<!-- navigation toc: --><li><ahref="._week41-bs001.html#plan-for-week-41"style="font-size: 80%;">Plan for week 41</a></li>
<!-- navigation toc: --><li><ahref="._week41-bs002.html#setting-up-the-back-propagation-algorithm"style="font-size: 80%;">Setting up the Back propagation algorithm</a></li>
<!-- navigation toc: --><li><ahref="._week41-bs003.html#setting-up-a-multi-layer-perceptron-model-for-classification"style="font-size: 80%;">Setting up a Multi-layer perceptron model for classification</a></li>
<!-- navigation toc: --><li><ahref="._week41-bs004.html#defining-the-cost-function"style="font-size: 80%;">Defining the cost function</a></li>
<!-- navigation toc: --><li><ahref="._week41-bs007.html#developing-a-code-for-doing-neural-networks-with-back-propagation"style="font-size: 80%;">Developing a code for doing neural networks with back propagation</a></li>
<!-- navigation toc: --><li><ahref="._week41-bs029.html#collect-and-pre-process-data"style="font-size: 80%;">Collect and pre-process data</a></li>
<!-- navigation toc: --><li><ahref="._week41-bs009.html#train-and-test-datasets"style="font-size: 80%;">Train and test datasets</a></li>
<!-- navigation toc: --><li><ahref="._week41-bs010.html#define-model-and-architecture"style="font-size: 80%;">Define model and architecture</a></li>
<!-- navigation toc: --><li><ahref="._week41-bs015.html#choose-cost-function-and-optimizer"style="font-size: 80%;">Choose cost function and optimizer</a></li>
<!-- navigation toc: --><li><ahref="._week41-bs016.html#optimizing-the-cost-function"style="font-size: 80%;">Optimizing the cost function</a></li>
<!-- navigation toc: --><li><ahref="._week41-bs021.html#evaluate-model-performance-on-test-data"style="font-size: 80%;">Evaluate model performance on test data</a></li>
<!-- navigation toc: --><li><ahref="._week41-bs029.html#collect-and-pre-process-data"style="font-size: 80%;">Collect and pre-process data</a></li>
<!-- navigation toc: --><li><ahref="._week41-bs030.html#the-breast-cancer-data-now-with-keras"style="font-size: 80%;">The Breast Cancer Data, now with Keras</a></li>
<!-- navigation toc: --><li><ahref="._week41-bs033.html#which-activation-function-should-i-use"style="font-size: 80%;">Which activation function should I use?</a></li>
<!-- navigation toc: --><li><ahref="._week41-bs034.html#is-the-logistic-activation-function-sigmoid-our-choice"style="font-size: 80%;">Is the Logistic activation function (Sigmoid) our choice?</a></li>
<!-- navigation toc: --><li><ahref="._week41-bs035.html#the-derivative-of-the-logistic-funtion"style="font-size: 80%;">The derivative of the Logistic funtion</a></li>
<!-- navigation toc: --><li><ahref="._week41-bs036.html#the-relu-function-family"style="font-size: 80%;">The RELU function family</a></li>
<!-- navigation toc: --><li><ahref="._week41-bs037.html#which-activation-function-should-we-use"style="font-size: 80%;">Which activation function should we use?</a></li>
<!-- navigation toc: --><li><ahref="._week41-bs038.html#more-on-activation-functions-output-layers"style="font-size: 80%;">More on activation functions, output layers</a></li>
<!-- navigation toc: --><li><ahref="._week41-bs042.html#a-very-nice-website-on-neural-networks"style="font-size: 80%;">A very nice website on Neural Networks</a></li>
<!-- navigation toc: --><li><ahref="._week41-bs043.html#a-top-down-perspective-on-neural-networks"style="font-size: 80%;">A top-down perspective on Neural networks</a></li>
<!-- navigation toc: --><li><ahref="._week41-bs044.html#limitations-of-supervised-learning-with-deep-networks"style="font-size: 80%;">Limitations of supervised learning with deep networks</a></li>
<!-- navigation toc: --><li><ahref="._week41-bs046.html#regular-nns-don-t-scale-well-to-full-images"style="font-size: 80%;">Regular NNs don’t scale well to full images</a></li>
