lars/FYS-STK4155
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

correcting typos

Browse Source
This commit is contained in:
Morten Hjorth-Jensen
2024-10-28 05:40:23 +01:00
parent cf17ada92c
commit f64815fcd2
8 changed files with 988 additions and 526 deletions
Download Patch File Download Diff File Expand all files Collapse all files
doc/pub/week44/html/._week44-bs001.html
+5 -5
View File
@@ -359,12 +359,12 @@ MathJax.Hub.Config({
<li> Convolutional Neural Networks</li>
<li> Readings and Videos:</li>
<ul>
<li> These lecture notes</li>
<li> These lecture notes at <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/pub/week44/ipynb/week44.ipynb" target="_self"><tt>https://github.com/CompPhysics/MachineLearning/blob/master/doc/pub/week44/ipynb/week44.ipynb</tt></a></li>
<li> For a more in depth discussion on neural networks we recommend Goodfellow et al chapter 9. See also chapter 11 and 12 on practicalities and applications</li>
<li> Reading suggestions for implementation of CNNs: <a href="https://github.com/rasbt/machine-learning-book/tree/main/ch14" target="_self">Rashcka et al.'s chapter 14</a>. T</li>
<li> <a href="https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi" target="_self">Video on Deep Learning</a></li>
<li> <a href="https://www.youtube.com/watch?v=iaSUYvmCekI&ab_channel=AlexanderAmini" target="_self">Video on Convolutional Neural Networks from MIT</a></li>
<li> <a href="https://www.youtube.com/watch?v=bNb2fEVKeEo&list=PLC1qU-LWwrF64f4QKQT-Vg5Wr4qEE1Zxk&index=6&ab_channel=StanfordUniversitySchoolofEngineering" target="_self">Video on CNNs from Stanford</a></li>
<li> Reading suggestions for implementation of CNNs see <a href="Rashcka et al.'s chapter 14" target="_self"><tt>Rashcka et al.'s chapter 14</tt></a>:"https://github.com/rasbt/machine-learning-book/tree/main/ch14".</li>
<li> Video on Deep Learning at <a href="https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi" target="_self"><tt>https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi</tt></a></li>
<li> Video on Convolutional Neural Networks from MIT at <a href="https://www.youtube.com/watch?v=iaSUYvmCekI&ab_channel=AlexanderAmini" target="_self"><tt>https://www.youtube.com/watch?v=iaSUYvmCekI&ab_channel=AlexanderAmini</tt></a></li>
<li> Video on CNNs from Stanford at <a href="https://www.youtube.com/watch?v=bNb2fEVKeEo&list=PLC1qU-LWwrF64f4QKQT-Vg5Wr4qEE1Zxk&index=6&ab_channel=StanfordUniversitySchoolofEngineering" target="_self"><tt>https://www.youtube.com/watch?v=bNb2fEVKeEo&list=PLC1qU-LWwrF64f4QKQT-Vg5Wr4qEE1Zxk&index=6&ab_channel=StanfordUniversitySchoolofEngineering</tt></a></li>
</ul>
</ol>
</div>
doc/pub/week44/html/._week44-bs038.html
+1 -1
View File
@@ -356,7 +356,7 @@ MathJax.Hub.Config({
<p>In total we have</p>
$$
\left(F\times F\times D_1)\right) \times K+(K\mathrm{--biases}),
\left(F\times F\times D_1\right) \times K+K_{\mathrm{biases}},
$$
<p>parameters to train by back propagation.</p>
doc/pub/week44/html/week44-reveal.html
+6 -6
View File
@@ -205,17 +205,17 @@ MathJax.Hub.Config({
<p><li> Readings and Videos:</li>
<ul>
<p><li> These lecture notes</li>
<p><li> These lecture notes at <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/pub/week44/ipynb/week44.ipynb" target="_blank"><tt>https://github.com/CompPhysics/MachineLearning/blob/master/doc/pub/week44/ipynb/week44.ipynb</tt></a></li>
<p><li> For a more in depth discussion on neural networks we recommend Goodfellow et al chapter 9. See also chapter 11 and 12 on practicalities and applications</li>
<p><li> Reading suggestions for implementation of CNNs: <a href="https://github.com/rasbt/machine-learning-book/tree/main/ch14" target="_blank">Rashcka et al.'s chapter 14</a>. T</li>
<p><li> Reading suggestions for implementation of CNNs see <a href="Rashcka et al.'s chapter 14" target="_blank"><tt>Rashcka et al.'s chapter 14</tt></a>:"https://github.com/rasbt/machine-learning-book/tree/main/ch14".</li>
<p><li> <a href="https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi" target="_blank">Video on Deep Learning</a></li>
<p><li> Video on Deep Learning at <a href="https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi" target="_blank"><tt>https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi</tt></a></li>
