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Update notebooks to latest nbformat

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Aurélien Geron 2020-04-06 19:13:12 +12:00
parent d507ec815a
commit 2f7ab70295
22 changed files with 112 additions and 194 deletions
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4
01_the_machine_learning_landscape.ipynb
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@ -785,7 +785,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
"version": "3.7.6"
},
"nav_menu": {},
"toc": {
@ -806,5 +806,5 @@
}
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"nbformat": 4,
"nbformat_minor": 1
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}

8
02_end_to_end_machine_learning_project.ipynb
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@ -1664,9 +1664,7 @@
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"metadata": {},
"source": [
"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?"
]
@ -2170,7 +2168,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
"version": "3.7.6"
},
"nav_menu": {
"height": "279px",
@ -2188,5 +2186,5 @@
}
},
"nbformat": 4,
"nbformat_minor": 1
"nbformat_minor": 4
}

8
03_classification.ipynb
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@ -1163,9 +1163,7 @@
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"metadata": {},
"source": [
"# Exercise solutions"
]
@ -2553,7 +2551,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
"version": "3.7.6"
},
"nav_menu": {},
"toc": {
@ -2567,5 +2565,5 @@
}
},
"nbformat": 4,
"nbformat_minor": 1
"nbformat_minor": 4
}

4
04_training_linear_models.ipynb
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@ -1797,7 +1797,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
"version": "3.7.6"
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@ -1811,5 +1811,5 @@
}
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12
05_support_vector_machines.ipynb
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@ -403,9 +403,7 @@
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"metadata": {},
"source": [
"# Non-linear classification"
]
@ -1241,9 +1239,7 @@
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"metadata": {},
"source": [
"See appendix A."
]
@ -1834,7 +1830,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
"version": "3.7.6"
},
"nav_menu": {},
"toc": {
@ -1848,5 +1844,5 @@
}
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12
06_decision_trees.ipynb
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@ -465,9 +465,7 @@
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"metadata": {},
"source": [
"# Exercise solutions"
]
@ -488,9 +486,7 @@
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"metadata": {},
"source": [
"## 7."
]
@ -733,7 +729,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
"version": "3.7.6"
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@ -750,5 +746,5 @@
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8
07_ensemble_learning_and_random_forests.ipynb
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@ -924,9 +924,7 @@
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"metadata": {},
"source": [
"# Exercise solutions"
]
@ -1394,7 +1392,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
"version": "3.7.6"
},
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"height": "252px",
@ -1411,5 +1409,5 @@
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20
08_dimensionality_reduction.ipynb
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@ -212,9 +212,7 @@
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"metadata": {},
"source": [
"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:"
]
@ -1481,9 +1479,7 @@
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"metadata": {},
"source": [
"# Exercise solutions"
]
@ -1504,9 +1500,7 @@
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"metadata": {},
"source": [
"## 9."
]
@ -1917,9 +1911,7 @@
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"metadata": {},
"source": [
"*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.*"
]
@ -2264,9 +2256,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
"version": "3.7.6"
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},
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16
09_unsupervised_learning.ipynb
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@ -975,9 +975,7 @@
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"scrolled": false
},
"metadata": {},
"outputs": [],
"source": [
"minibatch_kmeans = MiniBatchKMeans(n_clusters=10, batch_size=10, random_state=42)\n",
@ -1418,9 +1416,7 @@
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"scrolled": false
},
"metadata": {},
"outputs": [],
"source": [
"plt.figure(figsize=(10, 3.2))\n",
@ -1706,9 +1702,7 @@
{
"cell_type": "code",
"execution_count": 91,
"metadata": {
"scrolled": false
},
"metadata": {},
"outputs": [],
"source": [
"grid_clf.score(X_test, y_test)"
@ -3792,9 +3786,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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12
10_neural_nets_with_keras.ipynb
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@ -1597,9 +1597,7 @@
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"metadata": {},
"source": [
"# Exercise solutions"
]
@ -1613,9 +1611,7 @@
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"metadata": {},
"source": [
"See appendix A."
]
@ -2015,7 +2011,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
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@ -2032,5 +2028,5 @@
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8
11_training_deep_neural_networks.ipynb
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@ -2103,9 +2103,7 @@
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"metadata": {},
"source": [
"# Exercises"
]
@ -2684,7 +2682,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
"version": "3.7.6"
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"height": "360px",
@ -2701,5 +2699,5 @@
}
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}

