diff --git a/README.md b/README.md
index 78bd164a0..f22760dac 100644
--- a/README.md
+++ b/README.md
@@ -355,6 +355,7 @@ Recommended prereading: Chapters 1-2 (linear algebra) and chapter 3 (statistics)
 ###  Week 42 October 18-22
 - Lab Wednesday: Work on project 2
 - Lecture Thursday: Solving differential equations with neural networks and start Convolutional Neural Networks and classification problems
+  - Video of Lecture at https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureOctober21.mp4?vrtx=view-as-webpage
 - Lecture Friday: Convolutional Neural Networks and classification problems
 - Reading recommendations:
   - See lecture notes for week 42 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
diff --git a/doc/LectureNotes/_build/.doctrees/Clustering.doctree b/doc/LectureNotes/_build/.doctrees/Clustering.doctree
index cef131b01..62bb5e25e 100644
Binary files a/doc/LectureNotes/_build/.doctrees/Clustering.doctree and b/doc/LectureNotes/_build/.doctrees/Clustering.doctree differ
diff --git a/doc/LectureNotes/_build/.doctrees/chapter1.doctree b/doc/LectureNotes/_build/.doctrees/chapter1.doctree
index 0c4e5642b..62f132775 100644
Binary files a/doc/LectureNotes/_build/.doctrees/chapter1.doctree and b/doc/LectureNotes/_build/.doctrees/chapter1.doctree differ
diff --git a/doc/LectureNotes/_build/.doctrees/chapter10.doctree b/doc/LectureNotes/_build/.doctrees/chapter10.doctree
index ea0cecd85..88d74cf11 100644
Binary files a/doc/LectureNotes/_build/.doctrees/chapter10.doctree and b/doc/LectureNotes/_build/.doctrees/chapter10.doctree differ
diff --git a/doc/LectureNotes/_build/jupyter_execute/Clustering.ipynb b/doc/LectureNotes/_build/jupyter_execute/Clustering.ipynb
index 802668d7d..b8611853c 100644
--- a/doc/LectureNotes/_build/jupyter_execute/Clustering.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/Clustering.ipynb
@@ -475,7 +475,7 @@
      "output_type": "stream",
      "text": [
       "Converged at iteration 5\n",
-      "Runtime: 0.4787557125091553 seconds\n"
+      "Runtime: 0.5011799335479736 seconds\n"
      ]
     }
    ],
@@ -604,7 +604,7 @@
      "output_type": "stream",
      "text": [
       "Converged at iteration: 5\n",
-      "Runtime: 0.4250478744506836 seconds\n"
+      "Runtime: 0.4376199245452881 seconds\n"
      ]
     }
    ],
@@ -745,7 +745,7 @@
      "output_type": "stream",
      "text": [
       "Converged at iteration: 11\n",
-      "Runtime: 0.8499350547790527 seconds\n",
+      "Runtime: 0.8795850276947021 seconds\n",
       " "
      ]
     }
@@ -877,7 +877,7 @@
      "output_type": "stream",
      "text": [
       "Converged at iteration: 5\n",
-      "Runtime: 0.003952980041503906 seconds\n"
+      "Runtime: 0.003922939300537109 seconds\n"
      ]
     }
    ],
diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter1.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter1.ipynb
index e4f0ebb84..a5fd69df9 100644
--- a/doc/LectureNotes/_build/jupyter_execute/chapter1.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/chapter1.ipynb
@@ -310,7 +310,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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Wcs+IcbGZLatmoyIiEh9lAwHJJT08fWod0yIiIhGo6cpiksvrlRAREYmGppgQEck4BQIRkYxTIBARyTgFApFaZH0mTUkFBQKRauVWSVu1ys0Lk1slrXswULCQmFMgEKmWn1XS/AYLkQgpEIhUy88qaWlbUlO1m1RSIBCplp8ZItO0pKZqN6mlQCBSLT8zRKZp2uW01W7kXxQIRKrV2gq0tblVo8zcfVtb11XS0jTtcppqN9KFAoFILVpb3dKBnZ3uvvtSmX6CRVKkqXYjXSgQiAStXLBIijTVbqSLwAOBmY0xs7+Y2VIzW2JmlwS9zchpZIWkUZpqN9JF2YVpat6AW+d4BMm/m9lAAIsBfILk0lLvSfTCNLmRFYWdav376wcjIoEKevH6mpB8leTfvb+3AFgGYFTQ242MRlaISMKE2kdgZmMBHAO33GX352aZWYeZdaxfvz7MZNWXRlaISMKEFgjMbG8AvwZwKcnN3Z8n2UayhWTL8OHDw0pW/WlkhYiEZetW4K9/rbnDPpRAYGZ94IJAO8n7wthmZLIyskId4pIEaTtOX3sNuO8+4LLLgPe9Dxg0CPjAB4D//M/aPpdkoDcABuBuAN/x+56JEycy0ebOJZubSTN3P3du1Cmqr7lzyf79STfRgLv175+8/Uz795R1ST9OOzvJpUvJtjZy5kxy/Pj8fvTtS55wAnnVVeQDD5BvvEEAHayynA5j1NA0AP8D4FkAnd7DXyX5YKn3JHrUUBaMHevmmemuudmNk08Cje5Kh/Z2NxBj9WrX/DpnTv77S9pxun070NEBzJ+fv23c6J4bNgyYNg2YOtXdH3ss0NjY5e21jBoKPBBUQ4Eg5hoa3HlJd2buoqkkSFohIXsqFswbG4GBA10BWqpsi8txumEDsGCBK/DnzXNBYMcO99zBB7tCP1fwH3SQS3cPagkEvat5k2RcU1PxQjRJHeIa3ZV8xYZq79gBvPFGz++L4jglgZdeyhf68+cDzz/vnuvTB2hpAS65xBX8xx8PhDxgRoEgjXqqLtfDnDnFm1WS1CGehmCWddUE7bCO0x07gCefzBf68+cDr7/unhsyxBX4M2e6+5YWoF+/4NPUAwWCtOleXc7NGQ/ULxjkPifIYBO0NASzrCsVzIsxC/Y43bSpazPP44+7Nn8AGDcOOOWUfDPPhAmueTVG1EeQNmr79i/ompMEq1gfQTH1PvZJ93m5M/1584AlS9zjvXq5jtxcoX/88cCIEfXbdg/URyB5avuWrOheM913X2DzZmDnzvxr6lHL27ULePrpru37a9e65/bZB5gyBfjMZ1zhP2kSMGBAbduLgAJB2qjt258wmtAkeK2tXb+vetTytmwBFi7MF/oLFwLvvOOea2oCTjwxf8Z/2GGuFpBwahpKG42P90dNaJKzZk2+0J83D3jmGTe8tKEBOOqo/DDOqVOBMWOiTm1JsZ59VEJWjznj03ZZfjFqQsum3btdM88Pfwh89rPu9zFmDHDWWcAddwBDh7rpGh56CHjzTeDvfwe+9z1gxoyuQSBtv5FqL0kO8pb4KSaSLOmX5fs1dGjXfczdhg6NOmW1S/LUGfVO+9tvk488Ql5/PXnyyeQ+++S/65EjyTPOIG+5hezoIHfu9J/GUr+RCPMeNUwxEXmhX+ymQFBHlR6Yzc3FC8jm5uDTGqa0BoIkB/J6pH