<p><li> <a href="https://www.youtube.com/watch?v=iaSUYvmCekI&ab_channel=AlexanderAmini" target="_blank">Video on Convolutional Neural Networks from MIT</a></li>
<p><li> Video on Convolutional Neural Networks from MIT at <a href="https://www.youtube.com/watch?v=iaSUYvmCekI&ab_channel=AlexanderAmini" target="_blank"><tt>https://www.youtube.com/watch?v=iaSUYvmCekI&ab_channel=AlexanderAmini</tt></a></li>
<p><li> <a href="https://www.youtube.com/watch?v=bNb2fEVKeEo&list=PLC1qU-LWwrF64f4QKQT-Vg5Wr4qEE1Zxk&index=6&ab_channel=StanfordUniversitySchoolofEngineering" target="_blank">Video on CNNs from Stanford</a></li>
<p><li> Video on CNNs from Stanford at <a href="https://www.youtube.com/watch?v=bNb2fEVKeEo&list=PLC1qU-LWwrF64f4QKQT-Vg5Wr4qEE1Zxk&index=6&ab_channel=StanfordUniversitySchoolofEngineering" target="_blank"><tt>https://www.youtube.com/watch?v=bNb2fEVKeEo&list=PLC1qU-LWwrF64f4QKQT-Vg5Wr4qEE1Zxk&index=6&ab_channel=StanfordUniversitySchoolofEngineering</tt></a></li>
</ul>
<p>
</ol>
@@ -1190,7 +1190,7 @@ $$
<p>In total we have</p>
<p>&nbsp;<br>
$$
\left(F\times F\times D_1)\right) \times K+(K\mathrm{--biases}),
\left(F\times F\times D_1\right) \times K+K_{\mathrm{biases}},
$$
<p>&nbsp;<br>
doc/pub/week44/html/week44-solarized.html
+6 -6
View File
@@ -299,12 +299,12 @@ MathJax.Hub.Config({
<li> Convolutional Neural Networks</li>
<li> Readings and Videos:</li>
<ul>
<li> These lecture notes</li>
<li> These lecture notes at <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/pub/week44/ipynb/week44.ipynb" target="_blank"><tt>https://github.com/CompPhysics/MachineLearning/blob/master/doc/pub/week44/ipynb/week44.ipynb</tt></a></li>
<li> For a more in depth discussion on neural networks we recommend Goodfellow et al chapter 9. See also chapter 11 and 12 on practicalities and applications</li>
<li> Reading suggestions for implementation of CNNs: <a href="https://github.com/rasbt/machine-learning-book/tree/main/ch14" target="_blank">Rashcka et al.'s chapter 14</a>. T</li>
<li> <a href="https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi" target="_blank">Video on Deep Learning</a></li>
<li> <a href="https://www.youtube.com/watch?v=iaSUYvmCekI&ab_channel=AlexanderAmini" target="_blank">Video on Convolutional Neural Networks from MIT</a></li>
<li> <a href="https://www.youtube.com/watch?v=bNb2fEVKeEo&list=PLC1qU-LWwrF64f4QKQT-Vg5Wr4qEE1Zxk&index=6&ab_channel=StanfordUniversitySchoolofEngineering" target="_blank">Video on CNNs from Stanford</a></li>
<li> Reading suggestions for implementation of CNNs see <a href="Rashcka et al.'s chapter 14" target="_blank"><tt>Rashcka et al.'s chapter 14</tt></a>:"https://github.com/rasbt/machine-learning-book/tree/main/ch14".</li>
<li> Video on Deep Learning at <a href="https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi" target="_blank"><tt>https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi</tt></a></li>
<li> Video on Convolutional Neural Networks from MIT at <a href="https://www.youtube.com/watch?v=iaSUYvmCekI&ab_channel=AlexanderAmini" target="_blank"><tt>https://www.youtube.com/watch?v=iaSUYvmCekI&ab_channel=AlexanderAmini</tt></a></li>
<li> Video on CNNs from Stanford at <a href="https://www.youtube.com/watch?v=bNb2fEVKeEo&list=PLC1qU-LWwrF64f4QKQT-Vg5Wr4qEE1Zxk&index=6&ab_channel=StanfordUniversitySchoolofEngineering" target="_blank"><tt>https://www.youtube.com/watch?v=bNb2fEVKeEo&list=PLC1qU-LWwrF64f4QKQT-Vg5Wr4qEE1Zxk&index=6&ab_channel=StanfordUniversitySchoolofEngineering</tt></a></li>
</ul>
</ol>
</div>
@@ -1169,7 +1169,7 @@ $$
<p>In total we have</p>
$$
\left(F\times F\times D_1)\right) \times K+(K\mathrm{--biases}),
\left(F\times F\times D_1\right) \times K+K_{\mathrm{biases}},
$$
<p>parameters to train by back propagation.</p>
doc/pub/week44/html/week44.html
+6 -6
View File
@@ -376,12 +376,12 @@ MathJax.Hub.Config({
<li> Convolutional Neural Networks</li>
<li> Readings and Videos:</li>
<ul>
<li> These lecture notes</li>
<li> These lecture notes at <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/pub/week44/ipynb/week44.ipynb" target="_blank"><tt>https://github.com/CompPhysics/MachineLearning/blob/master/doc/pub/week44/ipynb/week44.ipynb</tt></a></li>
