From bc73a7d17baa81637600734604cb13def136543c Mon Sep 17 00:00:00 2001
From: Morten Hjorth-Jensen <morten.hjorth-jensen@fys.uio.no>
Date: Tue, 22 Oct 2024 12:04:58 +0200
Subject: [PATCH] adding material with video

---
 doc/HandWrittenNotes/2024/NotesOctober21.pdf  |  Bin 0 -> 6502387 bytes
 .../_build/.doctrees/environment.pickle       |  Bin 599926 -> 601137 bytes
 .../_build/.doctrees/week43.doctree           |  Bin 751158 -> 599632 bytes
 .../_build/html/_sources/week43.ipynb         |  626 +++---
 doc/LectureNotes/_build/html/searchindex.js   |    2 +-
 doc/LectureNotes/_build/html/week43.html      |  639 +-----
 .../_build/jupyter_execute/week43.ipynb       | 1785 +++--------------
 .../_build/jupyter_execute/week43.py          |    4 +-
 doc/LectureNotes/week43.ipynb                 |  633 +++---
 doc/pub/week43/html/week43-bs.html            |    5 +-
 doc/pub/week43/html/week43-reveal.html        |    7 +-
 doc/pub/week43/html/week43-solarized.html     |    5 +-
 doc/pub/week43/html/week43.html               |    5 +-
 doc/pub/week43/ipynb/ipynb-week43-src.tar.gz  |  Bin 192 -> 192 bytes
 doc/pub/week43/ipynb/week43.ipynb             |  629 +++---
 doc/src/week43/week43.do.txt                  |    5 +-
 16 files changed, 1322 insertions(+), 3023 deletions(-)
 create mode 100644 doc/HandWrittenNotes/2024/NotesOctober21.pdf

diff --git a/doc/HandWrittenNotes/2024/NotesOctober21.pdf b/doc/HandWrittenNotes/2024/NotesOctober21.pdf
new file mode 100644
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diff --git a/doc/LectureNotes/_build/.doctrees/week43.doctree b/doc/LectureNotes/_build/.doctrees/week43.doctree
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diff --git a/doc/LectureNotes/_build/html/_sources/week43.ipynb b/doc/LectureNotes/_build/html/_sources/week43.ipynb
index 3a2029a79..0a4d1c0e5 100644
--- a/doc/LectureNotes/_build/html/_sources/week43.ipynb
+++ b/doc/LectureNotes/_build/html/_sources/week43.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "6528e21e",
+   "id": "6d7c1084",
    "metadata": {
     "editable": true
    },
@@ -14,7 +14,7 @@
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   {
    "cell_type": "markdown",
-   "id": "3ee1b36e",
+   "id": "e958ea0e",
    "metadata": {
     "editable": true
    },
@@ -27,7 +27,7 @@
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   {
    "cell_type": "markdown",
-   "id": "ce7aadd0",
+   "id": "4c8f7e83",
    "metadata": {
     "editable": true
    },
@@ -46,7 +46,7 @@
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   {
    "cell_type": "markdown",
-   "id": "3b148f15",
+   "id": "2d33e55c",
    "metadata": {
     "editable": true
    },
@@ -63,7 +63,7 @@
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   {
    "cell_type": "markdown",
-   "id": "de5149e1",
+   "id": "f323c4f8",
    "metadata": {
     "editable": true
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@@ -79,7 +79,7 @@
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   {
    "cell_type": "markdown",
-   "id": "4dfacc15",
+   "id": "e08af3d1",
    "metadata": {
     "editable": true
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@@ -90,7 +90,7 @@
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-   "id": "07178d65",
+   "id": "9121b0dd",
    "metadata": {
     "editable": true
    },
@@ -104,7 +104,7 @@
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   {
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-   "id": "b4b2da11",
+   "id": "4ac0aea8",
    "metadata": {
     "editable": true
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@@ -117,7 +117,7 @@
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   {
    "cell_type": "markdown",
-   "id": "c264977f",
+   "id": "4575ab3d",
    "metadata": {
     "editable": true
    },
@@ -134,7 +134,7 @@
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   {
    "cell_type": "markdown",
-   "id": "9e588fe4",
+   "id": "1d042d4e",
    "metadata": {
     "editable": true
    },
@@ -144,7 +144,7 @@
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   {
    "cell_type": "markdown",
-   "id": "fd47ec8c",
+   "id": "1708e607",
    "metadata": {
     "editable": true
    },
@@ -171,7 +171,7 @@
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   {
    "cell_type": "markdown",
-   "id": "97fdb112",
+   "id": "04d244ea",
    "metadata": {
     "editable": true
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@@ -194,7 +194,7 @@
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   {
    "cell_type": "markdown",
-   "id": "98139ea4",
+   "id": "5d630b18",
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@@ -206,7 +206,7 @@
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   {
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-   "id": "6eab3a16",
+   "id": "542da6c0",
    "metadata": {
     "editable": true
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@@ -218,7 +218,7 @@
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   {
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-   "id": "3bd14855",
+   "id": "6f86a7b3",
    "metadata": {
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@@ -228,7 +228,7 @@
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   {
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-   "id": "e1ec5ba9",
+   "id": "802e6d13",
    "metadata": {
     "editable": true
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@@ -240,7 +240,7 @@
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   {
    "cell_type": "markdown",
-   "id": "4a2edd2d",
+   "id": "1939edba",
    "metadata": {
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@@ -254,7 +254,7 @@
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@@ -266,7 +266,7 @@
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@@ -278,7 +278,7 @@
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@@ -288,7 +288,7 @@
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@@ -300,7 +300,7 @@
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@@ -312,7 +312,7 @@
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@@ -322,7 +322,7 @@
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@@ -334,7 +334,7 @@
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@@ -346,7 +346,7 @@
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@@ -369,7 +369,7 @@
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@@ -381,7 +381,7 @@
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@@ -393,7 +393,7 @@
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@@ -403,7 +403,7 @@
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@@ -415,7 +415,7 @@
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@@ -436,7 +436,7 @@
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@@ -450,7 +450,7 @@
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@@ -462,7 +462,7 @@
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@@ -484,7 +484,7 @@
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@@ -506,7 +506,7 @@
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@@ -534,7 +534,7 @@
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@@ -546,7 +546,7 @@
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@@ -556,7 +556,7 @@
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@@ -568,7 +568,7 @@
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@@ -579,7 +579,7 @@
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@@ -591,7 +591,7 @@
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@@ -604,7 +604,7 @@
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@@ -629,7 +629,7 @@
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@@ -642,7 +642,7 @@
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@@ -654,7 +654,7 @@
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@@ -666,7 +666,7 @@
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@@ -676,7 +676,7 @@
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@@ -688,7 +688,7 @@
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@@ -700,7 +700,7 @@
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@@ -712,7 +712,7 @@
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@@ -724,7 +724,7 @@
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@@ -734,7 +734,7 @@
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@@ -746,7 +746,7 @@
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@@ -756,7 +756,7 @@
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@@ -768,7 +768,7 @@
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@@ -781,7 +781,7 @@
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@@ -793,7 +793,7 @@
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@@ -803,7 +803,7 @@
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@@ -815,7 +815,7 @@
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@@ -826,7 +826,7 @@
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@@ -838,7 +838,7 @@
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@@ -848,7 +848,7 @@
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@@ -860,7 +860,7 @@
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@@ -870,7 +870,7 @@
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@@ -881,7 +881,7 @@
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@@ -894,7 +894,7 @@
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@@ -904,7 +904,7 @@
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@@ -916,7 +916,7 @@
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@@ -926,7 +926,7 @@
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@@ -938,7 +938,7 @@
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@@ -948,7 +948,7 @@
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@@ -972,7 +972,7 @@
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@@ -1022,7 +1022,7 @@
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@@ -1077,7 +1077,7 @@
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@@ -1098,7 +1098,7 @@
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@@ -1136,7 +1136,7 @@
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@@ -1180,7 +1180,7 @@
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@@ -1220,7 +1220,7 @@
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@@ -1241,7 +1241,7 @@
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@@ -1267,7 +1267,7 @@
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@@ -1295,7 +1295,7 @@
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@@ -1332,7 +1332,7 @@
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@@ -1378,7 +1378,7 @@
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@@ -1409,7 +1409,7 @@
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@@ -1447,7 +1447,7 @@
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@@ -1481,7 +1481,7 @@
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@@ -1522,7 +1522,7 @@
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@@ -1601,7 +1601,7 @@
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@@ -1622,7 +1622,7 @@
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@@ -1636,7 +1636,7 @@
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@@ -1746,7 +1746,7 @@
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@@ -1765,7 +1765,7 @@
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@@ -1792,7 +1792,7 @@
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@@ -1806,7 +1806,7 @@
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@@ -1837,7 +1837,7 @@
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@@ -1848,7 +1848,7 @@
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@@ -1892,7 +1892,7 @@
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@@ -1915,7 +1915,7 @@
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@@ -1942,7 +1942,7 @@
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@@ -1953,7 +1953,7 @@
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@@ -1998,7 +1998,7 @@
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@@ -2016,7 +2016,7 @@
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@@ -2051,7 +2051,7 @@
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@@ -2063,7 +2063,7 @@
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@@ -2075,7 +2075,7 @@
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@@ -2088,7 +2088,7 @@
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@@ -2099,7 +2099,7 @@
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@@ -2112,7 +2112,7 @@
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@@ -2127,7 +2127,7 @@
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@@ -2139,7 +2139,7 @@
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@@ -2151,7 +2151,7 @@
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@@ -2164,7 +2164,7 @@
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@@ -2219,7 +2219,7 @@
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@@ -2248,7 +2248,7 @@
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@@ -2278,7 +2278,7 @@
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@@ -2305,7 +2305,7 @@
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@@ -2347,7 +2347,7 @@
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@@ -2358,7 +2358,7 @@
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@@ -2535,7 +2535,7 @@
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@@ -2554,7 +2554,7 @@
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@@ -2576,7 +2576,7 @@
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@@ -2717,7 +2717,7 @@
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@@ -2733,7 +2733,7 @@
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-   "id": "678df2fb",
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@@ -2746,7 +2746,7 @@
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@@ -2758,7 +2758,7 @@
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@@ -2780,7 +2780,7 @@
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@@ -2796,7 +2796,7 @@
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-   "id": "7c8aa0cf",
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@@ -2834,7 +2834,7 @@
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@@ -2847,7 +2847,7 @@
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-   "id": "5a9d9d29",
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@@ -2868,7 +2868,7 @@
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@@ -2884,7 +2884,7 @@
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@@ -2942,7 +2942,7 @@
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@@ -2957,7 +2957,7 @@
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-   "id": "feed7401",
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@@ -2978,7 +2978,7 @@
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@@ -3002,7 +3002,7 @@
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-   "id": "41311a29",
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@@ -3474,7 +3474,7 @@
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@@ -3486,7 +3486,7 @@
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-   "id": "58ad0c6a",
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@@ -3530,7 +3530,7 @@
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@@ -3546,7 +3546,7 @@
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-   "id": "a8a25f75",
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@@ -3561,7 +3561,7 @@
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@@ -3572,7 +3572,7 @@
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@@ -3587,7 +3587,7 @@
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@@ -3603,7 +3603,7 @@
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@@ -3617,7 +3617,7 @@
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@@ -3632,7 +3632,7 @@
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@@ -3658,7 +3658,7 @@
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@@ -3673,7 +3673,7 @@
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@@ -3684,7 +3684,7 @@
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@@ -3699,7 +3699,7 @@
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@@ -3710,7 +3710,7 @@
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@@ -3730,7 +3730,7 @@
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@@ -3745,7 +3745,7 @@
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@@ -3760,7 +3760,7 @@
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@@ -3797,7 +3797,7 @@
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@@ -3810,7 +3810,7 @@
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@@ -3833,7 +3833,7 @@
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@@ -3843,7 +3843,7 @@
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@@ -3858,9 +3858,9 @@
     "\n",
     "[An Introduction to Neural Network Methods for Differential Equations](https://www.springer.com/gp/book/9789401798150), by Yadav and Kumar.\n",
     "\n",
-    "**Master thesis on applying deep learning to problems in mechanics.**\n",
+    "**Physics informed neural networks.**\n",
     "\n",
-    "[Using Deep Reinforcement Learning for Active Flow Control](https://www.duo.uio.no/handle/10852/79212), by Marius Holm\n",
+    "[Scientific Machine Learning Through Physics–Informed Neural Networks: Where we are and What’s Next](https://link.springer.com/article/10.1007/s10915-022-01939-z), by Cuomo et al\n",
     "\n",
     "**Thanks to Kristine Baluka Hein.**\n",
     "\n",
@@ -3870,7 +3870,7 @@
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@@ -3884,7 +3884,7 @@
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@@ -3901,7 +3901,7 @@
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@@ -3917,7 +3917,7 @@
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@@ -3929,7 +3929,7 @@
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@@ -3947,7 +3947,7 @@
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@@ -3968,7 +3968,7 @@
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@@ -3985,7 +3985,7 @@
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@@ -3997,7 +3997,7 @@
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@@ -4008,7 +4008,7 @@
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@@ -4025,7 +4025,7 @@
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@@ -4036,7 +4036,7 @@
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@@ -4052,7 +4052,7 @@
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@@ -4064,7 +4064,7 @@
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@@ -4081,7 +4081,7 @@
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@@ -4093,7 +4093,7 @@
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@@ -4111,7 +4111,7 @@
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@@ -4121,7 +4121,7 @@
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@@ -4133,7 +4133,7 @@
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@@ -4150,7 +4150,7 @@
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@@ -4162,7 +4162,7 @@
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@@ -4173,7 +4173,7 @@
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@@ -4185,7 +4185,7 @@
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@@ -4195,7 +4195,7 @@
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@@ -4215,7 +4215,7 @@
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@@ -4232,7 +4232,7 @@
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@@ -4251,7 +4251,7 @@
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@@ -4263,7 +4263,7 @@
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@@ -4273,7 +4273,7 @@
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@@ -4290,7 +4290,7 @@
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@@ -4300,7 +4300,7 @@
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@@ -4316,7 +4316,7 @@
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@@ -4328,7 +4328,7 @@
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@@ -4340,7 +4340,7 @@
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@@ -4352,7 +4352,7 @@
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@@ -4362,7 +4362,7 @@
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@@ -4374,7 +4374,7 @@
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@@ -4391,7 +4391,7 @@
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@@ -4401,7 +4401,7 @@
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@@ -4413,7 +4413,7 @@
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@@ -4427,7 +4427,7 @@
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@@ -4443,7 +4443,7 @@
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@@ -4455,7 +4455,7 @@
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@@ -4477,7 +4477,7 @@
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@@ -4489,7 +4489,7 @@
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@@ -4512,7 +4512,7 @@