<!-- navigation toc: --><li><ahref="._week41-bs047.html#3d-volumes-of-neurons"style="font-size: 80%;">3D volumes of neurons</a></li>
<!-- navigation toc: --><li><ahref="._week41-bs048.html#layers-used-to-build-cnns"style="font-size: 80%;">Layers used to build CNNs</a></li>
<!-- navigation toc: --><li><ahref="._week41-bs050.html#cnns-in-brief"style="font-size: 80%;">CNNs in brief</a></li>
<!-- navigation toc: --><li><ahref="._week41-bs051.html#cnns-in-more-detail-building-convolutional-neural-networks-in-tensorflow-and-keras"style="font-size: 80%;">CNNs in more detail, building convolutional neural networks in Tensorflow and Keras</a></li>
<!-- navigation toc: --><li><ahref="._week41-bs052.html#setting-it-up"style="font-size: 80%;">Setting it up</a></li>
<!-- navigation toc: --><li><ahref="._week41-bs002.html#videos-on-neural-networks"style="font-size: 80%;">Videos on Neural Networks</a></li>
<!-- navigation toc: --><li><ahref="._week41-bs003.html#setting-up-the-back-propagation-algorithm"style="font-size: 80%;">Setting up the Back propagation algorithm</a></li>
<!-- navigation toc: --><li><ahref="._week41-bs004.html#setting-up-a-multi-layer-perceptron-model-for-classification"style="font-size: 80%;">Setting up a Multi-layer perceptron model for classification</a></li>
<!-- navigation toc: --><li><ahref="._week41-bs005.html#defining-the-cost-function"style="font-size: 80%;">Defining the cost function</a></li>
<!-- navigation toc: --><li><ahref="._week41-bs008.html#developing-a-code-for-doing-neural-networks-with-back-propagation"style="font-size: 80%;">Developing a code for doing neural networks with back propagation</a></li>
<!-- navigation toc: --><li><ahref="._week41-bs030.html#collect-and-pre-process-data"style="font-size: 80%;">Collect and pre-process data</a></li>
<!-- navigation toc: --><li><ahref="._week41-bs010.html#train-and-test-datasets"style="font-size: 80%;">Train and test datasets</a></li>
<!-- navigation toc: --><li><ahref="._week41-bs011.html#define-model-and-architecture"style="font-size: 80%;">Define model and architecture</a></li>
<!-- navigation toc: --><li><ahref="._week41-bs016.html#choose-cost-function-and-optimizer"style="font-size: 80%;">Choose cost function and optimizer</a></li>
<!-- navigation toc: --><li><ahref="._week41-bs017.html#optimizing-the-cost-function"style="font-size: 80%;">Optimizing the cost function</a></li>
<!-- navigation toc: --><li><ahref="._week41-bs022.html#evaluate-model-performance-on-test-data"style="font-size: 80%;">Evaluate model performance on test data</a></li>
<!-- navigation toc: --><li><ahref="._week41-bs030.html#collect-and-pre-process-data"style="font-size: 80%;">Collect and pre-process data</a></li>
<!-- navigation toc: --><li><ahref="._week41-bs031.html#the-breast-cancer-data-now-with-keras"style="font-size: 80%;">The Breast Cancer Data, now with Keras</a></li>
<!-- navigation toc: --><li><ahref="._week41-bs034.html#which-activation-function-should-i-use"style="font-size: 80%;">Which activation function should I use?</a></li>
<!-- navigation toc: --><li><ahref="._week41-bs035.html#is-the-logistic-activation-function-sigmoid-our-choice"style="font-size: 80%;">Is the Logistic activation function (Sigmoid) our choice?</a></li>
<!-- navigation toc: --><li><ahref="._week41-bs036.html#the-derivative-of-the-logistic-funtion"style="font-size: 80%;">The derivative of the Logistic funtion</a></li>
<!-- navigation toc: --><li><ahref="._week41-bs037.html#the-relu-function-family"style="font-size: 80%;">The RELU function family</a></li>
<!-- navigation toc: --><li><ahref="._week41-bs038.html#which-activation-function-should-we-use"style="font-size: 80%;">Which activation function should we use?</a></li>