3tWvLee8lLLyWPO47s3dt9jhl5+OHkBReQP/0puXy5m8unGqV+I0OHRpr3tQQC9RGkWTX9BWmYPsKPtO5nkvs+Kk17ZyewbFnXYZzLl7vn+vZ1s3PmOnWnTAEGD65POksdO6WElPeaa0iKq6ZQSHJBUom07meSA1y5tG/fDjzxRL7QX7DAteMDwH775Qv9qVOBY47ZY1K2uil17JRSz7zvYVRULYEg8magYjc1DdWJWfEqrFnp9yS5aaESQexnHNrmk9y0Vyrt++xDTplCNjbmH5swgTzvPPKOO8gXX6y+macapY6dUs2N9cr7Mscs1EcgRVVbKMShQAtDPfczLgE0LumoVGcnefPNXQv73K1XL3LqVPKKK8jf/Y5cvz7q1BY/doLO+zK/ZwUCKS6phUISxelMvJoAF3bwf/ddcsEC8qabyNNPJ4cPz+dZQ4O7HzyYvOYactu2YNNST0HmY5kafi2BQFcWp1kaJodLijhdl9D9attywrjK+s03u07K9sQT+UnZxo8Hpk/Pt+8ffHDsJmWLhQBnDVBnsUg9JLnzud5pJ4EVK7rOzbNkiXuud283KVuu0J86Fdh//1pSHx9BX9Vf5vM1akgkakme2qPWkUY7d7qrdQunaVi3zj03aJCbgTNX6E+a5PIljcI4GQho1JCahkTqIcnNcJU2OWzeDDz2WL7QX7QoHwDHjgVOOqnrpGxZaeYJo3mw0mY/nxQIROoloB9p4Mot0rN6dddmnmefzU/KdvTRwHnn5Zt6Ro2KZBdiIcEz/2YkVIskWNATnHWfqHDECODTnwYeeMAVYs3NboK2u+92a+lecw3w8MNuVa7Fi4Hvfhc488xsBwHABc7uzV4JWfVONQKROAt6RM8777imnX/+043W2bgRePVVV+iPGpU/0582DTjiCNfZK8UluHlQncUicVbvDsi1a7vOzfPUU25qZjNX0BdO09DU5B6XRFBncVZpzd30q6UDsrMTWLq0a/v+ihXuuX793KRsV13lCv3Jk+s3KZskTnwDgQq5nmmpxWwo1QHZ0OBuhb+NbdvchVq5Qn/BAteOD7ix+tOmARdfnJ+UrU+fUHdF4iueTUPjxrHjtdeSOSY7LEm+gEn8K3Z9Qne9e7vvffXq/MLthxzStX1/3Dg186Rc+paqfOWVPQ/8rVtdDaGekrzcXJymNJDg5Eb05Nrri43J37XLrbt72WXA738PbNjgmoTa2oCZM4H3vEdBoJwklwV1EM+moR07ij9ez0Iu6U0rCR6zLD68+64bmplr39+6NT/NWDE7dgA33BBuGtMi6WVBHcSzRlBqQYlShVw10fzqq8OpdQQlwWOWpYiNG924/a98BTjhBDc1w9SpwBVXuFW4Pv5x4PbbgZEji79fJwDVS3pZUA/VTlsa5G3igQfuOX1ynz5u4Yfu07tWO9VypYu2xHGO/jimScrr7CRfeom8807y/PPJQw7pepxPnkzOnk3edx/52mtd36upxeuvmgWcYgipXI+gsJAbOnTPBStyB3+188BX8j79+KQWO3aQixaR3/42+clPkvvvnz+OBg8mp08n58wh//Y3cuvW8p+nE4D6itNaEjVIZyAo1NMXVW00r6RwT8mBIiHZtIn84x/Jq68mTzyR7Ncvf8wceCB59tnkj35EPvssuXt31KmtryQGqZSc6KU/EPRU2NdSSPs9aFNSdZQAdHaSK1e6Y+fCC8kjj8wfL716kRMnkpdcQt5zD/nKK1GnNlhJLlCTGMC6qSUQxHPUUHc9jZApN3NiT/zOFqkROpKzaxfwzDNdp2l45RX33MCBwJQpwKc+5cbuT5oE7L13tOkNU0+drlGPvtEFqj2rNoL4vQH4CYDXATzn9z171AjKnWkEHc2TfKYjtdmyhXz4YfK668gPfYjce+/8MTB6NDljBvn975NPPknu2hV1aqMV15qzn/IjBb9vxLlpCMC/ATi2pkBARl91i3r7Eo41a8hf/pK8+GLy2GNd806uMDvqKPKii8if/YxctSrqlEar2O8hrn1p5dIV13RXqJZAEMoUE2Y2FsADJA/383rNPiqh6Ox0a+nm5uaZPz8/PUf//m5Sttw0DZMnu7H9UnpZzpkzgbvuit/UMOWW4qx1qc6YSMXso2Y2C8AsAGhS27sEYetW4PHH8237jz0GvPWWe+6AA1yhf8kl7v6oozQpWyml+gIefNAV+nFriy/Xx6c+wHBGDQEYi1qbhpJMzUrRWLeO/PWvycsuIydNInv3zlf7DzuMnDWLvPtu8p//dKN/kiiKYyuufQGlqI8g+j4CZj0QpOQgi73OTnLpUvK228iZM8nx4/P5vdde5AknkFddRT7wAPnGG1Gntj6iOraS2KZeLmCm4GRNgaASYX/hSfzRkPH/YWzbRs6bR95wA/nxj5P77pvP22HDyE98grzpJnLBAnL79qhTG4yoji2d3MRSrAMBgJ8DeBXATgBrAJxX7j2BBYIoDuCkVaPJeP7Q168nf/c78ooryOOP7zrlyMEHk1/4AvnjH5MvvJDcZp5KRXlsxf1EIUwxyYtYB4JqboEFgijOoJJYI4g6zZ2d5IsvknfcQZ53HjlhQj4NffqQU6aQl19O/va35Ouvh5OmOIr6e/IjJoVkYGJ00qRA4FcUZ1AxOlB8CyqfShUK775LLlxI3nwz+e//Tu63X36bQ4aQp55KfvOb5KOP+puULSvifmzFPX31EKNgrEDgV5RtqsUKwLieLQWRT8UKhd693dl+3775x8aNIz//efLWW8klS9I3KVu9xfUYImNVSAYmRk2/CgR+xekMJU5pCTJtnZ3k8uVuKvFiP5jGRvLSS8l77yXXrq3/vkh0wq5ZRiFGwU6BoBJxOYhidAAVVW0+7dxJdnSQt9xCnnEGOXJk8f1MQqe51CasmmWUJ1AxSo8CQRLFqEpZk82byYceIq+5hjzpJHLAgPy+NDWRZ51F/uAH5IgR8Q58SRCXkxi/gigk43gCFZPvpZZAEJspJjInqZe1r1nTdW6ep5/Oz9dy5JHAuee6uXmmTgXGjMm/b9Cg6qcLl2QusJ5LVz2nnFi9urLHw+B3Ovs4qzaCBHmrqEYQk2hcsRhVKUvatYt86il3Rv/Zz7oz/FxaBwxwNYBrrnE1grfeKv95Sf2u4iCOZ8JRyEI+VPk7QWabhpJQmPYkbgXj22+TjzxCXn89efLJ5D775PN15EjX5n/LLa4PYOfOaNOaNWlpSqxV0n/z5dSwf7UEglCmoa6U72mox44t3rzS3JyfTlhKW7cuPxPn/PnAk0+6FbjMgMMOy0/BPHWqy2uzqFOcXTrW89K82lgN33Mt01AnOxCkZB7xPQRxoHd2As8/ny/0580Dli93z/Xt6+bezxX6U6YAQ4bUvh9SP6XWAIh6rn+prxrKtFSsR1CVpHa49qRenYLbtwNPPJEv9BcsAN580z03fLg727/oInd/zDFAY2N990PqK4iOV4mfiMq0ZNcI0niWVG3VcP16V9jnzvgXLwZ27HDPTZjgzvRzTT3jx6uZRySOaijTslsjSONZkp/hcSTwj390Hcb5wgvuucZGoKUFuPRSV+gffzwwbFjgyRaROoioTEt2jSCNStUIDjgAmD07X/CvX+8e33fffNv+tGnAxImuzV8ql+ZOSEm9WmoEDfVOjNRozhygX789H1+3Dvjyl4HnngOmT3dVxaVLXUC4/37gyitdMFAQqE6uSr5qlatx5fpm2tujTpnktLe7E6WGBnev76ZuVCOIGgmsWNF1GOeSJfnnGxuBD34QOP9818xzwAHRpTXNNDwz3tLYH1hn2R0+mkQ7d7ppGQqHca5b554bNMgV9rmmnkmT3MEuwUvrUOS0UKAuK7udxUmweTPw2GP5tv2FC/NnNc3NwEkn5dv3DzvMFUgSvjCH7akvonJxnGMoRRQI6m316nyhP28e8Oyz+UnZjj4aOO+8/DDOUaOiTq3kzJkTzqR4SZw8Lg7SeM1QnFQ7N0WQt8RMQ71rF/nkk+T3v0/OmEGOGZOfH2TvvckPfYi87jry4YfddM0Sb2HM/RTWpGlxm8eqVmmfY6gOkNm5hsL2zjvAokX59v3HHgO2bHHPjRrVdW6eI48EeqvCJd2E0ReR1o5VNan1KJ2dxV/6UvRf+tq1XZt5nnoK2L3b/WiPOKLr+P2mJl2tK+WF0empjtVMSl8gGDeOHa+9Fu4ZTWenG5dfOIxzxQr3XL9++UnZpk0DJk8GBg8OJh2SbmGcrQdZ69BZeWylLxDstRc7cvPkFKrnGc22bXtOyrZpk3tu//27zs1zzDFAnz712a5kS7GCEwi2MA2qRpDWJqeUqCUQRN4xXOw2MYiFzl97jbzvPnL2bHLyZLJPn/znHnIIef755J13kv/4B9nZWf12JFxx7hSNqoMzqO1mYXWwOPJ5jCN1K5Q1NtZ2wHV2ksuWkbffTp57LnnQQfnP2Gsvcto08soryd//ntywwd9nSvzEfSRJlAVnEAFSq6SFr4JjPH2BYPjwPQ+67jtfeKA3NZHXXkveeCN52mnksGH59w0d6h678UZy/nxy+/bqvhCJnygK2koK2LQVnH7zO861tKSp4BhPXyBoaNjzh3Phhfk9vvVWd2ZfLIMOOog85xxXG1i2TM081UjKDznsgrbSGkjamlL87H/ca2lJU8Exnr5AUGzHhw517fiHHlo8YwBy9Ohas12S9EMuVdAOHRpMIKu0YE9SXvpV7iQhbcEvapmuEZQq6AcPJqdPLx0IklrljpMk/ZCLFbSNjV0HAtSz8K2mBpKU2lW9pK05LGqZ7iModiCNGEHu3u32OEmFVdIk7YfcvaAdOrR4+utRS9BxV57yqP4yO2rIT0dxVMPy0n52