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@@ -4528,7 +4528,7 @@
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@@ -4540,7 +4540,7 @@
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@@ -4564,7 +4564,7 @@
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@@ -4576,7 +4576,7 @@
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@@ -4597,7 +4597,7 @@
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@@ -4609,7 +4609,7 @@
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@@ -4628,7 +4628,7 @@
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@@ -4638,7 +4638,7 @@
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@@ -4652,7 +4652,7 @@
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@@ -4664,7 +4664,7 @@
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diff --git a/doc/LectureNotes/_build/html/searchindex.js b/doc/LectureNotes/_build/html/searchindex.js
index ad7935636..bfee9483b 100644
--- a/doc/LectureNotes/_build/html/searchindex.js
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Week 42: Logistic Regression and Optimization, reminders from week 38 and week 40","<span class=\"section-number\">3. </span>Linear Regression","<span class=\"section-number\">14. </span>Building a Feed Forward Neural Network","<span class=\"section-number\">15. </span>Solving Differential Equations  with Deep Learning","<span class=\"section-number\">16. </span>Convolutional Neural Networks","<span class=\"section-number\">17. </span>Recurrent neural networks: Overarching view","<span class=\"section-number\">4. </span>Ridge and Lasso Regression","<span class=\"section-number\">5. </span>Resampling Methods","<span class=\"section-number\">6. </span>Logistic Regression","<span class=\"section-number\">8. </span>Support Vector Machines, overarching aims","<span class=\"section-number\">9. </span>Decision trees, overarching aims","<span class=\"section-number\">10. </span>Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods","<span class=\"section-number\">11. </span>Basic ideas of the Principal Component Analysis (PCA)","<span class=\"section-number\">13. </span>Neural networks","<span class=\"section-number\">7. </span>Optimization, the central part of any Machine Learning algortithm","<span class=\"section-number\">12. </span>Clustering and Unsupervised Learning","Exercises week 34","Exercises week 35","Exercises week 36","Exercises week 37","Exercises week 38","Exercises week 39","Exercises week 41","Exercises week 42","Exercises week 43","Applied Data Analysis and Machine Learning","<span class=\"section-number\">2. </span>Linear Algebra, Handling of Arrays and more Python Features","Project 1 on Machine Learning, deadline October 7 (midnight), 2024","Project 2 on Machine Learning, deadline November 4 (Midnight)","Teaching schedule with links to material","<span class=\"section-number\">1. </span>Elements of Probability Theory and Statistical Data Analysis","Teachers and Grading","Textbooks","Week 34: Introduction to the course, Logistics and Practicalities","Week 35: From Ordinary Linear Regression to Ridge and Lasso Regression","Week 36: Linear Regression and Statistical interpretations","Week 37: Statistical interpretations and Resampling Methods","Week 38: Logistic Regression and Optimization","Week 39: Optimization and  Gradient Methods","Week 40: Gradient descent methods (continued) and start Neural networks","Week 41 Neural networks and constructing a neural network code","Week 42 Constructing a Neural Network code with examples","Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations"],titleterms:{"0":41,"1":[1,16,17,18,19,23,24,27,33,34,40,41,42],"11":40,"14":41,"16":37,"2":[1,16,17,18,19,23,24,28,33,34,35,40,41,42],"2023":31,"2024":[27,38,39,40,41],"21":42,"23":38,"26":34,"27":38,"3":[1,16,17,23,24,33,34,40,41,42],"30":39,"34":[16,33],"35":[17,34],"36":[18,35],"37":[19,36],"38":[0,20,37],"39":[21,22,38],"4":[1,23,24,28,34,41],"40":[0,22,39],"41":[22,40],"42":[0,23,41],"43":[24,42],"5":[1,23,24],"6":[23,24],"7":[23,24,27,40],"8":24,"9":36,"case":[9,11,30,34,35,37,38],"class":[0,37,40],"do":[2,35,36,38,39,40,41,42],"final":[13,22,28,34,35,38,39,40,41,42],"float":40,"function":[0,1,2,7,8,9,11,12,13,14,22,27,28,30,33,34,35,36,37,38,39,40,41,42],"import":[6,22,26,33,34,35,39,40,41],"new":[5,35,36,40],A:[0,1,2,5,9,10,22,33,35,36,37,39,40,41,42],AND:[39,40],And:[22,33,34,35,37,38,39],But:[22,38,39],For:34,In:[31,40],Is:[40,41],Ising:7,OR:[39,40],The:[0,1,2,3,4,6,7,8,9,10,12,13,18,25,33,34,35,36,37,38,39,40,41,42],To:[33,34],With:[5,35],about:[33,34],abov:[35,40,41,42],activ:[2,13,23,28,35,39,40,41,42],ad:[1,7,18,27,33,34,39,40,41],adaboost:11,adagrad:[14,22,38,39],adam:[14,22,38,39],adapt:[11,22,38,39],adjust:[2,41,42],advanc:[22,39],adversari:5,again:[0,4,10,37],ai:[27,33],aim:[9,10,18,19,20,21,22,23,24,33],aka:[33,34],al:[22,39],algebra:[26,33],algorithm:[10,11,12,13,22,28,34,38,39,40,41,42],algortithm:[14,37,38],all:[9,40,41],an:[1,5,11,33,40],analys:[6,34],analysi:[1,6,7,12,25,27,28,30,33,34,35,36,40],analyt:[1,17,18,22,42],analyz:[40,41],ani:[14,24,37,38],anoth:[10,35,36],appli:25,approach:[1,9,15,33,36,38,39],approxim:[13,40],architectur:[2,41,42],argument:[38,39],arrai:[26,33],artifici:[39,40],assist:31,assumpt:[35,36],august:34,autocorrel:30,autograd:[3,14,22,24,38,39,42],automat:[14,22,38,39,40,42],avoid:[],b:[18,27,28,38],back:[2,12,13,40,41,42],background:[25,27,28,36],backpropag:24,bag:11,base:[14,22,36,38,39],basic:[1,6,8,10,11,12,26,34,35,36,37,40],batch:[2,24,38,39,40,41],bay:[6,35,36],befor:12,bengio:[40,41],beta:[35,36],better:[9,39,40],bia:[7,27,36],bias:[40,41,42],binari:[2,41,42],bind:33,bird:11,boldsymbol:[34,35,36],book:[40,41,42],boost:11,bootstrap:[7,11,36],boston:1,breast:[2,41,42],brief:[33,36,37,38],bring:[13,40,41],build:[2,4,10,41,42],c:[27,28,33],calcul:34,can:[22,33,36,38,39,40],cancer:[0,2,8,10,12,37,41,42],cart:10,center:34,central:[14,25,30,36,37,38],chain:[13,40,41],challeng:37,chang:11,channel:33,chi:[1,33],choic:[40,41],choos:[2,41,42],cifar01:4,classic:12,classif:[0,2,10,11,28,37,41,42],classifi:[9,37],clip:[2,40,41],cluster:15,cnn:4,code:[1,2,3,6,10,12,13,14,15,22,28,33,34,35,36,37,38,39,40,41,42],collect:[2,4,41,42],come:37,commun:33,compact:[0,37,40,41],compar:[3,11,42],comparison:35,compet:[22,38,39],complet:[34,40,41],complex:[1,7,27,34],complic:[7,38,39,40],compon:12,comput:[10,38,39],computation:36,computerlab:33,con:10,concept:30,condit:[35,36,37,38],confid:36,conjug:[14,38],consider:[40,41],construct:[40,41,42],continu:39,contn:33,convex:[9,14,37,38],convolut:[4,13,39,40],correctli:[35,36],correl:[0,12,34,37],correspond:[37,38],cost:[0,2,11,34,35,36,37,38,40,41,42],count:40,cours:[25,32,33],covari:[6,12,30,34],cover:33,critic:28,cross:[0,7,27,36,37],custom:23,cython:33,d:[27,28],data:[0,1,2,4,7,8,10,12,16,17,23,25,27,30,33,34,35,37,40,41,42],dataset:[2,4,41,42],deadlin:[27,28,33],decai:[3,38,39,42],decis:[10,11],decomposit:[6,12,18,26,34],deep:[2,3,33,37,40,41,42],defin:[2,33,40,41,42],definit:[40,41],degre:[1,34],deliveri:[27,28],delta:36,dens:[1,33],deriv:[6,13,34,35,36,37,38,40,41],descent:[0,3,11,14,22,28,37,38,39,42],descript:27,design:34,detail:[4,33,42],develop:[2,41,42],diagon:12,differ:[9,28,38,39],different:22,differenti:[3,14,38,39,40,42],diffus:[3,42],dimension:[3,4,9,27,34,42],directli:[38,39],disadvantag:10,discret:30,discuss:[0,37],distribut:[6,30,35,36],distrubut:36,doe:[34,35,39,40],domain:30,dot:38,down:[2,40,41],dropout:[2,40,41],e:[27,28],each:[23,37],economi:34,electron:[27,28],element:[1,30,33,38,39],elimin:26,elu:[40,41,42],energi:33,ensembl:11,entri:[40,41],entropi:[0,10,37],environ:[1,16,33],equat:[1,3,13,33,34,35,37,38,40,41,42],error:[1,11,33,34,36],essenti:33,estim:[35,36],et:[22,39],etc:33,euler:[3,42],evalu:[2,28,40,41,42],exampl:[0,1,2,3,4,5,7,8,9,10,11,22,33,34,35,36,37,38,39,40,41,42],exercis:[0,1,7,16,17,18,19,20,21,22,23,24,33,34,40,42],expect:[19,30,35,36],expens:36,experi:30,explicit:[40,41],explod:[40,41],explor:[1,16,17,33],exponenti:[3,42],express:[0,18,19,34,37,38,40,41],extend:[0,37,38,40],extrapol:5,extrem:[11,33],ey:11,f:[27,28],fall:31,famili:[2,33,34,40,41,42],famou:26,fantast:34,featur:[10,26,34],feed:[2,13,24,39,40,41,42],find:[36,38],fine:[2,40,41],first:[5,13,28,33,34,35,37,38,40,41,42],fit:[1,11,33,35],fix:34,fold:[36,37],forc:4,forest:11,format:[27,28,33],forward:[2,3,13,24,39,40,41,42],fourier:4,frank:[7,27,34],freedom:[1,34],frequent:34,frequentist:[1,33],fridai:[],from:[0,6,11,13,22,28,34,35,36,37,38,39,40,41],full:[3,41,42],funtion:[40,41],further:[4,6,34],g:27,gan:5,gate:[39,40,4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Week 42: Logistic Regression and Optimization, reminders from week 38 and week 40","<span class=\"section-number\">3. </span>Linear Regression","<span class=\"section-number\">14. </span>Building a Feed Forward Neural Network","<span class=\"section-number\">15. </span>Solving Differential Equations  with Deep Learning","<span class=\"section-number\">16. </span>Convolutional Neural Networks","<span class=\"section-number\">17. </span>Recurrent neural networks: Overarching view","<span class=\"section-number\">4. </span>Ridge and Lasso Regression","<span class=\"section-number\">5. </span>Resampling Methods","<span class=\"section-number\">6. </span>Logistic Regression","<span class=\"section-number\">8. </span>Support Vector Machines, overarching aims","<span class=\"section-number\">9. </span>Decision trees, overarching aims","<span class=\"section-number\">10. </span>Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods","<span class=\"section-number\">11. </span>Basic ideas of the Principal Component Analysis (PCA)","<span class=\"section-number\">13. </span>Neural networks","<span class=\"section-number\">7. </span>Optimization, the central part of any Machine Learning algortithm","<span class=\"section-number\">12. </span>Clustering and Unsupervised Learning","Exercises week 34","Exercises week 35","Exercises week 36","Exercises week 37","Exercises week 38","Exercises week 39","Exercises week 41","Exercises week 42","Exercises week 43","Applied Data Analysis and Machine Learning","<span class=\"section-number\">2. </span>Linear Algebra, Handling of Arrays and more Python Features","Project 1 on Machine Learning, deadline October 7 (midnight), 2024","Project 2 on Machine Learning, deadline November 4 (Midnight)","Teaching schedule with links to material","<span class=\"section-number\">1. </span>Elements of Probability Theory and Statistical Data Analysis","Teachers and Grading","Textbooks","Week 34: Introduction to the course, Logistics and Practicalities","Week 35: From Ordinary Linear Regression to Ridge and Lasso Regression","Week 36: Linear Regression and Statistical interpretations","Week 37: Statistical interpretations and Resampling Methods","Week 38: Logistic Regression and Optimization","Week 39: Optimization and  Gradient Methods","Week 40: Gradient descent methods (continued) and start Neural networks","Week 41 Neural networks and constructing a neural network code","Week 42 Constructing a Neural Network code with examples","Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations"],titleterms:{"0":41,"1":[1,16,17,18,19,23,24,27,33,34,40,41,42],"11":40,"14":41,"16":37,"2":[1,16,17,18,19,23,24,28,33,34,35,40,41,42],"2023":31,"2024":[27,38,39,40,41],"21":42,"23":38,"26":34,"27":38,"3":[1,16,17,23,24,33,34,40,41,42],"30":39,"34":[16,33],"35":[17,34],"36":[18,35],"37":[19,36],"38":[0,20,37],"39":[21,22,38],"4":[1,23,24,28,34,41],"40":[0,22,39],"41":[22,40],"42":[0,23,41],"43":[24,42],"5":[1,23,24],"6":[23,24],"7":[23,24,27,40],"8":24,"9":36,"case":[9,11,30,34,35,37,38],"class":[0,37,40],"do":[2,35,36,38,39,40,41,42],"final":[13,22,28,34,35,38,39,40,41,42],"float":40,"function":[0,1,2,7,8,9,11,12,13,14,22,27,28,30,33,34,35,36,37,38,39,40,41,42],"import":[6,22,26,33,34,35,39,40,41],"new":[5,35,36,40],A:[0,1,2,5,9,10,22,33,35,36,37,39,40,41,42],AND:[39,40],And:[22,33,34,35,37,38,39],But:[22,38,39],For:34,In:[31,40],Is:[40,41],Ising:7,OR:[39,40],The:[0,1,2,3,4,6,7,8,9,10,12,13,18,25,33,34,35,36,37,38,39,40,41,42],To:[33,34],With:[5,35],about:[33,34],abov:[35,40,41,42],activ:[2,13,23,28,35,39,40,41,42],ad:[1,7,18,27,33,34,39,40,41],adaboost:11,adagrad:[14,22,38,39],adam:[14,22,38,39],adapt:[11,22,38,39],adjust:[2,41,42],advanc:[22,39],adversari:5,again:[0,4,10,37],ai:[27,33],aim:[9,10,18,19,20,21,22,23,24,33],aka:[33,34],al:[22,39],algebra:[26,33],algorithm:[10,11,12,13,22,28,34,38,39,40,41,42],algortithm:[14,37,38],all:[9,40,41],an:[1,5,11,33,40],analys:[6,34],analysi:[1,6,7,12,25,27,28,30,33,34,35,36,40],analyt:[1,17,18,22,42],analyz:[40,41],ani:[14,24,37,38],anoth:[10,35,36],appli:25,approach:[1,9,15,33,36,38,39],approxim:[13,40],architectur:[2,41,42],argument:[38,39],arrai:[26,33],artifici:[39,40],assist:31,assumpt:[35,36],august:34,autocorrel:30,autograd:[3,14,22,24,38,39,42],automat:[14,22,38,39,40,42],avoid:[],b:[18,27,28,38],back:[2,12,13,40,41,42],background:[25,27,28,36],backpropag:24,bag:11,base:[14,22,36,38,39],basic:[1,6,8,10,11,12,26,34,35,36,37,40],batch:[2,24,38,39,40,41],bay:[6,35,36],befor:12,bengio:[40,41],beta:[35,36],better:[9,39,40],bia:[7,27,36],bias:[40,41,42],binari:[2,41,42],bind:33,bird:11,boldsymbol:[34,35,36],book:[40,41,42],boost:11,bootstrap:[7,11,36],boston:1,breast:[2,41,42],brief:[33,36,37,38],bring:[13,40,41],build:[2,4,10,41,42],c:[27,28,33],calcul:34,can:[22,33,36,38,39,40],cancer:[0,2,8,10,12,37,41,42],cart:10,center:34,central:[14,25,30,36,37,38],chain:[13,40,41],challeng:37,chang:11,channel:33,chi:[1,33],choic:[40,41],choos:[2,41,42],cifar01:4,classic:12,classif:[0,2,10,11,28,37,41,42],classifi:[9,37],clip:[2,40,41],cluster:15,cnn:4,code:[1,2,3,6,10,12,13,14,15,22,28,33,34,35,36,37,38,39,40,41,42],collect:[2,4,41,42],come:37,commun:33,compact:[0,37,40,41],compar:[3,11,42],comparison:35,compet:[22,38,39],complet:[34,40,41],complex:[1,7,27,34],complic:[7,38,39,40],compon:12,comput:[10,38,39],computation:36,computerlab:33,con:10,concept:30,condit:[35,36,37,38],confid:36,conjug:[14,38],consider:[40,41],construct:[40,41,42],continu:39,contn:33,convex:[9,14,37,38],convolut:[4,13,39,40],correctli:[35,36],correl:[0,12,34,37],correspond:[37,38],cost:[0,2,11,34,35,36,37,38,40,41,42],count:40,cours:[25,32,33],covari:[6,12,30,34],cover:33,critic:28,cross:[0,7,27,36,37],custom:23,cython:33,d:[27,28],data:[0,1,2,4,7,8,10,12,16,17,23,25,27,30,33,34,35,37,40,41,42],dataset:[2,4,41,42],deadlin:[27,28,33],decai:[3,38,39,42],decis:[10,11],decomposit:[6,12,18,26,34],deep:[2,3,33,37,40,41,42],defin:[2,33,40,41,42],definit:[40,41],degre:[1,34],deliveri:[27,28],delta:36,dens:[1,33],deriv:[6,13,34,35,36,37,38,40,41],descent:[0,3,11,14,22,28,37,38,39,42],descript:27,design:34,detail:[4,33,42],develop:[2,41,42],diagon:12,differ:[9,28,38,39],different:22,differenti:[3,14,38,39,40,42],diffus:[3,42],dimension:[3,4,9,27,34,42],directli:[38,39],disadvantag:10,discret:30,discuss:[0,37],distribut:[6,30,35,36],distrubut:36,doe:[34,35,39,40],domain:30,dot:38,down:[2,40,41],dropout:[2,40,41],e:[27,28],each:[23,37],economi:34,electron:[27,28],element:[1,30,33,38,39],elimin:26,elu:[40,41,42],energi:33,ensembl:11,entri:[40,41],entropi:[0,10,37],environ:[1,16,33],equat:[1,3,13,33,34,35,37,38,40,41,42],error:[1,11,33,34,36],essenti:33,estim:[35,36],et:[22,39],etc:33,euler:[3,42],evalu:[2,28,40,41,42],exampl:[0,1,2,3,4,5,7,8,9,10,11,22,33,34,35,36,37,38,39,40,41,42],exercis:[0,1,7,16,17,18,19,20,21,22,23,24,33,34,40,42],expect:[19,30,35,36],expens:36,experi:30,explicit:[40,41],explod:[40,41],explor:[1,16,17,33],exponenti:[3,42],express:[0,18,19,34,37,38,40,41],extend:[0,37,38,40],extrapol:5,extrem:[11,33],ey:11,f:[27,28],fall:31,famili:[2,33,34,40,41,42],famou:26,fantast:34,featur:[10,26,34],feed:[2,13,24,39,40,41,42],find:[36,38],fine:[2,40,41],first:[5,13,28,33,34,35,37,38,40,41,42],fit:[1,11,33,35],fix:34,fold:[36,37],forc:4,forest:11,format:[27,28,33],forward:[2,3,13,24,39,40,41,42],fourier:4,frank:[7,27,34],freedom:[1,34],frequent:34,frequentist:[1,33],fridai:[],from:[0,6,11,13,22,28,34,35,36,37,38,39,40,41],full:[3,41,42],funtion:[40,41],further:[4,6,34],g:27,gan:5,gate:[39,40,4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\ No newline at end of file
diff --git a/doc/LectureNotes/_build/html/week43.html b/doc/LectureNotes/_build/html/week43.html
index 84a0df184..7eaffab86 100644
--- a/doc/LectureNotes/_build/html/week43.html
+++ b/doc/LectureNotes/_build/html/week43.html
@@ -2351,7 +2351,7 @@ the <em>Hadamard product</em>, meaning element-wise multiplication.</p>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Old accuracy on training data: 0.1440501043841336
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/953065564.py:4: RuntimeWarning: overflow encountered in exp
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_96681/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 </pre></div>
 </div>
@@ -2667,265 +2667,30 @@ Lambda =  0.001
 Accuracy score on test set:  0.9472222222222222
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate  =  0.01
-Lambda =  0.01
-Accuracy score on test set:  0.9305555555555556
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate  =  0.01
-Lambda =  0.1
-Accuracy score on test set:  0.9555555555555556
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate  =  0.01
-Lambda =  1.0
-Accuracy score on test set:  0.7694444444444445
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate  =  0.01
-Lambda =  10.0
-Accuracy score on test set:  0.19166666666666668
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/953065564.py:4: RuntimeWarning: overflow encountered in exp
-  return 1/(1 + np.exp(-x))
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate  =  0.1
-Lambda =  1e-05
-Accuracy score on test set:  0.10555555555555556
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/953065564.py:4: RuntimeWarning: overflow encountered in exp
-  return 1/(1 + np.exp(-x))
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate  =  0.1
-Lambda =  0.0001
-Accuracy score on test set:  0.08611111111111111
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/953065564.py:4: RuntimeWarning: overflow encountered in exp
-  return 1/(1 + np.exp(-x))
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate  =  0.1
-Lambda =  0.001
-Accuracy score on test set:  0.10555555555555556
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/953065564.py:4: RuntimeWarning: overflow encountered in exp
-  return 1/(1 + np.exp(-x))
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate  =  0.1
-Lambda =  0.01
-Accuracy score on test set:  0.08888888888888889
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/953065564.py:4: RuntimeWarning: overflow encountered in exp
-  return 1/(1 + np.exp(-x))
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate  =  0.1
-Lambda =  0.1
-Accuracy score on test set:  0.08611111111111111
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/953065564.py:4: RuntimeWarning: overflow encountered in exp
-  return 1/(1 + np.exp(-x))
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate  =  0.1
-Lambda =  1.0
-Accuracy score on test set:  0.08888888888888889
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/953065564.py:4: RuntimeWarning: overflow encountered in exp
-  return 1/(1 + np.exp(-x))
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate  =  0.1
-Lambda =  10.0
-Accuracy score on test set:  0.09166666666666666
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/953065564.py:4: RuntimeWarning: overflow encountered in exp
-  return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/1630775253.py:43: RuntimeWarning: overflow encountered in exp
-  exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
-  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate  =  1.0
-Lambda =  1e-05
-Accuracy score on test set:  0.07777777777777778
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/953065564.py:4: RuntimeWarning: overflow encountered in exp
-  return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/1630775253.py:43: RuntimeWarning: overflow encountered in exp
-  exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
-  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate  =  1.0
-Lambda =  0.0001
-Accuracy score on test set:  0.07777777777777778
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/953065564.py:4: RuntimeWarning: overflow encountered in exp
-  return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/1630775253.py:43: RuntimeWarning: overflow encountered in exp
-  exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
-  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate  =  1.0
-Lambda =  0.001
-Accuracy score on test set:  0.07777777777777778