<!-- navigation toc: --><li><ahref="._week41-bs039.html#more-on-activation-functions-output-layers"style="font-size: 80%;">More on activation functions, output layers</a></li>
<!-- navigation toc: --><li><ahref="._week41-bs043.html#a-very-nice-website-on-neural-networks"style="font-size: 80%;">A very nice website on Neural Networks</a></li>
<!-- navigation toc: --><li><ahref="._week41-bs044.html#a-top-down-perspective-on-neural-networks"style="font-size: 80%;">A top-down perspective on Neural networks</a></li>
<!-- navigation toc: --><li><ahref="._week41-bs045.html#limitations-of-supervised-learning-with-deep-networks"style="font-size: 80%;">Limitations of supervised learning with deep networks</a></li>
<!-- navigation toc: --><li><ahref="._week41-bs047.html#regular-nns-don-t-scale-well-to-full-images"style="font-size: 80%;">Regular NNs don’t scale well to full images</a></li>
<!-- navigation toc: --><li><ahref="._week41-bs048.html#3d-volumes-of-neurons"style="font-size: 80%;">3D volumes of neurons</a></li>
<!-- navigation toc: --><li><ahref="._week41-bs049.html#layers-used-to-build-cnns"style="font-size: 80%;">Layers used to build CNNs</a></li>
<!-- navigation toc: --><li><ahref="._week41-bs051.html#cnns-in-brief"style="font-size: 80%;">CNNs in brief</a></li>
<!-- navigation toc: --><li><ahref="._week41-bs052.html#cnns-in-more-detail-building-convolutional-neural-networks-in-tensorflow-and-keras"style="font-size: 80%;">CNNs in more detail, building convolutional neural networks in Tensorflow and Keras</a></li>
<!-- navigation toc: --><li><ahref="._week41-bs053.html#setting-it-up"style="font-size: 80%;">Setting it up</a></li>
<center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
<br>
<p> <br>
<center><h4>Oct 9, 2021</h4></center><!-- date -->
<center><h4>Oct 11, 2021</h4></center><!-- date -->
<br>
<p>
@@ -162,14 +162,24 @@ MathJax.Hub.Config({
<h2id="plan-for-week-41">Plan for week 41 </h2>
<ul>
<p><li> Thursday: Building our own Feed-forward Neural Network.</li>
<p><li> Thursday: Building our own Feed-forward Neural Network and discussion of project 2.</li>
<p><li> Friday: Playing around with our own Feed-forward Neural Network and introduction to TensorFlow. Start convolutional Neural Networks (CNN).</li>
</ul>
<p>
Reading suggestions for both days: <ahref="https://github.com/CompPhysics/MachineLearning/blob/master/doc/T\
For amore in depth discussion on neural networks we recommend Goodfellow et al chapters 6 and 7. For CNNs, see Goodfellow et al chapter 9. chapter 11 and 12 on practicalities and applications
For amore in depth discussion on neural networks we recommend Goodfellow et al chapters 6 and 7. For CNNs, see Goodfellow et al chapter 9. chapter 11 and 12 on practicalities and applications. We strongly recommend reading these chapters from Goodfellow et on Deep Learning, that is chapters 6-12. Bishop's chapter 5 on Neural Networks is an additional good read.
</section>
<section>
<h2id="videos-on-neural-networks">Videos on Neural Networks </h2>
<ul>
<p><li><ahref="https://www.youtube.com/watch?v=CqOfi41LfDw"target="_blank">Video on Neural Networks</a></li>
<p><li><ahref="https://www.youtube.com/watch?v=Ilg3gGewQ5U"target="_blank">Video on the back propagation algorithm</a></li>
For amore in depth discussion on neural networks we recommend Goodfellow et al chapters 6 and 7. For CNNs, see Goodfellow et al chapter 9. chapter 11 and 12 on practicalities and applications
For amore in depth discussion on neural networks we recommend Goodfellow et al chapters 6 and 7. For CNNs, see Goodfellow et al chapter 9. chapter 11 and 12 on practicalities and applications. We strongly recommend reading these chapters from Goodfellow et on Deep Learning, that is chapters 6-12. Bishop's chapter 5 on Neural Networks is an additional good read.
