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Morten Hjorth-Jensen
2025-10-20 17:20:40 +02:00
parent 8abfd003cc
commit 658005b69c
11 changed files with 4800 additions and 9472 deletions
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doc/pub/week41/ipynb/week41.ipynb
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}
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{
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"output_type": "stream",
"text": [
"x = Symbol('x')\n",
"e = exp(x**2)\n",
"x = Symbol('x')\n",
"e = 2*x*exp(x**2)\n"
]
}
],
"source": [
"from __future__ import division\n",
"from sympy import *\n",
@@ -1552,7 +1566,10 @@
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@@ -2449,7 +2466,10 @@
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@@ -3816,7 +3836,25 @@
]
}
],
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"language": "python",
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doc/pub/week42/ipynb/.ipynb_checkpoints/week42-checkpoint.ipynb
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doc/pub/week42/ipynb/week42.ipynb
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doc/pub/week43/html/week43-bs.html
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@@ -400,9 +400,9 @@ MathJax.Hub.Config({
<li> Reminder from last week, see also lecture notes from week 42 at <a href="https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/week42.html" target="_self"><tt>https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/week42.html</tt></a> as well as those from week 41, see see <a href="https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/week41.html" target="_self"><tt>https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/week41.html</tt></a>.</li>
<li> Building our own Feed-forward Neural Network.</li>
<li> Coding examples using Tensorflow/Keras and Pytorch examples. The Pytorch examples are adapted from Rashcka's text, see chapters 11-13..</li>
<li> Start discussions on how to use neural networks for solving differential equations (ordinary and partial ones). This topic continues next week as well.
<!-- * Video of lecture at <a href="https://youtu.be/vkBNTn-MLqs" target="_self"><tt>https://youtu.be/vkBNTn-MLqs</tt></a> -->
<!-- * Whiteboard notes on solving differential equations at <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2025/FYSSTKweek43.pdf" target="_self"><tt>https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2025/FYSSTKweek43.pdf</tt></a> --></li>
<li> Start discussions on how to use neural networks for solving differential equations (ordinary and partial ones). This topic continues next week as well.</li>
<li> Video of lecture at <a href="https://youtu.be/Gi6mzxAT0Ew" target="_self"><tt>https://youtu.be/Gi6mzxAT0Ew</tt></a></li>
<li> Whiteboard notes at <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2025/FYSSTKweek43.pdf" target="_self"><tt>https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2025/FYSSTKweek43.pdf</tt></a></li>
</ol>
</div>
</div>
doc/pub/week43/html/week43-reveal.html
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@@ -201,9 +201,9 @@ MathJax.Hub.Config({
<p><li> Reminder from last week, see also lecture notes from week 42 at <a href="https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/week42.html" target="_blank"><tt>https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/week42.html</tt></a> as well as those from week 41, see see <a href="https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/week41.html" target="_blank"><tt>https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/week41.html</tt></a>.</li>
<p><li> Building our own Feed-forward Neural Network.</li>
<p><li> Coding examples using Tensorflow/Keras and Pytorch examples. The Pytorch examples are adapted from Rashcka's text, see chapters 11-13..</li>
<p><li> Start discussions on how to use neural networks for solving differential equations (ordinary and partial ones). This topic continues next week as well.
<!-- * Video of lecture at <a href="https://youtu.be/vkBNTn-MLqs" target="_blank"><tt>https://youtu.be/vkBNTn-MLqs</tt></a> -->
<!-- * Whiteboard notes on solving differential equations at <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2025/FYSSTKweek43.pdf" target="_blank"><tt>https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2025/FYSSTKweek43.pdf</tt></a> --></li>
<p><li> Start discussions on how to use neural networks for solving differential equations (ordinary and partial ones). This topic continues next week as well.</li>
<p><li> Video of lecture at <a href="https://youtu.be/Gi6mzxAT0Ew" target="_blank"><tt>https://youtu.be/Gi6mzxAT0Ew</tt></a></li>
<p><li> Whiteboard notes at <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2025/FYSSTKweek43.pdf" target="_blank"><tt>https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2025/FYSSTKweek43.pdf</tt></a></li>
</ol>
</div>
</section>
doc/pub/week43/html/week43-solarized.html
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@@ -321,9 +321,9 @@ MathJax.Hub.Config({
<li> Reminder from last week, see also lecture notes from week 42 at <a href="https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/week42.html" target="_blank"><tt>https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/week42.html</tt></a> as well as those from week 41, see see <a href="https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/week41.html" target="_blank"><tt>https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/week41.html</tt></a>.</li>
<li> Building our own Feed-forward Neural Network.</li>
<li> Coding examples using Tensorflow/Keras and Pytorch examples. The Pytorch examples are adapted from Rashcka's text, see chapters 11-13..</li>
<li> Start discussions on how to use neural networks for solving differential equations (ordinary and partial ones). This topic continues next week as well.