18
12_custom_models_and_training_with_tensorflow.ipynb
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@ -1012,9 +1012,7 @@
{
"cell_type": "code",
"execution_count": 78,
"metadata": {
"scrolled": false
},
"metadata": {},
"outputs": [],
"source": [
"model.fit(X_train_scaled, y_train, epochs=2,\n",
@ -1158,9 +1156,7 @@
{
"cell_type": "code",
"execution_count": 90,
"metadata": {
"scrolled": false
},
"metadata": {},
"outputs": [],
"source": [
"model.fit(X_train_scaled, y_train, epochs=2,\n",
@ -1856,9 +1852,7 @@
{
"cell_type": "code",
"execution_count": 142,
"metadata": {
"scrolled": false
},
"metadata": {},
"outputs": [],
"source": [
"model.compile(loss=\"mse\", optimizer=\"nadam\")\n",
@ -3527,9 +3521,7 @@
{
"cell_type": "code",
"execution_count": 261,
"metadata": {
"scrolled": false
},
"metadata": {},
"outputs": [],
"source": [
"model = keras.models.Sequential([keras.layers.Dense(1, input_shape=[8])])\n",
@ -3914,5 +3906,5 @@
}
},
"nbformat": 4,
"nbformat_minor": 2
"nbformat_minor": 4
}

6
13_loading_and_preprocessing_data.ipynb
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@ -466,9 +466,7 @@
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"scrolled": false
},
"metadata": {},
"outputs": [],
"source": [
"n_inputs = 8 # X_train.shape[-1]\n",
@ -2785,5 +2783,5 @@
}
},
"nbformat": 4,
"nbformat_minor": 1
"nbformat_minor": 4
}

14
14_deep_computer_vision_with_cnns.ipynb
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@ -1230,9 +1230,7 @@
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"metadata": {},
"source": [
"# Exercises"
]
@ -1312,9 +1310,7 @@
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"metadata": {},
"source": [
"## 10. Use transfer learning for large image classification"
]
@ -1348,9 +1344,7 @@
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"metadata": {},
"source": [
"Simply open the Colab and follow its instructions."
]
@ -1386,5 +1380,5 @@
}
},
"nbformat": 4,
"nbformat_minor": 1
"nbformat_minor": 4
}

12
15_processing_sequences_using_rnns_and_cnns.ipynb
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@ -972,9 +972,7 @@
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"scrolled": false
},
"metadata": {},
"outputs": [],
"source": [
"np.random.seed(42)\n",
@ -1213,9 +1211,7 @@
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"metadata": {},
"source": [
"# Exercise solutions"
]
@ -1974,9 +1970,7 @@
{
"cell_type": "code",
"execution_count": 100,
"metadata": {
"scrolled": false
},
"metadata": {},
"outputs": [],
"source": [
"new_chorale_v2_hot = generate_chorale_v2(model, seed_chords, 56, temperature=1.5)\n",

2
16_nlp_with_rnns_and_attention.ipynb
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@ -1248,5 +1248,5 @@
}
},
"nbformat": 4,
"nbformat_minor": 2
"nbformat_minor": 4
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4
extra_gradient_descent_comparison.ipynb
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@ -257,9 +257,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.6"
"version": "3.7.6"
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12
index.ipynb
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@ -48,9 +48,7 @@
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"metadata": {},
"source": [
"## Prerequisites\n",
"### To understand\n",
@ -65,9 +63,7 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"metadata": {},
"outputs": [],
"source": []
}
@ -88,7 +84,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.8"
"version": "3.7.6"
},
"nav_menu": {},
"toc": {
@ -102,5 +98,5 @@
}
},
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"nbformat_minor": 1
"nbformat_minor": 4
}

35
math_linear_algebra.ipynb
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@ -152,7 +152,10 @@
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"outputs": [],
"source": [
@ -190,9 +193,7 @@
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"metadata": {},
"outputs": [],
"source": [
"def plot_vector2d(vector2d, origin=[0, 0], **options):\n",
@ -232,9 +233,7 @@
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"metadata": {},
"outputs": [],
"source": [
"a = np.array([1, 2, 8])\n",
@ -1671,9 +1670,7 @@
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"metadata": {},
"source": [
"## Converting 1D arrays to 2D arrays in NumPy\n",
"As we mentionned earlier, in NumPy (as opposed to Matlab, for example), 1D really means 1D: there is no such thing as a vertical 1D-array or a horizontal 1D-array. So you should not be surprised to see that transposing a 1D array does not do anything:"
@ -2001,7 +1998,10 @@
"cell_type": "code",
"execution_count": 90,
"metadata": {
"collapsed": true
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"outputs": [],
"source": [
@ -2739,7 +2739,10 @@
"cell_type": "code",
"execution_count": 122,
"metadata": {
"collapsed": true
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"outputs": [],
"source": [
@ -3039,9 +3042,7 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"metadata": {},
"outputs": [],
"source": []
}
@ -3062,7 +3063,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.2"
"version": "3.7.6"
},
"toc": {
"toc_cell": false,
@ -3072,5 +3073,5 @@
}
},
"nbformat": 4,
"nbformat_minor": 1
"nbformat_minor": 4
}

21
tools_matplotlib.ipynb
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@ -554,9 +554,7 @@
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"scrolled": false
},
"metadata": {},
"outputs": [],
"source": [
"plt.plot(x, x**2, px, py, \"ro\")\n",
@ -1022,9 +1020,7 @@
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"scrolled": false
},
"metadata": {},
"outputs": [],
"source": [
"plt.imshow(img, cmap=\"hot\")\n",
@ -1065,9 +1061,7 @@
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"scrolled": false
},
"metadata": {},
"outputs": [],
"source": [
"plt.imshow(img, interpolation=\"nearest\")\n",
@ -1086,7 +1080,10 @@
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": true
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"outputs": [],
"source": [
@ -1175,7 +1172,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.4"
"version": "3.7.6"
},
"toc": {
"toc_cell": true,
@ -1186,5 +1183,5 @@
}
},
"nbformat": 4,
"nbformat_minor": 1
"nbformat_minor": 4
}

38
tools_numpy.ipynb
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@ -21,9 +21,7 @@
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"metadata": {},
"outputs": [],
"source": [
"import numpy as np"
@ -357,9 +355,7 @@
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"scrolled": false
},
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
@ -500,9 +496,7 @@
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"scrolled": false
},
"metadata": {},
"outputs": [],
"source": [
"f = np.array([[1,2],[1000, 2000]], dtype=np.int32)\n",
@ -663,9 +657,7 @@
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"scrolled": false
},
"metadata": {},
"outputs": [],
"source": [
"a = np.array([14, 23, 32, 41])\n",
@ -1243,9 +1235,7 @@
{
"cell_type": "code",
"execution_count": 74,
"metadata": {
"scrolled": false
},
"metadata": {},
"outputs": [],
"source": [
"try:\n",
@ -1542,9 +1532,7 @@
{
"cell_type": "code",
"execution_count": 96,
"metadata": {
"scrolled": false
},
"metadata": {},
"outputs": [],
"source": [
"c[..., 3] # all matrices, all rows, column 3. This is equivalent to c[:, :, 3]"
@ -2700,7 +2688,10 @@
"cell_type": "code",
"execution_count": 175,
"metadata": {
"collapsed": true
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"outputs": [],
"source": [
@ -2746,7 +2737,10 @@
"cell_type": "code",
"execution_count": 178,
"metadata": {
"collapsed": true
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"outputs": [],
"source": [
@ -2839,7 +2833,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.4"
"version": "3.7.6"
},
"toc": {
"toc_cell": false,
@ -2857,5 +2851,5 @@
}
},
"nbformat": 4,
"nbformat_minor": 1
"nbformat_minor": 4
}

32
tools_pandas.ipynb
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@ -1756,9 +1756,7 @@
{
"cell_type": "code",
"execution_count": 97,
"metadata": {
"scrolled": false
},
"metadata": {},
"outputs": [],
"source": [
"grades >= 5"
@ -1978,9 +1976,7 @@
{
"cell_type": "code",
"execution_count": 110,
"metadata": {
"scrolled": false
},
"metadata": {},
"outputs": [],
"source": [
"bonus_points.interpolate(axis=1)"
@ -2242,9 +2238,7 @@
{
"cell_type": "code",
"execution_count": 125,
"metadata": {
"scrolled": false
},
"metadata": {},
"outputs": [],
"source": [
"much_data = np.fromfunction(lambda x,y: (x+y*y)%17*11, (10000, 26))\n",
@ -2264,9 +2258,7 @@
{
"cell_type": "code",
"execution_count": 126,
"metadata": {
"scrolled": false
},
"metadata": {},
"outputs": [],
"source": [
"large_df.head()"
@ -2298,9 +2290,7 @@
{
"cell_type": "code",
"execution_count": 128,
"metadata": {
"scrolled": false
},
"metadata": {},
"outputs": [],
"source": [
"large_df.info()"
@ -2322,9 +2312,7 @@
{
"cell_type": "code",
"execution_count": 129,
"metadata": {
"scrolled": false
},
"metadata": {},
"outputs": [],
"source": [
"large_df.describe()"
@ -2775,9 +2763,7 @@
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"metadata": {},
"source": [
"# What next?\n",
"As you probably noticed by now, pandas is quite a large library with *many* features. Although we went through the most important features, there is still a lot to discover. Probably the best way to learn more is to get your hands dirty with some real-life data. It is also a good idea to go through pandas' excellent [documentation](http://pandas.pydata.org/pandas-docs/stable/index.html), in particular the [Cookbook](http://pandas.pydata.org/pandas-docs/stable/cookbook.html)."
@ -2807,7 +2793,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.4"
"version": "3.7.6"
},
"toc": {
"toc_cell": false,
@ -2818,5 +2804,5 @@
}
},
"nbformat": 4,
"nbformat_minor": 1
"nbformat_minor": 4
}