diff --git a/CYFI445/lectures/01_linear_regression_concept/0_review_np_array.ipynb b/CYFI445/lectures/01_linear_regression_concept/0_review_np_array.ipynb
index cc1d12f..e1e28c0 100644
--- a/CYFI445/lectures/01_linear_regression_concept/0_review_np_array.ipynb
+++ b/CYFI445/lectures/01_linear_regression_concept/0_review_np_array.ipynb
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 152,
+   "execution_count": 76,
    "id": "323b62e8",
    "metadata": {},
    "outputs": [
@@ -29,7 +29,7 @@
        "6"
       ]
      },
-     "execution_count": 152,
+     "execution_count": 76,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -40,7 +40,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 153,
+   "execution_count": 77,
    "id": "6cdd612e",
    "metadata": {},
    "outputs": [
@@ -50,7 +50,7 @@
        "8"
       ]
      },
-     "execution_count": 153,
+     "execution_count": 77,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -61,7 +61,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 154,
+   "execution_count": 78,
    "id": "5e7c9a36",
    "metadata": {},
    "outputs": [
@@ -71,7 +71,7 @@
        "1"
       ]
      },
-     "execution_count": 154,
+     "execution_count": 78,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -82,7 +82,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 155,
+   "execution_count": 79,
    "id": "c497160d",
    "metadata": {},
    "outputs": [
@@ -101,7 +101,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 156,
+   "execution_count": 80,
    "id": "e81329e5",
    "metadata": {},
    "outputs": [
@@ -111,7 +111,7 @@
        "[1, 2, 3]"
       ]
      },
-     "execution_count": 156,
+     "execution_count": 80,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -123,7 +123,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 157,
+   "execution_count": 81,
    "id": "0f886f3a",
    "metadata": {},
    "outputs": [
@@ -133,7 +133,7 @@
        "1"
       ]
      },
-     "execution_count": 157,
+     "execution_count": 81,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -144,7 +144,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 158,
+   "execution_count": 82,
    "id": "bd694c5c",
    "metadata": {},
    "outputs": [
@@ -154,7 +154,7 @@
        "[1, 2, 3, 4]"
       ]
      },
-     "execution_count": 158,
+     "execution_count": 82,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -166,7 +166,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 159,
+   "execution_count": 83,
    "id": "f47c854d",
    "metadata": {},
    "outputs": [
@@ -176,7 +176,7 @@
        "4"
       ]
      },
-     "execution_count": 159,
+     "execution_count": 83,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -187,7 +187,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 160,
+   "execution_count": 84,
    "id": "e2d7b456",
    "metadata": {},
    "outputs": [
@@ -197,7 +197,7 @@
        "(1, 2, 3)"
       ]
      },
-     "execution_count": 160,
+     "execution_count": 84,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -209,7 +209,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 161,
+   "execution_count": 85,
    "id": "4e37998a",
    "metadata": {},
    "outputs": [
@@ -219,7 +219,7 @@
        "1"
       ]
      },
-     "execution_count": 161,
+     "execution_count": 85,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -238,7 +238,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 162,
+   "execution_count": 86,
    "id": "d9c0f925",
    "metadata": {},
    "outputs": [
@@ -248,7 +248,7 @@
        "[1, 4, 9]"
       ]
      },
-     "execution_count": 162,
+     "execution_count": 86,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -259,7 +259,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 163,
+   "execution_count": 87,
    "id": "799c1063",
    "metadata": {},
    "outputs": [
@@ -269,7 +269,7 @@
        "[1, 4, 9, 16, 25]"
       ]
      },
-     "execution_count": 163,
+     "execution_count": 87,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -282,7 +282,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 164,
+   "execution_count": 88,
    "id": "7a9d00ab",
    "metadata": {},
    "outputs": [
@@ -292,7 +292,7 @@
        "[0, 2, 4, 6, 8]"
       ]
      },
-     "execution_count": 164,
+     "execution_count": 88,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -305,7 +305,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 165,
+   "execution_count": 89,
    "id": "0b3a893b",
    "metadata": {},
    "outputs": [
@@ -315,7 +315,7 @@
        "[1, 2]"
       ]
      },
-     "execution_count": 165,
+     "execution_count": 89,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -337,7 +337,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 166,
+   "execution_count": 90,
    "id": "0df3abc9",
    "metadata": {},
    "outputs": [],
@@ -347,7 +347,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 167,
+   "execution_count": 91,
    "id": "2a7a1daf",
    "metadata": {},
    "outputs": [
@@ -357,7 +357,7 @@
        "array([1, 2, 3, 4, 5, 6])"
       ]
      },
-     "execution_count": 167,
+     "execution_count": 91,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -369,7 +369,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 168,
+   "execution_count": 92,
    "id": "1f8e61c2",
    "metadata": {},
    "outputs": [
@@ -379,7 +379,7 @@
        "np.int64(1)"
       ]
      },
-     "execution_count": 168,
+     "execution_count": 92,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -390,7 +390,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 169,
+   "execution_count": 93,
    "id": "61e20947",
    "metadata": {},
    "outputs": [],
@@ -400,7 +400,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 170,
+   "execution_count": 94,
    "id": "18251de4",
    "metadata": {},
    "outputs": [
@@ -410,7 +410,7 @@
        "np.float32(2.0)"
       ]
      },
-     "execution_count": 170,
+     "execution_count": 94,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -421,7 +421,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 171,
+   "execution_count": 95,
    "id": "11f35a16",
    "metadata": {},
    "outputs": [
@@ -431,7 +431,7 @@
        "array([1, 2, 3])"
       ]
      },
-     "execution_count": 171,
+     "execution_count": 95,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -442,7 +442,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 172,
+   "execution_count": 96,
    "id": "57ef6d6c",
    "metadata": {},
    "outputs": [
@@ -452,7 +452,7 @@
        "array([4, 5, 6])"
       ]
      },
-     "execution_count": 172,
+     "execution_count": 96,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -463,7 +463,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 173,
+   "execution_count": 97,
    "id": "db3cc4e7",
    "metadata": {},
    "outputs": [
@@ -475,7 +475,7 @@
        "       [ 9, 10, 11, 12]])"
       ]
      },
-     "execution_count": 173,
+     "execution_count": 97,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -487,7 +487,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 174,
+   "execution_count": 98,
    "id": "7ca7a696",
    "metadata": {},
    "outputs": [
@@ -497,7 +497,7 @@
        "np.int64(8)"
       ]
      },
-     "execution_count": 174,
+     "execution_count": 98,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -518,7 +518,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 175,
+   "execution_count": 99,
    "id": "53d75c62",
    "metadata": {},
    "outputs": [
@@ -528,7 +528,7 @@
        "array([-10.,  -8.,  -6.,  -4.,  -2.,   0.,   2.,   4.,   6.,   8.,  10.])"
       ]
      },
-     "execution_count": 175,
+     "execution_count": 99,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -552,7 +552,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 176,
+   "execution_count": 100,
    "id": "25a9b316",
    "metadata": {},
    "outputs": [
@@ -592,7 +592,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 177,
+   "execution_count": 101,
    "id": "d86d32ee",
    "metadata": {},
    "outputs": [
@@ -648,7 +648,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 178,
+   "execution_count": 102,
    "id": "ba1d7074",
    "metadata": {},
    "outputs": [
@@ -658,7 +658,7 @@
        "True"
       ]
      },
-     "execution_count": 178,
+     "execution_count": 102,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -669,7 +669,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 179,
+   "execution_count": 103,
    "id": "124e1812",
    "metadata": {},
    "outputs": [
@@ -679,7 +679,7 @@
        "4"
       ]
      },
-     "execution_count": 179,
+     "execution_count": 103,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -691,7 +691,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 180,
+   "execution_count": 104,
    "id": "f300cebf",
    "metadata": {},
    "outputs": [
@@ -701,7 +701,7 @@
        "3"
       ]
      },
-     "execution_count": 180,
+     "execution_count": 104,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -719,7 +719,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 181,
+   "execution_count": 105,
    "id": "6e9dceae",
    "metadata": {},
    "outputs": [
@@ -729,7 +729,7 @@
        "(3, 2, 4)"
       ]
      },
-     "execution_count": 181,
+     "execution_count": 105,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -740,7 +740,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 182,
+   "execution_count": 106,
    "id": "a5ee181d",
    "metadata": {},
    "outputs": [
@@ -750,7 +750,7 @@
        "24"
       ]
      },
-     "execution_count": 182,
+     "execution_count": 106,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -761,7 +761,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 183,
+   "execution_count": 107,
    "id": "b251a416",
    "metadata": {},
    "outputs": [
@@ -771,7 +771,7 @@
        "dtype('int64')"
       ]
      },
-     "execution_count": 183,
+     "execution_count": 107,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -783,7 +783,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 184,
+   "execution_count": 108,
    "id": "a043cd1c",
    "metadata": {},
    "outputs": [
@@ -793,7 +793,7 @@
        "dtype('float32')"
       ]
      },
-     "execution_count": 184,
+     "execution_count": 108,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -813,7 +813,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 185,
+   "execution_count": 109,
    "id": "75b22e31",
    "metadata": {},
    "outputs": [
@@ -823,7 +823,7 @@
        "array([0., 0.])"
       ]
      },
-     "execution_count": 185,
+     "execution_count": 109,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -834,7 +834,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 186,
+   "execution_count": 110,
    "id": "4b542c1f",
    "metadata": {},
    "outputs": [
@@ -844,7 +844,7 @@
        "array([1., 1.])"
       ]
      },
-     "execution_count": 186,
+     "execution_count": 110,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -855,7 +855,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 187,
+   "execution_count": 111,
    "id": "c22cdd54",
    "metadata": {},
    "outputs": [
@@ -865,7 +865,7 @@
        "array([0, 1, 2, 3])"
       ]
      },
-     "execution_count": 187,
+     "execution_count": 111,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -876,7 +876,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 188,
+   "execution_count": 112,
    "id": "367eacd6",
    "metadata": {},
    "outputs": [
@@ -886,7 +886,7 @@
        "array([2, 4, 6, 8])"
       ]
      },
-     "execution_count": 188,
+     "execution_count": 112,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -897,19 +897,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 189,
+   "execution_count": 113,
    "id": "0fa059e6",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[0.38240945, 0.11043883, 0.50139229, 0.17452479],\n",
-       "       [0.78513498, 0.33784318, 0.55672234, 0.56790213],\n",
-       "       [0.89555496, 0.17454204, 0.07973506, 0.30038232]])"
+       "array([[0.28983984, 0.28097321, 0.00238871, 0.10468329],\n",
+       "       [0.63055002, 0.74313965, 0.25371978, 0.41665696],\n",
+       "       [0.92790019, 0.99431098, 0.5256084 , 0.60452614]])"
       ]
      },
-     "execution_count": 189,
+     "execution_count": 113,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -920,19 +920,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 190,
+   "execution_count": 114,
    "id": "eae03b14",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 9,  3,  5,  9],\n",
-       "       [10, 10,  5,  3],\n",
-       "       [ 1, 10,  7,  4]], dtype=int32)"
+       "array([[10,  7,  1,  6],\n",
+       "       [ 7,  5,  6,  1],\n",
+       "       [ 5,  7,  8,  5]], dtype=int32)"
       ]
      },
-     "execution_count": 190,
+     "execution_count": 114,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -943,18 +943,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 191,
+   "execution_count": 115,
    "id": "86c88e48",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[-1.41440474,  0.09745154, -0.14707384,  0.54301773],\n",
-       "       [-1.00944703,  2.22044378,  1.48886965,  0.02239155]])"
+       "array([[ 0.03937364, -1.90673202, -0.75857225, -0.26479652],\n",
+       "       [ 0.20350195,  0.21107386,  1.2605615 , -0.49659832]])"
       ]
      },
-     "execution_count": 191,
+     "execution_count": 115,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -977,7 +977,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 192,
+   "execution_count": 116,
    "id": "b26242be",
    "metadata": {},
    "outputs": [
@@ -987,7 +987,7 @@
        "(6,)"
       ]
      },
-     "execution_count": 192,
+     "execution_count": 116,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -999,7 +999,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 193,
+   "execution_count": 117,
    "id": "18db087b",
    "metadata": {},
    "outputs": [
@@ -1009,7 +1009,7 @@
        "(1, 6)"
       ]
      },
-     "execution_count": 193,
+     "execution_count": 117,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1021,7 +1021,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 194,
+   "execution_count": 118,
    "id": "f213bce6",
    "metadata": {},
    "outputs": [
@@ -1031,7 +1031,7 @@
        "array([[1, 2, 3, 4, 5, 6]])"
       ]
      },
-     "execution_count": 194,
+     "execution_count": 118,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1042,7 +1042,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 195,
+   "execution_count": 119,
    "id": "25c7b822",
    "metadata": {},
    "outputs": [
@@ -1057,7 +1057,7 @@
        "       [6]])"
       ]
      },
-     "execution_count": 195,
+     "execution_count": 119,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1068,7 +1068,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 196,
+   "execution_count": 120,
    "id": "bbd43b79",
    "metadata": {},
    "outputs": [
@@ -1079,7 +1079,7 @@
        "       [4, 5, 6]])"
       ]
      },
-     "execution_count": 196,
+     "execution_count": 120,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1104,7 +1104,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 197,
+   "execution_count": 121,
    "id": "9af72192",
    "metadata": {},
    "outputs": [
@@ -1115,7 +1115,7 @@
        "       [4, 5]])"
       ]
      },
-     "execution_count": 197,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1131,7 +1131,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 198,
+   "execution_count": 122,
    "id": "4216c512",
    "metadata": {},
    "outputs": [
@@ -1142,7 +1142,7 @@
        "       [ 4, 14]])"
       ]
      },
-     "execution_count": 198,
+     "execution_count": 122,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1157,7 +1157,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 199,
+   "execution_count": 123,
    "id": "0eec244e",
    "metadata": {},
    "outputs": [
@@ -1169,7 +1169,7 @@
        "       [6, 6]])"
       ]
      },
-     "execution_count": 199,
+     "execution_count": 123,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1185,7 +1185,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 200,
+   "execution_count": 124,
    "id": "bb8d128a",
    "metadata": {},
    "outputs": [
@@ -1195,7 +1195,7 @@
        "array([1.6, 3.2])"
       ]
      },
-     "execution_count": 200,
+     "execution_count": 124,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1212,7 +1212,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 201,
+   "execution_count": 125,
    "id": "af55cce3",
    "metadata": {},
    "outputs": [
@@ -1224,7 +1224,7 @@
        "       [10, 20, 30]])"
       ]
      },
-     "execution_count": 201,
+     "execution_count": 125,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1277,7 +1277,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 202,
+   "execution_count": 126,
    "id": "0194c4b4",
    "metadata": {},
    "outputs": [
@@ -1287,7 +1287,7 @@
        "np.int64(1)"
       ]
      },
-     "execution_count": 202,
+     "execution_count": 126,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1300,7 +1300,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 203,
+   "execution_count": 127,
    "id": "5b201131",
    "metadata": {},
    "outputs": [
@@ -1310,7 +1310,7 @@
        "6"
       ]
      },
-     "execution_count": 203,
+     "execution_count": 127,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1323,7 +1323,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 204,
+   "execution_count": 128,
    "id": "e73fbd22",
    "metadata": {},
    "outputs": [
@@ -1333,7 +1333,7 @@
        "array([5, 7])"
       ]
      },
-     "execution_count": 204,
+     "execution_count": 128,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1347,7 +1347,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 205,
+   "execution_count": 129,
    "id": "01229cfb",
    "metadata": {},
    "outputs": [
@@ -1357,7 +1357,7 @@
        "array([7, 4, 6])"
       ]
      },
-     "execution_count": 205,
+     "execution_count": 129,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1371,7 +1371,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 206,
+   "execution_count": 130,
    "id": "fd882d6c",
    "metadata": {},
    "outputs": [
@@ -1381,7 +1381,7 @@
        "np.float64(2.5)"
       ]
      },
-     "execution_count": 206,
+     "execution_count": 130,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1393,51 +1393,51 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 207,
+   "execution_count": 131,
    "id": "ba88c2f7",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([2., 3.])"