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/953065564.py:4: RuntimeWarning: overflow encountered in exp
-  return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/1630775253.py:43: RuntimeWarning: overflow encountered in exp
-  exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
-  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate  =  1.0
-Lambda =  0.01
-Accuracy score on test set:  0.07777777777777778
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/953065564.py:4: RuntimeWarning: overflow encountered in exp
-  return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/1630775253.py:43: RuntimeWarning: overflow encountered in exp
-  exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
-  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate  =  1.0
-Lambda =  0.1
-Accuracy score on test set:  0.07777777777777778
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/953065564.py:4: RuntimeWarning: overflow encountered in exp
-  return 1/(1 + np.exp(-x))
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate  =  1.0
-Lambda =  1.0
-Accuracy score on test set:  0.10555555555555556
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/953065564.py:4: RuntimeWarning: overflow encountered in exp
-  return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/1630775253.py:43: RuntimeWarning: overflow encountered in exp
-  exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
-  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate  =  1.0
-Lambda =  10.0
-Accuracy score on test set:  0.07777777777777778
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/953065564.py:4: RuntimeWarning: overflow encountered in exp
-  return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/1630775253.py:43: RuntimeWarning: overflow encountered in exp
-  exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
-  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate  =  10.0
-Lambda =  1e-05
-Accuracy score on test set:  0.07777777777777778
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/953065564.py:4: RuntimeWarning: overflow encountered in exp
-  return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/1630775253.py:43: RuntimeWarning: overflow encountered in exp
-  exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
-  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate  =  10.0
-Lambda =  0.0001
-Accuracy score on test set:  0.07777777777777778
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/953065564.py:4: RuntimeWarning: overflow encountered in exp
-  return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/1630775253.py:43: RuntimeWarning: overflow encountered in exp
-  exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
-  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate  =  10.0
-Lambda =  0.001
-Accuracy score on test set:  0.07777777777777778
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/953065564.py:4: RuntimeWarning: overflow encountered in exp
-  return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/1630775253.py:43: RuntimeWarning: overflow encountered in exp
-  exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
-  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate  =  10.0
-Lambda =  0.01
-Accuracy score on test set:  0.07777777777777778
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/953065564.py:4: RuntimeWarning: overflow encountered in exp
-  return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/1630775253.py:43: RuntimeWarning: overflow encountered in exp
-  exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
-  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate  =  10.0
-Lambda =  0.1
-Accuracy score on test set:  0.07777777777777778
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/953065564.py:4: RuntimeWarning: overflow encountered in exp
-  return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/1630775253.py:43: RuntimeWarning: overflow encountered in exp
-  exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
-  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate  =  10.0
-Lambda =  1.0
-Accuracy score on test set:  0.07777777777777778
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/953065564.py:4: RuntimeWarning: overflow encountered in exp
-  return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/1630775253.py:43: RuntimeWarning: overflow encountered in exp
-  exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
-  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate  =  10.0
-Lambda =  10.0
-Accuracy score on test set:  0.07777777777777778
+<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
+<span class="ne">KeyboardInterrupt</span><span class="g g-Whitespace">                         </span>Traceback (most recent call last)
+<span class="n">Cell</span> <span class="n">In</span><span class="p">[</span><span class="mi">8</span><span class="p">],</span> <span class="n">line</span> <span class="mi">11</span>
+<span class="g g-Whitespace">      </span><span class="mi">8</span> <span class="k">for</span> <span class="n">j</span><span class="p">,</span> <span class="n">lmbd</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">lmbd_vals</span><span class="p">):</span>
+<span class="g g-Whitespace">      </span><span class="mi">9</span>     <span class="n">dnn</span> <span class="o">=</span> <span class="n">NeuralNetwork</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">Y_train_onehot</span><span class="p">,</span> <span class="n">eta</span><span class="o">=</span><span class="n">eta</span><span class="p">,</span> <span class="n">lmbd</span><span class="o">=</span><span class="n">lmbd</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="n">epochs</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span>
+<span class="g g-Whitespace">     </span><span class="mi">10</span>                         <span class="n">n_hidden_neurons</span><span class="o">=</span><span class="n">n_hidden_neurons</span><span class="p">,</span> <span class="n">n_categories</span><span class="o">=</span><span class="n">n_categories</span><span class="p">)</span>
+<span class="ne">---&gt; </span><span class="mi">11</span>     <span class="n">dnn</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>
+<span class="g g-Whitespace">     </span><span class="mi">13</span>     <span class="n">DNN_numpy</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">dnn</span>
+<span class="g g-Whitespace">     </span><span class="mi">15</span>     <span class="n">test_predict</span> <span class="o">=</span> <span class="n">dnn</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
+
+<span class="nn">Cell In[6], line 98,</span> in <span class="ni">NeuralNetwork.train</span><span class="nt">(self)</span>
+<span class="g g-Whitespace">     </span><span class="mi">95</span> <span class="bp">self</span><span class="o">.</span><span class="n">X_data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">X_data_full</span><span class="p">[</span><span class="n">chosen_datapoints</span><span class="p">]</span>
+<span class="g g-Whitespace">     </span><span class="mi">96</span> <span class="bp">self</span><span class="o">.</span><span class="n">Y_data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">Y_data_full</span><span class="p">[</span><span class="n">chosen_datapoints</span><span class="p">]</span>
+<span class="ne">---&gt; </span><span class="mi">98</span> <span class="bp">self</span><span class="o">.</span><span class="n">feed_forward</span><span class="p">()</span>
+<span class="g g-Whitespace">     </span><span class="mi">99</span> <span class="bp">self</span><span class="o">.</span><span class="n">backpropagation</span><span class="p">()</span>
+
+<span class="nn">Cell In[6], line 38,</span> in <span class="ni">NeuralNetwork.feed_forward</span><span class="nt">(self)</span>
+<span class="g g-Whitespace">     </span><span class="mi">36</span> <span class="k">def</span> <span class="nf">feed_forward</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="g g-Whitespace">     </span><span class="mi">37</span>     <span class="c1"># feed-forward for training</span>
+<span class="ne">---&gt; </span><span class="mi">38</span>     <span class="bp">self</span><span class="o">.</span><span class="n">z_h</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">X_data</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">hidden_weights</span><span class="p">)</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">hidden_bias</span>
+<span class="g g-Whitespace">     </span><span class="mi">39</span>     <span class="bp">self</span><span class="o">.</span><span class="n">a_h</span> <span class="o">=</span> <span class="n">sigmoid</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">z_h</span><span class="p">)</span>
+<span class="g g-Whitespace">     </span><span class="mi">41</span>     <span class="bp">self</span><span class="o">.</span><span class="n">z_o</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">a_h</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_weights</span><span class="p">)</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_bias</span>
+
+<span class="ne">KeyboardInterrupt</span>: 
 </pre></div>
 </div>
 </div>
@@ -2971,22 +2736,6 @@ Accuracy score on test set:  0.07777777777777778
 </pre></div>
 </div>
 </div>
-<div class="cell_output docutils container">
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/953065564.py:4: RuntimeWarning: overflow encountered in exp
-  return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/953065564.py:4: RuntimeWarning: overflow encountered in exp
-  return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/953065564.py:4: RuntimeWarning: overflow encountered in exp
-  return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/953065564.py:4: RuntimeWarning: overflow encountered in exp
-  return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/953065564.py:4: RuntimeWarning: overflow encountered in exp
-  return 1/(1 + np.exp(-x))
-</pre></div>
-</div>
-<img alt="_images/week43_96_1.png" src="_images/week43_96_1.png" />
-<img alt="_images/week43_96_2.png" src="_images/week43_96_2.png" />
-</div>
 </div>
 </div>
 <div class="section" id="scikit-learn-implementation">
@@ -3022,328 +2771,6 @@ performance overall.</p>
 </pre></div>
 </div>
 </div>
-<div class="cell_output docutils container">
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn&#39;t converged yet.
-  warnings.warn(
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate  =  1e-05
-Lambda =  1e-05
-Accuracy score on test set:  0.18333333333333332
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn&#39;t converged yet.
-  warnings.warn(
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate  =  1e-05
-Lambda =  0.0001
-Accuracy score on test set:  0.18611111111111112
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn&#39;t converged yet.
-  warnings.warn(
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate  =  1e-05
-Lambda =  0.001
-Accuracy score on test set:  0.13055555555555556
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn&#39;t converged yet.
-  warnings.warn(
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate  =  1e-05
-Lambda =  0.01
-Accuracy score on test set:  0.24444444444444444
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn&#39;t converged yet.
-  warnings.warn(
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate  =  1e-05
-Lambda =  0.1
-Accuracy score on test set:  0.23333333333333334
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn&#39;t converged yet.
-  warnings.warn(
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate  =  1e-05
-Lambda =  1.0
-Accuracy score on test set:  0.12777777777777777
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn&#39;t converged yet.
-  warnings.warn(
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate  =  1e-05
-Lambda =  10.0
-Accuracy score on test set:  0.1527777777777778
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn&#39;t converged yet.
-  warnings.warn(
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate  =  0.0001
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-Accuracy score on test set:  0.9111111111111111
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn&#39;t converged yet.
-  warnings.warn(
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate  =  0.0001
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-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn&#39;t converged yet.
-  warnings.warn(
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate  =  0.0001
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-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn&#39;t converged yet.
-  warnings.warn(
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate  =  0.0001
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-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn&#39;t converged yet.
-  warnings.warn(
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-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn&#39;t converged yet.
-  warnings.warn(
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-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn&#39;t converged yet.
-  warnings.warn(
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate  =  0.0001
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-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn&#39;t converged yet.
-  warnings.warn(
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate  =  0.001
-Lambda =  1e-05
-Accuracy score on test set:  0.975
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn&#39;t converged yet.
-  warnings.warn(
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate  =  0.001
-Lambda =  0.0001
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-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn&#39;t converged yet.
-  warnings.warn(
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate  =  0.001
-Lambda =  0.001
-Accuracy score on test set:  0.9805555555555555
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-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn&#39;t converged yet.
-  warnings.warn(
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate  =  0.001
-Lambda =  0.01
-Accuracy score on test set:  0.9861111111111112
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-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn&#39;t converged yet.
-  warnings.warn(
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate  =  0.001
-Lambda =  0.1
-Accuracy score on test set:  0.9805555555555555
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-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn&#39;t converged yet.
-  warnings.warn(
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate  =  0.001
-Lambda =  1.0
-Accuracy score on test set:  0.9777777777777777
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn&#39;t converged yet.
-  warnings.warn(
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate  =  0.001
-Lambda =  10.0
-Accuracy score on test set:  0.9444444444444444
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn&#39;t converged yet.
-  warnings.warn(
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate  =  0.01
-Lambda =  1e-05
-Accuracy score on test set:  0.9861111111111112
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate  =  0.01
-Lambda =  0.0001
-Accuracy score on test set:  0.9888888888888889
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate  =  0.01
-Lambda =  0.001
-Accuracy score on test set:  0.9888888888888889
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate  =  0.01
-Lambda =  0.01
-Accuracy score on test set:  0.9861111111111112
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate  =  0.01
-Lambda =  0.1
-Accuracy score on test set:  0.9888888888888889
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate  =  0.01
-Lambda =  1.0
-Accuracy score on test set:  0.9722222222222222
-
-Learning rate  =  0.01
-Lambda =  10.0
-Accuracy score on test set:  0.9527777777777777
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate  =  0.1
-Lambda =  1e-05
-Accuracy score on test set:  0.9027777777777778
-
-Learning rate  =  0.1
-Lambda =  0.0001
-Accuracy score on test set:  0.8583333333333333
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate  =  0.1
-Lambda =  0.001
-Accuracy score on test set:  0.8722222222222222
-
-Learning rate  =  0.1
-Lambda =  0.01
-Accuracy score on test set:  0.9055555555555556
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate  =  0.1
-Lambda =  0.1
-Accuracy score on test set:  0.8805555555555555
-
-Learning rate  =  0.1
-Lambda =  1.0
-Accuracy score on test set:  0.8722222222222222
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate  =  0.1
-Lambda =  10.0
-Accuracy score on test set:  0.8666666666666667
-
-Learning rate  =  1.0
-Lambda =  1e-05
-Accuracy score on test set:  0.08611111111111111
-
-Learning rate  =  1.0
-Lambda =  0.0001
-Accuracy score on test set:  0.10555555555555556
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate  =  1.0
-Lambda =  0.001
-Accuracy score on test set:  0.10555555555555556
-
-Learning rate  =  1.0
-Lambda =  0.01
-Accuracy score on test set:  0.17777777777777778
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate  =  1.0
-Lambda =  0.1
-Accuracy score on test set:  0.08333333333333333
-
-Learning rate  =  1.0
-Lambda =  1.0
-Accuracy score on test set:  0.08888888888888889
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate  =  1.0
-Lambda =  10.0
-Accuracy score on test set:  0.09444444444444444
-
-Learning rate  =  10.0
-Lambda =  1e-05
-Accuracy score on test set:  0.17222222222222222
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate  =  10.0
-Lambda =  0.0001
-Accuracy score on test set:  0.11666666666666667
-
-Learning rate  =  10.0
-Lambda =  0.001
-Accuracy score on test set:  0.10555555555555556
-
-Learning rate  =  10.0
-Lambda =  0.01
-Accuracy score on test set:  0.1388888888888889
-
-Learning rate  =  10.0
-Lambda =  0.1
-Accuracy score on test set:  0.11388888888888889
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate  =  10.0
-Lambda =  1.0
-Accuracy score on test set:  0.10555555555555556
-
-Learning rate  =  10.0
-Lambda =  10.0
-Accuracy score on test set:  0.09444444444444444
-</pre></div>
-</div>
-</div>
 </div>
 </div>
 <div class="section" id="id1">
@@ -3387,10 +2814,6 @@ Accuracy score on test set:  0.09444444444444444
 </pre></div>
 </div>
 </div>
-<div class="cell_output docutils container">
-<img alt="_images/week43_100_0.png" src="_images/week43_100_0.png" />
-<img alt="_images/week43_100_1.png" src="_images/week43_100_1.png" />
-</div>
 </div>
 </div>
 <div class="section" id="building-neural-networks-in-tensorflow-and-keras">
@@ -3429,14 +2852,6 @@ how simple solving a machine learning problem can be.</p>
 </pre></div>
 </div>
 </div>
-<div class="cell_output docutils container">
-<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span>  <span class="n">Cell</span> <span class="n">In</span><span class="p">[</span><span class="mi">12</span><span class="p">],</span> <span class="n">line</span> <span class="mi">1</span>
-    <span class="n">pip3</span> <span class="n">install</span> <span class="n">tensorflow</span>
-         <span class="o">^</span>
-<span class="ne">SyntaxError</span>: invalid syntax
-</pre></div>
-</div>
-</div>
 </div>
 <p>and/or if you use <strong>anaconda</strong>, just write (or install from the graphical user interface)
 (current release of CPU-only TensorFlow)</p>
@@ -4840,8 +4255,8 @@ approximate any function at a single hidden layer along with one input
 and output layer to any given precision.</p>
 <p><strong>Book on solving differential equations with ML methods.</strong></p>
 <p><a class="reference external" href="https://www.springer.com/gp/book/9789401798150">An Introduction to Neural Network Methods for Differential Equations</a>, by Yadav and Kumar.</p>
-<p><strong>Master thesis on applying deep learning to problems in mechanics.</strong></p>
-<p><a class="reference external" href="https://www.duo.uio.no/handle/10852/79212">Using Deep Reinforcement Learning for Active Flow Control</a>, by Marius Holm</p>
+<p><strong>Physics informed neural networks.</strong></p>
+<p><a class="reference external" href="https://link.springer.com/article/10.1007/s10915-022-01939-z">Scientific Machine Learning Through Physics–Informed Neural Networks: Where we are and What’s Next</a>, by Cuomo et al</p>
 <p><strong>Thanks to Kristine Baluka Hein.</strong></p>
 <p>The lectures on differential equations were developed by Kristine Baluka Hein, now PhD student at IFI.