<!-- * Video of lecture at <a href="https://youtu.be/vkBNTn-MLqs" target="_blank"><tt>https://youtu.be/vkBNTn-MLqs</tt></a> -->
<!-- * Whiteboard notes on solving differential equations at <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2025/FYSSTKweek43.pdf" target="_blank"><tt>https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2025/FYSSTKweek43.pdf</tt></a> --></li>
<li> Start discussions on how to use neural networks for solving differential equations (ordinary and partial ones). This topic continues next week as well.</li>
<li> Video of lecture at <a href="https://youtu.be/Gi6mzxAT0Ew" target="_blank"><tt>https://youtu.be/Gi6mzxAT0Ew</tt></a></li>
<li> Whiteboard notes at <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2025/FYSSTKweek43.pdf" target="_blank"><tt>https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2025/FYSSTKweek43.pdf</tt></a></li>
</ol>
</div>
doc/pub/week43/html/week43.html
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@@ -398,9 +398,9 @@ MathJax.Hub.Config({
<li> Reminder from last week, see also lecture notes from week 42 at <a href="https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/week42.html" target="_blank"><tt>https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/week42.html</tt></a> as well as those from week 41, see see <a href="https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/week41.html" target="_blank"><tt>https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/week41.html</tt></a>.</li>
<li> Building our own Feed-forward Neural Network.</li>
<li> Coding examples using Tensorflow/Keras and Pytorch examples. The Pytorch examples are adapted from Rashcka's text, see chapters 11-13..</li>
<li> Start discussions on how to use neural networks for solving differential equations (ordinary and partial ones). This topic continues next week as well.
<!-- * Video of lecture at <a href="https://youtu.be/vkBNTn-MLqs" target="_blank"><tt>https://youtu.be/vkBNTn-MLqs</tt></a> -->
<!-- * Whiteboard notes on solving differential equations at <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2025/FYSSTKweek43.pdf" target="_blank"><tt>https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2025/FYSSTKweek43.pdf</tt></a> --></li>
<li> Start discussions on how to use neural networks for solving differential equations (ordinary and partial ones). This topic continues next week as well.</li>
<li> Video of lecture at <a href="https://youtu.be/Gi6mzxAT0Ew" target="_blank"><tt>https://youtu.be/Gi6mzxAT0Ew</tt></a></li>
<li> Whiteboard notes at <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2025/FYSSTKweek43.pdf" target="_blank"><tt>https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2025/FYSSTKweek43.pdf</tt></a></li>
</ol>
</div>
doc/pub/week43/ipynb/.ipynb_checkpoints/week43-checkpoint.ipynb
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doc/pub/week43/ipynb/ipynb-week43-src.tar.gz
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doc/pub/week43/ipynb/week43.ipynb
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doc/src/week43/week43.do.txt
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@@ -10,8 +10,8 @@ o Reminder from last week, see also lecture notes from week 42 at URL:"https://c
o Building our own Feed-forward Neural Network.
o Coding examples using Tensorflow/Keras and Pytorch examples. The Pytorch examples are adapted from Rashcka's text, see chapters 11-13..
o Start discussions on how to use neural networks for solving differential equations (ordinary and partial ones). This topic continues next week as well.
# * Video of lecture at URL:"https://youtu.be/vkBNTn-MLqs"
# * Whiteboard notes on solving differential equations at URL:"https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2025/FYSSTKweek43.pdf"
o Video of lecture at URL:"https://youtu.be/Gi6mzxAT0Ew"
o Whiteboard notes at URL:"https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2025/FYSSTKweek43.pdf"
!eblock