Y17Cx8trjQ6jlWRf9VW5sqolkzq15YFGw6wKv2I0XHszuurd5N6qIhXh3ejTXNPo+OIi/CHvYWXpMiFfqeRYrdCjw2MonuoH498vo1DheVGx7EbP+06xqxfsxjXO4zB3VoZHUeYQfsgb14YGsOm7EKmyd7C/yHFboVcnZ14d2w8APd8slXe+TeD3KIKHlyaRudWvjwtSwbYlREJodwYH8qMn/aybq9jr0N1ihS7lWrT3JPXR3QnLaeYf34n8+0Xo6aunvsWp1BTW8/Mm+Nxd5ElA+zNi8O60CHIm6lLUjlWIvPtUuxWbGBMMLdeEsGHv+3nu+0y336hXlu1i6QDJ3hleFfaBnoZHUc0AU9XZ967OZ7y6jruXZxCbZ1jH+VKsVu5JwZF072NP498vk2utLsAq9KPMHtdNuN7hzM09n9WkxZ2JLKlDy/fEMPm7ELe+H6P0XEMJcVu5VydnXjv5nhcTIopH2+lvLrW6Eg2Iyu/lIc/20b3Nv48PSTa6DjCAobHhzKmZxjv/7LPoTeykWK3ASH+HkwfE8eevJM88cV2WU/mHFRU13HXoq24mBTv3RyPm7PMqzuK56/vTPdQPx7+LM1hj3Kl2G3EpZFBPHR1R1ak5rLg9/1Gx7FqWmseW7aN3cdO8s7oOEJkizuH4uZs4r1xCbiYFHd+nExZleMd5Uqx25C7Lu/AVdEteOmbDDbsO250HKs169csvkrL5eFrOnFZR8ferctRhfh7MGNMPJl5pTy6bJvDHeVKsdsQJyfFW6NiCQ/w5O5PtsrFS3/ilz35/GvVLq7tGsxdl7c3Oo4wUL/IQB4dGMU3244wc22m0XEsSordxvi6uzD7lkRq6uqZvDBZ3kw9zf6CMu79ZCsdW/rw+ojusr664I7+7RgW25o3vt/D9zsc581UKXYb1C7Imxlj4th9tIRHPttGvayRQXFFDZMWbMHJSTFrfCJebs5GRxJWQCnFqzd2o1uoHw98msruoyeNjmQRUuw26vJOLXh8UBTfbD/Cmz/sNjqOoWrq6rlrUTIHC8t5f1wCYQGyuJf4f+4uJmaNT8TTzZlJC7aQf7LK6EhNTordhv390naM6RnGzLX7WLrlkNFxDKG15pkv0/kt8zj/HN6N3u0CjI4krFCwnztzbkmkoLSK2z9KoqLavvc7kGK3YUopXhjahUsjA3ly+XbW7y0wOpLFffBrFku2HOKeKzowIiHU6DjCinVv48/00XFsyyniPjtf5leK3ca5mBquTO3QwpspHyezM7fE6EgW88XWHF79bheDu7WSnZDEObmmSzDPDenMDzuP8eLKnXZ7GqQUux3wcXdh3sQeeLs7c8u8zewvKDM6UpP7adcxHvl8G33bB/DWTd1xcpIzYMS5mXhJW27v15b5v+/nvZ/3GR2nSUix24nW/h4snNSTuvp6xs3dZNdbhSUfKOSuRVuJbuXDB+MTZLkAcd6evDaaG+JCeH31bru8kluK3Y50aOHDgtt6cqKsmlvmbeJEWbXRkcwu/XAxt81PopWfB/Nv7YmPu4vRkYQNcnJSvD6iG1d3bslzX+1gWXKO0ZHMSordznQL9Wf2hET2Hy/n5jmbKLSjck8/XMzNczbh7ebMR7f1JNDbzehIwoY5m5yYMSaOSzoE8OiybXyzzX72PJBit0N92wcy+5ZE9uWXMnb2Ro6X2v55uztyixk3t6HUl0zuLRtRC7M4dY57fJg/9y7eyvIU+xi5S7Hbqcs6BjF3Qg+yC8oYM3ujTV+UsT2nYaTu6WJi8d+l1IV5ebk5M//WnvRqG8CDS9Ps4poQKXY71i8ykA8n9uBgYTk3fbCBA8dt72yZX/fkM2rWBrxcnVkyuY9cVSqahJebM/Mm9qBfh0AeXbaN+b9lGx3pokix27m+HQJZdHsvTpRXM/y930k7VGR0pHP2Zcphbpu/hfAAL5bf1VdKXTQpD1cTs29J5OrOLXn+6528tHKnza7DJMXuABLCm7NsSl88XE2MnrWRNRnHjI70l7TWzFybydRPU0mMaMand/Smha+70bGEA3B3MfH+uAQm9o1gzvps7lq01SaXH5BidxDtg7z54q6+dGjhze0fJTHtx71WORopraplysdbeX31bq7v3pr5t/bEV05pFBZkclI8f30XnhnSmdU7jzJq1gab2/tAit2BtPBx59M7ejMsNoS3f9zDbQu2UFRuPadD7ssvZdjM3/gh4xhPD45m2uhY3F3k4iNhjEn92vLBuASy88sYPH2dTa3nLsXuYDxdnXnrpu68NCyG3zOPM3j6en7LNHbxsPp6zYLf9zNk+noKy6pZOKknt1/aTjbKEIa7pkswK+/rR1iAJ5MXJvPiyp1U1lj/1IwyYhGcxMREnZSUZPHHFX+UeqiIBz5NbTglsmcbnrg22uLTHocKy3ls2TZ+33ec/h2D+NeNXWnlJ5tPC+tSVVvHy99k8NGGA7QN9OKVG7rSp73ll4hWSiVrrRPPejspdsdWWVPH2z/sYfa6LFr4uPPYoE5c3z0EUxMvqlVWVcusX7OYvS4LBTw9pDOje7SRUbqwauv3FvDk8u0NpxAnhvLI36II8rHcFdBS7OK8pB0q4snl29mRW0JUsA8PX9OJAdEtzF60VbV1LEs+zNs/7iH/ZBWDu7bi8UFRctGRsBkV1XVMW7OX2euycDEpJvSJYHL/dgRYYIkLixS7Umok8DwQDfTUWp9TW0uxW6f6es0324/w1g97yC4oIyrYh7G9whgaG4Kfx8VN0RwpruCTTQdZvPkgBaXVJIY348nB0cSHNTNTeiEsK7ugjBlr9vJl6mHcXUyMSAhlZEIbYkJ8m+zI01LFHg3UAx8AD0ux