l/QfcqlA1v1WzfGSxqaeelMeRSZ9geDAA8sXuGEXylk5wJO+n6UCWb2CWxZOBmqlPIpELYEgnlcWx3HSuSzN35LkaQSKXf1aihadkRTRwjRhyNLCGK2tySn4u8uluzCQvf028MYbe75Wc9mLAAhp8XozO8XMXjCzl8zsK2Fss+5KFRoqTOKntdXV0jo73f0tt+y55GcQi86IJFTggcDMegH4AYCPAjgUwFlmdmjQ2627OXNUmCRVa6ubGK252TUHNTdrojSRAmHUCCYBeInkcpI7APwCwOl130p7u2vHb2hw9+3t9f38OBcmQe97GnSvJcThexOJicA7i83s0wBOIfm/vP8/B+B9JL/Y7XWzAHiLt+JwAM/53cYwYN8moNkKAhuBztXAqg3Axpp3IlrDAGzo4ck073uhHvMhY5QXecqLvINJDqzmjbHpLCbZBqANAMyso9re77RRXjjKhzzlRZ7yIs/Mqh5qGUbT0CsAxhT8P9p7TEREYiCMQPAEgIPM7EAzawQwA8D9IWxXRER8CLxpiOQuM/sigD8D6AXgJySXlHlbW9DpShDlhaN8yFNe5Ckv8qrOi1heWSwiIuEJ5YIyERGJLwUCEZGMiywQlJt2wsz2MrNfes8vMrOxESQzFD7y4jIzW2pmz5jZ/zOz5ijSGQa/05GY2afMjGaW2qGDfvLCzM70jo0lZvazsNMYFh+/kSYz+4uZPen9TqZHkc4wmNlPzOx1Myt6rZU53/Xy6hkzO7bsh1Y7bWktN7hO438CGAegEcDTAA7t9pqLAPzI+3sGgF9GkdaY5MUHAPT3/r4wy3nhvW4ggEcBLATQEnW6IzwuDgLwJIAh3v/7RZ3uCPOiDcCF3t+HAlgZdboDzI9/A3AsgOdKPD8dwB8BGIDJABaV+8yoagR+pp04HcBd3t+/AnCSmVmIaQxL2bwg+ReSuXmVF8Jdi5FGfqcjuR7AfwPYHmbiQuYnL84H8AOSbwIAyddDTmNY/OQFAezj/T0IwNoQ0xcqko+i51kDTgdwN52FAAab2YiePjOqQDAKwMsF/6/xHiv6GpK7ALwFYGgoqQuXn7wodB5ctE+jsnnhVXPHkPxDmAmLgJ/j4r0A3mtm881soZmdElrqwuUnL64DcLaZrQHwIICLw0laLFVapsRnigkpz8zOBtAC4P1RpyUKZtYA4NsAzok4KXHRG6556ES4WuKjZnYEyU1RJioiZwG4k+S3zGwKgJ+a2eEktfKQD1HVCPxMO/Gv15hZb7jqXpHVRRLP1xQcZvYhAFcDOI3kuyGlLWzl8mIg3ISEfzWzlXDtn/entMPYz3GxBsD9JHeSXAHgRbjAkDZ+8uI8APcAAMnHAPSFm5Auiyqe1ieqQOBn2on7Acz0/v40gEfo9YSkTNm8MLNjANwKFwTS2g4MlMkLkm+RHEZyLMmxcP0lp5GM2bqmdeHnN/JbuNoAzGwYXFPR8hDTGBY/ebEawEkAYGaHwAWC9aGmMj7uB/B5b/TQZABvkXy1pzdE0jTEEtNOmNnX4RZgvh/Aj+Gqdy/BdYzMiCKtQfOZFzcB2BvAvV5/+WqSp0WW6ID4zItM8JkXfwbwETNbCmA3gC+TTF2t2WdezAZwm5l9Ca7j+JyUnjjCzH4OdwIwzOsTuRZAHwAg+SO4PpLpAF4CsBXAuWU/M6V5JSIiPunKYhGRjFMgEBHJOAUCEZGMUyAQEck4BQIRkYxTIBARyTgFAhGRjFMgEPHBm+v+w97f3zCz70WdJpF60aRzIv5cC+DrZrYfgGMApO7KbskuXVks4pOZ/Q1uqo8TSW6JOj0i9aKmIREfzOwIACMA7FAQkLRRIBApw1vdqR1u5ae3U7wAjGSUAoFID8ysP4D7AMwmuQxumcxro02VSH2pj0BEJONUIxARyTgFAhGRjFMgEBHJOAUCEZGMUyAQEck4BQIRkYxTIBARybj/D0V5cnlMMivlAAAAAElFTkSuQmCC\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -461,7 +461,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -526,18 +526,18 @@
      "output_type": "stream",
      "text": [
       "The intercept alpha: \n",
-      " [1.76691438]\n",
+      " [1.94994109]\n",
       "Coefficient beta : \n",
-      " [[5.37626646]]\n",
-      "Mean squared error: 0.25\n",
-      "Variance score: 0.92\n",
+      " [[5.0707273]]\n",
+      "Mean squared error: 0.24\n",
+      "Variance score: 0.90\n",
       "Mean squared log error: 0.01\n",
-      "Mean absolute error: 0.40\n"
+      "Mean absolute error: 0.42\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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yUBro4osVBApKgUDEBWENlbVG0vpG0/PPB37843wakLduDX8KuOGG9M8vqVAgEHFBUAPmlClexXveed7722/30kHLluXTgEwC++03flsVFoyvAAUCERc0dpecNs2rYDdubL7rz3pFrqA00Le/7UYAKOIYCwcpEIi4or675L77eoOy6tXu+rPqB//WW+FpoG67WiZRgRdxjIWjFAhEXNTqrj8ojUR6FWFSd8UkcPDB47d1mwaqVf6kl+7qtgIv0hgLxykQiLio1V1/fRoJ8CrWWgXd7V1xUBroe9/rPg1Uf/cONB8vTgWuResTo0Ag4qJ2o19raaT+/mQq1TfeCE8DfetbnR0rSNDde6NOK3BNFZEYBQIRF0WdayeJu2ISeM97xm9LujdQlPJ0WoFrqojEKBCIuCrKXDvd3BUHpYGuuy6d3kDtyhOnAk9zYrqKUSAQKbI4d8Vr14angS65JNny1YQ1cANjg+QWL+68F1FaE9NVjAKBSJF1eldc26deFoPCgsp5++1jXVHzGiktADTpnEg1BD0B3HKLN0103sIm3HNlYr2C6GbSOU1DLVJmq1YBxxzTvN2lG0B1A82dAoFIWRVliui+vuAnAnUDzYzaCERcFmcqhqDeQIODbgWB+t9r69bmpS3VDTRTeiIQcVXYqmVAcGPwU08Bc+Y0b3cpAADNv9fGjcDkyd5Ee5s2acWwHKixWMRVnTSiFiUNBIT/XtOmeZPtafnIWJxfoYzkapK/JvkMSdXwIlFEaUQNSgPddZe7QQAI/702blQX0pxk2Ubwh2Z2XNyIJVI5rUYNL18e/hRwzjnplqtbURuBNZNoZtRYLOKqsFHDa9YAH/3o+O1FWiks6PcKoy6kmcgqEBiAX5BcQXJh0A4kF5IcJjk8MjKSUbFEEtKud0+c3j+No3GB5hk8ly0rTgCoCRplPG1a8L7qQpoNM0v9BWCm/+cMAL8CcGqr/efMmWMihTE4aNbTU7sn9149Pd72KJ+3c//9479be5VJt9dIDMCwxayjM+81RPIqAFvN7L+G7aNeQ1Io7Xr3dDOFQpF6A3VraMhrE1CvoVi66TWUeiAguQ+ACWa2xf/5AQBXm9k/hn1HgUAKZcKE4MqZ9GbFbPd5kKAA8NBDwGmndVVUKS/Xu48eAuBRkr8C8K8A/qFVEBApnHZrAnSyZsC994Y/BSgISEpSDwRm9pKZfdh/fdDMNG5cyqXdmgBR1wwggc9+dvy2IvQGitMQLk5R91GRbrVbE6Dd50GDwp580v0AAIxflF4DwQpLU0yI5OW224LXA3Dw/2QorSXgDK1HIFI0ZekNpLUESkGpIZEsBaWBfv3reEHAhdx8Jw3h4iwFApEs3HBD+FPA7NmdHy+N3HycwBK1IVycpjYCkbSlkQZKOjffuEYA4FXo9Y3arb6rgWC5c3pAWRwKBFIKQQHgxReBI4/s/thxBqm1okbfwnN9QJlItXz/++FPAUkEASD53LwafStNgUAkSSRw+eXjt4UNCuumsTfp3LwafStNgUDc40JvmE4F9QZavz68LaDbxt6gQWrnn+/l6l0ILFIscactTfOlaagrrGjTEV9+ebwpovv7g7/X3x+vHElct8FB7/yk96er11wCoUjTUEehxuKCSqL3SJEaLbvpDaTGXkmYGoslf0n1ay9Co2VQGmjDhvEVe7v0lhp7xSEKBJKMxYubl1GMs/i4y42WCxeGPwX09o69jxIU1dgrDlEgkGQkdUfqaqMlCdx88/htYb2BogTFdjOSdsrV6yaFoEAgyUjqjjTpCrJbQWmgzZtbtwVEDYoDA17+fnTU+7Ob39G16yaFosZiSUY3UxS4aP584O67m7dH+f+ihlvJgRqLJX9luiMlm4NAJyuFuZqmKeL4DMmEAoEkJ06qI27ldPHFwKRJXqU9aZL3PopW5wtKA23d2vkEcS4GRa0kJq3EHYCQ5ksDyhyUxmCjuIOgFi0KHoy1aFG88x17bLxBYUWS9AA2cQ40oExSlVb+P24ufdIkYM+e5u0TJwK7d3d+vkYO/p/oWtID2MQ5aiOQdCU1RqBR3C6nQUGg1faox92+vZxBANA4A2lJgUDaS2vUatzKaeLEzrZHOa4ZMHVq6+8XmasN2OKEtoGA5AMkP5xFYcRRSd9N1hps16xpbpyNUjktXNjZ9pqgtFBPDzA42Pp7ZeBiA7a4o10jAoATADwI4IcADo3bGNHJS43FjmnVqNtpI3LQscixhsuojdCLFplNnOh9b+LE1g3FQY2kmmFTSgZZNBaT/ByAPwPwUwDfN7N30wlNaix2UtDMokDnjchZDrY68UTgySebt5e1HUAqLfU1i0kSwAcBfBTAdwFsB/AtM7s9zknbUSAoiDiVela9V9JYMF7EYan2GiK5HMB6ANcCmAngAgCnAziR5NI4J5WSiNOInHbvlaBBYXv2tA4CaY641WheKYBJEfZZCGClNT86fJXkCymUSYqiry/4iaBVpb5kSXA6qdveK0ceCbz0UvP2dk8BjWMkaiNuge4bUtM8tkiC2j4RmNnzAUGg5hMJl0eKJE6XxDR6r5DNQaDWLNxOWmMk0j62SIK6GkdgZgG3YFIKUVIacSv1pKZfDkoDjY521hbQzRiJdtdIq4ZJQURJDSWC5EQAwwDWm9knszqvxNBJSmNgIPs0x0EHeWsCNIrTGBwnvQVEu0Zxjy2SsSxHFl8CQG0KReBySoNsDgJR00BB4o64jXKNNJpXCiKTQEDycHjtCbdkcT7pkospjaA0UDcBoCZueivKNdJoXimITGYfJXkPgL8AsB+Ay4JSQyQXwuuhhL6+vjlroswSKelwaYWtqVOBnTubt+c9JsClayQCx2cfJflJABvMbEWr/cxsqZnNNbO5vb29aRdLWnElpUE2B4EkngKS4Mo1EklAFqmhUwB8iuRqAD8B8DGSFZjlqwDCer3kndJIKw2UpLyvkUiCMl2YhuTpCEkN1dMUExlwcbH5oGkhgGQDQNCcSaq8pQScTg2Jo7rpGZTGtAlhcwMlHQTirNuraSKk5LRUZVXFnfwt6SeJLCeHi9PA6+KTk0iA1GcfzZoCQQbi9npJqrdMFmmgRnGCn3oHSUEoNSSdi9vrJYkxBlmkgYLEmfnUxTEVIglTIKiquL1euplGOu/eQHGCnxZ9lwpQIKiyOJO/xalMgwIAkH130DjBT+MFpAIUCKQznVameaWBwnQa/DReQCpAgUA6F1SZNnaxjJsGcrGrZlLTZos4KrNpqKXEgqZkbhTlCUAreonkQk8E0r2gwWk1naSB2g1yc/FpQaQE9EQg3QubKTZsrECYVl019bQgkho9EZRJ0nfM7Y4X1huoptMulq26arq8WI5IwSkQlEXceXTiHq/d3X6cLpatumpqYJdIahQIyiLpO+aw4y1YENwbaHCw+y6WrbpqamCXSGo011BZxJ1EDmiemnnePOBv/zbaebP696PJ30Ra0lxDEv+OOSgFFCUIZD0oTAO7RFKjQFAW7aZCCGv4bdX1M8jgYHFGBYtIJOo+Wha1SjFo9a1WXS87aWwdHFTlK1JCaiOoglZz6gPh4wAa9y36/PtaplJKTG0E0lqrrpdBKaVGZZhtM+nutSIlokBQBa0akhcsaG4j6O0tX6OsBqSJhFIbQRUsWRLc9TLu5HBFpAFpIqH0RFAFjV0vgea74zzXCMiCBqSJhFIgqIqBgbH8eL0vfKHcAaBGK42JhFIgaCftqY+jHL/bMoyOhq8UdscdnZe5iDQgTSScmTn3mjNnjjlhcNCsp6eWNPFePT3e9qyO320Z6r9Xe4lI6QAYtph1rsYRtNKq/30SfeqjHD9uGYKeAC65BLjuuo6LKSLu62YcgXoNtZJ2T5Mox++0DLt3A5MnN293MOCLiBvURtBK2j1Nohy/kzKQzUGg7L2BRKRrCgStpN3TJMrxo+wTtFLYlVcqAIhIJAoEraTd0yTK8Vvts2tXeG+gq64ae69F30WkBTUWF9HQkDc1RKOgv0st6CJSCU5POkdyL5L/SvJXJJ8n+edpn7PUyOYgMGWKN0V0EM2xIyJtZJEa2gHgY2b2YQDHATiL5MkZnLdcduwIXzB+587wil1z7IhIG6kHAn+sw1b/7WT/5V4+ymUksNderfcJq9g1x46ItJFJYzHJiSSfAbABwANm9kTAPgtJDpMcHhkZyaJY6UmqcTaoN1Bvb/C+YRW75tgRkTYyCQRmtsfMjgNwOIATSc4O2Gepmc01s7m9YZVdESSxAMq774b3Brr22s4qds2xIyJtZNp91Mw2A3gQwFlZnjdT3TbOks0Vff2gsCgVe+MTCaBF30UkVBa9hnpJHuj/vDeAjwP4t7TPm5u4jbNBaaAHHgjuEjowEF6xa0lGEelQFk8EhwJ4kOSzAJ6E10bw8wzOm49OG2d/97vwNNCZZ3Z+/nZPJBpcJiINUp90zsyeBXB82udxRtiykEE5/LAA0I1WTySNg8tqTwuA0kUiFaYpJpIWJYcflAZ6/PFk5gZq9USiwWUiEkCBIA1hOfx33gl/CjjppGTO3aq7qAaXiUgABYKskMABB4zflsYU0a2eSDS4TEQCKBCkLSgNtGJFulNEhz2RaHCZiARQIEjL5s3haaATTsi8OAA0uExEApUrELjSNZIEDjpo/DZXVgprNQZBRCqpPIHAhYFUQWmgF15wIwCIiIQoTyDIs2vkpk3haaBjjkn//CIiXUh9QFlm8uoamcagMBGRDJXniSDrrpFBaaCXX1YQEJHCKU8gyKpr5MhI+FNAbaZPEZECcT8QRO0JlEXXSBKYMWP8Nld6A4mIxOR2IOi0J1BaXSOD0kDr1jUHgCy7r9afa/p075V3t1kRKSS3A0FYT6AvfzmbCveNN8LTQDNnjt+WZffVxnNt3Oi9tP6AiMRAczCtMXfuXBseHvYq+ijl6+lJJw3UqFVZZs3yKuFG/f3e00mSws6V9nlFxFkkV5jZ3DjfdfuJIGqPnyTHCwSlgd54o31AyrL7apRjakZREYnI7UDQSY+fbiu+V18NfwpobCAOkmX31SjH1IyiIhKR24FgYACYNi3avt1UfGRzzr/T3kBZzuwZdK4szisipeR2IACA669vXekBwRVflB48QWmgWqNrp7Kc2bPxXNOmeS/NKCoiMbjdWFwzNOS1Aaxd6935z5sHLFs29n7JkvEVX+PavMD4BuX164HDD28+sYPXQkQkim4ai4sRCDrVqgdP0HYHr4GISCfK22sorrCG48Yg8M47CgIiUnnlDATtGo4PO8wLAPvtl015REQcVs5A0KpXjZnXRiAiIgDKGggWLGiemuLWW5UGEhEJUJ6FaYDg3kCnnQY89FAuxRERKYLyBAKtFCYiEkvxU0MHHdQcBLZvdycIZDk1tYhIDMUNBK+84gWAzZvHtl10kRcApk7NrVjjZDk1tYhITMUcUFaUNFCWU1OLSKVVZ0DZjTc2B4GdO90MAkC2U1OLiMSUeiAgeQTJB0muJPk8yUs6PsjOnV4AWLRobFtvr7ftqKPcTbVkOTW1iEhMWTwR7AZwqZkdC+BkAP+R5LEdHeHss8e/J4GREffz7llOTS0iElPqgcDMXjOzp/yftwB4AcDM1t9qcOqpwOTJ9Qcd/3mSK5QlKcupqUVEYsq0sZjkLACPAJhtZu80fLYQwEIA6Ovrm7OmvpE1yhq9JDA6mmh5RUSKohCNxST3BfD3AL7WGAQAwMyWmtlcM5vb29s7/sMojavKu4uIxJJJICA5GV4QGDKzn3Z8gHaVvPLuIiKxZdFriABuBfCCmf1VrIMENbrWupEmnXfXSGARqZgsnghOAXAegI+RfMZ/zevoCEGNrrff7jUar14dLwgEVfgaCSwiFVTMkcXdClvTeO+9vcXrG2kksIg4rpvG4vLMPtqJxYub1yvYtq15W41GAotIiRVriomkdFqxq0eSiJRYNQNBWMU+bZpGAotI5VQzEIRN/XD99RoJLCKVU802glrFvnixlybq6/OCQ227Kn4RqZBqBgLAq+xV4YuIVDQ1JCIi/58CgYhIxSkQiIhUnAKBiEjFKRCIiFScAoGISMUpEIiIVJwCgYhIxTk5DTXJLQBW5V0OR0wH8GbehXCArsMYXYsxuhZjjjaz/eJ80dWRxavizqtdNiSHdS10HerpWozRtRhDMvYiLkoNiYhUnAKBiEjFuRoIluZdAIfoWnh0HcboWozRtRgT+1o42VgsIiLZcfWJQEREMqJAICJScbkFApJnkVxF8kWS3wz4fCrJO/3PnyA5K4diZiLCtfgGyZUknyX5zyT78yhnFtpdi7r9PkfSSJa262CUa0Fyvv9v43mSd2RdxqxE+D/SR/JBkk/7/0/m5VHOLJC8jeQGks+FfE6Sf+1fq2dJntD2oGaW+QvARAC/BfA+AFMA/ArAsQ37XAzgRv/ncwHcmUdZHbkWfwigx/95UZWvhb/ffgAeAfA4gLl5lzvHfxdHAXgawEH++xl5lzvHa7EUwCL/52MBrM673Clej1MBnADguZDP5wH43wAI4GQAT7Q7Zl5PBCcCeNHMXjKznQB+AuDTDft8GsCP/Z/vAXAGSWZYxqy0vRZm9qCZbfPfPg7g8IzLmJUo/y4A4D8D+EsA27MsXMaiXIs/AXCDmb0FAGa2IeMyZiXKtTAA+/s/HwDg1QzLlykzewTApha7fBrA/zDP4wAOJHloq2PmFQhmAnil7v06f1vgPma2G8DbAKZlUrpsRbkW9S6CF+3LqO218B9zjzCzf8iyYDmI8u/iAwA+QHI5ycdJnpVZ6bIV5VpcBWAByXUAlgH4ajZFc1KndYqzU0xIAJILAMwFcFreZckDyQkA/grABTkXxRWT4KWHTof3lPgIyQ+Z2eY8C5WTLwD4kZldQ/IjAG4nOdvMRvMuWBHk9USwHsARde8P97cF7kNyErzHvY2ZlC5bUa4FSJ4JYDGAT5nZjozKlrV212I/ALMBPERyNbz8530lbTCO8u9iHYD7zGyXmb0M4DfwAkPZRLkWFwG4CwDM7DEAe8GbkK6KItUp9fIKBE8COIrke0lOgdcYfF/DPvcBON//+fMA/sX8lpCSaXstSB4P4CZ4QaCseWCgzbUws7fNbLqZzTKzWfDaSz5lZrEn23JYlP8jP4P3NACS0+Glil7KsIxZiXIt1gI4AwBI/h68QDCSaSndcR+AL/q9h04G8LaZvdbqC7mkhsxsN8mvALgfXo+A28zseZJXAxg2s/sA3Arv8e5FeA0j5+ZR1rRFvBY/ALAvgLv99vK1Zvap3AqdkojXohIiXov7AfwRyZUA9gD4T2ZWuqfmiNfiUgA3k/w6vIbjC0p64wiSfwfvBmC63yZyJYDJAGBmN8JrI5kH4EUA2wBc2PaYJb1WIiISkUYWi4hUnAKBiEjFKRCIiFScAoGISMUpEIiIVJwCgYhIxSkQiIhUnAKBSAT+XPcf93/+Lsm/ybtMIknRpHMi0VwJ4GqSMwAcD6B0I7ulujSyWCQikg/Dm+rjdDPbknd5RJKi1JBIBCQ/BOBQADsVBKRsFAhE2vBXdxqCt/LT1hIvACMVpUAg0gLJHgA/BXCpmb0Ab5nMK/MtlUiy1EYgIlJxeiIQEak4BQIRkYpTIBARqTgFAhGRilMgEBGpOAUCEZGKUyAQEam4/wc9bEx9S0SXRgAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -730,7 +730,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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EZJUxJsvTY3qGqlLKGvxJtVh9RYwFaG0ZpZQ11MOzSAv+PA4TpqyFBnellDXUs7NI85+eQvqkx1g66aWwHF/TMkopa6hHqZaSRYtJu/cuFp/ch5b/nhiW19DgrpRSEWSysym5chgH01tQOv11upzYJCyvo8FdKRXbivbBN0PhwAoQgWZZcN4H1lw9U1REzoWX0aDwKIv/M5sb+p8etpfSnLtSKrYtHg4HlgMOMBXO21Y8Y9UYfh1+Ixk/reet2//F9b+/NKwvpyN3pVRs87Rk0kpnrLrW5O99aQsnfryXNy4fzU2P3o6IhPVldeSulIptnpZMWmkZ5eLhHPpsLS3f2cf8rn0ZfEs2KYn2sL+sBnelVGw7dxY0Pwuwgdidty20jLJk3Q8k/qeUH1ueROv/y6Fl0dKIvK6mZZRSsS21FVy8LNqt8MgcPMihiYnYbZBzRzMGNv0BmvaLyGvryF0ppcKhvJydFw+l6YFDfH3rZQzsvKX6yVlhrkmvI3ellAo1Y/j5+tF0XLGIGWMe5NrHJziXaboL8xWZdOSulFIhtuPBR+k4azofDL6B30550PPKmDDXpNfgrpRSIbTnjXdo/8jf+OaMAfzm3ZdITvCyMibMhdI0uCulVIjy3weXfkfj0SPZdGJnOn78Hk0apnjfOcyF0jTnrpRSIch/F/+yi4rLryA/uSGOOXNo165F7U8Ic6E0HbkrpVSQ+W9HwRH2DbyYlMICtr86gzPODF/NGF9pcFdKqWDy3xUVbB58FW1/+YklDz/LOVdfGI4W+i2o4C4id4vIBhFZLyIzRCRFRDqKyHIR2SoiM0UkKVSNVUqpsAg0/20MG4aP4rRlC/j8D/cz+M+/D287/RBwzl1E2gB3AF2NMUUi8g5wLXAZMNkY87aIvACMBp4PSWuVUiocAsx/r7/zr3R//w3mXX4TF7/wz7AXA/NHsGmZBCBVRBKANGAPcAEw2/X4NOCqIF9DKaUsZ+Ok/9D92UdZ0vciznvvJew26wR2CCK4G2N2A5OAnTiDej6wCjhkTOXMBNlAG0/PF5ExIrJSRFbm5uYG2gyllIq4bTM+oPP9d7K2cy96fPEeyUmJ0W7ScQIO7iLSFBgKdAROBBoAl/j6fGPMVGNMljEmKyMjI9BmKKVURGUv/JZWN1/PzpbtOHHBJ6Q3bhjtJnkUTFrmQuBnY0yuMaYMeA84B2jiStMAtAV2B9lGpVQsCXNBrGjav2EzyVddwZHkNBI//4yMdidEu0leBRPcdwJni0iaOGcRBgEbgYXA/7j2GQl8EFwTlVIxpfKEoPKCYycExYHDu/ZSOGgwySXF5L/7Ae17dIl2k2oVTM59Oc6J09XAOtexpgL3A/eIyFagOfByCNqplIoVYS6IFQ1HDxxi74ALaLX/V3a8/CanXtg/2k2qU1DlB4wx44HxNTZvB/oGc1ylVAxrmnnsVP4wFMSKtOIjhWwfMJjTd/7E2qdfoc8NQ6PdJJ/oGapKqdAKtiCWhXL25aVlbLjgSs7YtILvx0+iz+0jo9YWf2nhMKVUaAVbECvMF7HwlaPCwYpLr6XfigWsuP2vnDn+7oi3IRg6cldKWUuocvZBfAMwxrB4xBj6ffkeK68fy5nPPBKyY0eKBnellLWE6iIWQaza+eqP93Peey+z+tIR9Hl9SkiPHSka3JVS1hKqi1gE+A3gyzv/wfkvP8EP/QbTa+6biM1DmIyBFUGac1dKWUuoLmIRwKqdBfc/xqBnxrOx9wC6fzkXSfASImNgRZCO3JVS4RPN3LSf3wAWjH+agU/8hZ/OOJtTF32OLSU5ZMeOBh25K6XCJ5orX/z4BjD/Xy8w8OF72H5qJicvmYc9LdXzjkX7nH04uMY5Wr9ii/N1LEhH7kqp8ImB3PT8f7/GeX+/nZ0nd6Pjt1+SkF5LIbAYmEitpCN3pVTo1BzZNu4Keassm5teMGUG595/C3vanUK7ZV+R0KRx7U+IgQ+rSjpyV0qFTs2RLVg2N/3Ff2bS7+6b2d+qHScs/5rE5s3qflKolmlGgI7clVKhU3Nkm78RRhyOapM8+fTZt/jNvb/nYMaJtFj2DcmtWvr2xHNnVf9mYqEPq5o0uCulQqe2JYI1UzbnzorKZOTHT73BBX/+A3mt2pDx3RKSTvSjJnuolmlGgKZllFKhU9sSQQtMRn4waRqD/jyavNbtaLlyqX+BPcboyF0pVTt/Rty1jWyjOBlpjOH9x1/jsr+N5cCJJ9Fq+SISTrDmEsZQ0ZG7Uqp2oRpxR2ky0hjDu/96mcv/egsH2nTghBVL4j6wgwZ3pVRdQjXijsJZnQ6HYcb457li/K0caNeJ1iuWYG+ZEfbXtQJNyyilaheqOioRnowsr3Aw40+TuPaZv5DT6TRaf/s1thbN636iRSZ+gxXUyF1EmojIbBH5UUQ2iUg/EWkmIvNEZIvrZ9NQNVYpFQUxUEelppLyCt68ZQLXP/0AOd16ceLKJb4FdrDExG8oBJuWeRr4zBhzGtAT2ASMAxYYYzoDC1z3lVKxqnLEfcUW5/0PO0fvAhU+FCI7WlLOO7/7MyNffpg9Z55Lm2VfI43rOPPUXQydhVqbgIO7iDQGzgNeBjDGlBpjDgFDgWmu3aYBVwXXRKWUJVhhRFtHG/KPljJn+K387u3J7D7/Etp+8wWkpfn3GjF0Fmptghm5dwRygVdF5HsReUlEGgCtjDF7XPvsBWIvWaWUOp4VRrS1tCEnv4jPrryZGz58kd1X/A9tvvgQkmsp2+tNDKahPAkmuCcAvYHnjTG9gKPUSMEYYwxgPD1ZRMaIyEoRWZmbmxtEM5RSEWGFEa2XNmzbc4hlg4dzzZdvsee6UbSZMxO8XWijLpVpqBGHnT9jcDIVggvu2UC2MWa56/5snMF+n4i0BnD9zPH0ZGPMVGNMljEmKyOjfixNUiqmWWFE66EN32/+lV8uuJwrv/uYfbfdQ+s3XwFPl8arZwJeCmmM2Ssiu0TkVGPMT8AgYKPr30hgouvnByFpqVIquqxQV6VGG75evpm04b9lYPZG8h59glbj/hTFxllLsOvcbwfeFJEkYDtwM85vA++IyGjgF2BEkK+hlIp3Aawtn/PxCk6/eTid8nZz5NXpNBt5Y0SaGiuCCu7GmDVAloeHBgVzXKVUGFnxJB0/LsdnjGH6a58z6K6RNC85Qvncj2h02cWhbY8V3yM/aWJKqfrGCksaa/JxJU5puYMpE9/kiv+7hsZSTsKir0kNdWAHa75HftLgrlS88nbCjxWWNNbkw0qc/MIyptz2GH94cDS2Jo1puGIZiWd6ShyEgBXfIz9pcFcqXnkbfVphSWNNdazE2bn/KDOuuYM7//tXjnY9gyZrVyGdO4evPVZ8j/ykhcOUilfeRp9WvFRcLStxVm3LYdt1oxm74iMOXDaU5rNnQGpqeNtjxffITxrclYoHniYAvVVztMKSxrq4+vPxhiQaPHeIEdtWcej2e2j+1BORWcMeC+9RHTQto1Q88JSCscJJRwFyLBrOi4tb0OHRnxnw8/ccve10mjzzpJ6c5AcduSsVDzylYGJ09Hm0pJxnP+jMzdPn0Li0gIp7oUFWdrSbFXP0Y1CpeBAHE4AAu/IKee4PD3H3f18nNbmY5IfKSOpli0x/fCgnHEs0uCsVD2IpBeMliC7dvI/5Q2/m/jceoahXLxq90APpFMH+xMHadneallEqHsRSCqbG2ahm0XBmyBRa3foHbt76HQUjf0+Tqc9DUlJk2xUHa9vd6chdKRVZbkG0uEJ4/MvO9Ll+CAO3r6L4qWdIf+3lyAd2iJvUViUN7kqpyHIF0V2lrXj0s99zy9Pv0L4kH/n8c1LuvN3344Q6Rx5LqS0fiPN6GtGVlZVlVq5cGe1mKKUioWgfC9+7j03/LWTsonc5eupppH/yEXTq5N9x5p1XfR1/i36xk5oKERFZZYzxWINBR+5KqYipcBimfLoTx/hN3LpoNoXXXEf66pX+B3aIuxx5qGlwV0pFxMGjpYx/+E2GjL6S3+z4nrJnn6PhjDf8v4B1pTjLkYearpZRSoXdql/y+PjPT/DX9ydjmjbB/s3XSP/+wR00Duq/hJMGd6VU2Dgchpe+WE/T++/lwR/mceTsc2g4511oFYILX8TS8s8o0LSMUioscgtKeOCfMxl44xCuXjefknEP0HDRV6EJ7KpOOnJXSoXckq37mX//Y0yY+yykpyOffUby4MGBHSwOLnkXDUGP3EXELiLfi8hHrvsdRWS5iGwVkZmui2crpawqhOvFyyocPD1nNXuHXcP49ybhOKsvqRt+QAIN7BB3ZQEiJRRpmTuBTW73HwMmG2NOAQ4Co0PwGkqpcAlR8Nyee4T7H3iVIaOHMmzDV5Q+OJ4GXy+E1q2Da58ueQxIUMFdRNoClwMvue4LcAEw27XLNOCqYF5DKRVmQQZPYwwzlv7M+9fcwWNP3sKJCRXYFswn6aEJYLcH3z5d8hiQYEfuTwH3AQ7X/ebAIWMq/6eQDbTx9EQRGSMiK0VkZW5ubpDNUEpV8TfNEkTw3H+khPsnf0THa6/k3oWvUn7lUFI3rYfzzw+4+ceJs7IAkRLwhKqIDAFyjDGrRGSgv883xkwFpoKz/ECg7VBK1VCj6mLVVZm8TUr6s17cbXJzYcVvmf/eCfztw+dIsYHjlVdJHTUSRELbH13yGJBgVsucA1wpIpcBKUAj4GmgiYgkuEbvbYHdwTdTKeUzT2kWTwG/MmD6EzwXD+fwvrU8ueU6Mmdv5p8bp1GY1ZekmTMCKyGgwibgtIwx5gFjTFtjTAfgWuBLY8wNwELgf1y7jQQ+CLqVStUXoVi54p5mwQ4I5C4KyaTk1784eOij3/O//57N0B+/puzqVNK+XaKB3YLCcRLT/cA9IrIVZw7+5TC8hlLxKRQrV9xz1AkNoKKw+uMBTEoWFJcxYfpi9j2TypMzn6Jx4yPY/mEj8ZYsSNDTZawoJL8VY8xXwFeu29uBvqE4rlL1TiiW/bmnWd5pdOx4lfyclPxmcy7vP/oyf373SU44mkf5Ne1JvSIPWvbSyU0L049cpaykaWb1GuXBLvureTw/ap7nHS1l0jvLOeOpR5j8wxcUndIF21ufYDvzzODapCJCa8soFWm15dVDvewvgOMZY5i9chf/Gv0wd95xFdesn0/5PfeSum4taGCPGXolJqUizcJXEPp5/1GefGUBV770KIO3LKP4jJ6kvPoy9OkT7aYpD2q7EpOmZZSKNAueTl9a7uC/C7eQ9+TTTFw4jRRx4HjsMVLuuUcnTGOU/taUirRQ59WD9N3Pebw05X1umTGJPr/+SOn5g0h4aWrwyxvjpZpjjPZDc+5KRZpFTqffm1/MuFe+YcPwUTz/7zGcUZQL06eTtGBeaNatx0s1xxjth47clYq0KJ9OX1JewcuLtrPzqf9y74JXaFF4iPIxt5D0r39Cs2bVdw5m1GrB9FNAYrQfOnJXKhJCWDM9GF/+uI9b//wqWTddxcS5T9LotFOQFStIfOH54wM7BDdqjZdqjjHaDw3uSkVCpL7ae/kQ2Z57hNuf+YLs60cz9elb6HVkL7z0EsnfLat9JUwwo1aLpJ+CFqP90LSMUpEQqa/2NQqEHVr4O14ofRzz3HP8c/HbNCwvxowdS+IjD3seqdcUzOSvFas5BpJmCkU/ojApqyN3pQLlT6olUl/tXR8ixY5Epu4bwoRXzuDamy/jgQUvk3zeudh++AH7f6b4FtghZketXkVrcjQKr6sjd6UC5U/ddH9qpgfB0SSTD7Ym8eHqcxjzxXuM2TWHki6nwduvknzxxf4f0Iqj72D4+w3KlxG3L/tEYVJWR+5KBaq2uuk1R2iVQXLE4WPBMlQTrK5vEItf6s8tH19M0jPFvPLyP+hzaAs8NZHkDesgkMDu4+tGe5LYLzW/QTXuWnsffBlx+7JPFCZlNbgrFShPf7C1jdDcg+GHXUL2NX3tJ7dzxxdnsevZdJ5/9kEu3rEc8/e/k5idA3feH74zTGNx/XfNNBPU3gdfRty+7BOF9JamZZQKlKdUi3uqpuYIzf0xdwF+TV+/O58X53xHt2nlPLH6aRKowAw2JFydDH/8RxAd81Esrv+umWZyL4nsqQ++TCj7sk8U0lsa3JUKlKc/2Npy6+7B0J2fX9M37TnMCx+upv3rL/HIivdpUFpExbl27Fc7oGUCtOgdQGcCEIoyCtE+tb+uPvgyVxKh+RR/aVVIpfwVaEByrwaJ3XmVJIzPx9i8r4CpH6yizfQXGb1qLo2Kj1B2xZUkjr8H8v4e2gAZqonEukS7Qma0P1yCVFtVSA3uSvkr0IAUYCDZ8Gs+0z9cRbvpUxm56kPSSwpdQf3B8JXijVTQfaeRM99dKSHdOemsfKIlf5Wqjb9BN9Bcsx95V2MMy3/O4425K+j+9kuMX/0xqeUllA0dBhMeJLFnT99eM1CRyqdbrEJmPAl4tYyItBORhSKyUUQ2iMidru3NRGSeiGxx/WwauuYqFQZ1rfqoueSvcdewLWtzOAzzNu5j7CPvsnHE75l031Xc8t17JAwbiqxbR9L770K4AztEbulevJ0kZSHBjNzLgXuNMatFJB1YJSLzgFHAAmPMRBEZB4wD7g++qUqFSV2j1MXDIXcpUAG5i8DeEJr1gfyNzqCX9R9n0A8ib1tW4eDDtb+y4I2PueTTN5myeQk2sWEGtkCGFJDU7Vfo1CLorvosUpOE8XaSlIUEHNyNMXuAPa7bBSKyCWgDDAUGunabBnyFBndlZXWlBg6uASqO3a84ArakY7lh9/x05cjfx4B14EgJM5btYPv0WYz4aiZTdq2nrEFDbHfdja33IrCtDui4Qasr6Mb4RGR9EJKcu4h0AHoBy4FWrsAPsBfw+BsXkTHAGID27duHohlKBaauUWrTTOeI3Z376L7myD93iTP41RLsNu05zOtfbkLeeoubl73HKXnZFLdug5k0icQ//hEaNXJNNlp0Hbmn0gv1fQRusQ+8oIO7iDQE3gXuMsYcFpGqx4wxRkQ8LscxxkwFpoJztUyw7VAqYHWNUs+d5TyjtNw1Uq85uj8u+Ds8BrsKh2HBpn18PGcJp899i/vWzaNJUQHFZ/SEZx8jZfhwSEysftxoTDZatFaK5VnsAy+o4C4iiTgD+5vGmPdcm/eJSGtjzB4RaQ3kBNtIpaIqtRVcsdn76P7cWTCnDRi31M3BNVVBMifnZ2YfuYYd3zbhkkUfMPnnVSA2yocOhdtvI2XgQHAbFFU7bijz3r6OLH0JUlZd5RLN0bPFPvACXucuziH6NCDPGHOX2/YngANuE6rNjDH31XYsXeeuLCGYwFBjXbijeT8W53Vg7vrmZCzJ47rvP6d9/j6KM1qRNPYWbLeMgTZt/H/9ELbR69p1X9aeWywFUSWaJ0VF4bXDchKTiJwLLALWAQ7X5r/gzLu/A7QHfgFGGGPyajuWBncVEsEGnGD+OGuM0rctb875337K4C3fklRRTtGpSaReaoPHD1dPvbi3GYGKwtpfP5g2+nrCULTPGg1GNE+KisIHXlhOYjLGLAY8fJcEYFCgx1XKL96CYyA5zwC/VpeUV7Bwm2HRmrs5cc5Mhq2bxokF+yltkIxtkIELILW9A1qcVT2wg//FxIL56u9rKsWitVJ8Es10kcWWdeoZqiq2+RocfRlVNc08tp4dAPG66sUYw/e7DvHxki2UvfMOl6/6nH/uWo/DZqP4/Ath7B9JurAvrLix9iDpbzGxYIKXr0HbYkHKL7H8wRRiWltGxbaaX8Mr1Uwn+JJqKNp3/KqYGvvtyitk7vKf2TPzfc5a/gWDtn1HWlkJhSd1IvmPo7GPGnl8Lr02/hYTs2quW0WF1pZR8aNmcGvcFfJWHT/6tad5L7dryiFvtTOw5q0GsQMOaNqLY9NHVI3+9+QX8ema3fzy7sec/vUn3PjTEhqXHKWkcVNk1Ei46XeknXOO5xUvdfF2WT5vYnlUrSJKg7uKLd5KARxYXmNHUz1I1kxniP34dM7+b50fCpLAnpLGfHpoANs3nszJj1/DkB8X0/LoQUpTG1B21ZUw8nckX3jh8Tl0f2mwVmGiwV1Zh68nz9QsBZC3EhIaVV9pUtdFFw5+f9xof2dxcxYcPIc9a1vTac0Wrty6kBaFcyhPsFGS1Qpuf5GkYTeQlJoa2j7Fg/rSzxiiOXdlHb7kxeedd3wpAKBavrpxV+emysJengLNvPNw5C5jzdFOfJPTi8LVqfTYuJWB21fSsLSI0rQGlPZpQsNee6CHA9ICXBIYytrvYN0AGsvLJ2OY5txVbKjr4tKLhztz5NipNnoH133jXNNcSyGvwtJyFm/OZf3asdg/a8+ZW9Zxa/ZskhzlFDZKo2L4MLjxdySdfz5JH2RAucNze0LRp9p4OksULHV6ezUWOztTaXBXVlLbMr9qSx7tzjRM+RGqJkDd93cLNA5HBRt3H2LZx2vI/+gz2iz7igHbVjO4IBeA/M6nYe68E64eRtrZZ4Pd7lt7QtGn2ngLllYNoFYtR1CPaXBX0ec+KrenUbVyxevFpV2j9GG/ekxd5KSdw+JtpWRvzCB5UxmZOzYzcncWiY4KSlIbUDBgIOW/vZKEyy+jcdu23tsVijXTgR7DW7AMZwANJm+u68stR3PuKnK85ZG9rS338dT8nMPFLN+SQ/aCxSR8/RWnblzJmdkbSS0vwSFC/qldSbr4YhoMuxL69/dthYu3QBepicNw59w9Hd/921Go8+Y64RoWeoFsZQ2eJt3g+AnSynogXk7w+TV1AKsaTmDf12uwffstJ29ZS6/dP9KotBCAAx27wAXn0/TyS7AN/A00DeBKj94mCONl4tBTPw6uCV9dlnh53yxGJ1SVNfg66eaWOy+uENYXnsbmX0+i6Kc00n8xnL59HZfmnE2CceAQ4VCHzpSNGEHF5Rdjv+ACmrdsGb62xsvEoad+BJo31/rvlqTBXUWOt+DhOimp3NjYXtaFbcUTyP/LM8i353HCzl/ptnc7WYU/AlCSnEJet17kXTuM5oMvwN6/H82aNIlcW30JgLGQgvDUj0Dz5rFc/z2OaVpGRY5b0Ctp3IfNnV5kx5qt5M97nYTN+zkh+wDd9mwjo/AQAA6xsb91C8pOstGoR1PSb5wMZ10Q/FmhfrbV75x7LKQgQvkBFMv132Oc5txVaPnxh+pwGHYfLGTHhu0cWvE9FT+sI/WnjbTauZXO+3fSoKzYuZ/Y2N+uE0Xdu5Ha+meanbSNhMyecKHrAl+xFBiiWVM8GmLhwyxOac5dBadmMHeUHivWlbsUPuxCuRF228/l14T/49CGnZRu2kDyT2tplrOfU3KzGVB0LLgdTm/KwRMbk3NqMxqddISmHY9i63UWLa9c4goUG53HPvJdcCfvFO2Db4bCgRXOol7NsuC8D5yPhfPDIlIpiFCMhkNxjHAug7RKH2OQjtzjnft/7LpOy6/5R5D1H1h5K+QuARyUOBLIPtya3L1NOLw3lbKcRJL2ldE49ygnHNhPm/xc7OZYVcWCtDQOZTSkpE0ySV0yaDb8URpmZULLlt5Ht562Q2AjYU+lCjIGOH+Gc6QZqWASihGz1Ufd9aGPQdCRezzxN3C4T3a5V07MXQpzT3GuHXeNag8XJ5KzZRcH97fh6P5CKqb8EXtuGWl5p9Hk4BFa5udxcvEuTmZX1WEKk5LZ36IJRzuk8OMJJ5HW1kHGiGdpuOMq0hsUko5zeSIJh+Cywcde398Jy0DP8vS2LZwrN/yt9FjX79Tbmvf9S49dlDvS5REiJRTts3ofw0SDe6yo/AN3jaIB56j0/ROP7WNPgYpiZ7Bu0hNsiThylpNfkM7BQ+kUHEqjOC+Z8kN2OAgJ+RWk5pfQoKCIJgWbaF54mEY1XrYwKZkDTZpQ0DSNX05uTXZGBQ1aFtHshHwatSokrUkZ7W0HaqxdfwIaOGocSZyj8srg5O2rvLftgZ7leVyRMXGVLai8a4GVG3WtNvFWZ8bUqK8TyfIIkRLNEhAxLmxpGRG5BHgaZ5Wnl4wxE73tGxdpmdpWV3jL+1Zuh+qBuWZuOHcV5kgxJYV2jh5JofBoKoVHUyg+mkRZYQKOIzY4AvYCB4lHy0gpLCP1aDHphYWkFxdWS5VUKrPZOdigEfnpDShslEpZ4wSkqSGxaTkNmhXRJKOAJq0KsKcb71fKBZwPSrUPlONrq+P8o6r842rWB2xJkTnL0/29t6c63+PKUZzYoUX/6Odg65qA9SVVhc1ZjiHe8tGac69VxFfLiIgd2AxcBGQDK4DrjDEbPe0fUHCvViXQdWo6QEIaGAdgO7atMnAGerusCMoBUqC0GCrAYdKoKAdHuZ2KklIqKpy3y8vtlJUnUF6eQHmZnfJyGxWldhwlNhylgikRKBNMqYFSoFSgFKTUIKUGe5mDxJJyUopLSSkpIa2kmNSy0rrfjoQk8lMbciQtjaMNUihJS6K8oR0aCvb0CpIalZHaqJiGzYto0qKABo2KnRcg8kdlgPao8mA1qzV6quBoA7HVfcm7UP9BWnUVS105YW9n9sZ6HjmOg26kRCPn3hfYaozZ7mrA28BQwGNwD8ji4az9IIeMN1IRDGKSEGMQwOYaqYppiM0YwCAmDZtxOAehJhmbMQgViElADAhliLG5jlHi/GnAVpkzBqCo6paNo9iCaH6ZzU5xQhLFicmUJCRRmpBAWWICZUkJHE1LpaxZAmUpCVSk2nCk2CAVSAV7WgXJaaWkNCihQYMSGjQoJL3RURqmFpMqeUBeEK3ywn207X5Zuooit9RAzQDuktDgWN2YquNJ3TlQX06M8ZdVv57XtdoklKkqKwnH71hVCVdwbwNus27O0ftZ7juIyBhgDED79u39f4WDa0ht3IRfO2VgBIzr+pUOsYFQtc240gbu+xgRjAgOEee+rm2IYMA5sMTg3GTABiZBMHbAXvnT+c8kCGI3GLsz5tkTHNgTKkio/JdYgT2pgsTkChKSy0lMLiMpuZzkxDKSpIwmtgISxUtg9MbeEJp0g0PrcX5rKXU2uuobR800jN0toHq4CHO1cro1iN05KqzrZB1PMgZ4CNw2Z9qpcimltyAbjkkwq1YurGsC1tvjsR4I6+lEZ6REbULVGDMVmArOtIzfB2iaSZfu30I3b2mCWFJbYHY9ntCQaqVwa/v66m1Jo7evv+5Bz5erGHl6HgLlR6kawSc08l5p0JcgG45Rtl6v1Fqs+k0qToQr594PmGCMudh1/wEAY8yjnva3fM7d0+2ar+M+oVg1oj62zPC4SVT37TWXvdW1jxWFukSu5mPjn/6OgxaNCdUEnBOqg4DdOCdUrzfGbPC0f1ysllFKqQiL+ISqMaZcRG4DPseZnX7FW2BXSikVemHLuRtjPgE+CdfxlVJKeRfMaj6llFIWpcFdKaXikAZ3pZSKQxrclVIqDlminruI5AK/1LFbC2B/BJpjRfW571C/+1+f+w71u/++9P0kY0yGpwcsEdx9ISIrva3njHf1ue9Qv/tfn/sO9bv/wfZd0zJKKRWHNLgrpVQciqXgPjXaDYii+tx3qN/9r899h/rd/6D6HjM5d6WUUr6LpZG7UkopH2lwV0qpOGS54C4il4jITyKyVUTGeXg8WURmuh5fLiIdotDMsPCh7/eIyEYR+UFEFojISdFoZ7jU1X+3/a4WESMicbNEzpe+i8gI1+9/g4i8Fek2hosP/+/bi8hCEfne9X//smi0MxxE5BURyRGR9V4eFxF5xvXe/CAivX0+uDHGMv9wlgfeBnQCkoC1QNca+9wKvOC6fS0wM9rtjmDfzwfSXLf/N1767mv/XfulA98Ay4CsaLc7gr/7zsD3QFPX/ZbRbncE+z4V+F/X7a7Ajmi3O4T9Pw/oDaz38vhlwKeAAGcDy309ttVG7lUX1jbGlAKVF9Z2NxSY5ro9Gxgk4ro4amyrs+/GmIXGmMordi8D2ka4jeHky+8e4GHgMaA4ko0LM1/6/kdgijHmIIAxJifCbQwXX/pugEau242BXyPYvrAyxnxD7Ve1HwpMN07LgCYi0tqXY1stuHu6sHYbb/sYY8qBfKB5RFoXXr703d1onJ/o8aLO/ru+krYzxnwcyYZFgC+/+y5AFxFZIiLLROSSiLUuvHzp+wTgRhHJxnmNiNsj0zRL8DcuVInaBbJV4ETkRiAL+E202xIpImID/g2MinJToiUBZ2pmIM5vbN+IyBnGmEPRbFSEXAe8Zox50nV95tdFpLsxxtPV5JWL1Ubuu4F2bvfburZ53Md1rdbGwIGItC68fOk7InIh8FfgSmNMSYTaFgl19T8d6A58JSI7cOYf58bJpKovv/tsYK4xpswY8zPOaxR3jlD7wsmXvo8G3gEwxnwLpOAsqlUf+BQXPLFacF8BdBaRjiKShHPCdG6NfeYCI123/wf40rhmHmJcnX0XkV7Af3EG9njJuVaqtf/GmHxjTAtjTAdjTAeccw5XGmPi4crqvvy/n4Nz1I6ItMCZptkewTaGiy993wkMAhCR03EG99yItjJ65gI3uVbNnA3kG2P2+PTMaM8We5kd3oxzBv2vrm3/wPmHDM5f7CxgK/Ad0CnabY5g3+cD+4A1rn9zo93mSPa/xr5fESerZXz83QvOtNRGYB1wbbTbHMG+dwWW4FxJswYYHO02h7DvM4A9QBnOb2ejgbHAWLff+xTXe7POn//zWn5AKaXikNXSMkoppUJAg7tSSsUhDe5KKRWHNLgrpVQc0uCulFJxSIO7UkrFIQ3uSikVh/4fMLcUomZa3ZMAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -747,7 +747,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.0050000000000000044\n"
+      "0.005000000000000007\n"
      ]
     }
    ],
@@ -1322,7 +1322,7 @@
       "270 3344  160  110  270      Ds  7.253775  7.253775\n",
       "\n",
       "[267 rows x 6 columns]\n",
-      "0.009883615646716186\n"
+      "0.00988361564671618\n"
      ]
     }
    ],
@@ -1392,16 +1392,6 @@
      "text": [
       "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
-      "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n",
-      "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
       "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
       "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
@@ -1431,6 +1421,8 @@
       "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
       "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n"
      ]
     },
@@ -1443,6 +1435,8 @@
       "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