 A great thanks to Kristine.</p>
diff --git a/doc/LectureNotes/_build/jupyter_execute/week43.ipynb b/doc/LectureNotes/_build/jupyter_execute/week43.ipynb
index ec58a8e51..75ada85c4 100644
--- a/doc/LectureNotes/_build/jupyter_execute/week43.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/week43.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "6528e21e",
+   "id": "6d7c1084",
    "metadata": {
     "editable": true
    },
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3ee1b36e",
+   "id": "e958ea0e",
    "metadata": {
     "editable": true
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@@ -27,7 +27,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ce7aadd0",
+   "id": "4c8f7e83",
    "metadata": {
     "editable": true
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@@ -46,7 +46,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3b148f15",
+   "id": "2d33e55c",
    "metadata": {
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@@ -63,7 +63,7 @@
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@@ -79,7 +79,7 @@
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@@ -90,7 +90,7 @@
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@@ -104,7 +104,7 @@
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@@ -117,7 +117,7 @@
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@@ -134,7 +134,7 @@
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@@ -144,7 +144,7 @@
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@@ -171,7 +171,7 @@
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@@ -194,7 +194,7 @@
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@@ -206,7 +206,7 @@
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@@ -218,7 +218,7 @@
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@@ -228,7 +228,7 @@
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@@ -240,7 +240,7 @@
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@@ -254,7 +254,7 @@
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@@ -266,7 +266,7 @@
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@@ -278,7 +278,7 @@
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@@ -288,7 +288,7 @@
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@@ -300,7 +300,7 @@
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@@ -312,7 +312,7 @@
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@@ -322,7 +322,7 @@
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@@ -334,7 +334,7 @@
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@@ -346,7 +346,7 @@
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@@ -369,7 +369,7 @@
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@@ -381,7 +381,7 @@
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@@ -393,7 +393,7 @@
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@@ -403,7 +403,7 @@
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@@ -415,7 +415,7 @@
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@@ -436,7 +436,7 @@
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@@ -450,7 +450,7 @@
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@@ -462,7 +462,7 @@
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@@ -484,7 +484,7 @@
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@@ -506,7 +506,7 @@
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@@ -534,7 +534,7 @@
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@@ -546,7 +546,7 @@
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@@ -556,7 +556,7 @@
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@@ -568,7 +568,7 @@
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@@ -579,7 +579,7 @@
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@@ -591,7 +591,7 @@
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@@ -604,7 +604,7 @@
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@@ -629,7 +629,7 @@
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@@ -642,7 +642,7 @@
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@@ -654,7 +654,7 @@
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@@ -666,7 +666,7 @@
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@@ -676,7 +676,7 @@
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@@ -688,7 +688,7 @@
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@@ -700,7 +700,7 @@
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@@ -712,7 +712,7 @@
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@@ -724,7 +724,7 @@
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@@ -734,7 +734,7 @@
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@@ -746,7 +746,7 @@
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@@ -756,7 +756,7 @@
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@@ -768,7 +768,7 @@
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@@ -781,7 +781,7 @@
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@@ -793,7 +793,7 @@
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@@ -803,7 +803,7 @@
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@@ -815,7 +815,7 @@
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@@ -826,7 +826,7 @@
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@@ -838,7 +838,7 @@
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@@ -848,7 +848,7 @@
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@@ -860,7 +860,7 @@
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@@ -870,7 +870,7 @@
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@@ -881,7 +881,7 @@
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@@ -894,7 +894,7 @@
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@@ -904,7 +904,7 @@
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@@ -916,7 +916,7 @@
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@@ -926,7 +926,7 @@
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@@ -938,7 +938,7 @@
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@@ -948,7 +948,7 @@
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@@ -972,7 +972,7 @@
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@@ -1022,7 +1022,7 @@
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-   "id": "bb8dc476",
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     "editable": true
@@ -1101,7 +1101,7 @@
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@@ -1122,7 +1122,7 @@
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-   "id": "84b61a0e",
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@@ -1169,7 +1169,7 @@
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@@ -1213,7 +1213,7 @@
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@@ -1253,7 +1253,7 @@
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@@ -1274,7 +1274,7 @@
   {
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-   "id": "bf84cbc3",
+   "id": "23a1395e",
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@@ -1300,7 +1300,7 @@
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@@ -1328,7 +1328,7 @@
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@@ -1365,7 +1365,7 @@
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-   "id": "9c60a3f7",
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@@ -1428,7 +1428,7 @@
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@@ -1459,7 +1459,7 @@
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@@ -1497,7 +1497,7 @@
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@@ -1531,7 +1531,7 @@
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@@ -1572,7 +1572,7 @@
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-   "id": "b2d49c0e",
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@@ -1589,7 +1589,7 @@
      "name": "stderr",
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-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_96681/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
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@@ -1674,7 +1674,7 @@
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@@ -1695,7 +1695,7 @@
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@@ -1709,7 +1709,7 @@
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@@ -1819,7 +1819,7 @@
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@@ -1838,7 +1838,7 @@
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@@ -1873,7 +1873,7 @@
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@@ -1887,7 +1887,7 @@
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@@ -2134,473 +2134,16 @@
      ]
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-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  0.01\n",
-      "Lambda =  0.01\n",
-      "Accuracy score on test set:  0.9305555555555556\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  0.01\n",
-      "Lambda =  0.1\n",
-      "Accuracy score on test set:  0.9555555555555556\n",
-      "\n"
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-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  0.01\n",
-      "Lambda =  1.0\n",
-      "Accuracy score on test set:  0.7694444444444445\n",
-      "\n"
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-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  0.01\n",
-      "Lambda =  10.0\n",
-      "Accuracy score on test set:  0.19166666666666668\n",
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-     "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
-      "  return 1/(1 + np.exp(-x))\n"
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-    {
-     "name": "stdout",
-     "output_type": "stream",
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-      "Learning rate  =  0.1\n",
-      "Lambda =  1e-05\n",
-      "Accuracy score on test set:  0.10555555555555556\n",
-      "\n"
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-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
-      "  return 1/(1 + np.exp(-x))\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  0.1\n",
-      "Lambda =  0.0001\n",
-      "Accuracy score on test set:  0.08611111111111111\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
-      "  return 1/(1 + np.exp(-x))\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  0.1\n",
-      "Lambda =  0.001\n",
-      "Accuracy score on test set:  0.10555555555555556\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
-      "  return 1/(1 + np.exp(-x))\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  0.1\n",
-      "Lambda =  0.01\n",
-      "Accuracy score on test set:  0.08888888888888889\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
-      "  return 1/(1 + np.exp(-x))\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  0.1\n",
-      "Lambda =  0.1\n",
-      "Accuracy score on test set:  0.08611111111111111\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
-      "  return 1/(1 + np.exp(-x))\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  0.1\n",
-      "Lambda =  1.0\n",
-      "Accuracy score on test set:  0.08888888888888889\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
-      "  return 1/(1 + np.exp(-x))\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  0.1\n",
-      "Lambda =  10.0\n",
-      "Accuracy score on test set:  0.09166666666666666\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
-      "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
-      "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/1630775253.py:44: RuntimeWarning: invalid value encountered in divide\n",
-      "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  1.0\n",
-      "Lambda =  1e-05\n",
-      "Accuracy score on test set:  0.07777777777777778\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
-      "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
-      "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/1630775253.py:44: RuntimeWarning: invalid value encountered in divide\n",
-      "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  1.0\n",
-      "Lambda =  0.0001\n",
-      "Accuracy score on test set:  0.07777777777777778\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
-      "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
-      "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/1630775253.py:44: RuntimeWarning: invalid value encountered in divide\n",
-      "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  1.0\n",
-      "Lambda =  0.001\n",
-      "Accuracy score on test set:  0.07777777777777778\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
-      "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
-      "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/1630775253.py:44: RuntimeWarning: invalid value encountered in divide\n",
-      "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  1.0\n",
-      "Lambda =  0.01\n",
-      "Accuracy score on test set:  0.07777777777777778\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
-      "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
-      "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/1630775253.py:44: RuntimeWarning: invalid value encountered in divide\n",
-      "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  1.0\n",
-      "Lambda =  0.1\n",
-      "Accuracy score on test set:  0.07777777777777778\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
-      "  return 1/(1 + np.exp(-x))\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  1.0\n",
-      "Lambda =  1.0\n",
-      "Accuracy score on test set:  0.10555555555555556\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
-      "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
-      "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/1630775253.py:44: RuntimeWarning: invalid value encountered in divide\n",
-      "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  1.0\n",
-      "Lambda =  10.0\n",
-      "Accuracy score on test set:  0.07777777777777778\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
-      "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
-      "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/1630775253.py:44: RuntimeWarning: invalid value encountered in divide\n",
-      "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  10.0\n",
-      "Lambda =  1e-05\n",
-      "Accuracy score on test set:  0.07777777777777778\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
-      "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
-      "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/1630775253.py:44: RuntimeWarning: invalid value encountered in divide\n",
-      "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  10.0\n",
-      "Lambda =  0.0001\n",
-      "Accuracy score on test set:  0.07777777777777778\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
-      "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
-      "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/1630775253.py:44: RuntimeWarning: invalid value encountered in divide\n",
-      "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  10.0\n",
-      "Lambda =  0.001\n",
-      "Accuracy score on test set:  0.07777777777777778\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
-      "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
-      "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/1630775253.py:44: RuntimeWarning: invalid value encountered in divide\n",
-      "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  10.0\n",
-      "Lambda =  0.01\n",
-      "Accuracy score on test set:  0.07777777777777778\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
-      "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
-      "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/1630775253.py:44: RuntimeWarning: invalid value encountered in divide\n",
-      "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  10.0\n",
-      "Lambda =  0.1\n",
-      "Accuracy score on test set:  0.07777777777777778\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
-      "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
-      "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/1630775253.py:44: RuntimeWarning: invalid value encountered in divide\n",
-      "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  10.0\n",
-      "Lambda =  1.0\n",
-      "Accuracy score on test set:  0.07777777777777778\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
-      "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
-      "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/1630775253.py:44: RuntimeWarning: invalid value encountered in divide\n",
-      "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  10.0\n",
-      "Lambda =  10.0\n",
-      "Accuracy score on test set:  0.07777777777777778\n",
-      "\n"
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[8], line 11\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m j, lmbd \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(lmbd_vals):\n\u001b[1;32m      9\u001b[0m     dnn \u001b[38;5;241m=\u001b[39m NeuralNetwork(X_train, Y_train_onehot, eta\u001b[38;5;241m=\u001b[39meta, lmbd\u001b[38;5;241m=\u001b[39mlmbd, epochs\u001b[38;5;241m=\u001b[39mepochs, batch_size\u001b[38;5;241m=\u001b[39mbatch_size,\n\u001b[1;32m     10\u001b[0m                         n_hidden_neurons\u001b[38;5;241m=\u001b[39mn_hidden_neurons, n_categories\u001b[38;5;241m=\u001b[39mn_categories)\n\u001b[0;32m---> 11\u001b[0m     \u001b[43mdnn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     13\u001b[0m     DNN_numpy[i][j] \u001b[38;5;241m=\u001b[39m dnn\n\u001b[1;32m     15\u001b[0m     test_predict \u001b[38;5;241m=\u001b[39m dnn\u001b[38;5;241m.\u001b[39mpredict(X_test)\n",
+      "Cell \u001b[0;32mIn[6], line 98\u001b[0m, in \u001b[0;36mNeuralNetwork.train\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     95\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mX_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mX_data_full[chosen_datapoints]\n\u001b[1;32m     96\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mY_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mY_data_full[chosen_datapoints]\n\u001b[0;32m---> 98\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfeed_forward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     99\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbackpropagation()\n",
+      "Cell \u001b[0;32mIn[6], line 38\u001b[0m, in \u001b[0;36mNeuralNetwork.feed_forward\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     36\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfeed_forward\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m     37\u001b[0m     \u001b[38;5;66;03m# feed-forward for training\u001b[39;00m\n\u001b[0;32m---> 38\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mz_h \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmatmul\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mX_data\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhidden_weights\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhidden_bias\n\u001b[1;32m     39\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39ma_h \u001b[38;5;241m=\u001b[39m sigmoid(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mz_h)\n\u001b[1;32m     41\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mz_o \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mmatmul(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39ma_h, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_weights) \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_bias\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
      ]
     }
    ],
@@ -2629,7 +2172,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9510511f",
+   "id": "4329a8ec",
    "metadata": {
     "editable": true
    },
@@ -2640,57 +2183,12 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "f97645b0",
+   "id": "28b0ce14",
    "metadata": {
     "collapsed": false,
     "editable": true
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
-      "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
-      "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
-      "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
-      "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94373/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
-      "  return 1/(1 + np.exp(-x))\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x1000 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "filenames": {
-       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week43_96_1.png"
-      }
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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",
-      "text/plain": [
-       "<Figure size 1000x1000 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "filenames": {
-       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week43_96_2.png"
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# visual representation of grid search\n",
     "# uses seaborn heatmap, you can also do this with matplotlib imshow\n",
@@ -2729,7 +2227,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ac92ed55",
+   "id": "6d949b8f",
    "metadata": {
     "editable": true
    },
@@ -2752,601 +2250,12 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "cc76da2f",
+   "id": "a7601b67",
    "metadata": {
     "collapsed": false,
     "editable": true
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  1e-05\n",
-      "Lambda =  1e-05\n",
-      "Accuracy score on test set:  0.18333333333333332\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  1e-05\n",
-      "Lambda =  0.0001\n",
-      "Accuracy score on test set:  0.18611111111111112\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  1e-05\n",
-      "Lambda =  0.001\n",
-      "Accuracy score on test set:  0.13055555555555556\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  1e-05\n",
-      "Lambda =  0.01\n",
-      "Accuracy score on test set:  0.24444444444444444\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  1e-05\n",
-      "Lambda =  0.1\n",
-      "Accuracy score on test set:  0.23333333333333334\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  1e-05\n",
-      "Lambda =  1.0\n",
-      "Accuracy score on test set:  0.12777777777777777\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  1e-05\n",
-      "Lambda =  10.0\n",
-      "Accuracy score on test set:  0.1527777777777778\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  0.0001\n",
-      "Lambda =  1e-05\n",
-      "Accuracy score on test set:  0.9111111111111111\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  0.0001\n",
-      "Lambda =  0.0001\n",
-      "Accuracy score on test set:  0.8888888888888888\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  0.0001\n",
-      "Lambda =  0.001\n",
-      "Accuracy score on test set:  0.8722222222222222\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  0.0001\n",
-      "Lambda =  0.01\n",
-      "Accuracy score on test set:  0.8305555555555556\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  0.0001\n",
-      "Lambda =  0.1\n",
-      "Accuracy score on test set:  0.8888888888888888\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  0.0001\n",
-      "Lambda =  1.0\n",
-      "Accuracy score on test set:  0.8805555555555555\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  0.0001\n",
-      "Lambda =  10.0\n",
-      "Accuracy score on test set:  0.8944444444444445\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  0.001\n",
-      "Lambda =  1e-05\n",
-      "Accuracy score on test set:  0.975\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  0.001\n",
-      "Lambda =  0.0001\n",
-      "Accuracy score on test set:  0.9777777777777777\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  0.001\n",
-      "Lambda =  0.001\n",
-      "Accuracy score on test set:  0.9805555555555555\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  0.001\n",
-      "Lambda =  0.01\n",
-      "Accuracy score on test set:  0.9861111111111112\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  0.001\n",
-      "Lambda =  0.1\n",
-      "Accuracy score on test set:  0.9805555555555555\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  0.001\n",
-      "Lambda =  1.0\n",
-      "Accuracy score on test set:  0.9777777777777777\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  0.001\n",
-      "Lambda =  10.0\n",
-      "Accuracy score on test set:  0.9444444444444444\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  0.01\n",