24eaunqWbz3Mgg372ZFbgruLE1d3DubSDoH07RBAaLOzj6611mTmlfLz7nzW7s5jU3Yh9VozIKoFt/SJ4NLIQJl2EXYhM6+UmWsz+Wb7Eapr64kK9mFIt1b0ahdAt1A/sy4rbdGpGKXUz0ix26XtOcV8svkgP+w8RkHjYmIh/h6EB3jSppknrfzdMSlFvYa6+noOF1WSXVBKVkEZReU1AHRs6c1V0S0Z0zNMplyE3Sour+Hrbbl8lpzznyu8XZ2d6NLal9b+HgT7utPS141BMa0u+PfA6opdKTUZmAwQFhaWcODAgYt+XGE5Wmv2HCvlt8wCUg4VcaiwnJwTFf8p+1OCfd1pG+hF2yAvurT25fJOLQjxl7NchGMpLKsmaX8hm7MLSc8t5lhJFUeLK6moqePjSb3oFxl4QfdrtmJXSv0IBP/Jl57SWq9ovM3PyIjdIdXU1QNgUgqlkOkVIc5Aa01pVS2uzk4XPD1zrsXufA5hrrqgBMIhuJjkGjchzoVSymI7fslvpRBC2JmLKnal1A1KqRygD/CNUmq1eWIJIYS4UIZcoKSUygcu9N3TQMDYxU2MJ6+BvAaO/vzBMV+DcK110NluZEixXwylVNK5vHlgz+Q1kNfA0Z8/yGvwV2SOXQgh7IwUuxBC2BlbLPZZRgewAvIayGvg6M8f5DU4I5ubYxdCCPHXbHHELoQQ4i9IsQshhJ2xqWJXSg1USu1WSmUqpR43Oo8lKaXaKKXWKqUylFI7lFL3G53JKEopk1IqRSm10ugsRlBK+SulPldK7Wr899DH6EyWppR6oPH3IF0ptVgp5W50JmtiM8WulDIBM4FBQGdgjFKqs7GpLKoWeEhrHQ30Bu52sOd/uvuBDKNDGGgasEprHQV0x8FeC6VUCHAfkKi1jgFMwGhjU1kXmyl2oCeQqbXO0lpXA0uAoQZnshit9RGt9dbGv5+k4Zc5xNhUlqeUCgUGA3OMzmIEpZQv0B+YC6C1rtZaFxmbyhDOgIdSyhnwBHINzmNVbKnYQ4BDp32cgwMWG4BSKgKIAzYZm8QQ7wCP0rBzlyNqB+QDHzZOR81RSnkZHcqStNaHgTeAg8ARoFhr/b2xqayLLRX7ny307XDnaiqlvIFlwFStdYnReSxJKTUEyNNaJxudxUDOQDzwb611HFAGONr7Tc1oOFpvC7QGvJRS44xNZV1sqdhzgDanfRyKgx1+KaVcaCj1RVrrL4zOY4BLgOuVUvtpmIq7Uin1sbGRLC4HyNFanzpa+5yGonckVwHZWut8rXUN8AXQ1+BMVsWWin0LEKmUaquUcqXhzZKvDM5kMapha6K5QIbW+i2j8xhBa/2E1jpUax1Bw8//J621Q43UtNZHgUNKqU6NnxoA7DQwkhEOAr2VUp6NvxcDcLA3kM/mrDsoWQutda1S6h5gNQ3vgs/TWu8wOJYlXQKMB7YrpVIbP/ek1vpbAzMJY9wLLGoc4GQBtxqcx6K01puUUp8DW2k4WywFWV7gD2RJASGEsDO2NBUjhBDiHEixCyGEnZFiF0IIOyPFLoQQdkaKXQgh7IwUuxBC2BkpdiGEsDP/BwhV8/ssWIDfAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113c8da20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute the x and y coordinates for points on sine and cosine curves\n",
    "x = np.arange(0, 3 * np.pi, 0.1)\n",
    "y_sin = np.sin(x)\n",
    "y_cos = np.cos(x)\n",
    "\n",
    "# Set up a subplot grid that has height 2 and width 1,\n",
    "# and set the first such subplot as active.\n",
    "plt.subplot(2, 1, 1)\n",
    "\n",
    "# Make the first plot\n",
    "plt.plot(x, y_sin)\n",
    "plt.title('Sine')\n",
    "\n",
    "# Set the second subplot as active, and make the second plot.\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.plot(x, y_cos)\n",
    "plt.title('Cosine')\n",
    "\n",
    "# Show the figure.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Kaggle Example\n",
    "\n",
    "https://www.kaggle.com/sarvajna/dogs-vs-cats-keras-solution/notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%javascript\n",
    "$('.nbp-app-bar').toggle()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read More\n",
    "[Matrix Multiplication](https://www.mathsisfun.com/algebra/matrix-multiplying.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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