       "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n"
      ]
     },
@@ -1452,16 +1446,6 @@
      "text": [
       "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
-      "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n",
-      "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
       "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
       "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
@@ -1479,6 +1463,8 @@
       "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
       "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n"
      ]
     },
@@ -1491,6 +1477,8 @@
       "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
       "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n"
      ]
     },
@@ -1506,8 +1494,6 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n",
       "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n"
      ]
@@ -1520,28 +1506,16 @@
       "  warnings.warn(\n"
      ]
     },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n",
-      "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n",
-      "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 720x720 with 2 Axes>"
       ]
      },
      "metadata": {
       "filenames": {
-       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter1_61_13.png"
+       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter1_61_10.png"
       }
      },
      "output_type": "display_data"
@@ -3288,15 +3262,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[1.95042842 0.23671948 4.75963006]\n",
+      "[ 2.01879166 -0.10539223  5.12224228]\n",
       "Training R2\n",
-      "0.9951052834261356\n",
+      "0.9953896988605513\n",
       "Training MSE\n",
-      "0.009330116621524378\n",
+      "0.008627230881866453\n",
       "Test R2\n",
-      "0.9946376411631981\n",
+      "0.9933901005631264\n",
       "Test MSE\n",
-      "0.014653795785269241\n"
+      "0.011519511227899348\n"
      ]
     }
    ],
@@ -3400,13 +3374,13 @@
      "output_type": "stream",
      "text": [
       "Training R2\n",
-      "0.9999867383482111\n",
+      "0.9999901872822963\n",
       "Training MSE\n",
-      "6.594186465773493\n",
+      "3.8316530831689173\n",
       "Test R2\n",
-      "0.9999026871593244\n",
+      "0.999959390714798\n",
       "Test MSE\n",
-      "6.7878553902416146\n"
+      "24.997147499722345\n"
      ]
     }
    ],
diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter10.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter10.ipynb
index 6f379efd6..398d4d5cf 100644
--- a/doc/LectureNotes/_build/jupyter_execute/chapter10.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/chapter10.ipynb
@@ -2284,15 +2284,15 @@
       "Learning rate  =  0.1\n",
       "Lambda =  1e-05\n",
       "Accuracy score on test set:  0.9111111111111111\n",
-      "\n"
+      "\n",
+      "Learning rate  =  0.1\n",
+      "Lambda =  0.0001\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Learning rate  =  0.1\n",
-      "Lambda =  0.0001\n",
       "Accuracy score on test set:  0.9222222222222223\n",
       "\n",
       "Learning rate  =  0.1\n",
@@ -2400,13 +2400,7 @@
       "Learning rate  =  10.0\n",
       "Lambda =  0.01\n",
       "Accuracy score on test set:  0.08888888888888889\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "\n",
       "Learning rate  =  10.0\n",
       "Lambda =  0.1\n",
       "Accuracy score on test set:  0.10555555555555556\n",
diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter1_11_0.png b/doc/LectureNotes/_build/jupyter_execute/chapter1_11_0.png
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diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter1_3_0.png b/doc/LectureNotes/_build/jupyter_execute/chapter1_3_0.png
index e3b21a8a9..85d690ad6 100644
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diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter1_61_10.png b/doc/LectureNotes/_build/jupyter_execute/chapter1_61_10.png
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diff --git a/doc/LectureNotes/schedule.md b/doc/LectureNotes/schedule.md
index 572474bbf..0e80f50d0 100644
--- a/doc/LectureNotes/schedule.md
+++ b/doc/LectureNotes/schedule.md
@@ -124,6 +124,7 @@ For the reading assignments we use the following abbreviations:
 ###  Week 42 October 18-22
 - Lab Wednesday: Work on project 2
 - Lecture Thursday: Solving differential equations with neural networks and start Convolutional Neural Networks and classification problems
+  - Vieo of Lecture at https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureOctober21.mp4?vrtx=view-as-webpage
 - Lecture Friday: Convolutional Neural Networks and classification problems
 - Reading recommendations:
   - See lecture notes for week 42 at https://compphysics.github.io/MachineLearning/doc/web/course.html.