+       "array([2., 5.])"
       ]
      },
-     "execution_count": 207,
+     "execution_count": 131,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "np.array([[1, 2], \n",
-    "          [3, 4]]).mean(axis=0) # change (2,2) to (1,2)"
+    "          [3, 8]]).mean(axis=0) # change (2,2) to (1,2)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 208,
+   "execution_count": 132,
    "id": "b49ff53c",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([1.5, 3.5])"
+       "array([1.5, 5.5])"
       ]
      },
-     "execution_count": 208,
+     "execution_count": 132,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "np.array([[1, 2], \n",
-    "          [3, 4]]).mean(axis=1) # change (2,2) to (2,1)"
+    "          [3, 8]]).mean(axis=1) # change (2,2) to (2,1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 209,
+   "execution_count": 133,
    "id": "fa48f9a5",
    "metadata": {},
    "outputs": [
@@ -1447,7 +1447,7 @@
        "array([1.5, 3.5])"
       ]
      },
-     "execution_count": 209,
+     "execution_count": 133,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1460,7 +1460,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 210,
+   "execution_count": 134,
    "id": "3ee7e4c9",
    "metadata": {},
    "outputs": [
@@ -1470,7 +1470,7 @@
        "np.int64(5)"
       ]
      },
-     "execution_count": 210,
+     "execution_count": 134,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1478,7 +1478,7 @@
    "source": [
     "# Example array\n",
     "array = np.array([[4, 1, 3],\n",
-    "                  [2, 5, 0]])\n",
+    "                  [2, 15, 0]])\n",
     "\n",
     "# Find index of minimum value in the entire array (flattened)\n",
     "min_index_flat = np.argmin(array)\n",
@@ -1497,13 +1497,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 211,
+   "execution_count": 135,
    "id": "6d37c412",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1520,7 +1520,7 @@
     "Y = np.array([0, 1, 4, 9, 16, 25], dtype=np.float32)\n",
     "\n",
     "# Create dots\n",
-    "plt.scatter(X, Y, color='blue')  # draw data points\n",
+    "plt.scatter(X, Y, color='red')  # draw data points\n",
     "\n",
     "# Add labels and title\n",
     "plt.xlabel('X Axis')\n",
@@ -1533,13 +1533,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 212,
+   "execution_count": 137,
    "id": "b0a7a95f",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1550,8 +1550,8 @@
    ],
    "source": [
     "# y=x**2\n",
-    "X = np.array([0, 1, 2, 3, 4, 5], dtype=np.float32)\n",
-    "Y = Y**2\n",
+    "X = np.array([0, 1, 2, 3, 4, 5, 6], dtype=np.float32)\n",
+    "Y = X**2\n",
     "\n",
     "# Create the plot\n",
     "plt.plot(X, Y)\n",
@@ -1567,7 +1567,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 213,
+   "execution_count": null,
    "id": "713cbe61",
    "metadata": {},
    "outputs": [
@@ -1610,7 +1610,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 214,
+   "execution_count": null,
    "id": "0ef952bd",
    "metadata": {},
    "outputs": [
@@ -1658,7 +1658,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 215,
+   "execution_count": null,
    "id": "5afe68f8",
    "metadata": {},
    "outputs": [
@@ -1691,7 +1691,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 216,
+   "execution_count": null,
    "id": "a1c381ad",
    "metadata": {},
    "outputs": [
@@ -1727,7 +1727,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 217,
+   "execution_count": null,
    "id": "5085fc72",
    "metadata": {},
    "outputs": [
@@ -1767,7 +1767,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 218,
+   "execution_count": null,
    "id": "37870aa6",
    "metadata": {},
    "outputs": [
diff --git a/CYFI445/lectures/01_linear_regression_concept/1_plot_linear_regression.ipynb b/CYFI445/lectures/01_linear_regression_concept/1_plot_linear_regression.ipynb
index d5a3379..5e9b673 100644
--- a/CYFI445/lectures/01_linear_regression_concept/1_plot_linear_regression.ipynb
+++ b/CYFI445/lectures/01_linear_regression_concept/1_plot_linear_regression.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "08a3e0d1",
    "metadata": {},
    "outputs": [
@@ -65,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 15,
    "id": "76e04a69",
    "metadata": {},
    "outputs": [
@@ -73,13 +73,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean Squared Error: 10.135\n"
+      "Mean Squared Error: 0.389\n"
      ]
     }
    ],
    "source": [
-    "w= 3\n",
-    "y_pred = 3.0 * X  # Model: y = 2x\n",
+    "w= 1.85\n",
+    "y_pred = w * X  # Model: y = 2x\n",
     "mse = np.mean((Y - y_pred) ** 2)\n",
     "print(f\"Mean Squared Error: {mse:.3f}\")\n"
    ]
@@ -115,13 +115,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "id": "34821a6b",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -139,7 +139,7 @@
     "Y = np.array([2.3, 3.4, 6.5, 6.8], dtype=np.float32)\n",
     "\n",
     "# Generate weights from -5 to 5\n",
-    "w_values = np.arange(-5, 9.05, 0.05) # 281 values (281,)\n",
+    "w_values = np.arange(-5, 9.05, 0.5) # 281 values (281,)\n",
     "\n",
     "# Calculate MSE for each weight\n",
     "y_preds = w_values[:, np.newaxis] * X # (281,1) *(4) = (281,1) * (1,4) = (281,4)\n",
@@ -153,7 +153,7 @@
     "\n",
     "# Create plot\n",
     "# plt.figure(figsize=(10, 6))\n",
-    "plt.plot(w_values, mse_values)\n",
+    "plt.scatter(w_values, mse_values)\n",
     "plt.scatter(optimal_w, min_mse, color='red', \n",
     "           label=f'Minimum MSE (w={optimal_w:.2f}, MSE={min_mse:.2f})')\n",
     "plt.xlabel('Weight (w)', fontsize=12)\n",
@@ -166,7 +166,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 5,
    "id": "1870d1e3",
    "metadata": {},
    "outputs": [
@@ -176,7 +176,7 @@
        "(281, 4)"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -195,7 +195,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "id": "39d1d373",
    "metadata": {},
    "outputs": [
diff --git a/CYFI445/lectures/02_linear_regression_gradient_np/1_gradient.ipynb b/CYFI445/lectures/02_linear_regression_gradient_np/1_gradient.ipynb
index ea880a2..4525d57 100644
--- a/CYFI445/lectures/02_linear_regression_gradient_np/1_gradient.ipynb
+++ b/CYFI445/lectures/02_linear_regression_gradient_np/1_gradient.ipynb
@@ -13,7 +13,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 10,
    "id": "39d1d373",
    "metadata": {},
    "outputs": [],
@@ -36,7 +36,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 11,
    "id": "016485d6",
    "metadata": {},
    "outputs": [],
@@ -67,7 +67,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 12,
    "id": "e6ec992a",
    "metadata": {},
    "outputs": [
@@ -75,37 +75,47 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "epoch 1: w = 25.779, loss = 5939.33496094\n",
-      "epoch 2: w = 22.191, loss = 4291.27685547\n",
-      "epoch 3: w = 19.141, loss = 3100.55566406\n",
-      "epoch 4: w = 16.549, loss = 2240.25878906\n",
-      "epoch 5: w = 14.346, loss = 1618.69470215\n",
-      "epoch 6: w = 12.473, loss = 1169.61450195\n",
-      "epoch 7: w = 10.881, loss = 845.15423584\n",
-      "epoch 8: w = 9.528, loss = 610.73156738\n",
-      "epoch 9: w = 8.378, loss = 441.36120605\n",
-      "epoch 10: w = 7.400, loss = 318.99108887\n",
-      "epoch 11: w = 6.569, loss = 230.57872009\n",
-      "epoch 12: w = 5.863, loss = 166.70083618\n",
-      "epoch 13: w = 5.262, loss = 120.54901886\n",
-      "epoch 14: w = 4.752, loss = 87.20434570\n",
-      "epoch 15: w = 4.318, loss = 63.11280441\n",
-      "epoch 16: w = 3.949, loss = 45.70667267\n",
-      "epoch 17: w = 3.636, loss = 33.13073730\n",
-      "epoch 18: w = 3.370, loss = 24.04463005\n",
-      "epoch 19: w = 3.143, loss = 17.47991371\n",
-      "epoch 20: w = 2.951, loss = 12.73690891\n",
-      "epoch 21: w = 2.787, loss = 9.31008530\n",
-      "epoch 22: w = 2.648, loss = 6.83420563\n",
-      "epoch 23: w = 2.530, loss = 5.04538441\n",
-      "epoch 24: w = 2.429, loss = 3.75295949\n",
-      "epoch 25: w = 2.344, loss = 2.81918406\n",
-      "epoch 26: w = 2.271, loss = 2.14453101\n",
-      "epoch 27: w = 2.210, loss = 1.65709329\n",
-      "epoch 28: w = 2.157, loss = 1.30491960\n",
-      "epoch 29: w = 2.113, loss = 1.05047393\n",
-      "epoch 30: w = 2.075, loss = 0.86663759\n",
-      "Prediction after training: f(6) = 12.448\n"
+      "epoch 1: w = -33.721, loss = 13142.33496094\n",