<li> For a more in depth discussion on neural networks we recommend Goodfellow et al chapter 9. See also chapter 11 and 12 on practicalities and applications</li>
<li> Reading suggestions for implementation of CNNs: <a href="https://github.com/rasbt/machine-learning-book/tree/main/ch14" target="_blank">Rashcka et al.'s chapter 14</a>. T</li>
<li> <a href="https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi" target="_blank">Video on Deep Learning</a></li>
<li> <a href="https://www.youtube.com/watch?v=iaSUYvmCekI&ab_channel=AlexanderAmini" target="_blank">Video on Convolutional Neural Networks from MIT</a></li>
<li> <a href="https://www.youtube.com/watch?v=bNb2fEVKeEo&list=PLC1qU-LWwrF64f4QKQT-Vg5Wr4qEE1Zxk&index=6&ab_channel=StanfordUniversitySchoolofEngineering" target="_blank">Video on CNNs from Stanford</a></li>
<li> Reading suggestions for implementation of CNNs see <a href="Rashcka et al.'s chapter 14" target="_blank"><tt>Rashcka et al.'s chapter 14</tt></a>:"https://github.com/rasbt/machine-learning-book/tree/main/ch14".</li>
<li> Video on Deep Learning at <a href="https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi" target="_blank"><tt>https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi</tt></a></li>
<li> Video on Convolutional Neural Networks from MIT at <a href="https://www.youtube.com/watch?v=iaSUYvmCekI&ab_channel=AlexanderAmini" target="_blank"><tt>https://www.youtube.com/watch?v=iaSUYvmCekI&ab_channel=AlexanderAmini</tt></a></li>
<li> Video on CNNs from Stanford at <a href="https://www.youtube.com/watch?v=bNb2fEVKeEo&list=PLC1qU-LWwrF64f4QKQT-Vg5Wr4qEE1Zxk&index=6&ab_channel=StanfordUniversitySchoolofEngineering" target="_blank"><tt>https://www.youtube.com/watch?v=bNb2fEVKeEo&list=PLC1qU-LWwrF64f4QKQT-Vg5Wr4qEE1Zxk&index=6&ab_channel=StanfordUniversitySchoolofEngineering</tt></a></li>
</ul>
</ol>
</div>
@@ -1246,7 +1246,7 @@ $$
<p>In total we have</p>
$$
\left(F\times F\times D_1)\right) \times K+(K\mathrm{--biases}),
\left(F\times F\times D_1\right) \times K+K_{\mathrm{biases}},
$$
<p>parameters to train by back propagation.</p>
doc/pub/week44/ipynb/ipynb-week44-src.tar.gz
BIN
View File
Binary file not shown.
doc/pub/week44/ipynb/week44.ipynb
+958 -496
View File
File diff suppressed because it is too large Load Diff
doc/src/week44/week44.do.txt
+6 -6
View File
@@ -9,12 +9,12 @@ DATE: October 28-November 1
!bblock Material for the lecture Monday October 28, 2024
o Convolutional Neural Networks
o Readings and Videos:
* These lecture notes
* These lecture notes at URL:"https://github.com/CompPhysics/MachineLearning/blob/master/doc/pub/week44/ipynb/week44.ipynb"
* For a more in depth discussion on neural networks we recommend Goodfellow et al chapter 9. See also chapter 11 and 12 on practicalities and applications
* Reading suggestions for implementation of CNNs: "Rashcka et al.'s chapter 14":"https://github.com/rasbt/machine-learning-book/tree/main/ch14". T
* "Video on Deep Learning":"https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi"
* "Video on Convolutional Neural Networks from MIT":"https://www.youtube.com/watch?v=iaSUYvmCekI&ab_channel=AlexanderAmini"
* "Video on CNNs from Stanford":"https://www.youtube.com/watch?v=bNb2fEVKeEo&list=PLC1qU-LWwrF64f4QKQT-Vg5Wr4qEE1Zxk&index=6&ab_channel=StanfordUniversitySchoolofEngineering"
* Reading suggestions for implementation of CNNs see URL:"Rashcka et al.'s chapter 14":"https://github.com/rasbt/machine-learning-book/tree/main/ch14".
* Video on Deep Learning at URL:"https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi"
* Video on Convolutional Neural Networks from MIT at URL:"https://www.youtube.com/watch?v=iaSUYvmCekI&ab_channel=AlexanderAmini"
* Video on CNNs from Stanford at URL:"https://www.youtube.com/watch?v=bNb2fEVKeEo&list=PLC1qU-LWwrF64f4QKQT-Vg5Wr4qEE1Zxk&index=6&ab_channel=StanfordUniversitySchoolofEngineering"
!eblock
@@ -873,7 +873,7 @@ With parameter sharing, the convolution involves thus for each filter $F\times
In total we have
!bt
\[
\left(F\times F\times D_1)\right) \times K+(K\mathrm{--biases}),
\left(F\times F\times D_1\right) \times K+K_{\mathrm{biases}},
\]
!et
parameters to train by back propagation.