-      "Lambda =  1e-05\n",
-      "Accuracy score on test set:  0.9861111111111112\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  0.01\n",
-      "Lambda =  0.0001\n",
-      "Accuracy score on test set:  0.9888888888888889\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  0.01\n",
-      "Lambda =  0.001\n",
-      "Accuracy score on test set:  0.9888888888888889\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  0.01\n",
-      "Lambda =  0.01\n",
-      "Accuracy score on test set:  0.9861111111111112\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  0.01\n",
-      "Lambda =  0.1\n",
-      "Accuracy score on test set:  0.9888888888888889\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  0.01\n",
-      "Lambda =  1.0\n",
-      "Accuracy score on test set:  0.9722222222222222\n",
-      "\n",
-      "Learning rate  =  0.01\n",
-      "Lambda =  10.0\n",
-      "Accuracy score on test set:  0.9527777777777777\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  0.1\n",
-      "Lambda =  1e-05\n",
-      "Accuracy score on test set:  0.9027777777777778\n",
-      "\n",
-      "Learning rate  =  0.1\n",
-      "Lambda =  0.0001\n",
-      "Accuracy score on test set:  0.8583333333333333\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  0.1\n",
-      "Lambda =  0.001\n",
-      "Accuracy score on test set:  0.8722222222222222\n",
-      "\n",
-      "Learning rate  =  0.1\n",
-      "Lambda =  0.01\n",
-      "Accuracy score on test set:  0.9055555555555556\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  0.1\n",
-      "Lambda =  0.1\n",
-      "Accuracy score on test set:  0.8805555555555555\n",
-      "\n",
-      "Learning rate  =  0.1\n",
-      "Lambda =  1.0\n",
-      "Accuracy score on test set:  0.8722222222222222\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  0.1\n",
-      "Lambda =  10.0\n",
-      "Accuracy score on test set:  0.8666666666666667\n",
-      "\n",
-      "Learning rate  =  1.0\n",
-      "Lambda =  1e-05\n",
-      "Accuracy score on test set:  0.08611111111111111\n",
-      "\n",
-      "Learning rate  =  1.0\n",
-      "Lambda =  0.0001\n",
-      "Accuracy score on test set:  0.10555555555555556\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  1.0\n",
-      "Lambda =  0.001\n",
-      "Accuracy score on test set:  0.10555555555555556\n",
-      "\n",
-      "Learning rate  =  1.0\n",
-      "Lambda =  0.01\n",
-      "Accuracy score on test set:  0.17777777777777778\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  1.0\n",
-      "Lambda =  0.1\n",
-      "Accuracy score on test set:  0.08333333333333333\n",
-      "\n",
-      "Learning rate  =  1.0\n",
-      "Lambda =  1.0\n",
-      "Accuracy score on test set:  0.08888888888888889\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  1.0\n",
-      "Lambda =  10.0\n",
-      "Accuracy score on test set:  0.09444444444444444\n",
-      "\n",
-      "Learning rate  =  10.0\n",
-      "Lambda =  1e-05\n",
-      "Accuracy score on test set:  0.17222222222222222\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  10.0\n",
-      "Lambda =  0.0001\n",
-      "Accuracy score on test set:  0.11666666666666667\n",
-      "\n",
-      "Learning rate  =  10.0\n",
-      "Lambda =  0.001\n",
-      "Accuracy score on test set:  0.10555555555555556\n",
-      "\n",
-      "Learning rate  =  10.0\n",
-      "Lambda =  0.01\n",
-      "Accuracy score on test set:  0.1388888888888889\n",
-      "\n",
-      "Learning rate  =  10.0\n",
-      "Lambda =  0.1\n",
-      "Accuracy score on test set:  0.11388888888888889\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate  =  10.0\n",
-      "Lambda =  1.0\n",
-      "Accuracy score on test set:  0.10555555555555556\n",
-      "\n",
-      "Learning rate  =  10.0\n",
-      "Lambda =  10.0\n",
-      "Accuracy score on test set:  0.09444444444444444\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from sklearn.neural_network import MLPClassifier\n",
     "# store models for later use\n",
@@ -3368,7 +2277,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4b34882e",
+   "id": "a0053714",
    "metadata": {
     "editable": true
    },
@@ -3379,41 +2288,12 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "b0349437",
+   "id": "c3e7ffe9",
    "metadata": {
     "collapsed": false,
     "editable": true
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x1000 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "filenames": {
-       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week43_100_0.png"
-      }
-     },
-     "output_type": "display_data"
-    },
-    {
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",
-      "text/plain": [
-       "<Figure size 1000x1000 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "filenames": {
-       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week43_100_1.png"
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# optional\n",
     "# visual representation of grid search\n",
@@ -3453,7 +2333,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3dc3d83e",
+   "id": "c1b791ca",
    "metadata": {
     "editable": true
    },
@@ -3471,7 +2351,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "530560b4",
+   "id": "1ddb5273",
    "metadata": {
     "editable": true
    },
@@ -3506,28 +2386,19 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "705deef0",
+   "id": "afcb633b",
    "metadata": {
     "collapsed": false,
     "editable": true
    },
-   "outputs": [
-    {
-     "ename": "SyntaxError",
-     "evalue": "invalid syntax (2357089093.py, line 1)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;36m  Cell \u001b[0;32mIn[12], line 1\u001b[0;36m\u001b[0m\n\u001b[0;31m    pip3 install tensorflow\u001b[0m\n\u001b[0m         ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "pip3 install tensorflow"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "4c72975c",
+   "id": "0c181897",
    "metadata": {
     "editable": true
    },
@@ -3539,7 +2410,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "42580269",
+   "id": "355d2618",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -3552,7 +2423,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0b2fd909",
+   "id": "00b9e53a",
    "metadata": {
     "editable": true
    },
@@ -3563,7 +2434,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "e8f23bf0",
+   "id": "c28e21cc",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -3576,7 +2447,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "22efaba3",
+   "id": "56363aa5",
    "metadata": {
     "editable": true
    },
@@ -3591,7 +2462,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "fa995d4c",
+   "id": "977e44bf",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -3603,7 +2474,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d7e17232",
+   "id": "5406eebe",
    "metadata": {
     "editable": true
    },
@@ -3615,7 +2486,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "18ffc9ed",
+   "id": "f6ef3d40",
    "metadata": {
     "editable": true
    },
@@ -3628,7 +2499,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "50838e03",
+   "id": "b2e18b19",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -3683,7 +2554,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "ab5f30fe",
+   "id": "453ee3be",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -3712,7 +2583,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "30fff6f5",
+   "id": "2070cf50",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -3742,7 +2613,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "9e412c91",
+   "id": "04640634",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -3769,7 +2640,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "329f11e7",
+   "id": "01ebfb4b",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -3811,7 +2682,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "041d733f",
+   "id": "22efdc6e",
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@@ -3822,7 +2693,7 @@
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@@ -3999,7 +2870,7 @@
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@@ -4018,7 +2889,7 @@
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@@ -4040,7 +2911,7 @@
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@@ -4181,7 +3052,7 @@
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@@ -4197,7 +3068,7 @@
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@@ -4210,7 +3081,7 @@
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@@ -4222,7 +3093,7 @@
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@@ -4244,7 +3115,7 @@
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@@ -4260,7 +3131,7 @@
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@@ -4298,7 +3169,7 @@
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@@ -4311,7 +3182,7 @@
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-   "id": "5a9d9d29",
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@@ -4332,7 +3203,7 @@
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@@ -4348,7 +3219,7 @@
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@@ -4406,7 +3277,7 @@
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@@ -4421,7 +3292,7 @@
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-   "id": "feed7401",
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@@ -4442,7 +3313,7 @@
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@@ -4466,7 +3337,7 @@
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-   "id": "41311a29",
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@@ -4938,7 +3809,7 @@
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@@ -4950,7 +3821,7 @@
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-   "id": "58ad0c6a",
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@@ -4994,7 +3865,7 @@
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@@ -5010,7 +3881,7 @@
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-   "id": "a8a25f75",
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@@ -5025,7 +3896,7 @@
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@@ -5036,7 +3907,7 @@
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@@ -5051,7 +3922,7 @@
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@@ -5067,7 +3938,7 @@
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@@ -5081,7 +3952,7 @@
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@@ -5096,7 +3967,7 @@
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@@ -5122,7 +3993,7 @@
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@@ -5137,7 +4008,7 @@
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@@ -5148,7 +4019,7 @@
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@@ -5163,7 +4034,7 @@
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@@ -5174,7 +4045,7 @@
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@@ -5194,7 +4065,7 @@
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@@ -5209,7 +4080,7 @@
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@@ -5224,7 +4095,7 @@
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@@ -5261,7 +4132,7 @@
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@@ -5274,7 +4145,7 @@
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@@ -5297,7 +4168,7 @@
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@@ -5307,7 +4178,7 @@
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@@ -5322,9 +4193,9 @@
     "\n",
     "[An Introduction to Neural Network Methods for Differential Equations](https://www.springer.com/gp/book/9789401798150), by Yadav and Kumar.\n",
     "\n",
-    "**Master thesis on applying deep learning to problems in mechanics.**\n",
+    "**Physics informed neural networks.**\n",
     "\n",
-    "[Using Deep Reinforcement Learning for Active Flow Control](https://www.duo.uio.no/handle/10852/79212), by Marius Holm\n",
+    "[Scientific Machine Learning Through Physics–Informed Neural Networks: Where we are and What’s Next](https://link.springer.com/article/10.1007/s10915-022-01939-z), by Cuomo et al\n",
     "\n",
     "**Thanks to Kristine Baluka Hein.**\n",
     "\n",
@@ -5334,7 +4205,7 @@
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@@ -5348,7 +4219,7 @@
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@@ -5365,7 +4236,7 @@
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@@ -5381,7 +4252,7 @@
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@@ -5393,7 +4264,7 @@
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@@ -5411,7 +4282,7 @@
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@@ -5432,7 +4303,7 @@
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@@ -5449,7 +4320,7 @@
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@@ -5461,7 +4332,7 @@
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@@ -5472,7 +4343,7 @@
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@@ -5489,7 +4360,7 @@
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@@ -5500,7 +4371,7 @@
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@@ -5516,7 +4387,7 @@
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@@ -5528,7 +4399,7 @@
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@@ -5545,7 +4416,7 @@
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@@ -5557,7 +4428,7 @@
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@@ -5575,7 +4446,7 @@
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@@ -5585,7 +4456,7 @@
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@@ -5597,7 +4468,7 @@
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@@ -5614,7 +4485,7 @@
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@@ -5626,7 +4497,7 @@
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@@ -5637,7 +4508,7 @@
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@@ -5649,7 +4520,7 @@
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@@ -5659,7 +4530,7 @@
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@@ -5679,7 +4550,7 @@
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@@ -5696,7 +4567,7 @@
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@@ -5715,7 +4586,7 @@
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@@ -5727,7 +4598,7 @@
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@@ -5737,7 +4608,7 @@
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@@ -5754,7 +4625,7 @@
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@@ -5764,7 +4635,7 @@
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@@ -5780,7 +4651,7 @@
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@@ -5792,7 +4663,7 @@
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@@ -5804,7 +4675,7 @@
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@@ -5816,7 +4687,7 @@
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@@ -5826,7 +4697,7 @@
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@@ -5838,7 +4709,7 @@
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@@ -5855,7 +4726,7 @@
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@@ -5865,7 +4736,7 @@
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@@ -5877,7 +4748,7 @@
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@@ -5891,7 +4762,7 @@
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@@ -5907,7 +4778,7 @@
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@@ -5919,7 +4790,7 @@
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@@ -5941,7 +4812,7 @@
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@@ -5953,7 +4824,7 @@
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@@ -5976,7 +4847,7 @@
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@@ -5992,7 +4863,7 @@
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@@ -6004,7 +4875,7 @@
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@@ -6028,7 +4899,7 @@
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@@ -6040,7 +4911,7 @@
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@@ -6061,7 +4932,7 @@
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@@ -6073,7 +4944,7 @@
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@@ -6092,7 +4963,7 @@
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@@ -6102,7 +4973,7 @@
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@@ -6116,7 +4987,7 @@
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@@ -6128,7 +4999,7 @@
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@@ -6140,7 +5011,7 @@
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   {
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+   "id": "50ab1e89",
    "metadata": {
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@@ -6157,7 +5028,7 @@
   },
   {
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-   "id": "285ccc48",
+   "id": "4966a321",
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@@ -6169,7 +5040,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "94baf8e8",
+   "id": "c40440e9",
    "metadata": {
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@@ -6191,7 +5062,7 @@
   },
   {
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-   "id": "12025356",
+   "id": "a74e04ea",
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@@ -6206,7 +5077,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "42565de6",
+   "id": "c74ab9b7",
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@@ -6217,7 +5088,7 @@
   {
    "cell_type": "code",
    "execution_count": 41,
-   "id": "695b8df0",
+   "id": "db981810",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -6372,7 +5243,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "caaa6bf9",
+   "id": "ec284695",
    "metadata": {
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@@ -6387,7 +5258,7 @@
   {
    "cell_type": "code",
    "execution_count": 42,
-   "id": "2481727a",
+   "id": "e64c1080",
    "metadata": {
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     "editable": true
@@ -6556,7 +5427,7 @@
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   {
    "cell_type": "markdown",
-   "id": "94e67eb6",
+   "id": "14bd5616",
    "metadata": {
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@@ -6569,7 +5440,7 @@
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   {
    "cell_type": "markdown",
-   "id": "3c847d14",
+   "id": "fb3eb651",
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@@ -6586,7 +5457,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "88c997db",
+   "id": "eb56398a",
    "metadata": {
     "editable": true
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@@ -6602,7 +5473,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3ee360d6",
+   "id": "35a6ae74",
    "metadata": {
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@@ -6615,7 +5486,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "915df9b1",
+   "id": "c7ed6ac0",
    "metadata": {
     "editable": true
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@@ -6632,7 +5503,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3cce06b5",
+   "id": "2ff13e10",
    "metadata": {
     "editable": true
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@@ -6644,7 +5515,7 @@
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   {
    "cell_type": "markdown",
-   "id": "164a26c3",
+   "id": "dd6f9845",
    "metadata": {
     "editable": true
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@@ -6671,7 +5542,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a588ddab",
+   "id": "8c3f2ea3",
    "metadata": {
     "editable": true
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@@ -6684,7 +5555,7 @@
   {
    "cell_type": "code",
    "execution_count": 43,
-   "id": "76252b3a",
+   "id": "af7ec34a",
    "metadata": {
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     "editable": true
@@ -6863,7 +5734,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2e9f770a",
+   "id": "389473a6",
    "metadata": {
     "editable": true
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@@ -6883,7 +5754,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6613bf81",
+   "id": "2f06bb48",
    "metadata": {
     "editable": true
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@@ -6898,7 +5769,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4f2611c5",
+   "id": "a06afbb2",
    "metadata": {
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@@ -6912,7 +5783,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fbbfb78c",
+   "id": "e0f0d76c",
    "metadata": {
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@@ -6928,7 +5799,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fede632e",
+   "id": "926e6c32",
    "metadata": {
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@@ -6938,7 +5809,7 @@
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   {
    "cell_type": "markdown",
-   "id": "63169a3e",
+   "id": "8f3aa5e2",
    "metadata": {
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@@ -6960,7 +5831,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1638bb66",
+   "id": "a9afdd7b",
    "metadata": {
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@@ -6974,7 +5845,7 @@
   {
    "cell_type": "code",
    "execution_count": 44,
-   "id": "805cdc28",
+   "id": "d4316cb1",
    "metadata": {
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     "editable": true
@@ -7050,7 +5921,7 @@
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   {
    "cell_type": "markdown",
-   "id": "b958d38c",
+   "id": "fed0985f",
    "metadata": {
     "editable": true
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@@ -7062,7 +5933,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1208716b",
+   "id": "c2b87817",
    "metadata": {
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@@ -7079,7 +5950,7 @@
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   {
    "cell_type": "markdown",
-   "id": "9cd38ea9",
+   "id": "0edfcfb2",
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@@ -7091,7 +5962,7 @@
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   {
    "cell_type": "markdown",
-   "id": "9694bb51",
+   "id": "1aa4b8e9",
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@@ -7106,7 +5977,7 @@
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   {
    "cell_type": "markdown",
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+   "id": "5ba7d0ac",
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@@ -7118,7 +5989,7 @@
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   {
    "cell_type": "markdown",
-   "id": "db57a833",
+   "id": "e7b30a16",
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@@ -7130,7 +6001,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ebdeaa75",
+   "id": "dbac6763",
    "metadata": {
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@@ -7142,7 +6013,7 @@
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   {
    "cell_type": "markdown",
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+   "id": "20a4997e",
    "metadata": {
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@@ -7152,7 +6023,7 @@
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   {
    "cell_type": "markdown",
-   "id": "f7e8ffc6",
+   "id": "7cd569c7",
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@@ -7169,7 +6040,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8cfec0e6",
+   "id": "5ceb6d56",
    "metadata": {
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@@ -7181,7 +6052,7 @@
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   {
    "cell_type": "markdown",
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+   "id": "0911f3ba",
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@@ -7193,7 +6064,7 @@
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   {
    "cell_type": "markdown",
-   "id": "8aba4caa",
+   "id": "7968b5f7",
    "metadata": {
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@@ -7203,7 +6074,7 @@
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   {
    "cell_type": "markdown",
-   "id": "fd018426",
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@@ -7215,7 +6086,7 @@
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   {
    "cell_type": "markdown",
-   "id": "44764933",
+   "id": "5feceb81",
    "metadata": {
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@@ -7226,7 +6097,7 @@
   {
    "cell_type": "code",
    "execution_count": 45,
-   "id": "76170788",
+   "id": "a9e42eda",
    "metadata": {
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     "editable": true
@@ -7391,7 +6262,7 @@
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   {