+      "epoch 2: w = -28.384, loss = 9495.44531250\n",
+      "epoch 3: w = -23.847, loss = 6860.56689453\n",
+      "epoch 4: w = -19.991, loss = 4956.86718750\n",
+      "epoch 5: w = -16.714, loss = 3581.44433594\n",
+      "epoch 6: w = -13.927, loss = 2587.70117188\n",
+      "epoch 7: w = -11.559, loss = 1869.72167969\n",
+      "epoch 8: w = -9.546, loss = 1350.98144531\n",
+      "epoch 9: w = -7.835, loss = 976.19177246\n",
+      "epoch 10: w = -6.381, loss = 705.40625000\n",
+      "epoch 11: w = -5.145, loss = 509.76367188\n",
+      "epoch 12: w = -4.094, loss = 368.41192627\n",
+      "epoch 13: w = -3.201, loss = 266.28527832\n",
+      "epoch 14: w = -2.442, loss = 192.49880981\n",
+      "epoch 15: w = -1.797, loss = 139.18804932\n",
+      "epoch 16: w = -1.248, loss = 100.67105103\n",
+      "epoch 17: w = -0.782, loss = 72.84249878\n",
+      "epoch 18: w = -0.386, loss = 52.73636627\n",
+      "epoch 19: w = -0.049, loss = 38.20970154\n",
+      "epoch 20: w = 0.238, loss = 27.71417999\n",
+      "epoch 21: w = 0.481, loss = 20.13116455\n",
+      "epoch 22: w = 0.688, loss = 14.65243530\n",
+      "epoch 23: w = 0.864, loss = 10.69405556\n",
+      "epoch 24: w = 1.013, loss = 7.83412457\n",
+      "epoch 25: w = 1.140, loss = 5.76782560\n",
+      "epoch 26: w = 1.248, loss = 4.27492380\n",
+      "epoch 27: w = 1.340, loss = 3.19630289\n",
+      "epoch 28: w = 1.418, loss = 2.41699839\n",
+      "epoch 29: w = 1.484, loss = 1.85395169\n",
+      "epoch 30: w = 1.541, loss = 1.44715023\n",
+      "epoch 31: w = 1.588, loss = 1.15323627\n",
+      "epoch 32: w = 1.629, loss = 0.94088316\n",
+      "epoch 33: w = 1.664, loss = 0.78745776\n",
+      "epoch 34: w = 1.693, loss = 0.67660838\n",
+      "epoch 35: w = 1.718, loss = 0.59651941\n",
+      "epoch 36: w = 1.740, loss = 0.53865540\n",
+      "epoch 37: w = 1.758, loss = 0.49684834\n",
+      "epoch 38: w = 1.773, loss = 0.46664292\n",
+      "epoch 39: w = 1.786, loss = 0.44481942\n",
+      "epoch 40: w = 1.797, loss = 0.42905203\n",
+      "Prediction after training: f(6) = 10.783\n"
      ]
     }
    ],
@@ -116,8 +126,8 @@
     "\n",
     "# Configration\n",
     "learning_rate = 0.01\n",
-    "n_iters = 30\n",
-    "w_init = 30.0\n",
+    "n_iters = 40\n",
+    "w_init = -40.0\n",
     "traced_ws=train(learning_rate, n_iters, w_init, X, Y)\n",
     "\n",
     "print(f'Prediction after training: f(6) = {forward(traced_ws[-1], 6):.3f}')"
@@ -125,13 +135,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 13,
    "id": "42ee5787",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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nr732muU5e/bsoVu3boSGhhITE8OWLVt44IEHyMvLv906depUvv/+e2bPns3BgwcZM2YMgwcPJjo6+rZ/PyWhdPvyxD2JaOBn2cpj+6nLtKtdRe2QhBCVmSkD3gu4YxM7wOOuF1Lye47eD7Tudd9IBL3LXZsdO3YMRVFuOy4nJCSEK1eucPHiRXx8fABo3749r7/+OgD33XcfW7duZcaMGfTo0YOqVfN74T08PPDz87vja3t5efHZZ59hZ2dHvXr1mDZtGhkZGbzxxhsAjB8/nvfff58tW7bw6KOP3vR8d3d33N3dgfxxP1999RXr1q2zvO7TTz9taVurVi0+++wzWrVqRXp6Oq6urnh5eQHg4+ODh4fHbfOzfPlytm7dSrt27QCYP38+gYGBLFu2jP/85z9AfvE1e/ZsateuDcCoUaOYPHnyLa+p1Wrv+Np169Zl2rRphY7d2GNVs2ZN3nnnHYYPH86sWbMAmDZtGi1btrQ8hvy/O6PRSHZ2Nu+99x7r1q0jLCzMko8tW7bw1Vdf0alTp1vGWRJU7SGqWbPmLbsQR44cCUBWVhYjR47E29sbV1dXBgwYQHJycqFrxMfHExkZibOzMz4+Prz66qvk5uYWarNx40aaN2+Og4MDderUYe7cuWX1Fu+Jo05Lnyb5W3n8Eiu3zYQQAijUA3Q3BT9Ub3x8+PDhIr9mgwYNsLP750emr68vjRo1sjzWarV4e3tbbnPdTlxcHEOGDOGLL76gffv2luOxsbE88MAD1KhRAzc3N8sP/vj4eKtjPHz4MPb29rRp08ZyzNvbm3r16hV6z87OzpZiCMDf3/+ucd9OixYtbjq2bt06unXrRrVq1XBzc2PIkCFcvnyZjIwM4J8eols5fvw4GRkZ9OjRA1dXV8vX999/z4kTJ4oVo7VU7SHatWuXpYsM4MCBA/To0cNSxY4ZM4aVK1eyePFi3N3dGTVqFP3792fr1q0A5OXlERkZiZ+fH9u2beP8+fMMHToUnU7He++9B8CpU6eIjIxk+PDhzJ8/n/Xr1/PMM8/g7+9vud9Zng1sUZ2fdiaw+kASU/rm4uIgnXpCiFKic87vrbkDs9lMxpH1uC574u7XG/QLBLWz7nWtUKdOHTQaDYcPH+ahhx666fzhw4fx9PS09PyUJJ1OV+ixRqO55TGz2XzbayQlJfHggw/yzDPPMGzYMMvxa9euERERQUREBPPnz6dq1arEx8cTERFRKgOVbxV3UYrMG7m4FO7ZO336NH369GHEiBG8++67eHl5sWXLFoYNG0ZOTg7Ozs433e68UcFYo5UrV1KtWrVC5xwcHIoVo7VU7SGqWrUqfn5+lq8VK1ZQu3ZtOnXqRFpaGt9++y0ff/wxXbt2pUWLFsyZM4dt27axfft2ANauXcuhQ4f48ccfadq0Kb169WLKlCnMnDnT8iGaPXs2wcHBTJ8+nZCQEEaNGsXAgQOZMWOGmm/das1reBJcxYWMnDxW7b/1oDchhCgRGk3+rau7fOXWuB/FEABobnchMFSD2l2tuh6a212nMG9vb3r06MGsWbPIzMwsdC4pKYn58+fzyCOPoLnhegU/L258fOMtN51OV+gX89KSlZVF3759qV+/Ph9//HGhc0eOHOHy5cu8//773H///dSvX/+mHhu9Xg9wx1hDQkLIzc1lx44dlmOXL1/m6NGjhIaGFjt2a167QGxsLGazmenTp9O2bVvuu+8+EhMLF9mNGze+7bIGoaGhODg4EB8fT506dQp9BQZaeQu2mMpNd0NOTg4//vgjY8eORaPREBsbi8lkonv37pY29evXp0aNGsTExNC2bVtiYmJo1KgRvr6+ljYRERGMGDGCgwcP0qxZM2JiYgpdo6DNnRayys7OJjs72/LYaDQC+fddbxyYVhIKrnen6z7U1J+P1x1n8e4E+jW5833uysyaXIl8kivr2WquTCYTiqJgNpvv2Kvxb4qigJ0Wc/hU7H55EtCguWFwtXK9SFIipgIaKMK1rfHZZ5/RoUMHIiIimDx5MsHBwRw8eJBx48ZRrVo1pkyZUuj9bN26lQ8++IC+ffuybt06Fi9ezO+//25pU7NmTct4FQcHBzw9PS29JQVtFEWx5OrfubjdsRuPK4rCc889R0JCAlFRUYWGfhTMUNPr9Xz22Wc8//zzHDhwwLK2UMG1AgMD0Wg0LF++nN69e+Pk5ISrq2uh16xduzYPPvggzz77LF9++SVubm6MHz+eatWq8cADDxSK68b4bnXsRta8doFatWphMpn47LPP6NOnT6HB1gWvP27cOJo0acKIESN4/vnn0ev1bNiwgZ49e1KzZk1efvllxowZQ25uLh06dCAtLY1t27bh5ubGE0/c3DNpNptRFAWTyYRWqy10rij/rstNQbRs2TJSU1Mto+qTkpLQ6/U3DeDy9fUlKSnJ0ubGYqjgfMG5O7UxGo1kZmbesutu6tSpTJo06abja9euxdnZuq7doiqY9XArhmzQoGXn6Sv8sGQV3o6lEkKFcadcicIkV9aztVzZ29vj5+dHenp6sW7LXK3eGV2fL3HaOAlN+j+914qrH5md38ZUrRNc/2WyJPn6+rJ+/Xref/99HnnkEa5cuYKPjw+RkZGMGzcOe3t7yy+xZrOZkSNHsn37diZPnoybmxvvvvsuYWFhljaTJk3irbfe4ptvvsHf3599+/aRnZ1NXl5eoV+Gc3NzLY8BcnNzycnJKXTMbDaTlZWF0WgsNM386tWrbNy4kfPnz9OwYcNC7+f333+nQ4cOzJw5kylTpvD555/TuHFjJk6cyOOPP861a9cwGo2W4mb8+PEMGzaMRx99lFmzZt0Ux6effsrrr7/OAw88gMlkol27dixcuJDMzEwyMzPJyspCUZRCcRf0thlv8/dl7WsDBAcH8+677/LBBx/wxhtv0K5dO9566y1GjBjB1atXsbOzw8/PjyVLljB58mTatm2Lo6MjLVu2pE+fPly9epVXXnkFNzc3pk6dyunTp3F3d6dJkyaMGTPmljHm5OSQmZnJpk2bbhpDXDBuyRoapbg3DktYREQEer2e33//HYAFCxbw1FNPFeqpAWjdujVdunThgw8+4LnnnuPMmTOsWbPGcj4jIwMXFxdWrVpFr169uO+++3jqqacYP368pc2qVauIjIwkIyPjlgXRrXqIAgMDuXTpEgaDoUTft8lkIioqih49etx0X/dGT8zdzbYTKfy3S21e7Fr7tu0qM2tzJSRXRWGrucrKyiIhIYGaNWvi6Gj9b1mKonD16lXc3Nzyb02Z8yA+BtKTwNUPaoSBnfbuFyoDtWrV4qWXXuKll15S5fVvypW4rXvJVVZWFqdPnyYwMPCmz7LRaKRKlSqkpaXd9ed3ueghOnPmDOvWrWPJkiWWY35+fuTk5JCamlqolyg5OdkyTdHPz4+dO3cWulZBV+SNbf49My05ORmDwXDbgV0ODg63HLyl0+lK7Rvm3a79cMsabDuRwtK9iYzuUQ87O9v9x1Wafw+VjeTKeraWq7y8PDQaDXZ2doVmT91Nwe2RgudiZwe1OpZWmPfMEqcKbsqVuK17yZWdnZ1lkPu//w0X5d90ufgbmjNnjqXLs0CLFi3Q6XSFBl4dPXqU+Ph4yzTKsLAw9u/fX2jwWVRUFAaDwTKALCws7KbBW1FRUTdNxSzvIhr44epgT0JKJrtO3/vS9EIIIYT4h+o9RGazmTlz5vDEE08U2vPF3d2dYcOGMXbsWLy8vDAYDLz44ouEhYXRtm1bAMLDwwkNDWXIkCFMmzaNpKQk3nrrLUaOHGnp4Rk+fDhffPEFr732Gk8//TR//vknixYtYuXKlaq83+Jy0muJbOTPz7sT+CX2LG1q3ds+O0IIUdmdPn1a7RBEBaJ6D9G6deuIj48vtEpngRkzZtCnTx8GDBhAx44dLQOxCmi1WlasWIFWqyUsLIzBgwczdOjQQituBgcHs3LlSqKiomjSpAnTp0/nm2++qRBrEP3bwJbVAVi5/zzXsnPv0loIIYQQ1lK9hyg8PPy2C0I5Ojoyc+bMQpvx/VtQUBCrVq2642t07tyZuLi4e4qzPGgZ5EmQtzNnLmew+kASA1pUVzskIUQFV07m1QhRbCX1GVa9h0hYT6PRMKB5fhH061+ylYcQovgKBpsWZVqyEOVRwbIR/16DqKhU7yESRdO/eTU+jvqbbScuc/ZKBtU9S2ddJCFE5abVavHw8LBMSnF2drZqurPZbCYnJ4esrCyZOXUXkivrFTdXZrOZixcv4uzsXGgccnFIQVTBVPd0pl1tb7aduMySv87x32511Q5JCFFBFSxPUpSNPRVFsSxqK2vr3Jnkynr3kis7Oztq1KhxzzmWgqgCGtC8OttOXObXv87yYtc68g9NCFEsGo0Gf39/fHx8rN7iwGQysWnTJjp27GhT6zYVh+TKeveSK71eXyI9cFIQVUC9Gvkx4bcDnLmcwe4zV2hV00vtkIQQFZhWq7V6/IVWqyU3NxdHR0f5IX8XkivrlYdcyU3NCshZb0/vRv4A/LJbBlcLIYQQ90oKogpq4PUp9yv2JZKRI2sSCSGEEPdCCqIKqnWwF0HezlzLyWPlvvN3f4IQQgghbksKogpKo9HwcMtAABbtTlA5GiGEEKJik4KoAhvQvDp2Gth1+gonLqarHY4QQghRYUlBVIH5uTvSuZ4PAItlcLUQQghRbFIQVXAPt/xnK4/cPLPK0QghhBAVkxREFVzX+r54u+i5eDWbDUcvqh2OEEIIUSFJQVTB6e3t6N+8GgA/75LB1UIIIURxSEFUCTzSKn+22YajF7hgzFI5GiGEEKLikYKoEqjj40bzGh7kmRV+/euc2uEIIYQQFY4URJVEQS/R4t0JKIqicjRCCCFExSIFUSUR2TgAZ72Wk5eusev0FbXDEUIIISoUKYgqCVcHe/o0zt/wVQZXCyGEEEUjBVElUrCVx6r957maZVI5GiGEEKLikIKoEmkR5Emtqi5kmvJYIRu+CiGEEFaTgqgS0Wg0PHK9l0humwkhhBDWk4KokunfvDr2dhr2JKTyd/JVtcMRQgghKgQpiCqZqm4OdK2fv+Gr9BIJIYQQ1pGCqBIqWJNoadw5cnJlw1chhBDibqQgqoQ63VcVHzcHUq7lsPZQktrhCCGEEOWeFESVkL3WzjIFf+FOuW0mhBBC3I0URJXUI60C0Whgy/FLnL50Te1whBBCiHJNCqJKKtDLmY51qwKwUAZXCyGEEHckBVEl9ljrGgD8Epsgg6uFEEKIO5CCqBLrFuJDVTcHLqXnsO5wstrhCCGEEOWWFESVmE5rZ1m5esGOeJWjEUIIIcovKYgquRsHV5+5LIOrhRBCiFuRgqiSC/Ry5n4ZXC2EEELckeoF0blz5xg8eDDe3t44OTnRqFEjdu/ebTmvKAoTJkzA398fJycnunfvzrFjxwpdIyUlhUGDBmEwGPDw8GDYsGGkp6cXarNv3z7uv/9+HB0dCQwMZNq0aWXy/sqDx1vn3zZbvFsGVwshhBC3ompBdOXKFdq3b49Op+OPP/7g0KFDTJ8+HU9PT0ubadOm8dlnnzF79mx27NiBi4sLERERZGVlWdoMGjSIgwcPEhUVxYoVK9i0aRPPPfec5bzRaCQ8PJygoCBiY2P58MMPmThxIl9//XWZvl+1dAvxlcHVQgghyidzHpozW6iWEoPmzBYw56kShr0qr3rdBx98QGBgIHPmzLEcCw4Otvy/oih88sknvPXWW/Tt2xeA77//Hl9