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update schedule

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Morten Hjorth-Jensen
2022-09-29 15:23:47 +02:00
parent bde0e11769
commit da4b20036f
6 changed files with 10 additions and 2 deletions
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doc/LectureNotes/_build/.doctrees/environment.pickle
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doc/LectureNotes/_build/html/_sources/schedule.md
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@@ -90,6 +90,8 @@ For the reading assignments we use the following abbreviations:
### Week 39 September 26-30
- Lab Wednesday: Work on Project 1
- Lecture Thursday: * Thursday: Repetition of Logistic regression equations and classification problems and discussion of Gradient methods. Examples on how to implement Logistic Regression and discussion of stochastic gradient descent
- Video of lecture at https://youtu.be/rDBj50Lv3Go
* Friday: Stochastic Gradient descent with examples and automatic differentiation
* Reading recommendations:
doc/LectureNotes/_build/html/schedule.html
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@@ -524,7 +524,11 @@ const thebe_selector_output = ".output, .cell_output"
<h3>Week 39 September 26-30<a class="headerlink" href="#week-39-september-26-30" title="Permalink to this headline"></a></h3>
<ul class="simple">
<li><p>Lab Wednesday: Work on Project 1</p></li>
<li><p>Lecture Thursday: * Thursday: Repetition of Logistic regression equations and classification problems and discussion of Gradient methods. Examples on how to implement Logistic Regression and discussion of stochastic gradient descent</p></li>
<li><p>Lecture Thursday: * Thursday: Repetition of Logistic regression equations and classification problems and discussion of Gradient methods. Examples on how to implement Logistic Regression and discussion of stochastic gradient descent</p>
<ul>
<li><p>Video of lecture at <a class="reference external" href="https://youtu.be/rDBj50Lv3Go">https://youtu.be/rDBj50Lv3Go</a></p></li>
</ul>
</li>
</ul>
<ul class="simple">
<li><p>Friday: Stochastic Gradient descent with examples and automatic differentiation</p></li>
doc/LectureNotes/_build/html/searchindex.js
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doc/LectureNotes/schedule.md
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@@ -90,6 +90,8 @@ For the reading assignments we use the following abbreviations:
### Week 39 September 26-30
- Lab Wednesday: Work on Project 1
- Lecture Thursday: * Thursday: Repetition of Logistic regression equations and classification problems and discussion of Gradient methods. Examples on how to implement Logistic Regression and discussion of stochastic gradient descent
- Video of lecture at https://youtu.be/rDBj50Lv3Go
* Friday: Stochastic Gradient descent with examples and automatic differentiation
* Reading recommendations: