diff --git a/doc/pub/week40/html/._week40-bs001.html b/doc/pub/week40/html/._week40-bs001.html index 740be79eb..682a4d5fa 100644 --- a/doc/pub/week40/html/._week40-bs001.html +++ b/doc/pub/week40/html/._week40-bs001.html @@ -329,6 +329,11 @@ MathJax.Hub.Config({
  • Friday: Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model.
    • + + + Reading suggestions for both days: Aurelien Geron's chapter 10 and Hastie et al chapter 11. diff --git a/doc/pub/week40/html/week40-reveal.html b/doc/pub/week40/html/week40-reveal.html index 32a4d35b8..bb9639c9a 100644 --- a/doc/pub/week40/html/week40-reveal.html +++ b/doc/pub/week40/html/week40-reveal.html @@ -169,6 +169,12 @@ MathJax.Hub.Config({

  • Video of Lecture

  • Friday: Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model.
    • + + +

    diff --git a/doc/pub/week40/html/week40-solarized.html b/doc/pub/week40/html/week40-solarized.html index bfcf09ac9..5216c674b 100644 --- a/doc/pub/week40/html/week40-solarized.html +++ b/doc/pub/week40/html/week40-solarized.html @@ -278,6 +278,11 @@ MathJax.Hub.Config({

  • Friday: Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model.
    • + + + Reading suggestions for both days: Aurelien Geron's chapter 10 and Hastie et al chapter 11. diff --git a/doc/pub/week40/html/week40.html b/doc/pub/week40/html/week40.html index 8cdb7fdf3..335199555 100644 --- a/doc/pub/week40/html/week40.html +++ b/doc/pub/week40/html/week40.html @@ -283,6 +283,11 @@ MathJax.Hub.Config({
  • Friday: Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model.
    • + + + Reading suggestions for both days: Aurelien Geron's chapter 10 and Hastie et al chapter 11. diff --git a/doc/pub/week40/ipynb/ipynb-week40-src.tar.gz b/doc/pub/week40/ipynb/ipynb-week40-src.tar.gz index 506cea50c..51eed6eb8 100644 Binary files a/doc/pub/week40/ipynb/ipynb-week40-src.tar.gz and b/doc/pub/week40/ipynb/ipynb-week40-src.tar.gz differ diff --git a/doc/pub/week40/ipynb/week40.ipynb b/doc/pub/week40/ipynb/week40.ipynb index e1f728064..9b75a5667 100644 --- a/doc/pub/week40/ipynb/week40.ipynb +++ b/doc/pub/week40/ipynb/week40.ipynb @@ -26,6 +26,9 @@ "\n", "* Friday: Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model. \n", "\n", + " * [Video of Lecture](https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureOctober8.mp4?vrtx=view-as-webpage)\n", + "\n", + "\n", "Reading suggestions for both days: [Aurelien Geron's chapter 10](https://github.com/CompPhysics/MachineLearning/blob/master/doc/Textbooks/TensorflowML.pdf) and Hastie et al chapter 11.\n", "For Stochastic Gradient Descent, we recommend chapter 4 of Geron's text.\n", "For neural networks we recommend Goodfellow et al chapters 6 and 7 and Bishop 5.1-5.4\n", diff --git a/doc/pub/week41/ipynb/week41.ipynb b/doc/pub/week41/ipynb/week41.ipynb index e536eff6d..e0af85976 100644 --- a/doc/pub/week41/ipynb/week41.ipynb +++ b/doc/pub/week41/ipynb/week41.ipynb @@ -491,9 +491,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", @@ -562,9 +560,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split\n", @@ -686,9 +682,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# building our neural network\n", @@ -765,9 +759,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# setup the feed-forward pass, subscript h = hidden layer\n", @@ -932,9 +924,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# to categorical turns our integer vector into a onehot representation\n", @@ -1033,9 +1023,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "class NeuralNetwork:\n", @@ -1157,9 +1145,7 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "epochs = 100\n", @@ -1193,9 +1179,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "eta_vals = np.logspace(-5, 1, 7)\n", @@ -1230,9 +1214,7 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# visual representation of grid search\n", @@ -1292,9 +1274,7 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "from sklearn.neural_network import MLPClassifier\n", @@ -1325,9 +1305,7 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# optional\n", @@ -1410,9 +1388,7 @@ { "cell_type": "code", "execution_count": 12, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "pip3 install tensorflow" @@ -1429,9 +1405,7 @@ { "cell_type": "code", "execution_count": 13, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "conda create -n tf tensorflow\n", @@ -1448,9 +1422,7 @@ { "cell_type": "code", "execution_count": 14, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "conda create -n tf-gpu tensorflow-gpu\n", @@ -1471,9 +1443,7 @@ { "cell_type": "code", "execution_count": 15, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "conda install keras" @@ -1495,9 +1465,7 @@ { "cell_type": "code", "execution_count": 16, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# import necessary packages\n", @@ -1548,9 +1516,7 @@ { "cell_type": "code", "execution_count": 17, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "from tensorflow.keras.layers import Input\n", @@ -1575,9 +1541,7 @@ { "cell_type": "code", "execution_count": 18, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "\n", @@ -1603,9 +1567,7 @@ { "cell_type": "code", "execution_count": 19, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "DNN_keras = np.zeros((len(eta_vals), len(lmbd_vals)), dtype=object)\n", @@ -1628,9 +1590,7 @@ { "cell_type": "code", "execution_count": 20, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# optional\n", @@ -1676,9 +1636,7 @@ { "cell_type": "code", "execution_count": 21, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "\n", @@ -2398,9 +2356,7 @@ { "cell_type": "code", "execution_count": 22, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# import necessary packages\n", @@ -2455,9 +2411,7 @@ { "cell_type": "code", "execution_count": 23, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "from tensorflow.keras import datasets, layers, models\n", @@ -2495,9 +2449,7 @@ { "cell_type": "code", "execution_count": 24, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "def create_convolutional_neural_network_keras(input_shape, receptive_field,\n", @@ -2538,9 +2490,7 @@ { "cell_type": "code", "execution_count": 25, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "CNN_keras = np.zeros((len(eta_vals), len(lmbd_vals)), dtype=object)\n", @@ -2571,9 +2521,7 @@ { "cell_type": "code", "execution_count": 26, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# visual representation of grid search\n", @@ -2620,7 +2568,25 @@ ] } ], - "metadata": {}, + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, "nbformat": 4, "nbformat_minor": 4 } diff --git a/doc/src/week40/week40.do.txt b/doc/src/week40/week40.do.txt index d6de94ac3..a6740ce89 100644 --- a/doc/src/week40/week40.do.txt +++ b/doc/src/week40/week40.do.txt @@ -9,7 +9,7 @@ DATE: today * Thursday: Stochastic Gradient descent with examples and automatic differentiation and begin Neural Networks. * "Video of Lecture":"https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureOctober7.mp4?vrtx=view-as-webpage" * Friday: Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model. - + * "Video of Lecture":"https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureOctober8.mp4?vrtx=view-as-webpage" Reading suggestions for both days: "Aurelien Geron's chapter 10":"https://github.com/CompPhysics/MachineLearning/blob/master/doc/Textbooks/TensorflowML.pdf" and Hastie et al chapter 11. For Stochastic Gradient Descent, we recommend chapter 4 of Geron's text. For neural networks we recommend Goodfellow et al chapters 6 and 7 and Bishop 5.1-5.4