fX5YtW8ajjz7K4cOHWb16Nbt27aJly5YAfP755/Tu3ZuPPvqIgIAA5s+fT05ODt999x16vZ4GDRqwZ88ePv7440KFU4Hs7Gyys7Mtj41GIwAmkwmTyVSiOSi4Xklf998GNgvgy02nmL/9DD3qVynV1yotZZWrykByZT3JVdFIvqwnubo7zZEVaNe+gf3VRFoCnPkSxS2AvPD3UOr3uefrFyX3GkVRlHt+xWIKDQ0lIiKCs2fPEh0dTbVq1XjhhRd49tlnATh58iS1a9cmLi6Opk2bWp7XqVMnmjZtyqeffsp3333Hyy+/zJUrVyznc3NzcXR0ZPHixTz00EMMHToUo9HIsmXLLG02bNhA165dSUlJKdQjBTBx4kQmTZp0U7wLFizA2dm5ZJNQRi5nwZQ4LQoa/q9ZLlUc1Y5ICCGELfNP3UWrU58DoLnheEFRsiv4Rc57tLqn18jIyODxxx8nLS0Ng8Fwx7aq9hCdPHmSL7/8krFjx/LGG2+wa9cu/vvf/6LX63niiSdISsrfmNTX17fQ83x9fS3nkpKS8PHxKXTe3t4eLy+vQm1u7Hm68ZpJSUk3FUTjx49n7NixlsdGo5HAwEDCw8PvmtCiMplMREVF0aNHD3Q6XYle+982XI1l8/HLXHSry9AedUv1tUpDWeaqopNcWU9yVTSSL+tJru7AnIf9F68DhYuhgscKGlpdXkLuo2+BnbbYL1Nwh8caqhZEZrOZli1b8t577wHQrFkzDhw4wOzZs3niiSdUi8vBwQEHB4ebjut0ulL7UJfmtQsMahvE5uOX+eWvRF6OqI9Oq/qY+mIpi1xVFpIr60muikbyZT3J1S2c2g5XE297WoMCxnPoEndB8P3Ffpmi5F3Vn4j+/v6EhoYWOhYSEkJ8fP4ign5+fgAkJxceCJycnGw55+fnx4ULFwqdz83NJSUlpVCbW13jxtewBd1CfKni6sCl9GzWHZLB1UIIIVSSbuXPIGvblQBVC6L27dtz9OjRQsf+/vtvgoKCgPwB1n5+fqxfv95y3mg0smPHDsLCwgAICwsjNTWV2NhYS5s///wTs9lMmzZtLG02bdpUaHBVVFQU9erVu+l2WWWm09rxcMvqACzYKStXCyGEUImr793bFKVdCVC1IBozZgzbt2/nvffe4/jx4yxYsICvv/6akSNHAqDRaBg9ejTvvPMOy5cvZ//+/QwdOpSAgAD69esH5Pco9ezZk2effZadO3eydetWRo0axaOPPkpAQAAAjz/+OHq9nmHDhnHw4EF+/vlnPv3000LjhGxFwYavm49dIiElQ+VohBBC2KSgdmAI4PazujRgqJbfroyoWhC1atWKpUuX8tNPP9GwYUOmTJnCJ598wqBBgyxtXnvtNV588UWee+45WrVqRXp6OqtXr8bR8Z9pUvPnz6d+/fp069aN3r1706FDh0JrDLm7u7N27VpOnTpFixYtePnll5kwYcItp9xXdvkrV+dPu58v+5sJIYRQg50Wwt+7zcnrw6x7vn9PA6qLStVB1QB9+vShT5/brzWg0WiYPHkykydPvm0bLy8vFixYcMfXady4MZs3by52nJXJ4LZBbD52iUW7ExjToy4O9mX3gRNCCCEA4q9kUgPIUzRoNTf0FRkC8ouh0AfLNJ6KOc1I3JNu9X3wd3ck5VoOf+xPUjscIYQQNsgcMwuAKO/HyR28jN1BI8gdvAxG7y/zYgikILJJ9lo7y1iiH7afUTkaIYQQtib91E5qZuwnR9Hi03UUSlAHznmFoQR1KNPbZDeSgshGPdoqEHs7DbFnrnAo0fqFq4QQQoh7lbz2EwA26e+nWYMQdYO5TgoiG+VjcCSiYf4aTD/ukF4iIYQQZUMxnqfG+TUAZDZ7Fo3m32tVq0MKIhs2uE3+ek/L4s5hzJLNB4UQQpS+xHUz0ZFLrFKPTl0j1A7HQgoiG9a2lhd1fFzJyMlj6V/n1A5HCCFEZWfKwnDwBwAOBw3C4Fh+tjSRgsiGaTQahrTN7yX6cfsZFOX2S2QJIYQQ9+rq7oW45aVyVqlCk+6D1Q6nECmIbNxDzavhrNdy7EI6O06lqB2OEEKIykpRyN7yBQDrXB6kUQ1vlQMqTAoiG2dw1NG3aTVApuALIYQoPeaTm6hy7RgZigPuHYapHc5NpCASDG6bvybRmgNJXDBmqRyNEEKIyujyn58B8LumEz1blo+p9jeSgkjQIMCdFkGe5JoVFu5KUDscIYQQlU3KSbzPrc//3wZP4aQvf1tGSUEkACyDq3/aGU9unlnlaIQQQlQmxuiZ2KGwIa8Jvbp0UjucW5KCSADQq5EfXi56zqdlsf7IBbXDEUIIUVlkGXHYn78B+1/+j1CziovKAd2aFEQCAAd7LQ+3DATyp+ALIYQQJSFn9w84mDM4Zq5G864D1Q7ntqQgEhaD2tRAo4HNxy5x8mK62uEIIYSo6Mx5ZG/N39X+d8cH6HSfj8oB3Z4URMIi0MuZLvXyP6w/bo9XORohhBAVnfL3atwyz5KquODd/gns7MrHvmW3IgWRKGRIWP7g6sW7E7iWnatyNEIIISoy48bPAVisdKdf67oqR3NnUhCJQjrVrUpwFReuZuey5K+zaocjhBCioko6gHtSDLmKHSkNhuLuVH72LbsVKYhEIXZ2GoZe7yWau+207G8mhBCiWDI252/Tsdrcmr6dWqsczd1JQSRuMrBFdVz0Wk5cvMaW45fUDkcIIURFc+0S+kO/ArDd52Hq+xlUDujupCASN3Fz1DGwRXUA5m07rW4wQgghKpzcnd9ir+Swx1yLdp16qR2OVaQgErc0tF1NANYfuUD85Qx1gxFCCFFx5OaQu/1rAJboHqRHAz+VA7KOFETilmpXdaXjfVVRFPg+5rTa4QghhKgoDi7FMfsSSYonvm0fQaetGKVGxYhSqOKp671EP8sUfCGEENZQFMtg6gXmHjzctrbKAVlPCiJxW53uq0pNb2euZuWyNO6c2uEIIYQo7xJ24HxpH1mKjsv1Hqeqm4PaEVlNCiJxW/lT8GsC+YOrZQq+EEKIO8nekt87tDSvAwM6NlU3mCKSgkjc0cCW1XHWazl2IZ1tJy6rHY4QQojyKjUe3d8rAdhWZSDNAj3UjaeIpCASd2S4YQr+XJmCL4QQ4jbydnyNHWa25DWge+cuaDTld9+yW5GCSNxVwW2zdYeTSUiRKfhCCCH+JecaebvnAbDE4UF6NfRXOaCik4JI3FUdH1fur1sFRYEftp9ROxwhhBDljLLnJ/QmI6fMvtRu1x+9fcUrLypexEIVT16fgr9wZzwZOTIFXwghxHVmM1lbZgLwo9KLx9rUVDeeYpKCSFilSz0fgrydMcoUfCGEEDc68SdOxpMYFSdMjR7Dy0WvdkTFIgWRsMqNU/DnbD2N2SxT8IUQQkDWls8BWJTXmUEdG6gcTfGpWhBNnDgRjUZT6Kt+/fqW81lZWYwcORJvb29cXV0ZMGAAycnJha4RHx9PZGQkzs7O+Pj48Oqrr5KbW/iWzsaNG2nevDkODg7UqVOHuXPnlsXbq3QeblkdVwd7jl9IJ/rYRbXDEUIIobaLR3E8s5E8RcPB6o9Sz89N7YiKTfUeogYNGnD+/HnL15YtWyznxowZw++//87ixYuJjo4mMTGR/v37W87n5eURGRlJTk4O27ZtY968ecydO5cJEyZY2pw6dYrIyEi6dOnCnj17GD16NM888wxr1qwp0/dZGbg56ni0VSAA3205pXI0Qggh1GbaNguAdeYWPNA5TOVo7o296gHY2+Pnd/NOuGlpaXz77bcsWLCArl27AjBnzhxCQkLYvn07bdu2Ze3atRw6dIh169bh6+tL06ZNmTJlCuPGjWPixIno9Xpmz55NcHAw06dPByAkJIQtW7YwY8YMIiIibhlTdnY22dnZlsdGoxEAk8mEyWQq0fdfcL2Svm5pGdS6Ot9tPcXmY5c4kJBSpr8NVLRcqUlyZT3JVdFIvqxX6XOVeQXN3oUArHLpx7Rgz2K/19LKVVGup3pBdOzYMQICAnB0dCQsLIypU6dSo0YNYmNjMZlMdO/e3dK2fv361KhRg5iYGNq2bUtMTAyNGjXC19fX0iYiIoIRI0Zw8OBBmjVrRkxMTKFrFLQZPXr0bWOaOnUqkyZNuun42rVrcXZ2vvc3fQtRUVGlct3S0NjLjj2X7ZiyaCuP1zGX+etXpFypTXJlPclV0Ui+rFdZc1U7aQUNzVkcNAeh86jB6tV/3PM1SzpXGRnWr52nakHUpk0b5s6dS7169Th//jyTJk3i/vvv58CBAyQlJaHX6/Hw8Cj0HF9fX5KSkgBISkoqVAwVnC84d6c2RqORzMxMnJycbopr/PjxjB071vLYaDQSGBhIeHg4BoPhnt/3jUwmE1FRUfTo0QOdTlei1y4t/g1Tefh/O/krRcsnHbtQxbVsNu+riLlSi+TKepKropF8Wa9S5yrPRN6nrwHwk10kbw0Ox8Wh+CVFaeWq4A6PNVQtiHr16mX5/8aNG9OmTRuCgoJYtGjRLQuVsuLg4ICDw80/5HU6Xal9qEvz2iWtde2qNKvhQVx8Kgt3JzKmx31l+voVKVdqk1xZT3JVNJIv61XKXB39HV1mEhcVA26tHsHDtWR+Zpd0ropyLdUHVd/Iw8OD++67j+PHj+Pn50dOTg6pqamF2iQnJ1vGHPn5+d0066zg8d3aGAwGVYuuim5Yh2AAftx+hixTnsrRCCGEKEuZm/N3tV+Q151B7cv2l+LSUq4KovT0dE6cOIG/vz8tWrRAp9Oxfv16y/mjR48SHx9PWFj+SPawsDD279/PhQsXLG2ioqIwGAyEhoZa2tx4jYI2BdcQxdOzgR/VPJy4fC2H3/bIQo1CCGEzzsbilBxLjqLlfN3Hqe5ZOmNry5qqBdErr7xCdHQ0p0+fZtu2bTz00ENotVoee+wx3N3dGTZsGGPHjmXDhg3Exsby1FNPERYWRtu2bQEIDw8nNDSUIUOGsHfvXtasWcNbb73FyJEjLbe8hg8fzsmTJ3nttdc4cuQIs2bNYtGiRYwZM0bNt17h2WvtLNt5fLvlFIoiCzUKIYQtyNqa3zv0u7kdAzq1UDmakqNqQXT27Fkee+wx6tWrx8MPP4y3tzfbt2+natWqAMyYMYM+ffowYMAAOnbsiJ+fH0uWLLE8X6vVsmLFCrRaLWFhYQwePJihQ4cyefJkS5vg4GBWrlxJVFQUTZo0Yfr06XzzzTe3nXIvrPdI60Bc9Fr+Tk5n87FLaocjhBCitBkT0R35DYCt3v+hZZCnygGVHFUHVS9cuPCO5x0dHZk5cyYzZ868bZugoCBWrVp1x+t07tyZuLi4YsUobs/gqOPhVoHM2Xqab7acouN9VdUOSQghRCkybf8GnZLHDnN9unUNR6PRqB1SiSlXY4hExfNUu2DsNLDp74v8nXxV7XCEEEKUFlMmebu+BWC544NENPC9yxMqFimIxD2p4e1MeGj+jD7ZzkMIISov875FOJpSSTBX5b6Oj2CvrVwlROV6N0IVz9yfPwV/Sdw5Lqdn36W1EEKICkdRuBadP5h6kV0vBraqqW48pUAKInHPWgR50qS6Ozm5Zr6POaN2OEIIIUraqWjcjH9zTXFA12roPa1KXV5JQSTumUaj4dmOtQD4PuY0mTmyUKMQQlQmqRs+B2CpuROP3t9I5WhKhxREokT0bOBHDS9nrmSYWByboHY4QgghSsrlExgS8hc4Tqz/BD4GR5UDKh1SEIkSYa+1s4wl+mbzKXLzzCpHJIQQoiQYo7/ADoX1ec3o172T2uGUGimIRIn5T4tAPJ11xKdksPpgktrhCCGEuFdZaTgc+AmAv/wf4T5fN5UDKj1SEIkS46TX8sT17Ty+ij4p23kIIUQFl7FjHg7mTP42V6N9j4Fqh1OqpCASJWpoWE0cdXbsP5dGzMnLaocjhBCiuMx55G77EoDVrg8RVqeKygGVLimIRInyctHzcMtAIL+XSAghRMWUc2glhuxEriiu1Or6VKXapuNWpCASJe6ZDrWw00D03xc5fN6odjhCCCGKIfXPTwFYbh9ORLNaKkdT+qQgEiWuhrczvRr5A/C/TdJLJIQQFU1e4l58UnZjUrTo2jyHrpJt03Erlf8dClU8f32hxuV7EzmXmqlyNEIIIYri/JoZAKzTtOHBTq1UjqZsSEEkSkXj6h60q+1NrlmRTV+FEKICUa4m43vmdwBSGj2DayXcpuNWpCASpea5671EC3fGk5ZhUjkaIYQQ1kiImoWOXPYodekZ0UftcMqMFESi1HS6ryr1/dy4lpPHjztk01chhCj3crMxHJgHwPHgwXi7OqgcUNmRgkiUGo1GY+klmrP1NFkm2fRVCCHKs/jNP+JhvsJ5xYu2fZ5SO5wyJQWRKFUPNAkgwN2RS+nZ/PrXWbXDEUIIcTuKAttnARDrM5DqVdxVDqhsSUEkSpVOa8ez13uJZkefkE1fhRCivDHnwanNpKycRI3s42QqOupFvqh2VGVOCiJR6h5tVQNvFz0JKZms2Hde7XCEEEIUOLQcPmkI8/rgtTt/qr3GTkvdjD3qxqUCKYhEqXPSa3m6QzAAszYex2yWTV+FEEJ1h5bDoqFgTCx02EHJyj9+aLlKgalDCiJRJoaEBeHmYM/fyemsO5ysdjhCCGHbzHmwehxw8y+olh3LVr+e385GSEEkyoTBUcfQdkEAzNx4AkWRXiIhhFDNmW039QwVpoDxXH47GyEFkSgzT7UPxlFnx96EVLaduKx2OEIIYbvSreypt7ZdJSAFkSgzVVwdeLRVDQBmbjiucjRCCGHDXH1Ltl0lIAWRKFPPdayFvZ2GbScu81f8FbXDEUII2xTUDgwBtxhBVEADhmr57WyEFESiTAV4ONG/eTUAZm04oXI0Qghho+y05HR47TYnrw+r7vk+2GnLLCS1SUEkytzwTrXRaGDd4WSOJBnVDkcIIWzSyf0xaIAc/rWbvSEAHv4eQh9UJS612N+9iRAlq1ZVV3o38mflvvPM2nCCzx5rpnZIQghhU3IunaZ2wq8A/NliFj0bBuQPoHb1zb9NZkM9QwWkh0io4oXOtQFYsS+R05euqRyNEELYloTfJqMjlx2aRnTuORCC74dG1/+0wWIIpCASKmkQ4E6XelUxK/l7nAkhhCgbuRePE5SwFICk5i/jqLPNAujfpCASqhnVtQ4Av/51lnOpmSpHI4QQtiHxt0nYY2YLzegR8YDa4ZQb5aYgev/999FoNIwePdpyLCsri5EjR+Lt7Y2rqysDBgwgObnwIlHx8fFERkbi7OyMj48Pr776Krm5uYXabNy4kebNm+Pg4ECdOnWYO3duGbwjcTctgrxoV9sbU57ClxtlXSIhhChteclHqHZ2BQCJzV/GWS9DiQsUqyBKSEjg7Nmzlsc7d+5k9OjRfP3118UKYteuXXz11Vc0bty40PExY8bw+++/s3jxYqKjo0lMTKR///6W83l5eURGRpKTk8O2bduYN28ec+fOZcKECZY2p06dIjIyki5durBnzx5Gjx7NM888w5o1a4oVqyhZ/+1WF4BFu85yPk16iYQQojQlL38bLWbW04pe4T3VDqdcKVZp+Pjjj/Pcc88xZMgQkpKS6NGjBw0aNGD+/PkkJSUVKkjuJj09nUGDBvG///2Pd955x3I8LS2Nb7/9lgULFtC1a1cA5syZQ0hICNu3b6dt27asXbuWQ4cOsW7dOnx9fWnatClTpkxh3LhxTJw4Eb1ez+zZswkODmb69OkAhISEsGXLFmbMmEFERMQtY8rOziY7O9vy2GjMnxpuMpkwmUxFztedFFyvpK9bUbQINNC6pic7T19h5p/HeLtPyG3b2nquikJyZT3JVdFIvqxX3nKlJB0g4NxqAM42/i8dteUnttLKVVGuV6yC6MCBA7Ru3RqARYsW0bBhQ7Zu3cratWsZPnx4kQqikSNHEhkZSffu3QsVRLGxsZhMJrp37245Vr9+fWrUqEFMTAxt27YlJiaGRo0a4ev7z9LiERERjBgxgoMHD9KsWTNiYmIKXaOgzY235v5t6tSpTJo06abja9euxdnZ2er3VhRRUVGlct2KoLWzhp1o+WlnPHVNp/BwuHN7W85VUUmurCe5KhrJl/XKS67qHfmU+sBKc1t0ZoVVq1apHdJNSjpXGRkZVrctVkFkMplwcMj/qbVu3ToefDB/8ab69etz/vx5q6+zcOFC/vrrL3bt2nXTuaSkJPR6PR4eHoWO+/r6kpSUZGlzYzFUcL7g3J3aGI1GMjMzcXJyuum1x48fz9ixYy2PjUYjgYGBhIeHYzAYrH5/1jCZTERFRdGjRw90Ol2JXruiUBSF7d/uYveZVE7oa/F/vevfsp3kynqSK+tJropG8mW9cpWrxDh0cbHkKRrONn6Jp/v2