    "cell_type": "markdown",
-   "id": "afe64d3c",
+   "id": "0f40d867",
    "metadata": {
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@@ -7413,7 +6284,7 @@
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   {
    "cell_type": "markdown",
-   "id": "b579efba",
+   "id": "6e2f088a",
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@@ -7430,7 +6301,7 @@
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   {
    "cell_type": "markdown",
-   "id": "1f9afc2e",
+   "id": "f5391046",
    "metadata": {
     "editable": true
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@@ -7440,7 +6311,7 @@
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   {
    "cell_type": "markdown",
-   "id": "cdc8be09",
+   "id": "a7ac2bfe",
    "metadata": {
     "editable": true
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@@ -7455,7 +6326,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5955fec0",
+   "id": "323eeb6e",
    "metadata": {
     "editable": true
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@@ -7465,7 +6336,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ce2797bd",
+   "id": "c8fd016d",
    "metadata": {
     "editable": true
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@@ -7480,7 +6351,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dd635489",
+   "id": "f86d84b7",
    "metadata": {
     "editable": true
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@@ -7491,7 +6362,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2eddadc9",
+   "id": "f96f1132",
    "metadata": {
     "editable": true
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@@ -7511,7 +6382,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "65a61481",
+   "id": "3a969016",
    "metadata": {
     "editable": true
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@@ -7523,7 +6394,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7013915a",
+   "id": "3d749e66",
    "metadata": {
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@@ -7560,7 +6431,7 @@
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   {
    "cell_type": "markdown",
-   "id": "6b5d013b",
+   "id": "187e06c3",
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@@ -7570,7 +6441,7 @@
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   {
    "cell_type": "markdown",
-   "id": "3877834e",
+   "id": "0b45037f",
    "metadata": {
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@@ -7583,7 +6454,7 @@
   {
    "cell_type": "code",
    "execution_count": 46,
-   "id": "7e63f565",
+   "id": "5bc810a2",
    "metadata": {
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     "editable": true
@@ -7788,7 +6659,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2592748f",
+   "id": "9c6c362d",
    "metadata": {
     "editable": true
    },
@@ -7805,7 +6676,7 @@
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   {
    "cell_type": "markdown",
-   "id": "f76a2424",
+   "id": "93025367",
    "metadata": {
     "editable": true
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@@ -7822,7 +6693,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "06bb4068",
+   "id": "31722ff7",
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@@ -7832,7 +6703,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cf449250",
+   "id": "0dd431c6",
    "metadata": {
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@@ -7847,7 +6718,7 @@
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   {
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-   "id": "72fe7bb2",
+   "id": "ffb27c5a",
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@@ -7861,7 +6732,7 @@
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   {
    "cell_type": "markdown",
-   "id": "97d43e59",
+   "id": "f2a7f9cc",
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@@ -7874,7 +6745,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "26ed608f",
+   "id": "7e5166a0",
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     "editable": true
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@@ -7894,7 +6765,7 @@
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   {
    "cell_type": "markdown",
-   "id": "ae2b2d38",
+   "id": "322d3d0e",
    "metadata": {
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@@ -7906,7 +6777,7 @@
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   {
    "cell_type": "markdown",
-   "id": "0bc085b8",
+   "id": "13fd2713",
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@@ -7918,7 +6789,7 @@
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   {
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+   "id": "f6fd8a54",
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@@ -7930,7 +6801,7 @@
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   {
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-   "id": "4700b147",
+   "id": "3312eb36",
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@@ -7940,7 +6811,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "07da99c0",
+   "id": "12b7262b",
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@@ -7952,7 +6823,7 @@
   },
   {
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-   "id": "7d3e9d8a",
+   "id": "a2c24b60",
    "metadata": {
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@@ -7964,7 +6835,7 @@
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   {
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-   "id": "77e9e06c",
+   "id": "7907d074",
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@@ -7976,7 +6847,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ab19ecd2",
+   "id": "ff2dbe91",
    "metadata": {
     "editable": true
    },
@@ -7986,7 +6857,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "31ff9094",
+   "id": "d3bfa800",
    "metadata": {
     "editable": true
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@@ -8002,7 +6873,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3896b229",
+   "id": "450c71ab",
    "metadata": {
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@@ -8012,7 +6883,7 @@
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   {
    "cell_type": "markdown",
-   "id": "40d97099",
+   "id": "e332f6e8",
    "metadata": {
     "editable": true
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@@ -8024,7 +6895,7 @@
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   {
    "cell_type": "markdown",
-   "id": "32cda23a",
+   "id": "1d0c52d7",
    "metadata": {
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@@ -8041,7 +6912,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "140ba57b",
+   "id": "1c600851",
    "metadata": {
     "editable": true
    },
@@ -8051,7 +6922,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "68834674",
+   "id": "54f8ec74",
    "metadata": {
     "editable": true
    },
@@ -8067,7 +6938,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "198be6e0",
+   "id": "63362c9e",
    "metadata": {
     "editable": true
    },
@@ -8081,7 +6952,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e56f85f9",
+   "id": "7f5a6717",
    "metadata": {
     "editable": true
    },
@@ -8100,7 +6971,7 @@
   {
    "cell_type": "code",
    "execution_count": 47,
-   "id": "4ee57304",
+   "id": "78a296e0",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -8155,7 +7026,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0088cd21",
+   "id": "0a91555e",
    "metadata": {
     "editable": true
    },
@@ -8185,7 +7056,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c422d21e",
+   "id": "d0f43d3c",
    "metadata": {
     "editable": true
    },
@@ -8214,7 +7085,7 @@
   {
    "cell_type": "code",
    "execution_count": 48,
-   "id": "86acdd1c",
+   "id": "62625f51",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -8261,7 +7132,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6097ee56",
+   "id": "ba76ad6c",
    "metadata": {
     "editable": true
    },
@@ -8287,7 +7158,7 @@
   {
    "cell_type": "code",
    "execution_count": 49,
-   "id": "8cf456d6",
+   "id": "da1c7791",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -8521,7 +7392,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1f09e18d",
+   "id": "5cbbebd3",
    "metadata": {
     "editable": true
    },
@@ -8533,7 +7404,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2f8d1764",
+   "id": "c76b254a",
    "metadata": {
     "editable": true
    },
@@ -8545,7 +7416,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2e187f3c",
+   "id": "922fa6ef",
    "metadata": {
     "editable": true
    },
@@ -8557,7 +7428,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b9710ff1",
+   "id": "e3d14cd2",
    "metadata": {
     "editable": true
    },
@@ -8574,7 +7445,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "474b116e",
+   "id": "0419dde1",
    "metadata": {
     "editable": true
    },
@@ -8584,7 +7455,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f50a379c",
+   "id": "e81f4361",
    "metadata": {
     "editable": true
    },
@@ -8596,7 +7467,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b9be7c57",
+   "id": "4f3cbc2c",
    "metadata": {
     "editable": true
    },
@@ -8613,7 +7484,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e9a7c309",
+   "id": "a164460b",
    "metadata": {
     "editable": true
    },
@@ -8624,7 +7495,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "82102f94",
+   "id": "8abd8160",
    "metadata": {
     "editable": true
    },
@@ -8644,7 +7515,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "33008606",
+   "id": "07eb9f81",
    "metadata": {
     "editable": true
    },
@@ -8654,7 +7525,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a4efa0b7",
+   "id": "87159922",
    "metadata": {
     "editable": true
    },
@@ -8680,7 +7551,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2a1267ce",
+   "id": "ac102a8a",
    "metadata": {
     "editable": true
    },
@@ -8696,7 +7567,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0aa8903b",
+   "id": "f907980b",
    "metadata": {
     "editable": true
    },
@@ -8707,7 +7578,7 @@
   {
    "cell_type": "code",
    "execution_count": 50,
-   "id": "40349a4d",
+   "id": "14aba268",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -8938,7 +7809,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9e31fdb4",
+   "id": "c3a13d9b",
    "metadata": {
     "editable": true
    },
diff --git a/doc/LectureNotes/_build/jupyter_execute/week43.py b/doc/LectureNotes/_build/jupyter_execute/week43.py
index 075095e51..9039eeddd 100644
--- a/doc/LectureNotes/_build/jupyter_execute/week43.py
+++ b/doc/LectureNotes/_build/jupyter_execute/week43.py
@@ -2634,9 +2634,9 @@ scores = logistic_regression.fit(X, yXOR, scheduler, epochs=1000)
 # 
 # [An Introduction to Neural Network Methods for Differential Equations](https://www.springer.com/gp/book/9789401798150), by Yadav and Kumar.
 # 
-# **Master thesis on applying deep learning to problems in mechanics.**
+# **Physics informed neural networks.**
 # 
-# [Using Deep Reinforcement Learning for Active Flow Control](https://www.duo.uio.no/handle/10852/79212), by Marius Holm
+# [Scientific Machine Learning Through Physics–Informed Neural Networks: Where we are and What’s Next](https://link.springer.com/article/10.1007/s10915-022-01939-z), by Cuomo et al
 # 
 # **Thanks to Kristine Baluka Hein.**
 # 
diff --git a/doc/LectureNotes/week43.ipynb b/doc/LectureNotes/week43.ipynb
index 3a2029a79..0635c602d 100644
--- a/doc/LectureNotes/week43.ipynb
+++ b/doc/LectureNotes/week43.ipynb
@@ -2,7 +2,7 @@
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-   "id": "6528e21e",
+   "id": "6107bf3a",
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@@ -14,7 +14,7 @@
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@@ -27,7 +27,7 @@
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+   "id": "92454b8c",
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@@ -40,13 +40,16 @@
     "\n",
     "  * Solving differential equations with Neural Networks\n",
     "\n",
-    "  * \"Video of lecture at <https://youtu.be/vkBNTn-MLqs>\n",
-    "<!-- * [Whiteboard notes](https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesOct21.pdf) -->"
+    "  * Video of lecture at <https://youtu.be/vkBNTn-MLqs>\n",
+    "\n",
+    "  * Video os second part, solving differential equations with neural networks at <https://youtu.be/2N8To65I2wQ>\n",
+    "\n",
+    "  * Whiteboard notes on solving differential equations at <https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesOct21.pdf>"
    ]
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   {
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@@ -63,7 +66,7 @@
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@@ -79,7 +82,7 @@
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@@ -90,7 +93,7 @@
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@@ -104,7 +107,7 @@
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@@ -117,7 +120,7 @@
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@@ -134,7 +137,7 @@
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@@ -144,7 +147,7 @@
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@@ -171,7 +174,7 @@
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@@ -194,7 +197,7 @@
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@@ -206,7 +209,7 @@
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@@ -218,7 +221,7 @@
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@@ -240,7 +243,7 @@
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@@ -254,7 +257,7 @@
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@@ -266,7 +269,7 @@
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@@ -278,7 +281,7 @@
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@@ -288,7 +291,7 @@
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@@ -300,7 +303,7 @@
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@@ -312,7 +315,7 @@
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@@ -322,7 +325,7 @@
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@@ -334,7 +337,7 @@
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@@ -346,7 +349,7 @@
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@@ -369,7 +372,7 @@
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@@ -381,7 +384,7 @@
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@@ -393,7 +396,7 @@
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@@ -415,7 +418,7 @@
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@@ -436,7 +439,7 @@
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@@ -450,7 +453,7 @@
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@@ -462,7 +465,7 @@
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@@ -484,7 +487,7 @@
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@@ -506,7 +509,7 @@
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@@ -534,7 +537,7 @@
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@@ -546,7 +549,7 @@
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@@ -556,7 +559,7 @@
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@@ -568,7 +571,7 @@
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@@ -579,7 +582,7 @@
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@@ -591,7 +594,7 @@
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@@ -604,7 +607,7 @@
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@@ -629,7 +632,7 @@
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@@ -642,7 +645,7 @@
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@@ -654,7 +657,7 @@
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@@ -666,7 +669,7 @@
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@@ -676,7 +679,7 @@
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@@ -688,7 +691,7 @@
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@@ -700,7 +703,7 @@
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@@ -712,7 +715,7 @@
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@@ -724,7 +727,7 @@
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@@ -734,7 +737,7 @@
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@@ -746,7 +749,7 @@
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@@ -756,7 +759,7 @@
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@@ -768,7 +771,7 @@
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@@ -781,7 +784,7 @@
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@@ -793,7 +796,7 @@
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@@ -803,7 +806,7 @@
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@@ -815,7 +818,7 @@
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@@ -826,7 +829,7 @@
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@@ -838,7 +841,7 @@
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@@ -848,7 +851,7 @@
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@@ -860,7 +863,7 @@
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@@ -870,7 +873,7 @@
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@@ -881,7 +884,7 @@
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@@ -894,7 +897,7 @@
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@@ -904,7 +907,7 @@
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@@ -916,7 +919,7 @@
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@@ -926,7 +929,7 @@
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@@ -938,7 +941,7 @@
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@@ -948,7 +951,7 @@
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@@ -972,7 +975,7 @@
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@@ -1022,7 +1025,7 @@
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@@ -1077,7 +1080,7 @@
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@@ -1098,7 +1101,7 @@
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@@ -1136,7 +1139,7 @@
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@@ -1180,7 +1183,7 @@
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@@ -1220,7 +1223,7 @@
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@@ -1241,7 +1244,7 @@
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@@ -1267,7 +1270,7 @@
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@@ -1295,7 +1298,7 @@
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@@ -1332,7 +1335,7 @@
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@@ -1378,7 +1381,7 @@
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@@ -1409,7 +1412,7 @@
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@@ -1447,7 +1450,7 @@
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@@ -1522,7 +1525,7 @@
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@@ -1601,7 +1604,7 @@
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@@ -1622,7 +1625,7 @@
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@@ -1636,7 +1639,7 @@
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@@ -1746,7 +1749,7 @@
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@@ -1765,7 +1768,7 @@
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@@ -1806,7 +1809,7 @@
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@@ -1837,7 +1840,7 @@
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@@ -1848,7 +1851,7 @@
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@@ -1892,7 +1895,7 @@
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@@ -1915,7 +1918,7 @@
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-   "id": "cc76da2f",
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@@ -1942,7 +1945,7 @@
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@@ -1953,7 +1956,7 @@
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-   "id": "b0349437",
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@@ -1998,7 +2001,7 @@
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@@ -2016,7 +2019,7 @@
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@@ -2051,7 +2054,7 @@
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@@ -2063,7 +2066,7 @@
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@@ -2075,7 +2078,7 @@
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@@ -2088,7 +2091,7 @@
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@@ -2099,7 +2102,7 @@
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@@ -2112,7 +2115,7 @@
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@@ -2127,7 +2130,7 @@
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-   "id": "fa995d4c",
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@@ -2139,7 +2142,7 @@
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@@ -2151,7 +2154,7 @@
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@@ -2164,7 +2167,7 @@
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-   "id": "50838e03",
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@@ -2219,7 +2222,7 @@
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-   "id": "ab5f30fe",
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@@ -2248,7 +2251,7 @@
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@@ -2278,7 +2281,7 @@
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@@ -2305,7 +2308,7 @@
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@@ -2347,7 +2350,7 @@
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@@ -2358,7 +2361,7 @@
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@@ -2535,7 +2538,7 @@
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@@ -2554,7 +2557,7 @@
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@@ -2576,7 +2579,7 @@
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@@ -2717,7 +2720,7 @@
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@@ -2733,7 +2736,7 @@
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-   "id": "678df2fb",
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@@ -2746,7 +2749,7 @@
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@@ -2758,7 +2761,7 @@
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-   "id": "18f1eb25",
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@@ -2780,7 +2783,7 @@
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@@ -2796,7 +2799,7 @@
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-   "id": "7c8aa0cf",
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@@ -2834,7 +2837,7 @@
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@@ -2847,7 +2850,7 @@
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-   "id": "5a9d9d29",
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@@ -2868,7 +2871,7 @@
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@@ -2884,7 +2887,7 @@
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@@ -2942,7 +2945,7 @@
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@@ -2957,7 +2960,7 @@
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-   "id": "feed7401",
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@@ -2978,7 +2981,7 @@
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@@ -3002,7 +3005,7 @@
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-   "id": "41311a29",
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@@ -3474,7 +3477,7 @@
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@@ -3486,7 +3489,7 @@
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-   "id": "58ad0c6a",
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@@ -3530,7 +3533,7 @@
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@@ -3546,7 +3549,7 @@
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-   "id": "a8a25f75",
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@@ -3561,7 +3564,7 @@
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@@ -3572,7 +3575,7 @@
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@@ -3587,7 +3590,7 @@
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@@ -3603,7 +3606,7 @@
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@@ -3617,7 +3620,7 @@
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@@ -3632,7 +3635,7 @@
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@@ -3658,7 +3661,7 @@
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@@ -3673,7 +3676,7 @@
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@@ -3684,7 +3687,7 @@
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@@ -3699,7 +3702,7 @@
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@@ -3710,7 +3713,7 @@
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@@ -3730,7 +3733,7 @@
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@@ -3745,7 +3748,7 @@
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@@ -3760,7 +3763,7 @@
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@@ -3797,7 +3800,7 @@
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@@ -3810,7 +3813,7 @@
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@@ -3833,7 +3836,7 @@
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@@ -3843,7 +3846,7 @@
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@@ -3858,9 +3861,9 @@
     "\n",
     "[An Introduction to Neural Network Methods for Differential Equations](https://www.springer.com/gp/book/9789401798150), by Yadav and Kumar.\n",
     "\n",
-    "**Master thesis on applying deep learning to problems in mechanics.**\n",
+    "**Physics informed neural networks.**\n",
     "\n",
-    "[Using Deep Reinforcement Learning for Active Flow Control](https://www.duo.uio.no/handle/10852/79212), by Marius Holm\n",
+    "[Scientific Machine Learning Through Physics–Informed Neural Networks: Where we are and What’s Next](https://link.springer.com/article/10.1007/s10915-022-01939-z), by Cuomo et al\n",
     "\n",
     "**Thanks to Kristine Baluka Hein.**\n",
     "\n",
@@ -3870,7 +3873,7 @@
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@@ -3884,7 +3887,7 @@
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@@ -3901,7 +3904,7 @@
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@@ -3917,7 +3920,7 @@
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@@ -3929,7 +3932,7 @@
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@@ -3947,7 +3950,7 @@
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@@ -3968,7 +3971,7 @@
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@@ -3985,7 +3988,7 @@
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@@ -3997,7 +4000,7 @@
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@@ -4008,7 +4011,7 @@
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@@ -4025,7 +4028,7 @@
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@@ -4036,7 +4039,7 @@
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@@ -4052,7 +4055,7 @@
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@@ -4064,7 +4067,7 @@
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@@ -4081,7 +4084,7 @@
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@@ -4093,7 +4096,7 @@
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@@ -4111,7 +4114,7 @@
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@@ -4121,7 +4124,7 @@
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@@ -4133,7 +4136,7 @@
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@@ -4150,7 +4153,7 @@
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@@ -4162,7 +4165,7 @@
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@@ -4173,7 +4176,7 @@
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@@ -4185,7 +4188,7 @@
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@@ -4195,7 +4198,7 @@
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@@ -4215,7 +4218,7 @@
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@@ -4232,7 +4235,7 @@
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@@ -4251,7 +4254,7 @@
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@@ -4263,7 +4266,7 @@
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@@ -4273,7 +4276,7 @@
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@@ -4290,7 +4293,7 @@
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@@ -4300,7 +4303,7 @@
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@@ -4316,7 +4319,7 @@
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@@ -4328,7 +4331,7 @@
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@@ -4340,7 +4343,7 @@
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@@ -4352,7 +4355,7 @@
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@@ -4362,7 +4365,7 @@
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@@ -4374,7 +4377,7 @@
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@@ -4391,7 +4394,7 @@
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@@ -4401,7 +4404,7 @@
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@@ -4413,7 +4416,7 @@
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@@ -4427,7 +4430,7 @@
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@@ -4443,7 +4446,7 @@
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@@ -4455,7 +4458,7 @@
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@@ -4477,7 +4480,7 @@
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@@ -4489,7 +4492,7 @@
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@@ -4512,7 +4515,7 @@
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@@ -4528,7 +4531,7 @@
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@@ -4540,7 +4543,7 @@
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@@ -4564,7 +4567,7 @@
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@@ -4576,7 +4579,7 @@
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@@ -4597,7 +4600,7 @@
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@@ -4609,7 +4612,7 @@
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@@ -4628,7 +4631,7 @@
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@@ -4638,7 +4641,7 @@
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@@ -4652,7 +4655,7 @@
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@@ -4664,7 +4667,7 @@
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@@ -4676,7 +4679,7 @@
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@@ -4693,7 +4696,7 @@
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@@ -4705,7 +4708,7 @@
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@@ -4727,7 +4730,7 @@
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@@ -4742,7 +4745,7 @@
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@@ -4753,7 +4756,7 @@
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@@ -4908,7 +4911,7 @@
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@@ -4923,7 +4926,7 @@
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@@ -5092,7 +5095,7 @@
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@@ -5105,7 +5108,7 @@
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@@ -5122,7 +5125,7 @@
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@@ -5138,7 +5141,7 @@
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@@ -5151,7 +5154,7 @@
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@@ -5168,7 +5171,7 @@
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@@ -5180,7 +5183,7 @@
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@@ -5207,7 +5210,7 @@
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@@ -5220,7 +5223,7 @@
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@@ -5399,7 +5402,7 @@
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@@ -5419,7 +5422,7 @@
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@@ -5434,7 +5437,7 @@
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@@ -5448,7 +5451,7 @@
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@@ -5464,7 +5467,7 @@
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@@ -5474,7 +5477,7 @@
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@@ -5496,7 +5499,7 @@
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@@ -5510,7 +5513,7 @@
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@@ -5586,7 +5589,7 @@
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@@ -5598,7 +5601,7 @@
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@@ -5615,7 +5618,7 @@
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@@ -5627,7 +5630,7 @@
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@@ -5642,7 +5645,7 @@
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@@ -5654,7 +5657,7 @@
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@@ -5666,7 +5669,7 @@
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@@ -5678,7 +5681,7 @@
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@@ -5688,7 +5691,7 @@
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@@ -5705,7 +5708,7 @@
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@@ -5717,7 +5720,7 @@
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@@ -5729,7 +5732,7 @@
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@@ -5739,7 +5742,7 @@
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@@ -5751,7 +5754,7 @@
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@@ -5762,7 +5765,7 @@
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@@ -5927,7 +5930,7 @@
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@@ -5949,7 +5952,7 @@
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@@ -5966,7 +5969,7 @@
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@@ -5976,7 +5979,7 @@
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@@ -5991,7 +5994,7 @@
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@@ -6001,7 +6004,7 @@
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@@ -6016,7 +6019,7 @@
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@@ -6027,7 +6030,7 @@
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@@ -6047,7 +6050,7 @@
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@@ -6059,7 +6062,7 @@
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@@ -6096,7 +6099,7 @@
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@@ -6106,7 +6109,7 @@
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@@ -6119,7 +6122,7 @@
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@@ -6324,7 +6327,7 @@
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@@ -6341,7 +6344,7 @@
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@@ -6358,7 +6361,7 @@
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@@ -6368,7 +6371,7 @@
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@@ -6383,7 +6386,7 @@
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@@ -6397,7 +6400,7 @@
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@@ -6410,7 +6413,7 @@
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@@ -6430,7 +6433,7 @@
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@@ -6442,7 +6445,7 @@
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@@ -6454,7 +6457,7 @@
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@@ -6466,7 +6469,7 @@
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@@ -6476,7 +6479,7 @@
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@@ -6488,7 +6491,7 @@
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@@ -6500,7 +6503,7 @@
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@@ -6512,7 +6515,7 @@
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@@ -6522,7 +6525,7 @@
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@@ -6538,7 +6541,7 @@
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@@ -6548,7 +6551,7 @@
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@@ -6560,7 +6563,7 @@
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@@ -6577,7 +6580,7 @@
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@@ -6587,7 +6590,7 @@
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@@ -6603,7 +6606,7 @@
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@@ -6617,7 +6620,7 @@
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@@ -6636,7 +6639,7 @@
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@@ -6691,7 +6694,7 @@
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@@ -6721,7 +6724,7 @@
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@@ -6750,7 +6753,7 @@
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-   "id": "86acdd1c",
+   "id": "83ad594f",
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     "editable": true
@@ -6797,7 +6800,7 @@
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+   "id": "dbe6f74a",
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@@ -6823,7 +6826,7 @@
   {
    "cell_type": "code",
    "execution_count": 49,
-   "id": "8cf456d6",
+   "id": "a29e5348",
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     "editable": true
@@ -7057,7 +7060,7 @@
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   {
    "cell_type": "markdown",
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+   "id": "f2171b20",
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@@ -7069,7 +7072,7 @@
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   {
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@@ -7081,7 +7084,7 @@
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   {
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@@ -7093,7 +7096,7 @@
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+   "id": "6969d557",
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@@ -7110,7 +7113,7 @@
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@@ -7120,7 +7123,7 @@
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@@ -7132,7 +7135,7 @@
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@@ -7149,7 +7152,7 @@
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@@ -7160,7 +7163,7 @@
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@@ -7180,7 +7183,7 @@
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@@ -7190,7 +7193,7 @@
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@@ -7216,7 +7219,7 @@
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@@ -7232,7 +7235,7 @@
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@@ -7243,7 +7246,7 @@
   {
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@@ -7474,7 +7477,7 @@
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+   "id": "80a6f6ef",
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diff --git a/doc/pub/week43/html/week43-bs.html b/doc/pub/week43/html/week43-bs.html
index a6ba323b3..778488ec5 100644
--- a/doc/pub/week43/html/week43-bs.html
+++ b/doc/pub/week43/html/week43-bs.html
@@ -489,8 +489,9 @@ MathJax.Hub.Config({
 <ul>
   <li> Building our own Feed-forward Neural Network with intro to Tensorflow</li>
   <li> Solving differential equations with Neural Networks</li>
-  <li> "Video of lecture at <a href="https://youtu.be/vkBNTn-MLqs" target="_self"><tt>https://youtu.be/vkBNTn-MLqs</tt></a>
-<!-- * <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesOct21.pdf" target="_self">Whiteboard notes</a> --></li>
+  <li> Video of lecture at <a href="https://youtu.be/vkBNTn-MLqs" target="_self"><tt>https://youtu.be/vkBNTn-MLqs</tt></a></li>
+  <li> Video os second part, solving differential equations with neural networks at <a href="https://youtu.be/2N8To65I2wQ" target="_self"><tt>https://youtu.be/2N8To65I2wQ</tt></a></li>
+  <li> Whiteboard notes on solving differential equations at <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesOct21.pdf" target="_self"><tt>https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesOct21.pdf</tt></a></li>  
 </ul>
 </div>
 </div>
diff --git a/doc/pub/week43/html/week43-reveal.html b/doc/pub/week43/html/week43-reveal.html
index 411157dea..30320d347 100644
--- a/doc/pub/week43/html/week43-reveal.html
+++ b/doc/pub/week43/html/week43-reveal.html
@@ -206,8 +206,11 @@ MathJax.Hub.Config({
 
 <p><li> Solving differential equations with Neural Networks</li>
 
-<p><li> "Video of lecture at <a href="https://youtu.be/vkBNTn-MLqs" target="_blank"><tt>https://youtu.be/vkBNTn-MLqs</tt></a>
-<!-- * <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesOct21.pdf" target="_blank">Whiteboard notes</a> --></li>
+<p><li> Video of lecture at <a href="https://youtu.be/vkBNTn-MLqs" target="_blank"><tt>https://youtu.be/vkBNTn-MLqs</tt></a></li>
+
+<p><li> Video os second part, solving differential equations with neural networks at <a href="https://youtu.be/2N8To65I2wQ" target="_blank"><tt>https://youtu.be/2N8To65I2wQ</tt></a></li>
+
+<p><li> Whiteboard notes on solving differential equations at <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesOct21.pdf" target="_blank"><tt>https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesOct21.pdf</tt></a></li>  
 </ul>
 </div>
 </section>
diff --git a/doc/pub/week43/html/week43-solarized.html b/doc/pub/week43/html/week43-solarized.html
index 6faad34c2..2650fd3cb 100644
--- a/doc/pub/week43/html/week43-solarized.html
+++ b/doc/pub/week43/html/week43-solarized.html
@@ -385,8 +385,9 @@ MathJax.Hub.Config({
 <ul>
   <li> Building our own Feed-forward Neural Network with intro to Tensorflow</li>
   <li> Solving differential equations with Neural Networks</li>
-  <li> "Video of lecture at <a href="https://youtu.be/vkBNTn-MLqs" target="_blank"><tt>https://youtu.be/vkBNTn-MLqs</tt></a>
-<!-- * <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesOct21.pdf" target="_blank">Whiteboard notes</a> --></li>
+  <li> Video of lecture at <a href="https://youtu.be/vkBNTn-MLqs" target="_blank"><tt>https://youtu.be/vkBNTn-MLqs</tt></a></li>
+  <li> Video os second part, solving differential equations with neural networks at <a href="https://youtu.be/2N8To65I2wQ" target="_blank"><tt>https://youtu.be/2N8To65I2wQ</tt></a></li>
+  <li> Whiteboard notes on solving differential equations at <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesOct21.pdf" target="_blank"><tt>https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesOct21.pdf</tt></a></li>  
 </ul>
 </div>
 
diff --git a/doc/pub/week43/html/week43.html b/doc/pub/week43/html/week43.html
index 747239a96..9210219f9 100644
--- a/doc/pub/week43/html/week43.html
+++ b/doc/pub/week43/html/week43.html
@@ -462,8 +462,9 @@ MathJax.Hub.Config({
 <ul>
   <li> Building our own Feed-forward Neural Network with intro to Tensorflow</li>
   <li> Solving differential equations with Neural Networks</li>
-  <li> "Video of lecture at <a href="https://youtu.be/vkBNTn-MLqs" target="_blank"><tt>https://youtu.be/vkBNTn-MLqs</tt></a>
-<!-- * <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesOct21.pdf" target="_blank">Whiteboard notes</a> --></li>
+  <li> Video of lecture at <a href="https://youtu.be/vkBNTn-MLqs" target="_blank"><tt>https://youtu.be/vkBNTn-MLqs</tt></a></li>
+  <li> Video os second part, solving differential equations with neural networks at <a href="https://youtu.be/2N8To65I2wQ" target="_blank"><tt>https://youtu.be/2N8To65I2wQ</tt></a></li>
+  <li> Whiteboard notes on solving differential equations at <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesOct21.pdf" target="_blank"><tt>https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesOct21.pdf</tt></a></li>  
 </ul>
 </div>
 
diff --git a/doc/pub/week43/ipynb/ipynb-week43-src.tar.gz b/doc/pub/week43/ipynb/ipynb-week43-src.tar.gz
index 2d1c864feb266ddbdce0fdbe7fed87435365f1bc..27f05a66279fd7c06d284fa65fb22f776eb76209 100644
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diff --git a/doc/pub/week43/ipynb/week43.ipynb b/doc/pub/week43/ipynb/week43.ipynb
index 0a4d1c0e5..0635c602d 100644
--- a/doc/pub/week43/ipynb/week43.ipynb
+++ b/doc/pub/week43/ipynb/week43.ipynb
@@ -2,7 +2,7 @@
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-   "id": "6d7c1084",
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@@ -14,7 +14,7 @@
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@@ -27,7 +27,7 @@
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+   "id": "92454b8c",
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@@ -40,13 +40,16 @@
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-    "<!-- * [Whiteboard notes](https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesOct21.pdf) -->"
+    "  * Video of lecture at <https://youtu.be/vkBNTn-MLqs>\n",
+    "\n",
+    "  * Video os second part, solving differential equations with neural networks at <https://youtu.be/2N8To65I2wQ>\n",
+    "\n",
+    "  * Whiteboard notes on solving differential equations at <https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesOct21.pdf>"
    ]
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@@ -63,7 +66,7 @@
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@@ -90,7 +93,7 @@
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@@ -104,7 +107,7 @@
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@@ -144,7 +147,7 @@
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@@ -171,7 +174,7 @@
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@@ -194,7 +197,7 @@
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@@ -206,7 +209,7 @@
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@@ -218,7 +221,7 @@
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@@ -254,7 +257,7 @@
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@@ -278,7 +281,7 @@
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@@ -288,7 +291,7 @@
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@@ -312,7 +315,7 @@
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@@ -322,7 +325,7 @@
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@@ -334,7 +337,7 @@
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@@ -346,7 +349,7 @@
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@@ -369,7 +372,7 @@
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@@ -381,7 +384,7 @@
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@@ -393,7 +396,7 @@
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@@ -403,7 +406,7 @@
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@@ -415,7 +418,7 @@
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@@ -436,7 +439,7 @@
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@@ -450,7 +453,7 @@
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@@ -462,7 +465,7 @@
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@@ -484,7 +487,7 @@
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@@ -506,7 +509,7 @@
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@@ -534,7 +537,7 @@
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@@ -546,7 +549,7 @@
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+   "id": "40d7c3b7",
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@@ -556,7 +559,7 @@
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@@ -568,7 +571,7 @@
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@@ -579,7 +582,7 @@
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@@ -591,7 +594,7 @@
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@@ -676,7 +679,7 @@
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@@ -724,7 +727,7 @@
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@@ -734,7 +737,7 @@
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@@ -746,7 +749,7 @@
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@@ -756,7 +759,7 @@
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@@ -768,7 +771,7 @@
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@@ -781,7 +784,7 @@
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@@ -793,7 +796,7 @@
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@@ -803,7 +806,7 @@
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@@ -815,7 +818,7 @@
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@@ -826,7 +829,7 @@
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@@ -838,7 +841,7 @@
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@@ -848,7 +851,7 @@
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@@ -860,7 +863,7 @@
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@@ -870,7 +873,7 @@
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@@ -881,7 +884,7 @@
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@@ -894,7 +897,7 @@
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@@ -904,7 +907,7 @@
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@@ -916,7 +919,7 @@
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@@ -926,7 +929,7 @@
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@@ -938,7 +941,7 @@
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@@ -948,7 +951,7 @@
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@@ -972,7 +975,7 @@
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@@ -1022,7 +1025,7 @@
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-   "id": "6b4f0f8b",
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@@ -1077,7 +1080,7 @@
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@@ -1098,7 +1101,7 @@
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@@ -1136,7 +1139,7 @@
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@@ -1180,7 +1183,7 @@
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@@ -1220,7 +1223,7 @@
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@@ -1241,7 +1244,7 @@
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@@ -1267,7 +1270,7 @@
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@@ -1295,7 +1298,7 @@
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@@ -1332,7 +1335,7 @@
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-   "id": "1efd1c2e",
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@@ -1378,7 +1381,7 @@