For amore in depth discussion on neural networks we recommend Goodfellow et al chapters 6 and 7. For CNNs, see Goodfellow et al chapter 9. chapter 11 and 12 on practicalities and applications
For amore in depth discussion on neural networks we recommend Goodfellow et al chapters 6 and 7. For CNNs, see Goodfellow et al chapter 9. chapter 11 and 12 on practicalities and applications. We strongly recommend reading these chapters from Goodfellow et on Deep Learning, that is chapters 6-12. Bishop's chapter 5 on Neural Networks is an additional good read.
"**Morten Hjorth-Jensen**, Department of Physics, University of Oslo and Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University\n",
"\n",
"Date: **Oct 9, 2021**\n",
"Date: **Oct 11, 2021**\n",
"\n",
"Copyright 1999-2021, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license\n",
"\n",
@@ -19,13 +19,19 @@
"\n",
"## Plan for week 41\n",
"\n",
"* Thursday: Building our own Feed-forward Neural Network. \n",
"* Thursday: Building our own Feed-forward Neural Network and discussion of project 2.\n",
"\n",
"* Friday: Playing around with our own Feed-forward Neural Network and introduction to TensorFlow. Start convolutional Neural Networks (CNN). \n",
"\n",
"Reading suggestions for both days: [Aurelien Geron's chapters 10-11](https://github.com/CompPhysics/MachineLearning/blob/master/doc/T\\\n",
"extbooks/TensorflowML.pdf).\n",
"For amore in depth discussion on neural networks we recommend Goodfellow et al chapters 6 and 7. For CNNs, see Goodfellow et al chapter 9. chapter 11 and 12 on practicalities and applications\n",
"For amore in depth discussion on neural networks we recommend Goodfellow et al chapters 6 and 7. For CNNs, see Goodfellow et al chapter 9. chapter 11 and 12 on practicalities and applications. We strongly recommend reading these chapters from Goodfellow et on Deep Learning, that is chapters 6-12. Bishop's chapter 5 on Neural Networks is an additional good read.\n",
"\n",
"## Videos on Neural Networks\n",
"\n",
"* [Video on Neural Networks](https://www.youtube.com/watch?v=CqOfi41LfDw)\n",
"\n",
"* [Video on the back propagation algorithm](https://www.youtube.com/watch?v=Ilg3gGewQ5U)\n",
* Thursday: Building our own Feed-forward Neural Network.
* Thursday: Building our own Feed-forward Neural Network and discussion of project 2.
* Friday: Playing around with our own Feed-forward Neural Network and introduction to TensorFlow. Start convolutional Neural Networks (CNN).
Reading suggestions for both days: "Aurelien Geron's chapters 10-11":"https://github.com/CompPhysics/MachineLearning/blob/master/doc/T\
extbooks/TensorflowML.pdf".
For amore in depth discussion on neural networks we recommend Goodfellow et al chapters 6 and 7. For CNNs, see Goodfellow et al chapter 9. chapter 11 and 12 on practicalities and applications
For amore in depth discussion on neural networks we recommend Goodfellow et al chapters 6 and 7. For CNNs, see Goodfellow et al chapter 9. chapter 11 and 12 on practicalities and applications. We strongly recommend reading these chapters from Goodfellow et on Deep Learning, that is chapters 6-12. Bishop's chapter 5 on Neural Networks is an additional good read.
!split
===== Videos on Neural Networks =====
* "Video on Neural Networks":"https://www.youtube.com/watch?v=CqOfi41LfDw"
* "Video on the back propagation algorithm":"https://www.youtube.com/watch?v=Ilg3gGewQ5U"
!split
===== Setting up the Back propagation algorithm =====
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Morten Hjorth-Jensen