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Update notebooks to latest nbformat
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‎01_the_machine_learning_landscape.ipynb

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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.3"
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"version": "3.7.6"
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},
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"nav_menu": {},
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"toc": {
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}
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},
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"nbformat": 4,
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‎02_end_to_end_machine_learning_project.ipynb

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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Question: Try a Support Vector Machine regressor (`sklearn.svm.SVR`), with various hyperparameters such as `kernel=\"linear\"` (with various values for the `C` hyperparameter) or `kernel=\"rbf\"` (with various values for the `C` and `gamma` hyperparameters). Don't worry about what these hyperparameters mean for now. How does the best `SVR` predictor perform?"
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.3"
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"version": "3.7.6"
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},
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"nav_menu": {
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"height": "279px",
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}
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},
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"nbformat": 4,
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‎03_classification.ipynb

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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Exercise solutions"
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.3"
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"version": "3.7.6"
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"nav_menu": {},
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"toc": {
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}
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"nbformat": 4,
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‎04_training_linear_models.ipynb

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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.3"
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"version": "3.7.6"
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},
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"nav_menu": {},
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"toc": {
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}
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},
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"nbformat": 4,
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‎05_support_vector_machines.ipynb

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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Non-linear classification"
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"metadata": {},
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"source": [
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"See appendix A."
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]
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.3"
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"version": "3.7.6"
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"toc": {
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}
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"nbformat": 4,
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‎06_decision_trees.ipynb

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"cell_type": "markdown",
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"# Exercise solutions"
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},
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{
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"cell_type": "markdown",
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"source": [
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"## 7."
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.6"
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"height": "309px",
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}
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‎07_ensemble_learning_and_random_forests.ipynb

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},
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{
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"cell_type": "markdown",
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"source": [
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"# Exercise solutions"
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.3"
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"version": "3.7.6"
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},
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"height": "252px",
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}
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"nbformat": 4,
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‎08_dimensionality_reduction.ipynb

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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Notice that running PCA multiple times on slightly different datasets may result in different results. In general the only difference is that some axes may be flipped. In this example, PCA using Scikit-Learn gives the same projection as the one given by the SVD approach, except both axes are flipped:"
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},
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{
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"cell_type": "markdown",
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"# Exercise solutions"
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},
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"cell_type": "markdown",
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"## 9."
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},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"*Exercise: Alternatively, you can write colored digits at the location of each instance, or even plot scaled-down versions of the digit images themselves (if you plot all digits, the visualization will be too cluttered, so you should either draw a random sample or plot an instance only if no other instance has already been plotted at a close distance). You should get a nice visualization with well-separated clusters of digits.*"
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.3"
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"version": "3.7.6"
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}
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"nbformat": 4,
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‎09_unsupervised_learning.ipynb

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"execution_count": 47,
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"data": {
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"execution_count": 71,
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"execution_count": 91,
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"data": {
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.6"
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}
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‎10_neural_nets_with_keras.ipynb

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},
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{
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"cell_type": "markdown",
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"# Exercise solutions"
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},
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"cell_type": "markdown",
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"name": "python",
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"nbconvert_exporter": "python",
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}
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‎11_training_deep_neural_networks.ipynb

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},
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"source": [
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"# Exercises"
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"name": "python",
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}
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}

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