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@@ -1409,7 +1412,7 @@
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@@ -1447,7 +1450,7 @@
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@@ -1481,7 +1484,7 @@
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@@ -1522,7 +1525,7 @@
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@@ -1601,7 +1604,7 @@
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@@ -1622,7 +1625,7 @@
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@@ -1636,7 +1639,7 @@
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@@ -1746,7 +1749,7 @@
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@@ -1765,7 +1768,7 @@
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@@ -1792,7 +1795,7 @@
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@@ -1806,7 +1809,7 @@
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@@ -1837,7 +1840,7 @@
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@@ -1848,7 +1851,7 @@
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-   "id": "28b0ce14",
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@@ -1892,7 +1895,7 @@
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@@ -1915,7 +1918,7 @@
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@@ -1942,7 +1945,7 @@
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@@ -1953,7 +1956,7 @@
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-   "id": "c3e7ffe9",
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@@ -1998,7 +2001,7 @@
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@@ -2016,7 +2019,7 @@
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@@ -2051,7 +2054,7 @@
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-   "id": "afcb633b",
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@@ -2063,7 +2066,7 @@
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@@ -2075,7 +2078,7 @@
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-   "id": "355d2618",
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@@ -2088,7 +2091,7 @@
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@@ -2099,7 +2102,7 @@
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-   "id": "c28e21cc",
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@@ -2112,7 +2115,7 @@
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@@ -2127,7 +2130,7 @@
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-   "id": "977e44bf",
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@@ -2139,7 +2142,7 @@
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@@ -2151,7 +2154,7 @@
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@@ -2164,7 +2167,7 @@
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-   "id": "b2e18b19",
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@@ -2219,7 +2222,7 @@
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-   "id": "453ee3be",
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@@ -2248,7 +2251,7 @@
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-   "id": "2070cf50",
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@@ -2278,7 +2281,7 @@
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-   "id": "04640634",
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@@ -2305,7 +2308,7 @@
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-   "id": "01ebfb4b",
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@@ -2347,7 +2350,7 @@
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@@ -2358,7 +2361,7 @@
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-   "id": "4d4f1c0f",
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@@ -2535,7 +2538,7 @@
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@@ -2554,7 +2557,7 @@
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@@ -2576,7 +2579,7 @@
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-   "id": "dd2196ea",
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@@ -2717,7 +2720,7 @@
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@@ -2733,7 +2736,7 @@
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-   "id": "35eaab76",
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@@ -2746,7 +2749,7 @@
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@@ -2758,7 +2761,7 @@
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-   "id": "dba06f6e",
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@@ -2780,7 +2783,7 @@
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@@ -2796,7 +2799,7 @@
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-   "id": "ece525b8",
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@@ -2834,7 +2837,7 @@
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@@ -2847,7 +2850,7 @@
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-   "id": "7f18c8f0",
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@@ -2868,7 +2871,7 @@
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@@ -2884,7 +2887,7 @@
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-   "id": "e2d3f664",
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@@ -2942,7 +2945,7 @@
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@@ -2957,7 +2960,7 @@
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-   "id": "f03db125",
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@@ -2978,7 +2981,7 @@
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@@ -3002,7 +3005,7 @@
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-   "id": "a3eab883",
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@@ -3474,7 +3477,7 @@
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@@ -3486,7 +3489,7 @@
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@@ -3530,7 +3533,7 @@
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@@ -3546,7 +3549,7 @@
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@@ -3561,7 +3564,7 @@
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@@ -3572,7 +3575,7 @@
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@@ -3587,7 +3590,7 @@
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@@ -3603,7 +3606,7 @@
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@@ -3617,7 +3620,7 @@
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@@ -3632,7 +3635,7 @@
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@@ -3658,7 +3661,7 @@
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@@ -3673,7 +3676,7 @@
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@@ -3684,7 +3687,7 @@
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@@ -3699,7 +3702,7 @@
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@@ -3710,7 +3713,7 @@
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@@ -3730,7 +3733,7 @@
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@@ -3745,7 +3748,7 @@
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@@ -3760,7 +3763,7 @@
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@@ -3797,7 +3800,7 @@
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@@ -3810,7 +3813,7 @@
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@@ -3833,7 +3836,7 @@
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@@ -3843,7 +3846,7 @@
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@@ -3870,7 +3873,7 @@
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@@ -3884,7 +3887,7 @@
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@@ -3901,7 +3904,7 @@
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@@ -3917,7 +3920,7 @@
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@@ -3929,7 +3932,7 @@
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@@ -3947,7 +3950,7 @@
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@@ -3968,7 +3971,7 @@
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@@ -3985,7 +3988,7 @@
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@@ -3997,7 +4000,7 @@
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@@ -4008,7 +4011,7 @@
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@@ -4025,7 +4028,7 @@
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@@ -4036,7 +4039,7 @@
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@@ -4052,7 +4055,7 @@
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@@ -4064,7 +4067,7 @@
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@@ -4081,7 +4084,7 @@
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@@ -4093,7 +4096,7 @@
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@@ -4111,7 +4114,7 @@
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@@ -4121,7 +4124,7 @@
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@@ -4133,7 +4136,7 @@
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@@ -4150,7 +4153,7 @@
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@@ -4162,7 +4165,7 @@
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@@ -4173,7 +4176,7 @@
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@@ -4185,7 +4188,7 @@
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@@ -4195,7 +4198,7 @@
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@@ -4215,7 +4218,7 @@
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@@ -4232,7 +4235,7 @@
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@@ -4251,7 +4254,7 @@
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@@ -4263,7 +4266,7 @@
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@@ -4273,7 +4276,7 @@
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@@ -4290,7 +4293,7 @@
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@@ -4300,7 +4303,7 @@
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@@ -4316,7 +4319,7 @@
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@@ -4328,7 +4331,7 @@
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@@ -4340,7 +4343,7 @@
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@@ -4352,7 +4355,7 @@
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@@ -4362,7 +4365,7 @@
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@@ -4374,7 +4377,7 @@
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@@ -4391,7 +4394,7 @@
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@@ -4401,7 +4404,7 @@
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@@ -4413,7 +4416,7 @@
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@@ -4427,7 +4430,7 @@
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@@ -4443,7 +4446,7 @@
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@@ -4455,7 +4458,7 @@
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@@ -4477,7 +4480,7 @@
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@@ -4489,7 +4492,7 @@
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@@ -4512,7 +4515,7 @@
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@@ -4528,7 +4531,7 @@
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@@ -4540,7 +4543,7 @@
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@@ -4564,7 +4567,7 @@
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@@ -4576,7 +4579,7 @@
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@@ -4597,7 +4600,7 @@
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@@ -4609,7 +4612,7 @@
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@@ -4628,7 +4631,7 @@
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@@ -4638,7 +4641,7 @@
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@@ -4652,7 +4655,7 @@
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@@ -4664,7 +4667,7 @@
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@@ -4676,7 +4679,7 @@
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@@ -4693,7 +4696,7 @@
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@@ -4705,7 +4708,7 @@
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@@ -4727,7 +4730,7 @@
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@@ -4742,7 +4745,7 @@
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@@ -4753,7 +4756,7 @@
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@@ -4908,7 +4911,7 @@
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@@ -4923,7 +4926,7 @@
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-   "id": "e64c1080",
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@@ -5092,7 +5095,7 @@
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@@ -5105,7 +5108,7 @@
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@@ -5122,7 +5125,7 @@
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@@ -5138,7 +5141,7 @@
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@@ -5151,7 +5154,7 @@
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@@ -5168,7 +5171,7 @@
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@@ -5180,7 +5183,7 @@
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@@ -5207,7 +5210,7 @@
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@@ -5220,7 +5223,7 @@
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-   "id": "af7ec34a",
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@@ -5399,7 +5402,7 @@
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@@ -5419,7 +5422,7 @@
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@@ -5434,7 +5437,7 @@
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@@ -5448,7 +5451,7 @@
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@@ -5464,7 +5467,7 @@
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@@ -5474,7 +5477,7 @@
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@@ -5496,7 +5499,7 @@
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@@ -5510,7 +5513,7 @@
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@@ -5586,7 +5589,7 @@
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@@ -5598,7 +5601,7 @@
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@@ -5615,7 +5618,7 @@
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@@ -5627,7 +5630,7 @@
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@@ -5642,7 +5645,7 @@
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@@ -5654,7 +5657,7 @@
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@@ -5666,7 +5669,7 @@
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@@ -5678,7 +5681,7 @@
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@@ -5688,7 +5691,7 @@
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@@ -5705,7 +5708,7 @@
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@@ -5717,7 +5720,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0911f3ba",
+   "id": "4c506a87",
    "metadata": {
     "editable": true
    },
@@ -5729,7 +5732,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7968b5f7",
+   "id": "24701ca8",
    "metadata": {
     "editable": true
    },
@@ -5739,7 +5742,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "91a7d83f",
+   "id": "826b9055",
    "metadata": {
     "editable": true
    },
@@ -5751,7 +5754,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5feceb81",
+   "id": "323573be",
    "metadata": {
     "editable": true
    },
@@ -5762,7 +5765,7 @@
   {
    "cell_type": "code",
    "execution_count": 45,
-   "id": "a9e42eda",
+   "id": "4234c03b",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -5927,7 +5930,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0f40d867",
+   "id": "5f7a90b0",
    "metadata": {
     "editable": true
    },
@@ -5949,7 +5952,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6e2f088a",
+   "id": "532f1254",
    "metadata": {
     "editable": true
    },
@@ -5966,7 +5969,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f5391046",
+   "id": "ece44428",
    "metadata": {
     "editable": true
    },
@@ -5976,7 +5979,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a7ac2bfe",
+   "id": "c34e3e05",
    "metadata": {
     "editable": true
    },
@@ -5991,7 +5994,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "323eeb6e",
+   "id": "9186a55c",
    "metadata": {
     "editable": true
    },
@@ -6001,7 +6004,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c8fd016d",
+   "id": "0644e2f2",
    "metadata": {
     "editable": true
    },
@@ -6016,7 +6019,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f86d84b7",
+   "id": "b3bdd092",
    "metadata": {
     "editable": true
    },
@@ -6027,7 +6030,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f96f1132",
+   "id": "e1e12027",
    "metadata": {
     "editable": true
    },
@@ -6047,7 +6050,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3a969016",
+   "id": "334ba808",
    "metadata": {
     "editable": true
    },
@@ -6059,7 +6062,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3d749e66",
+   "id": "3e465af3",
    "metadata": {
     "editable": true
    },
@@ -6096,7 +6099,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "187e06c3",
+   "id": "c854491a",
    "metadata": {
     "editable": true
    },
@@ -6106,7 +6109,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0b45037f",
+   "id": "6f5435eb",
    "metadata": {
     "editable": true
    },
@@ -6119,7 +6122,7 @@
   {
    "cell_type": "code",
    "execution_count": 46,
-   "id": "5bc810a2",
+   "id": "aec3e689",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -6324,7 +6327,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9c6c362d",
+   "id": "349c11e3",
    "metadata": {
     "editable": true
    },
@@ -6341,7 +6344,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "93025367",
+   "id": "d4adb530",
    "metadata": {
     "editable": true
    },
@@ -6358,7 +6361,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "31722ff7",
+   "id": "8b9396bf",
    "metadata": {
     "editable": true
    },
@@ -6368,7 +6371,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0dd431c6",
+   "id": "059b4467",
    "metadata": {
     "editable": true
    },
@@ -6383,7 +6386,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ffb27c5a",
+   "id": "5a7fcf9e",
    "metadata": {
     "editable": true
    },
@@ -6397,7 +6400,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f2a7f9cc",
+   "id": "df2d65c5",
    "metadata": {
     "editable": true
    },
@@ -6410,7 +6413,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7e5166a0",
+   "id": "ae173d6c",
    "metadata": {
     "editable": true
    },
@@ -6430,7 +6433,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "322d3d0e",
+   "id": "bdaf49ce",
    "metadata": {
     "editable": true
    },
@@ -6442,7 +6445,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "13fd2713",
+   "id": "8fc692b7",
    "metadata": {
     "editable": true
    },
@@ -6454,7 +6457,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f6fd8a54",
+   "id": "3d9a8d27",
    "metadata": {
     "editable": true
    },
@@ -6466,7 +6469,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3312eb36",
+   "id": "8bb8693c",
    "metadata": {
     "editable": true
    },
@@ -6476,7 +6479,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "12b7262b",
+   "id": "8f12a71a",
    "metadata": {
     "editable": true
    },
@@ -6488,7 +6491,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a2c24b60",
+   "id": "92c02c54",
    "metadata": {
     "editable": true
    },
@@ -6500,7 +6503,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7907d074",
+   "id": "3574103a",
    "metadata": {
     "editable": true
    },
@@ -6512,7 +6515,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ff2dbe91",
+   "id": "0c5567ec",
    "metadata": {
     "editable": true
    },
@@ -6522,7 +6525,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d3bfa800",
+   "id": "e239c04b",
    "metadata": {
     "editable": true
    },
@@ -6538,7 +6541,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "450c71ab",
+   "id": "b32c6b70",
    "metadata": {
     "editable": true
    },
@@ -6548,7 +6551,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e332f6e8",
+   "id": "dfb3bad0",
    "metadata": {
     "editable": true
    },
@@ -6560,7 +6563,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1d0c52d7",
+   "id": "14519bf8",
    "metadata": {
     "editable": true
    },
@@ -6577,7 +6580,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1c600851",
+   "id": "a0882035",
    "metadata": {
     "editable": true
    },
@@ -6587,7 +6590,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "54f8ec74",
+   "id": "bf1f5906",
    "metadata": {
     "editable": true
    },
@@ -6603,7 +6606,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "63362c9e",
+   "id": "61a16dd6",
    "metadata": {
     "editable": true
    },
@@ -6617,7 +6620,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7f5a6717",
+   "id": "99949d7b",
    "metadata": {
     "editable": true
    },
@@ -6636,7 +6639,7 @@
   {
    "cell_type": "code",
    "execution_count": 47,
-   "id": "78a296e0",
+   "id": "f9faf581",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -6691,7 +6694,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0a91555e",
+   "id": "3cbeb7ac",
    "metadata": {
     "editable": true
    },
@@ -6721,7 +6724,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d0f43d3c",
+   "id": "04a66fd1",
    "metadata": {
     "editable": true
    },
@@ -6750,7 +6753,7 @@
   {
    "cell_type": "code",
    "execution_count": 48,
-   "id": "62625f51",
+   "id": "83ad594f",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -6797,7 +6800,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ba76ad6c",
+   "id": "dbe6f74a",
    "metadata": {
     "editable": true
    },
@@ -6823,7 +6826,7 @@
   {
    "cell_type": "code",
    "execution_count": 49,
-   "id": "da1c7791",
+   "id": "a29e5348",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -7057,7 +7060,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5cbbebd3",
+   "id": "f2171b20",
    "metadata": {
     "editable": true
    },
@@ -7069,7 +7072,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c76b254a",
+   "id": "e2c87638",
    "metadata": {
     "editable": true
    },
@@ -7081,7 +7084,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "922fa6ef",
+   "id": "d8595812",
    "metadata": {
     "editable": true
    },
@@ -7093,7 +7096,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e3d14cd2",
+   "id": "6969d557",
    "metadata": {
     "editable": true
    },
@@ -7110,7 +7113,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0419dde1",
+   "id": "f39d16ef",
    "metadata": {
     "editable": true
    },
@@ -7120,7 +7123,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e81f4361",
+   "id": "6c8e08dd",
    "metadata": {
     "editable": true
    },
@@ -7132,7 +7135,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4f3cbc2c",
+   "id": "699a4862",
    "metadata": {
     "editable": true
    },
@@ -7149,7 +7152,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a164460b",
+   "id": "3e5b810b",
    "metadata": {
     "editable": true
    },
@@ -7160,7 +7163,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8abd8160",
+   "id": "c3f9ac9b",
    "metadata": {
     "editable": true
    },
@@ -7180,7 +7183,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "07eb9f81",
+   "id": "56f7fba0",
    "metadata": {
     "editable": true
    },
@@ -7190,7 +7193,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "87159922",
+   "id": "2788d661",
    "metadata": {
     "editable": true
    },
@@ -7216,7 +7219,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ac102a8a",
+   "id": "2094e988",
    "metadata": {
     "editable": true
    },
@@ -7232,7 +7235,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f907980b",
+   "id": "aac9bb21",
    "metadata": {
     "editable": true
    },
@@ -7243,7 +7246,7 @@
   {
    "cell_type": "code",
    "execution_count": 50,
-   "id": "14aba268",
+   "id": "550bea3f",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -7474,7 +7477,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c3a13d9b",
+   "id": "80a6f6ef",
    "metadata": {
     "editable": true
    },
diff --git a/doc/src/week43/week43.do.txt b/doc/src/week43/week43.do.txt
index 17dfcc09a..6ebdc9147 100644
--- a/doc/src/week43/week43.do.txt
+++ b/doc/src/week43/week43.do.txt
@@ -8,8 +8,9 @@ DATE: October 21, 2024
 !bblock Material for the lecture on Monday October 21, 2024
   * Building our own Feed-forward Neural Network with intro to Tensorflow
   * Solving differential equations with Neural Networks
-  * "Video of lecture at URL:"https://youtu.be/vkBNTn-MLqs"
-#  * "Whiteboard notes":"https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesOct21.pdf"  
+  * Video of lecture at URL:"https://youtu.be/vkBNTn-MLqs"
+  * Video os second part, solving differential equations with neural networks at URL:"https://youtu.be/2N8To65I2wQ"
+  * Whiteboard notes on solving differential equations at URL:"https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesOct21.pdf"  
 !eblock