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corrected typos

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Morten Hjorth-Jensen
2024-09-24 05:55:16 +02:00
parent 140513be8f
commit 7434b6372b
6 changed files with 284 additions and 309 deletions
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doc/pub/week39/html/week39-reveal.html
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@@ -247,17 +247,10 @@ MathJax.Hub.Config({
<p><li> Work on project 1, in particular resampling methods like cross-validation and bootstrap. <b>For more discussions of project 1, chapter 5 of Goodfellow et al is a good read, in particular sections 5.1-5.5 and 5.7-5.11</b>.</li>
<p><li> <a href="https://youtu.be/tVW1ZDmZnwM" target="_blank">Video on how to write scientific reports recorded during one of the lab sessions</a></li>
</ul>
<p>
<p>These sections summarize neatly what we have done till now and point to what is coming with respect to deep learning. </p>
<ul>
<p><li> A general guideline can be found at <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/Projects/EvaluationGrading/EvaluationForm.md" target="_blank"><tt>https://github.com/CompPhysics/MachineLearning/blob/master/doc/Projects/EvaluationGrading/EvaluationForm.md</tt></a>.</li>
</ul>
</div>
<!-- rett opp tyrleif -->
</section>
<section>
doc/pub/week39/html/week39-solarized.html
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<li> Exercise for week 39 on how to write the abstract and the introduction of the report and how to include references.</li>
<li> Work on project 1, in particular resampling methods like cross-validation and bootstrap. <b>For more discussions of project 1, chapter 5 of Goodfellow et al is a good read, in particular sections 5.1-5.5 and 5.7-5.11</b>.</li>
<li> <a href="https://youtu.be/tVW1ZDmZnwM" target="_blank">Video on how to write scientific reports recorded during one of the lab sessions</a></li>
</ul>
<p>These sections summarize neatly what we have done till now and point to what is coming with respect to deep learning. </p>
<ul>
<li> A general guideline can be found at <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/Projects/EvaluationGrading/EvaluationForm.md" target="_blank"><tt>https://github.com/CompPhysics/MachineLearning/blob/master/doc/Projects/EvaluationGrading/EvaluationForm.md</tt></a>.</li>
</ul>
</div>
<!-- rett opp tyrleif -->
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="lecture-monday-september-23-optimization-the-central-part-of-any-machine-learning-algortithm">Lecture Monday September 23, Optimization, the central part of any Machine Learning algortithm </h2>
doc/pub/week39/html/week39.html
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@@ -460,16 +460,11 @@ MathJax.Hub.Config({
<li> Exercise for week 39 on how to write the abstract and the introduction of the report and how to include references.</li>
<li> Work on project 1, in particular resampling methods like cross-validation and bootstrap. <b>For more discussions of project 1, chapter 5 of Goodfellow et al is a good read, in particular sections 5.1-5.5 and 5.7-5.11</b>.</li>
<li> <a href="https://youtu.be/tVW1ZDmZnwM" target="_blank">Video on how to write scientific reports recorded during one of the lab sessions</a></li>
</ul>
<p>These sections summarize neatly what we have done till now and point to what is coming with respect to deep learning. </p>
<ul>
<li> A general guideline can be found at <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/Projects/EvaluationGrading/EvaluationForm.md" target="_blank"><tt>https://github.com/CompPhysics/MachineLearning/blob/master/doc/Projects/EvaluationGrading/EvaluationForm.md</tt></a>.</li>
</ul>
</div>
<!-- rett opp tyrleif -->
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="lecture-monday-september-23-optimization-the-central-part-of-any-machine-learning-algortithm">Lecture Monday September 23, Optimization, the central part of any Machine Learning algortithm </h2>
doc/pub/week39/ipynb/ipynb-week39-src.tar.gz
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doc/src/week39/week39.do.txt
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@@ -31,13 +31,10 @@ DATE: Week 39
* Exercise for week 39 on how to write the abstract and the introduction of the report and how to include references.
* Work on project 1, in particular resampling methods like cross-validation and bootstrap. _For more discussions of project 1, chapter 5 of Goodfellow et al is a good read, in particular sections 5.1-5.5 and 5.7-5.11_.
* "Video on how to write scientific reports recorded during one of the lab sessions":"https://youtu.be/tVW1ZDmZnwM"
These sections summarize neatly what we have done till now and point to what is coming with respect to deep learning.
* A general guideline can be found at URL:"https://github.com/CompPhysics/MachineLearning/blob/master/doc/Projects/EvaluationGrading/EvaluationForm.md".
!eblock
# rett opp tyrleif
!split