UDeefymtXBXc4bFGsQqiBg0aMHv2bCIjI4mKimLKlCkAJCYm4u3tbdU1EhISeOmll4iKisLR0bE4YZQaBwcHS8F3I51OV2of6tK8dkUwpkc9Bn2zg593n2VU17r4GG7/mbD1XBWF5Mp6kquikXxZrzzk6vLqd/AGVigdGNizm+rx3E5J56oo1yrWoOoPPviAr776is6dO/PYY4/RpEkTAJYvX265lXY3sbGxXLhwgebNm2Nvb4+9vT3R0dF89tln2Nvb4+vrS05ODqmpqYWel5ycjJ+fHwB+fn43zToreHy3NgaD4Za9Q0Id7Wp70yLIk+xcM7OjT6odjhBCVBpK/Ha8z0eTq9hxtvF/8Xa9y7gEG1Wsgqhz585cunSJS5cu8d1331mOP/fcc8yePduqa3Tr1o39+/ezZ88ey1fLli0ZNGiQ5f91Oh3r16+3POfo0aPEx8cTFhYGQFhYGPv37+fChQuWNlFRURgMBkJDQy1tbrxGQZuCa4jyQaPR8NL1GWfzd5zhwtUslSMSQojKIXXlRACWKJ35T3hHdYMpx4p1yywzMxNFUfD09ATgzJkzLF26lJCQkNvO3Po3Nzc3GjZsWOiYi4sL3t7eluPDhg1j7NixeHl5YTAYePHFFwkLC6Nt27YAhIeHExoaypAhQ5g2bRpJSUm89dZbjBw50nLLa/jw4XzxxRe89tprPP300/z5558sWrSIlStXFueti1J0f90qNKvhQVx8Kl9Hn+StPqFqhySEEBWacjIaz+QYchQtyU1G3XE4gq0rVg9R3759+f777wFITU2lTZs2TJ8+nX79+vHll1+WWHAzZsygT58+DBgwgI4dO+Ln58eSJUss57VaLStWrECr1RIWFsbgwYMZOnQokydPtrQJDg5m5cqVREVF0aRJE6ZPn84333xjdeEmys6NvUQ/7jjDpfTsuzxDCCHEbSkKaasmArBY6caj4R3UjaecK1YP0V9//cWMGTMA+OWXX/D19SUuLo5ff/2VCRMmMGLEiGIFs3HjxkKPHR0dmTlzJjNnzrztc4KCgu46dbBz587ExcUVKyZRtjrdV5Um1d3ZezaN/206yfjet1+XSAghxO0px9fjcekvshQdKc1fpKqbjB26k2L1EGVkZODm5gbkr83Tv39/7OzsaNu2LWfOnCnRAIVt0Wg0vNQ9v5fo+5gzXJZeIiGEKDpFwfjHRAAWEs7j3duoG08FUKyCqE6dOixbtoyEhATWrFlDeHg4ABcuXCjxdXqE7elSz4fG1d3JNOXx9SaZcSaEEEWlHF2Fe8p+MhQHjC1GycwyKxSrIJowYQKvvPIKNWvWpHXr1pYZW2vXrqVZs2YlGqCwPRqNhtHXe4nmxZyWGWdCCFEUZjNX/8gfSzufngzu1lLlgCqGYhVEAwcOJD4+nt27dxfaJLVbt26WsUVC3Isu9XxoVsODLJOZWRtOqB2OEEJUGOZDyzGkHcGoOJHVaiReLnq1Q6oQilUQQf6Ch82aNSMxMdGy833r1q2pX//W2y4IURQajYZXwusBsGBHPImpmSpHJIQQFYA5j4w1+b1DPxLJkK5y18ZaxSqIzGYzkydPxt3dnaCgIIKCgvDw8GDKlCmYzeaSjlHYqHa1vWkT7EVOnpkvNhxXOxwhhCj3zPt/wfXqCVIVF8xtXsDDWXqHrFWsgujNN9/kiy++4P333ycuLo64uDjee+89Pv/8c/7v//6vpGMUNkqj0fDy9V6iRbsSiE+xftdiIYSwOXm5ZK59B4C5mgcZ0rmxygFVLMVah2jevHl88803ll3uARo3bky1atV44YUXePfdd0ssQGHbWgd70fG+qmz6+yJfbDxJZ1lkVQghbsm8ZwEu1+K5pBjQtRuOu3P53MC1vCpWD1FKSsotxwrVr1+flJSUew5KiBu93OM+AH7bk0iyDCUSQoib5eaQuW4qAHM1fRnSqYHKAVU8xSqImjRpwhdffHHT8S+++ILGjaWLTpSsJoEedA/xxazA6oRizwMQQohKKzd2Hi6ZiSQrHrh1eB6Do/QOFVWxbplNmzaNyMhI1q1bZ1mDKCYmhoSEhLtuoyFEcYztcR/rDifz12U7jiRdpVGgl9ohCSFE+WDKIvvPadgD87QDGNVRtjwqjmL9ut2pUyf+/vtvHnroIVJTU0lNTaV///4cPHiQH374oaRjFILQAAO9G/oC8Nmfsi6REEIUyNnxLS7ZFzineOPfdTjO+mL1ddi8YmctICDgpsHTe/fu5dtvv+Xrr7++58CE+LcXu9TmjwNJRB2+wL6zqTSu7qF2SEIIoa6ca+RGf4Qe+FH/MGPa1lE7ogpLBmSICqOOjystqygATF/7t8rRCCGE+jK3zcbZlMIZsw91wp9Hby8/1otLMicqlJ6BZuztNET/fZHtJy+rHY4QQqgnywhbPgVgofNj9GtRU914KjgpiESFUsURHm5ZDYD3/ziCoigqRySEEOq4tukLnHLTOGH2p2nvZ9HaadQOqUIr0hii/v373/F8amrqvcQihFVGda7N0rjz7ElIZc3BZHo29FM7JCGEKFuZV9DumAnAEvchvNKousoBVXxFKojc3d3ven7o0KH3FJAQd1PVzYFhHYL5YsNxPlxzhO4hPthrpbNTCGE7jBs+wZCXzhFzIG0feAaNRnqH7lWRCqI5c+aUVhxCFMlznWoxf8cZTly8xq9/neWRVjXUDkkIIcrGtcvod38FwErvpxhb10flgCoH+bVaVEgGRx0ju+RPL50RdYwsU57KEQkhRNm4EvUhjuZM9ptr0qXfU9I7VEKkIBIV1uC2QVTzcCLJmMXcbafVDkcIIUrf1WRc9n4HwJ/+z9I8SFbtLylSEIkKy1GnZcz1jV9nbThOWoZJ5YiEEKJ0Ja96D72SzV/mOvTsJ2N2S5IURKJCe6hZNer5umHMyuXLaNnSQwhReSmpCXgd/hGA2FojqedvUDmiykUKIlGhae00vBpRD4A5W0+RlJalckRCCFE6EpZPQUcuO5UQHuj3mNrhVDpSEIkKr1uID61qepKda+aTdbKlhxCi8jFdOkXAyV8AONFwNH4eTipHVPlIQSQqPI1Gw+u96gOwaHcCxy9cVTkiIYQoWWeWTsSePGJoTJ8HBqgdTqUkBZGoFFoEedEj1Bezkr+lhxBCVBbXEo8QfG45ACmtX8XNUadyRJWTFESi0hjXsz5aOw3rDl9g24lLaocjhBDFZ86DU5th/y9cWjgKLWa2aVvSI7yP2pFVWlIQiUqjjo8rg9rkr1j97srDmM2y8asQogI6tBw+aQjz+sCvwwgy7gLAM7Qrenv5sV1aJLOiUnmpW13cHOw5mGhkadw5tcMRQoiiObQcFg0FY2KhwwpQf/+H+edFqZCCSFQq3q4OvHB9S48P1xwlM0e29BBCVBDmPFg9jvzypzDN9S9Wv57fTpQ4KYhEpfNU+5qWLT2+2XxS7XCEEMI6Z7bd1DNUmALGc/ntRImTgkhUOo46La/1zF+s8cvoE1y4Kos1CiEqgPTkkm0nikTVgujLL7+kcePGGAwGDAYDYWFh/PHHH5bzWVlZjBw5Em9vb1xdXRkwYADJyYU/CPHx8URGRuLs7IyPjw+vvvoqubm5hdps3LiR5s2b4+DgQJ06dZg7d25ZvD2hogcaB9CkujsZOXnMiDqmdjhCCHF3rr4l204UiaoFUfXq1Xn//feJjY1l9+7ddO3alb59+3Lw4EEAxowZw++//87ixYuJjo4mMTGR/v37W56fl5dHZGQkOTk5bNu2jXnz5jF37lwmTJhgaXPq1CkiIyPp0qULe/bsYfTo0TzzzDOsWbOmzN+vKDt2dhre6hMKwM+74jmaJIs1CiHKuaB2YAi4xQiiAhowVMtvJ0qcqgXRAw88QO/evalbty733Xcf7777Lq6urmzfvp20tDS+/fZbPv74Y7p27UqLFi2YM2cO27ZtY/v27QCsXbuWQ4cO8eOPP9K0aVN69erFlClTmDlzJjk5OQDMnj2b4OBgpk+fTkhICKNGjWLgwIHMmDFDzbcuykCrml70bOCHWYH3Vh1WOxwhhLgzOy2ZbV66zUlN/h893wc7bZmFZEvs1Q6gQF5eHosXL+batWuEhYURGxuLyWSie/fuljb169enRo0axMTE0LZtW2JiYmjUqBG+vv90H0ZERDBixAgOHjxIs2bNiImJKXSNgjajR4++bSzZ2dlkZ2dbHhuNRgBMJhMmk6mE3jGWa974p7i94uTqlR51WHc4mei/L/Ln4fPcX6dKaYVXrsjnynqSq6KRfFmvyLlSFM7tWEYdIAcdev55nmIIIK/Huyh1e0ElzH1pfa6Kcj3VC6L9+/cTFhZGVlYWrq6uLF26lNDQUPbs2YNer8fDw6NQe19fX5KSkgBISkoqVAwVnC84d6c2RqORzMxMnJxu3iBv6tSpTJo06abja9euxdnZudjv9U6ioqJK5bqVUVFz1d7HjugkO95YFMtrTfLQakopsHJIPlfWk1wVjeTLetbmyuViLN2NMeQoWuZUm0ID5zQcTalk6Ty47FoPTtrByVWlHK26SvpzlZGRYXVb1QuievXqsWfPHtLS0vjll1944okniI6OVjWm8ePHM3bsWMtjo9FIYGAg4eHhGAyGEn0tk8lEVFQUPXr0QKeT/WnupLi5ap9poscnW0jKMHHFuyFD29YoxSjLB/lcWU9yVTSSL+sVKVemDIwfvwLAH4b/8NSTw9BobOe3t9L6XBXc4bGG6gWRXq+nTp38hfRatGjBrl27+PTTT3nkkUfIyckhNTW1UC9RcnIyfn5+APj5+bFz585C1yuYhXZjm3/PTEtOTsZgMNyydwjAwcEBBweHm47rdLpS+wZQmteubIqaqyo6HS+H1+OtZQf4dP1xHmoeiJeLvhQjLD/kc2U9yVXRSL6sZ02uzv72PtVzL3BOqULoI5PR623je9S/lfTnqijXKnfrEJnNZrKzs2nRogU6nY7169dbzh09epT4+HjCwsIACAsLY//+/Vy4cMHSJioqCoPBQGhoqKXNjdcoaFNwDWEbHmtdg/p+bhizcvk46qja4QghhEXexWP4HvgagOjgl6lbXabVq0HVgmj8+PFs2rSJ06dPs3//fsaPH8/GjRsZNGgQ7u7uDBs2jLFjx7JhwwZiY2N56qmnCAsLo23btgCEh4cTGhrKkCFD2Lt3L2vWrOGtt95i5MiRlh6e4cOHc/LkSV577TWOHDnCrFmzWLRoEWPGjFHzrYsyprXTMPHBBgAs2BHPoUTru1GFEKLUKAoXf34RHblsphk9Bw5TOyKbpWpBdOHCBYYOHUq9evXo1q0bu3btYs2aNfTo0QOAGTNm0KdPHwYMGEDHjh3x8/NjyZIlludrtVpWrFiBVqslLCyMwYMHM3ToUCZPnmxpExwczMqVK4mKiqJJkyZMnz6db775hoiIiDJ/v0JdbWt5E9nIH7MCk34/iKLcfrUPIYQoCxl7l+B3KYZsRUdi2CS8XG8eriHKhqpjiL799ts7nnd0dGTmzJnMnDnztm2CgoJYterOo+47d+5MXFxcsWIUlcv43vVZdziZHadSWLU/icjG/mqHJISwVdnp5K4cB8BCfX8e63a/ygHZtnI3hkiI0lTd05nhnWoD+Ys1ZubIrtFCCHWk/PEOBtNF4s1Vqf3Q/6G3lx/JapLsC5szvFNtAtwdOZeaydebTqodjhDCBikXDmPYkz+QennAS3QIDVQ5IiEFkbA5Tnotb0SGAPBl9HHOpWaqHJEQwqYoCpcXv4Q9eaw3t6DvwzKQujyQgkjYpMhG/rQJ9iLLZJZ9zoQQZSorbhFVLu4gS9ER33oCgV6lswOCKBopiIRN0mg0vP1AA+w0sHLfebYcu6R2SEIIW5BlJPePNwD4Qf8fHouQgdTlhRREwmaFBhgYGlYTgAm/HSA7VwZYCyFKV+ofk3E1XeKU2ZdaD47HUSc715cXUhAJmzY2/D6qujlw8tI1vtl8Su1whBCVmJJ0ALe9+cvNLPF7ia4NZSB1eSIFkbBpBkcdb10fYP35n8dISLF+Z2QhhLCaopD6y0toMbPG3Jr+Dz9pU5u3VgRSEAmb92CTAMJqeZNlMjPp90NqhyOEqIRy/voJz0u7yVAcONniTYKruKgdkvgXKYiEzdNoNEzu2wB7Ow3rDiez7lCy2iEJISqTrDRMq/MHUs+z/w9De3ZQOSBxK1IQCQHU9XXjmftrATDx94OygrUQosQY/5iCi+kKJ8z+1Ok3DhcHVXfNErchBZEQ1/23Wx0C3B05eyWTWRuPqx2OEKIScLt2Go9D3wOwNGAsPRrVUDkicTtSEAlxnbPengkPNADgq+iTnLyYrnJEQogKTTFT+/T3aDGzyhzGY48OUTsicQdSEAlxg4gGvnSuV5WcPDMTfjuIoihqhySEqKAydv1AUM5x0hVHUu5/m2oeTmqHJO5ACiIhbqDRaJj0YAMc7O3YcvwSy/acUzskIURFlJGC9s/JACxweoxHurZROSBxN1IQCfEvQd4u/LdbXQCmrDhMyrUclSMSQlQ0ycvexDUvjaPm6jQb8Bo6rfy4Le/kb0iIW3iuYy3q+bqRci2Hd1bK2kRCCOuZ4mOp+vdPAPzs9gRNa1ZVOSJhDSmIhLgFndaO9wc0QqOBJX+dk81fhRDWMeeRsvhF7FBYpbmfurXqqR2RsJIURELcRrManjxxffPXN5bul7WJhBB3dXHT//C9ehCj4kRet8k4y5JDFYYURELcwSsR9fB3dyQ+JYNP1x9TOxwhRDlmTr+EY/Q7APzm+SQRrRuqHJEoCimIhLgDVwd7JvfN/6b2v80nOZRoVDkiIUR5dWLhq7gpVzmiBNF58BuyeWsFIwWREHfRI9SX3o38yDMrjF+yjzyzrE0khADMeXBqM+z/hdToWdQ9uwSAE60nEljFoHJwoqjk7qYQVpj4QAM2H7vE3rNpzNt2mqc7BKsdkhBCTYeWw+pxYEwEwOP64f26xvTs9ZBqYYnikx4iIazgY3Dk9V71Afho7VHiL2eoHJEQQjWHlsOioZZiqICiQEPTfrRHflcpMHEvpCASwkqPtapBm2AvMnLyGPfrPsxy60wI22POy+8Z4uZ//xoNaABWv57fTlQoUhAJYSU7Ow3TBjbGUWdHzMnLLNgZr3ZIQoiydmbbTT1DhSlgPJffTlQoUhAJUQRB3i6M65l/62zqqsMkpMitMyFsSnpyybYT5YYUREIU0RNhNWld04trOXm8vmQfiiK3zoSwGa6+JdtOlBtSEAlRRHZ2Gj64futs6/HL/LQzQe2QhBBlJagdGAJuMYKogAYM1fLbiQpFCiIhiiG4iguvRuTfOntv1WHOpWaqHJEQokzYaUn0aImG/FllhV1fiLHn+2CnLePAxL2SgkiIYnqyXU1aBnmSnp3L67/KrTMhbEHaiV34xK8EIMverfBJQwA8/D2EPqhCZOJeycKMQhST9vqss16fbmbzsUv8vCuBR1vXUDssIURpyblG1sKncCePTfbtaP3KMji/M38Atatv/m0y6RmqsKSHSIh7UKuqK69G1APgnZUy60yIyuzMgtH4mhI4r3hR5dHZODo6QPD90Ghg/p9SDFVoqhZEU6dOpVWrVri5ueHj40O/fv04evRooTZZWVmMHDkSb29vXF1dGTBgAMnJhaczxsfHExkZibOzMz4+Prz66qvk5uYWarNx40aaN2+Og4MDderUYe7cuaX99oSNeKp9MK1q5t86e3nRXtnrTIhKKDVuKUGnF2FWNGxp+A6hdYLUDkmUMFULoujoaEaOHMn27duJiorCZDIRHh7OtWvXLG3GjBnD77//zuLFi4mOjiYxMZH+/ftbzufl5REZGUlOTg7btm1j3rx5zJ07lwkTJljanDp1isjISLp06cKePXsYPXo0zzzzDGvWrCnT9ysqJ62dho8fboqLXsvO0yn8b/NJtUMSQpQgxXge7e//BWCJ00P0fegxlSMSpUHVMUSrV68u9Hju3Ln4+PgQGxtLx44dSUtL49tvv2XBggV07doVgDlz5hASEsL27dtp27Yta9eu5dChQ6xbtw5fX1+aNm3KlClTGDduHBMnTkSv1zN79myCg4OZPn06ACEhIWzZsoUZM2YQERFR5u9bVD6BXs68/WADXvtlH9PXHqVj3aqEBshu10JUeGYzyT88jZ/ZyEGlJo2GTENvL6NNKqNyNag6LS0NAC8vLwBiY2MxmUx0797d0qZ+/frUqFGDmJgY2rZtS0xMDI0aNcLX959FsCIiIhgxYgQHDx6kWbNmxMTEFLpGQZvRo0ffMo7s7Gyys7Mtj41GIwAmkwmTyVQi77VAwfVK+rqVUXnPVb/GvkQd9CHq8AVGL4xjyfA2OOjUGVNQ3nNVnkiuisbW8pW64TP8Lm4jU9ET1+IDHvHxsPq921qu7kVp5aoo1ys3BZHZbGb06NG0b9+ehg0bApCUlIRer8fDw6NQW19fX5KSkixtbiyGCs4XnLtTG6PRSGZmJk5OToXOTZ06lUmTJt0U49q1a3F2di7+m7yDqKioUrluZVSec9XZBWJ0Wv6+kM6L/4uiX02zqvGU51yVN5KrorGFfLlkxNPx6HsAfKUdRJA5m1WrVhX5OraQq5JS0rnKyLB+oku5KYhGjhzJgQMH2LJli9qhMH78eMaOHWt5bDQaCQwMJDw8HIOhZG+DmEwmoqKi6NGjBzqdrkSvXdlUlFxVrX+R536MY2OSHU/3bE3bWl5lHkNFyVV5ILkqGpvJlymTtM87oyeXjTSn3/BJVPMs2i/ENpOrElBauSq4w2ONclEQjRo1ihUrVrBp0yaqV69uOe7n50dOTg6pqamFeomSk5Px8/OztNm5c2eh6xXMQruxzb9npiUnJ2MwGG7qHQJwcHDAwcHhpuM6na7UPtSlee3KprznKrxhAI+3ucyCHfGMW3KA1WM6YnBUJ97ynqvyRHJVNJU9X8lLXsE38wQXFXcye39KTR/3Yl+rsueqJJV0ropyLVVHhimKwqhRo1i6dCl//vknwcHBhc63aNECnU7H+vXrLceOHj1KfHw8YWFhAISFhbF//34uXLhgaRMVFYXBYCA0NNTS5sZrFLQpuIYQJe3N3iHU9HYmMS2Lt5YekFWshahAMg7+ge/huQD8GvgGvdo0VjcgUSZULYhGjhzJjz/+yIIFC3BzcyMpKYmkpCQyM/P3hXJ3d2fYsGGMHTuWDRs2EBsby1NPPUVYWBht27YFIDw8nNDQUIYMGcLevXtZs2YNb731FiNHjrT08gwfPpyTJ0/y2muvceTIEWbNmsWiRYsYM2aMau9dVG4uDvZ8/EhTtHYalu9N5JfYs2qHJISwRvpFcpe8AMAibSSPDx6mckCirKhaEH355ZekpaXRuXNn/P39LV8///yzpc2MGTPo06cPAwYMoGPHjvj5+bFkyRLLea1Wy4oVK9BqtYSFhTF48GCGDh3K5MmTLW2Cg4NZuXIlUVFRNGnShOnTp/PNN9/IlHtRqprX8GRsj/sAmPDbQU5cTFc5IiHEHSkKST8+iyEvhaPm6gQ/+pFqt7tF2VN1DJE1txEcHR2ZOXMmM2fOvG2boKCgu47879y5M3FxcUWOUYh7MaJTbbaduMTW45d5cUEcS0e2w8FelvcXojy6svkr/JI2kK3YE9P0A56sG6B2SKIMyepSQpQiu+urWHu56Dl03sjUVUfUDkkIcQumpMM4/5m/w8EPrk8xqG9vlSMSZU0KIiFKma/Bken/aQLA3G2nWXco+S7PEEKUqdxsLn8/FAey2UoTIp6eiE4rPx5tjfyNC1EGutT3YViH/FmUr/6yl6S0LJUjEkIUiP/lDfwy/iZFcSWr9+cEeruqHZJQgRREQpSR13rWo2E1A1cyTIz+OY48s0zFF0JtKfujqH7kWwD+qPUW3Vo3UTkioRYpiIQoIw72Wj5/rDnOei3bT6bw6fpjaockhO0x58GpzbD/F/IOrURZ8jx2KKzS92TA48+pHZ1QUblYqVoIWxFcxYX3HmrE6J/38Pmfx2hew4PO9XzUDksI23BoOaweB8ZEALSAN5CseBD61Bc4qrQZsygfpIdIiDLWr1k1BrWpgaLAmJ/3cC41U+2QhKj8Di2HRUMtxVABRQEfTSo1r8SoFJgoL6QgEkIF/9cnlEbV3LmSYWLk/L/IyTWrHZIQlZc5L79niJvH7Wk0oEEDq1/PbydslhREQqjAUadl1qDmuDvp2JOQynurDqsdkhCV15ltN/UMFaaA8Vx+O2GzpCASQiWBXs58/PA/6xP9vvdO37CFEMWWbuXaX9a2E5WSFERCqKhbiC8vdK4NwOu/7uP4BdnvTIgS5+pbsu1EpSQFkRAqG9vjPsJqeXMtJ48RP8aSnp2rdkhCVC5B7ch19OL222dqwFANgtqVZVSinJGCSAiV2Wvt+OyxZvi4OXDsQjovL9qDWRZtFKLEXD13BFNWOhrNLYdV5//R832wk2n3tkwKIiHKgapuDswe0gK91o41B5P5YsNxtUMSolIwp18mY95AnMjhtKYaiqt/4QaGAHj4ewh9UJ0ARbkhCzMKUU40r+HJO/0a8tqv+/g46m9C/A30CJUxDUIUW24O574eSGBuImeVqmQOXoFdraD82WTpyfljhoLaSc+QAKSHSIhy5eFWgTwRFgTkL9p4/MJVlSMSooJSFM78+AKBxr+4qjhxuPP/CKlTK7/4Cb4fGg3M/1OKIXGdFERClDNv9QmlTbAX6dm5PPt9LGmZJrVDEqLCSVzzMUGnF5OnaFhV7116dOmidkiinJOCSIhyRqe1Y9ag5lTzcOLUpWu8tDCOPBlkLYTVUveswHf7OwD87PU8Ax99WuWIREUgBZEQ5ZC3qwNfDWmBo86OjUcvMm31EbVDEqJCyE48gP63Z9BiZpWuO32em4LWTqN2WKICkIJIiHKqYTV3pg3MX8n6q00nWbgzXuWIhCjflGuXSJ8zEGclk92EEPLMNxic9GqHJSoIKYiEKMcebBLA6O51AXhr2QG2Hr+kckRClFO5OSR9PRBv03nOKD7kDvieYF9PtaMSFYgUREKUcy91q0vfpgHkmhWG/xgrM8+E+DdFIeGH5/FPi8OoOBHb7kvaNrpP7ahEBSMFkRDlnEaj4YMBjWkZ5MnVrFyenruby+nZaoclRLmRsGoagWeWkKdoWFbnHR4K76Z2SKICkoJIiArAUaflqyEtqOHlTHxKBs//EEuWKU/tsIRQXfKuZVTbNRWARd4jGDRoGBqNDKIWRScFkRAVhLerA9892Qo3R3t2n7nCa7/skz3PhG0x58GpzbD/Fzi1mbSTsbitfB47FFY79KTv85NlRpkoNtm6Q4gKpI6PK7MHt+CJ73ayfG8ifu6OvNE7RO2whCh9h5bD6nFgTLQccsUOLWb+smtI8+Hf4OygUzFAUdFJD5EQFUz7OlWYNrAxAF9vOsk3m0+qHJEQpezQclg0tFAxBKDFjAL4dxqGj6ebOrGJSkMKIiEqoP7Nq/N6r/oAvLPyML/tOadyREKUEnNefs8Qt7897B/7UX47Ie6BFERCVFDPd6zFU+1rAvDK4r1sPnZR3YCEKA1ntt3UM3QjDYDxXH47Ie6BFERCVFAajYb/iwylT2N/THkKw3+IZf/ZNLXDEqJkpSeXbDshbkMKIiEqMDs7DdMfbkL7Ot5cy8njqbk7OXXpmtphCVFyXH1Ltp0QtyEFkRAVnIO9ltmDW9AgwMCl9BwG/W8751Iz1Q5LiJIR1A6T3nCHBhowVIOgdmUWkqicpCASohJwc9Qx7+nW1KrqQmJaFk/MiSUtR+2ohLh3Z/78H7ocI3CrYdXX1xzq+T7YacsyLFEJqVoQbdq0iQceeICAgAA0Gg3Lli0rdF5RFCZMmIC/vz9OTk50796dY8eOFWqTkpLCoEGDMBgMeHh4MGzYMNLT0wu12bdvH/fffz+Ojo4EBgYybdq00n5rQpS5Kq4OLHimLYFeTpxJyWDWIS0p16QqEhVXwp/fELjldQDiHFuDW0DhBoYAePh7CH1QhehEZaNqQXTt2jWaNGnCzJkzb3l+2rRpfPbZZ8yePZsdO3bg4uJCREQEWVlZljaDBg3i4MGDREVFsWLFCjZt2sRzzz1nOW80GgkPDycoKIjY2Fg+/PBDJk6cyNdff13q70+Isubn7siCZ9ria3AgKVPDU/NiScs0qR2WEEV2Nnoe1Ta9gh0Ka5z7UG/MKjRjDsATK2DAt/l/jt4vxZAoMaquVN2rVy969ep1y3OKovDJJ5/w1ltv0bdvXwC+//57fH19WbZsGY8++iiHDx9m9erV7Nq1i5YtWwLw+eef07t3bz766CMCAgKYP38+OTk5fPfdd+j1eho0aMCePXv4+OOPCxVOQlQWgV7OzHuyJQNnbeHQ+as8PXcX3z/dGhcHWZheVAznt87Hf8Po/GLIsRft/zvnn1Wog+9XNzhRaZXb75CnTp0iKSmJ7t27W465u7vTpk0bYmJiePTRR4mJicHDw8NSDAF0794dOzs7duzYwUMPPURMTAwdO3ZEr9db2kRERPDBBx9w5coVPD09b3rt7OxssrP/2U3caMy/f20ymTCZSva37YLrlfR1KyPJlfVqeOh5ITSP2X87EnvmCk/O2cn/BjeTougW5HNVNKWdr4s7F+ETNQotZtY4hNNyxDc4aDUV8u9HPlvWK61cFeV65fa7Y1JSEgC+voWnUvr6+lrOJSUl4ePjU+i8vb09Xl5ehdoEBwffdI2Cc7cqiKZOncqkSZNuOr527VqcnZ2L+Y7uLCoqqlSuWxlJrqxTzQWeqZPFrMNadp2+Qv9P1/N8SB6OMvb0luRzVTSlkS+Xi7F0SvgCe42ZVdxPap3H2bJxfYm/TlmTz5b1SjpXGRkZVrcttwWRmsaPH8/YsWMtj41GI4GBgYSHh2Mw3Gn6Z9GZTCaioqLo0aMHOp1sTHgnkivrFeTqmf49aJecwVPzYjl5NZeF5735dmgL3Bzln34B+VwVTWnlK3n3b/jGzUSnyWOdfSeav/Aj3m5OJXZ9Nchny3qllauCOzzWKLffFf38/ABITk7G39/fcjw5OZmmTZta2ly4cKHQ83Jzc0lJSbE838/Pj+TkwiuYFjwuaPNvDg4OODg43HRcp9OV2oe6NK9d2UiurKfT6WgZXIX5z7Rh8Dc7iEtI4+nv/+L7Ya0xOEoObySfq6K5p3yZ8/K32khPBldfzl64hM+a59GRywbd/TQetQAfd9eSDVhF8tmyXknnqijXKrfrEAUHB+Pn58f69f90lxqNRnbs2EFYWBgAYWFhpKamEhsba2nz559/YjabadOmjaXNpk2bCt1HjIqKol69ere8XSZEZdS4ugcLnm2Lu5OOPQmpDPlmh8w+E+o4tBw+aQjz+sCvw2BeH6r98SR6ctmsa0+T/y6qVMWQqDhULYjS09PZs2cPe/bsAfIHUu/Zs4f4+Hg0Gg2jR4/mnXfeYfny5ezfv5+hQ4cSEBBAv379AAgJCaFnz548++yz7Ny5k61btzJq1CgeffRRAgLy16t4/PHH0ev1DBs2jIMHD/Lzzz/z6aefFrolJoQtaFjNnQXPtsHTWcfes2k89vV2LqVn3/2JQpSUQ8th0dCbNmvVkL/oYvNeT+HlVjrjNIW4G1ULot27d9OsWTOaNWsGwNixY2nWrBkTJkwA4LXXXuPFF1/kueeeo1WrVqSnp7N69WocHR0t15g/fz7169enW7du9O7dmw4dOhRaY8jd3Z21a9dy6tQpWrRowcsvv8yECRNkyr2wSQ0C3FnwbFu8XfQcOm/k4dkxnL1i/aBDIYrNnAerx3Gr9abzaXDZ+HZ+OyFUoOoYos6dO6Mot/vHkb+b9+TJk5k8efJt23h5ebFgwYI7vk7jxo3ZvHlzseMUojIJ8TeweHgYQ77dyclL1/jP7Bh+GNaaOj5uaocmKrMz227qGbqRBgWM5/LbyVpDQgXldgyREKL01Krqyi8jwqhd1YXzaVn8Z3YM+86mqh2WqMzSk+/epijthChhUhAJYaP83Z1YPLwdjau7cyXDxGNfb2fb8UtqhyUqKcXZ27qGrr53byNEKZCCSAgb5uWiZ8GzbWlX25trOXk8MWcnS+POqh2WqGTMmWmcWPYeALcfJaEBQzUIaldmcQlxIymIhLBxrg72fPdkK3o38sOUpzDm5718tv7YHcf3CWGtrMvxJM7oTJ2rO8lW7POnlOX/5wbXH/d8H+xkKXWhDimIhBA46rR88Vhznu9YC4CPo/7mtV/2YcozqxyZqMhSTuzm2szOVM85yUXFnZhOC9A8/AMY/As3NATAw9/LzvVCVeV2pWohRNmys9MwvncI1b2cefu3AyyOPUtiWiazBrXA3UlW2RV38K+VpwlqR8Ku3/H+43mcyeI4gRgHLKBz48b57etH3tReeoaE2qQgEkIUMqRtENU9nBi54C+2Hr/MwC+38b+hLalZxUXt0ER5dGh5/vpCN0ypN+kMBOQY0WogVtuYKk//TPNqAf88x04rU+tFuSO3zIQQN+lS34dFz4fha3Dg2IV0HvxiC9F/X1Q7LFHe3GblaZ0pvxg6oG9M7dF/EHRjMSREOSUFkRDilhpWc+f3UR1oVsMDY1YuT83ZydebTshga5HvLitPK0ADx8t4uFTsHeuF7ZCCSAhxWz4GRxY+15ZHWgZiVuC9VUcY/fMeskyyvYKt0yTE3GXladAUrDwtRAUgBZEQ4o4c7LW8P6ARk/s2wN5Ow297Euk/axunL11TOzShJll5WlQyUhAJIe5Ko9EwNKwmPz7TxrIxbJ/Pt7Bi3+17CETlZtYbrGsoK0+LCkIKIiGE1drW8mblf++ndU0v0rNzGbUgjv9bdkBuoVVW5jw4tRn2/5L/Z8FO9GkJXPjlVUBWnhaVh0y7F0IUiZ+7IwuebcOMdX8zc8MJfth+hr/irzDz8eYyNb8yucV0egwBJHl3IOLkUhw1JtIUFwyaa+SPGLqxMpKVp0XFIz1EQogis9fa8WpEfeY+1QpPZx0HE430/mwzP+2Ml1lolcFtptMrxkQCTy3CUWNil30LrgyLkZWnRaUhPURCiGLrXM+HVS/dz+iFe9hxKoXxS/az7lAyUwc0wsfNUe3wRHHcYTq9hvxbZBkaZ0JGL8fV1RUIkpWnRaUgPURCiHvi7+7Egmfb8kbv+ui1dqw/coGen2xm9YEktUMTxXFm252n02vAhQwck3b9c7Bg5elGA/P/lGJIVEBSEAkh7pnWTsNzHWuz/MX2hPgbSLmWw/AfYxm9MI7L6dlqhyeKQqbTCxslBZEQosTU9zOwbGQ7hneqjUYDy/Yk0v3jaJb8dVbGFpU3t5lBlqN1tu75Mp1eVDIyhkgIUaIc7LW83qs+EQ18Gb9kP0eSrjJ20V6Wxp3jvYcaEehl5Q9cUXpuM4PsXLVeOBz+lSp3eKqChkydJ7rAsFIPU4iyJD1EQohS0ayGJ7+/2IFXI+qht7dj87FL9JgRzefrj8m6RWq6wwyyaoe/pQqpXMQDhfzip7D8xweqD5JxQqLSkYJICFFqdFo7Rnapw+qX7qdtLS+yTGamR/1N94+jWX3gvNxGK2tWzCDLsnPBYexeNA//gOYW0+nzBszhvEerMglXiLIkt8yEEKWuVlVXfnq2Lcv3JjJ11RHOXslk+I9/0a62N28/0IB6fm5qh1g5mfMKT4c35911Bpmj+RqOl/fnryF0i+n0Sp4ZTq4qwzchRNmQgkgIUSY0Gg19m1aje4gvX248wdebT7LtxGV6fbqJ/s2rM7p7Xap7yviiEnOrcUJOntY9t2AGWcF0+hvlmUsmPiHKGbllJoQoUy4O9rwSUY/1YzvRq6EfZgV+iT1L14+imbj8IBevyjT9e3a7cUKZV6x7vswgEzZIeoiEEKoI9HLmy8EtiIu/wodrjrLtxGXmbjvNot0JDA2rybAOwVR1c1A7zIrhxltjLlXhj9e40zghNNw0XNrSwhAgG7IKmyQFkRBCVc1qeLLg2bZsPX6JaWuOsjchldnRJ/hu6ykeblmd5zvWlqn6d3KrW2N3oLl1JYRsyCpsnRREQohyoX2dKiyr7c36wxeYufE4cfGp/Lg9np92JvBAY3+eah9Mk0APtcNU178HSV+7DL88ya16g+7KyQMyU/95bAjIL4ZkQ1Zho6QgEkKUGxqNhu6hvnQL8WH7yRRmbTzO5mOXWLYnkWV7EmkS6MHQtkFENvbHUWcDvRg3FkCXT0DsHLh6/p/zGjuKVQwB/Of7/OfLhqxCAFIQCSHKIY1GQ1htb8Jqe7P/bBrfbjnJqv1J7E1I5eWEVN5ddZiBLaozoHn1yjVl/24F0L8pxZnxdX2cUM0OUgAJcQMpiIQQ5Vqj6u588mgz3uqTzc+7Epi//QyJaVl8vekkX286SYMAAw81q0bfptUqziDsfw+CVhQ4tgb2/QwZl0vxhWWckBC3IwWREKJCqOLqwMgudXi+Yy3WH7nAr7Fn2XD0AgcTjRxMNPLeqsO0DvYiPNSPHqG+5WMgdkHhc/U8XLsIzt5wchMcXQFZaWUfj4wTEuK2pCASQlQo9lo7Ihr4EdHAjyvXclixL5ElceeIi09l+8kUtp9MYfKKQ4T6G+ha34d2dbxpXsOzdMYc5ebA9lmwdyFkpoHWAVy8Qe8Kehc4vQlyrpb861rl+q2xvrMg45KMExLiLmyqIJo5cyYffvghSUlJNGnShM8//5zWrVurHZYQopg8XfQMCavJkLCaJKRkEHUombWHkth5KoVD540cOm/kiw3HcbC3o1VNL8Jqe9OshgeNqrnj5qi7txdf+3+w7bObj6edurfrlogbbo3V7qxqJEJUFDZTEP3888+MHTuW2bNn06ZNGz755BMiIiI4evQoPj4+aocnhLhHgV7OPN0hmKc7BJNyLYc/j1xgy7GLbD1xmYtXs9ly/BJbjl8C8tfiqV3VlcbV3bnPx4WUKxoapmRQs6oBrd1tF+r5x+2KIbVo7AoPsJZbY0IUmc0URB9//DHPPvssTz31FACzZ89m5cqVfPfdd7z++usqRyeEKEleLnoGtqjOwBbVURSF4xfS2Xr8EjtPp7A3IY1zqZkcv5DO8Qvp15+h5esjW9Db2xHk5YyfuyN+Bkf83B3xMTji7qTDzcEeV0d7XLVm6sd8AdxuteeydD2CgXPyxyfJFHohis0mCqKcnBxiY2MZP3685ZidnR3du3cnJibmpvbZ2dlkZ/+zn5LRaATAZDJhMplKNLaC65X0dSsjyZX1JFeF1fRypGbr6gxqXR2Ay+nZ7DtnZP+5NP5Ovsq+0xe4nK0lJ9fMsQvpHLMUSjd7WruKCTp1Nji9vuvGP48NAeT1eBflvsjCDfPMpbYJq3y2rCe5sl5p5aoo17OJgujSpUvk5eXh61t4w0JfX1+OHDlyU/upU6cyadKkm46vXbsWZ+fSmbkSFRVVKtetjCRX1pNc3VkdoI479G4CZiWPlGy4nKUhLQdScyA1R4MxB7LyICtPQ1Ye1DEnl1l8/y6AMnVenPHuzDUHX7J0Hlx2rQcn7eDkqjKLqYB8tqwnubJeSecqIyPD6rY2URAV1fjx4xk7dqzlsdFoJDAwkPDwcAwGQ4m+lslkIioqih49eqDT3eMgz0pOcmU9yZX1CnIVEW5drux2nIR1ZfQDzi2AvGZDULxqgasvusAw6qh8K0w+W9aTXFmvtHJVcIfHGjZREFWpUgWtVktycuHf7JKTk/Hz87upvYODAw4ONy/wptPpSu1DXZrXrmwkV9aTXFnP6ly1HQ7r3y7mKtFWcK4CjR+Ger3RBLVDW07HAslny3qSK+uVdK6Kci2bKIj0ej0tWrRg/fr19OvXDwCz2cz69esZNWqUusEJISoWez2EjSqZWWZOXtD6OagRJmsFCaEymyiIAMaOHcsTTzxBy5Ytad26NZ988gnXrl2zzDoTQgirhU/J/7MoRZGjOzR+FDyD8rfrcPOX4keIcsRmCqJHHnmEixcvMmHCBJKSkmjatCmrV6++aaC1EEJYJXwKdP2/269UXa0puFTJ7/WR4keIcs9mCiKAUaNGyS0yIUTJsddDh9H5X0KICs1O7QCEEEIIIdQmBZEQQgghbJ4UREIIIYSweVIQCSGEEMLmSUEkhBBCCJsnBZEQQgghbJ4UREIIIYSweVIQCSGEEMLmSUEkhBBCCJtnUytVF5eiKAAYjcYSv7bJZCIjIwOj0Si7Id+F5Mp6kivrSa6KRvJlPcmV9UorVwU/twt+jt+JFERWuHr1KgCBgYEqRyKEEEKIorp69Sru7u53bKNRrCmbbJzZbCYxMRE3Nzc0Gk2JXttoNBIYGEhCQgIGg6FEr13ZSK6sJ7mynuSqaCRf1pNcWa+0cqUoClevXiUgIAA7uzuPEpIeIivY2dlRvXr1Un0Ng8Eg/2CsJLmynuTKepKropF8WU9yZb3SyNXdeoYKyKBqIYQQQtg8KYiEEEIIYfOkIFKZg4MDb7/9Ng4ODmqHUu5JrqwnubKe5KpoJF/Wk1xZrzzkSgZVCyGEEMLmSQ+REEIIIWyeFERCCCGEsHlSEAkhhBDC5klBJIQQQgibJwWRih588EFq1KiBo6Mj/v7+DBkyhMTExEJt9u3bx/3334+joyOBgYFMmzZNpWjVc/r0aYYNG0ZwcDBOTk7Url2bt99+m5ycnELtJFf53n33Xdq1a4ezszMeHh63bBMfH09kZCTOzs74+Pjw6quvkpubW7aBlhMzZ86kZs2aODo60qZNG3bu3Kl2SKrbtGkTDzzwAAEBAWg0GpYtW1bovKIoTJgwAX9/f5ycnOjevTvHjh1TJ1iVTZ06lVatWuHm5oaPjw/9+vXj6NGjhdpkZWUxcuRIvL29cXV1ZcCAASQnJ6sUsXq+/PJLGjdubFl8MSwsjD/++MNyXu08SUGkoi5durBo0SKOHj3Kr7/+yokTJxg4cKDlvNFoJDw8nKCgIGJjY/nwww+ZOHEiX3/9tYpRl70jR45gNpv56quvOHjwIDNmzGD27Nm88cYbljaSq3/k5OTwn//8hxEjRtzyfF5eHpGRkeTk5LBt2zbmzZvH3LlzmTBhQhlHqr6ff/6ZsWPH8vbbb/PXX3/RpEkTIiIiuHDhgtqhqeratWs0adKEmTNn3vL8tGnT+Oyzz5g9ezY7duzAxcWFiIgIsrKyyjhS9UVHRzNy5Ei2b99OVFQUJpOJ8PBwrl27ZmkzZswYfv/9dxYvXkx0dDSJiYn0799fxajVUb16dd5//31iY2PZvXs3Xbt2pW/fvhw8eBAoB3lSRLnx22+/KRqNRsnJyVEURVFmzZqleHp6KtnZ2ZY248aNU+rVq6dWiOXGtGnTlODgYMtjydXN5syZo7i7u990fNWqVYqdnZ2SlJRkOfbll18qBoOhUP5sQevWrZWRI0daHufl5SkBAQHK1KlTVYyqfAGUpUuXWh6bzWbFz89P+fDDDy3HUlNTFQcHB+Wnn35SIcLy5cKFCwqgREdHK4qSnxudTqcsXrzY0ubw4cMKoMTExKgVZrnh6empfPPNN+UiT9JDVE6kpKQwf/582rVrh06nAyAmJoaOHTui1+st7SIiIjh69ChXrlxRK9RyIS0tDS8vL8tjyZX1YmJiaNSoEb6+vpZjERERGI1Gy29qtiAnJ4fY2Fi6d+9uOWZnZ0f37t2JiYlRMbLy7dSpUyQlJRXKm7u7O23atJG8kf+9CbB8f4qNjcVkMhXKV/369alRo4ZN5ysvL4+FCxdy7do1wsLCykWepCBS2bhx43BxccHb25v4+Hh+++03y7mkpKRCP7QAy+OkpKQyjbM8OX78OJ9//jnPP/+85ZjkynqSq3yXLl0iLy/vlrmwpTwUVUFuJG83M5vNjB49mvbt29OwYUMgP196vf6m8Xy2mq/9+/fj6uqKg4MDw4cPZ+nSpYSGhpaLPElBVMJef/11NBrNHb+OHDliaf/qq68SFxfH2rVr0Wq1DB06FMVGFg8vaq4Azp07R8+ePfnPf/7Ds88+q1LkZa84uRJClK2RI0dy4MABFi5cqHYo5Va9evXYs2cPO3bsYMSIETzxxBMcOnRI7bAAsFc7gMrm5Zdf5sknn7xjm1q1aln+v0qVKlSpUoX77ruPkJAQAgMD2b59O2FhYfj5+d00wr7gsZ+fX4nHXtaKmqvExES6dOlCu3btbhosLbkqnKs78fPzu2kmVWXKlbWqVKmCVqu95efGlvJQVAW5SU5Oxt/f33I8OTmZpk2bqhSV+kaNGsWKFSvYtGkT1atXtxz38/MjJyeH1NTUQr0ftvo50+v11KlTB4AWLVqwa9cuPv30Ux555BHV8yQFUQmrWrUqVatWLdZzzWYzANnZ2QCEhYXx5ptvYjKZLOOKoqKiqFevHp6eniUTsIqKkqtz587RpUsXWrRowZw5c7CzK9y5KbmyXlhYGO+++y4XLlzAx8cHyM+VwWAgNDS0RF6jItDr9bRo0YL169fTr18/IP/f4Pr16xk1apS6wZVjwcHB+Pn5sX79eksBZDQaLb/x2xpFUXjxxRdZunQpGzduJDg4uND5Fi1aoNPpWL9+PQMGDADg6NGjxMfHExYWpkbI5YrZbCY7O7t85KlMhm6Lm2zfvl35/PPPlbi4OOX06dPK+vXrlXbt2im1a9dWsrKyFEXJn53g6+urDBkyRDlw4ICycOFCxdnZWfnqq69Ujr5snT17VqlTp47SrVs35ezZs8r58+ctXwUkV/84c+aMEhcXp0yaNElxdXVV4uLilLi4OOXq1auKoihKbm6u0rBhQyU8PFzZs2ePsnr1aqVq1arK+PHjVY687C1cuFBxcHBQ5s6dqxw6dEh57rnnFA8Pj0Iz8GzR1atXLZ8bQPn444+VuLg45cyZM4qiKMr777+veHh4KL/99puyb98+pW/fvkpwcLCSmZmpcuRlb8SIEYq7u7uycePGQt+bMjIyLG2GDx+u1KhRQ/nzzz+V3bt3K2FhYUpYWJiKUavj9ddfV6Kjo5VTp04p+/btU15//XVFo9Eoa9euVRRF/TxJQaSSffv2KV26dFG8vLwUBwcHpWbNmsrw4cOVs2fPFmq3d+9epUOHDoqDg4NSrVo15f3331cpYvXMmTNHAW75dSPJVb4nnnjilrnasGGDpc3p06eVXr16KU5OTkqVKlWUl19+WTGZTOoFraLPP/9cqVGjhqLX65XWrVsr27dvVzsk1W3YsOGWn6EnnnhCUZT8qff/93//p/j6+ioODg5Kt27dlKNHj6obtEpu971pzpw5ljaZmZnKCy+8oHh6eirOzs7KQw89VOgXOlvx9NNPK0FBQYper1eqVq2qdOvWzVIMKYr6edIoio2M4BVCCCGEuA2ZZSaEEEIImycFkRBCCCFsnhREQgghhLB5UhAJIYQQwuZJQSSEEEIImycFkRBCCCFsnhREQgghhLB5UhAJIYQQwuZJQSSEEEIImycFkRBCCCFsnhREQgghhLB5UhAJIWzKihUr8PDwIC8vD4A9e/ag0Wh4/fXXLW2eeeYZBg8erFaIQggVSEEkhLAp999/P1evXiUuLg6A6OhoqlSpwsaNGy1toqOj6dy5szoBCiFUIQWREMKmuLu707RpU0sBtHHjRsaMGUNcXBzp6emcO3eO48eP06lTJ3UDFUKUKSmIhBA2p1OnTmzcuBFFUdi8eTP9+/cnJCSELVu2EB0dTUBAAHXr1lU7TCFEGbJXOwAhhChrnTt35rvvvmPv3r3odDrq169P586d2bhxI1euXJHeISFskPQQCSFsTsE4ohkzZliKn4KCaOPGjTJ+SAgbJAWREMLmeHp60rhxY+bPn28pfjp27Mhff/3F33//LT1EQtggKYiEEDapU6dO5OXlWQoiLy8vQkND8fPzo169euoGJ4QocxpFURS1gxBCCCGEUJP0EAkhhBDC5klBJIQQQgibJwWREEIIIWyeFERCCCGEsHlSEAkhhBDC5klBJIQQQgibJwWREEIIIWyeFERCCCGEsHlSEAkhhBDC5klBJIQQQgibJwWREEIIIWze/wPuLF5azv1EJwAAAABJRU5ErkJggg==",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -179,7 +189,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 14,
    "id": "d10dad60",
    "metadata": {},
    "outputs": [
@@ -249,7 +259,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 15,
    "id": "a0158bf2",
    "metadata": {},
    "outputs": [
diff --git a/CYFI445/lectures/03_linear_regressioin_autogradient/2_autograd.ipynb b/CYFI445/lectures/03_linear_regressioin_autogradient/2_autograd.ipynb
index 978c89d..5bda07a 100644
--- a/CYFI445/lectures/03_linear_regressioin_autogradient/2_autograd.ipynb
+++ b/CYFI445/lectures/03_linear_regressioin_autogradient/2_autograd.ipynb
@@ -30,7 +30,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 11,
+      "execution_count": 37,
       "metadata": {},
       "outputs": [
         {
@@ -56,92 +56,47 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 12,
+      "execution_count": 38,
       "metadata": {},
       "outputs": [
         {
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "Tensor x: tensor([2., 3.], requires_grad=True)\n",
-            "Tensor y: tensor([1., 4.], requires_grad=True)\n"
+            "Tensor w: tensor([2., 3.], requires_grad=True)\n",
+            "Tensor b: tensor([1., 4.], requires_grad=True)\n"
           ]
         }
       ],
       "source": [
         "# Create a tensor with requires_grad=True\n",
-        "x = torch.tensor([2.0, 3.0], requires_grad=True)\n",
-        "print(\"Tensor x:\", x)\n",
+        "# I have two trainable parameters: w0 and w1\n",
+        "w = torch.tensor([2.0, 3.0], requires_grad=True)\n",
+        "print(\"Tensor w:\", w)\n",
         "\n",
         "# Create another tensor\n",
-        "y = torch.tensor([1.0, 4.0], requires_grad=True)\n",
-        "print(\"Tensor y:\", y)"
+        "b = torch.tensor([1.0, 4.0], requires_grad=True)\n",
+        "print(\"Tensor b:\", b)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Step 3: Build Computation Graph\n",
-        "When we perform operations on tensors with `requires_grad=True`, PyTorch builds a **computation graph** to track how the output depends on the inputs. Let's compute a simple expression: `z = x^2 + y`.\n",
-        "\n",
-        "\n",
-        "\n",
-        "       x [2.0, 3.0]\n",
-        "           |\n",
-        "           | **2 (PowBackward0)\n",
-        "           v\n",
-        "       temp [4.0, 9.0]\n",
-        "           |\n",
-        "           | + (AddBackward0)\n",
-        "           |        y [1.0, 4.0]\n",
-        "           |        /\n",
-        "           v       /\n",
-        "         z [5.0, 13.0]\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 13,
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Result z: tensor([ 5., 13.], grad_fn=<AddBackward0>)\n",
-            "z requires_grad: True\n"
-          ]
-        }
-      ],
-      "source": [
-        "# Compute z = x^2 + y (element-wise)\n",
-        "z = x**2 + y\n",
-        "print(\"Result z:\", z)\n",
-        "\n",
-        "# z is a tensor with gradients tracked\n",
-        "# Means x, y, z are the components of the computatioal graph\n",
-        "print(\"z requires_grad:\", z.requires_grad)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Step 4: Computing Gradients\n",
+        "## Step 3: Computing Gradients\n",
         "To compute gradients, we call `.backward()` on a scalar output. If the output is a tensor (like `z`), we need to reduce it to a scalar (e.g., using `.sum()`). The gradients are stored in the `.grad` attribute of the input tensors.\n",
         "\n",
-        "       x [2.0, 3.0]\n",
+        "       w [2.0, 3.0]\n",
         "           |\n",
         "           | **2 (PowBackward0)\n",
         "           v\n",
         "       temp [4.0, 9.0]\n",
         "           |\n",
         "           | + (AddBackward0)\n",
-        "           |        y [1.0, 4.0]\n",
+        "           |        b [1.0, 4.0]\n",
         "           |        /\n",
         "           v       /\n",
-        "         z [5.0, 13.0]\n",
+        "        z [5.0, 13.0]\n",
         "           |\n",
         "           | sum (SumBackward0)\n",
         "           v\n",
@@ -150,21 +105,32 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 14,
+      "execution_count": null,
       "metadata": {},
       "outputs": [
         {
           "name": "stdout",
           "output_type": "stream",
           "text": [
+            "Result z: tensor([ 5., 13.], grad_fn=<AddBackward0>)\n",
+            "z requires_grad: True\n",
             "Loss (sum of z): tensor(18., grad_fn=<SumBackward0>)\n",
-            "Gradient of x (∂loss/∂x): tensor([4., 6.])\n",
-            "Gradient of y (∂loss/∂y): tensor([1., 1.])\n"
+            "Gradient of w (∂loss/∂w): tensor([4., 6.])\n",
+            "Gradient of b (∂loss/∂b): tensor([1., 1.])\n"
           ]
         }
       ],
       "source": [
+        "# Compute z = w^2 + b (element-wise)\n",
+        "z = w**2 + b\n",
+        "print(\"Result z:\", z)\n",
+        "\n",
+        "# z is a tensor with gradients tracked\n",
+        "# Means w, b, z are the components of the computatioal graph\n",
+        "print(\"z requires_grad:\", z.requires_grad)\n",
+        "\n",
         "# Reduce z to a scalar by summing its elements\n",
+        "# both trainable parameters w and b affect the loss\n",
         "loss = z.sum()\n",
         "print(\"Loss (sum of z):\", loss)\n",
         "\n",
@@ -172,8 +138,8 @@
         "loss.backward()\n",
         "\n",
         "# Gradients are stored in x.grad and y.grad\n",
-        "print(\"Gradient of x (∂loss/∂x):\", x.grad)\n",
-        "print(\"Gradient of y (∂loss/∂y):\", y.grad)"
+        "print(\"Gradient of w (∂loss/∂w):\", w.grad)\n",
+        "print(\"Gradient of b (∂loss/∂b):\", b.grad)"
       ]
     },
     {
@@ -182,34 +148,34 @@
       "source": [
         "### Explanation\n",
         "\n",
-        "To reduce the loss, you can modify the four numbers corresponding to the elements of x and y (i.e., x1, x2, y1, y2). Here is why:\n",
+        "To reduce the loss, you can modify the four numbers corresponding to the elements of w and b (i.e., w1, w2, b1, b2). Here is why:\n",
         "\n",
-        "- For `z = x^2 + y`, the loss is `loss = z.sum()`.\n",
-        "- The gradient of `loss` with respect to `x` is `∂loss/∂x = 2x` (derivative of `x^2`).\n",
-        "  - For `x = [2.0, 3.0]`, this gives `2x = [4.0, 6.0]`.\n",
-        "- The gradient of `loss` with respect to `y` is `∂loss/∂y = 1` (derivative of `y`).\n",
-        "  - For `y = [1.0, 4.0]`, this gives `[1.0, 1.0]`.\n",
+        "- For `z = w^2 + b`, the loss is `loss = z.sum()`.\n",
+        "- The gradient of `loss` with respect to `w` is `∂loss/∂w = 2w` (derivative of `w^2`).\n",
+        "  - For `w = [2.0, 3.0]`, this gives `2w = [4.0, 6.0]`.\n",
+        "- The gradient of `loss` with respect to `b` is `∂loss/∂b = 1` (derivative of `b`).\n",
+        "  - For `w = [1.0, 4.0]`, this gives `[1.0, 1.0]`.\n",
         "\n",
-        "In gradient-based optimization (e.g., gradient descent), you update each element of x and y to reduce the loss:\n",
-        "- Update rule: `x[i] = x[i] - learning_rate * x.grad[i]`.\n",
-        "- Update rule: `y[i] = y[i] - learning_rate * y.grad[i]`.\n",
+        "In gradient-based optimization (e.g., gradient descent), you update each element of w and b to reduce the loss:\n",
+        "- Update rule: `w[i] = w[i] - learning_rate * w.grad[i]`.\n",
+        "- Update rule: `b[i] = b[i] - learning_rate * b.grad[i]`.\n",
         "\n",
         "or simply\n",
-        "- `x = x - learning_rate * x.grad`\n",
-        "- `y = y - learning_rate * y.grad`"
+        "- `w = w - learning_rate * w.grad`\n",
+        "- `b = b - learning_rate * b.grad`"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Step 5: A Simple Optimization Example (one parameter)\n",
+        "## Step 4: A Simple Optimization Example (one parameter)\n",
         "Autograd is often used to optimize parameters. Let's minimize a simple function `f(w) = w^2` using gradient descent."
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 15,
+      "execution_count": 40,
       "metadata": {},
       "outputs": [
         {
@@ -268,13 +234,13 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Step 6: Plot all weight changes - Gradient Descent\n",
+        "## Step 5: Plot all weight changes - Gradient Descent\n",
         "Autograd is often used to optimize parameters. Let's minimize a simple function `f(w) = w^2` using gradient descent."
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 16,
+      "execution_count": 41,
       "metadata": {},
       "outputs": [
         {
@@ -356,21 +322,21 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Step 7: Disabling Gradient Tracking\n",
+        "## Step 6: Disabling Gradient Tracking\n",
         "- When requires_grad=True, the tensor is part of PyTorch’s computation graph, and converting it to a NumPy array would break the graph.\n",
         "- Sometimes, you need to perform operations without gradient tracking (e.g., during inference). Use `torch.no_grad()` or `.detach()`."
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 20,
+      "execution_count": 42,
       "metadata": {},
       "outputs": [
         {
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "Result without gradient tracking: tensor([4., 9.])\n",
+            "Result without gradient tracking: tensor([1.7180])\n",
             "Detached tensor requires_grad: False\n",
             "tensor([1., 2.], requires_grad=True)\n",
             "access w with out participating in a computatioinal graph tensor([1., 2.])\n"
@@ -381,15 +347,15 @@
         "\n",
         "# Example with no_grad\n",
         "with torch.no_grad():\n",
-        "    result = x**2  # No computation graph is built\n",
+        "    result = w**2  # No computation graph is built\n",
         "print(\"Result without gradient tracking:\", result)\n",
         "\n",
         "# Detach a tensor\n",
-        "detached_x = x.detach()\n",
+        "detached_x = w.detach()\n",
         "print(\"Detached tensor requires_grad:\", detached_x.requires_grad)\n",
         "\n",
         "# create a new tensor that is a copy of x but detached from the computation graph and independent of gradient tracking\n",
-        "detached_copy_x = x.clone().detach()\n",
+        "detached_copy_x = w.clone().detach()\n",
         "\n",
         "# access to the tensor’s underlying data as another tensor, bypassing the autograd (gradient tracking) system.\n",
         "w = torch.tensor([1.0, 2.0], requires_grad=True)\n",
@@ -401,12 +367,12 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Step 8:  Simple Optimization Example (two parameters)"
+        "## Step 7:  Simple Optimization Example (two parameters)"
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 18,
+      "execution_count": 43,
       "metadata": {},
       "outputs": [
         {
@@ -465,7 +431,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 19,
+      "execution_count": 44,
       "metadata": {},
       "outputs": [
         {
diff --git a/CYFI445/lectures/03_linear_regressioin_autogradient/compute_autogradient.pptx b/CYFI445/lectures/03_linear_regressioin_autogradient/compute_autogradient.pptx
index 4d40e52..4028131 100644
Binary files a/CYFI445/lectures/03_linear_regressioin_autogradient/compute_autogradient.pptx and b/CYFI445/lectures/03_linear_regressioin_autogradient/compute_autogradient.pptx differ