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Merge branch 'master' into master

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Aurélien Geron 2021-05-27 14:55:45 +12:00 committed by GitHub
parent ae670cda67 2330a4dea3
commit 6dc55e93f4
28 changed files with 249 additions and 156 deletions
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7
01_the_machine_learning_landscape.ipynb
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@ -15,7 +15,10 @@
"source": [
"<table align=\"left\">\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/01_the_machine_learning_landscape.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
" <a href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/01_the_machine_learning_landscape.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
" </td>\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://kaggle.com/kernels/welcome?src=https://github.com/ageron/handson-ml2/blob/master/01_the_machine_learning_landscape.ipynb\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" /></a>\n",
" </td>\n",
"</table>"
]
@ -817,7 +820,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.4-final"
"version": "3.7.10"
},
"metadata": {
"interpreter": {

7
02_end_to_end_machine_learning_project.ipynb
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@ -17,7 +17,10 @@
"source": [
"<table align=\"left\">\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/02_end_to_end_machine_learning_project.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
" <a href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/02_end_to_end_machine_learning_project.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
" </td>\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://kaggle.com/kernels/welcome?src=https://github.com/ageron/handson-ml2/blob/master/02_end_to_end_machine_learning_project.ipynb\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" /></a>\n",
" </td>\n",
"</table>"
]
@ -2216,7 +2219,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.9"
"version": "3.7.10"
},
"nav_menu": {
"height": "279px",

20
03_classification.ipynb
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@ -15,7 +15,10 @@
"source": [
"<table align=\"left\">\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/03_classification.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
" <a href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/03_classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
" </td>\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://kaggle.com/kernels/welcome?src=https://github.com/ageron/handson-ml2/blob/add-kaggle-badge/03_classification.ipynb\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" /></a>\n",
" </td>\n",
"</table>"
]
@ -44,6 +47,10 @@
"import sys\n",
"assert sys.version_info >= (3, 5)\n",
"\n",
"# Is this notebook running on Colab or Kaggle?\n",
"IS_COLAB = \"google.colab\" in sys.modules\n",
"IS_KAGGLE = \"kaggle_secrets\" in sys.modules\n",
"\n",
"# Scikit-Learn ≥0.20 is required\n",
"import sklearn\n",
"assert sklearn.__version__ >= \"0.20\"\n",
@ -2330,12 +2337,9 @@
"metadata": {},
"outputs": [],
"source": [
"# if running this notebook on Colab, we just pip install urlextract\n",
"try:\n",
" import google.colab\n",
" !pip install -q -U urlextract\n",
"except ImportError:\n",
" pass # not running on Colab"
"# if running this notebook on Colab or Kaggle, we just pip install urlextract\n",
"if IS_COLAB or IS_KAGGLE:\n",
" !pip install -q -U urlextract"
]
},
{
@ -2601,7 +2605,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.9"
"version": "3.7.10"
},
"nav_menu": {},
"toc": {

7
04_training_linear_models.ipynb
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@ -20,7 +20,10 @@
"source": [
"<table align=\"left\">\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/04_training_linear_models.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
" <a href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/04_training_linear_models.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
" </td>\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://kaggle.com/kernels/welcome?src=https://github.com/ageron/handson-ml2/blob/add-kaggle-badge/04_training_linear_models.ipynb\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" /></a>\n",
" </td>\n",
"</table>"
]
@ -1790,7 +1793,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.9"
"version": "3.7.10"
},
"nav_menu": {},
"toc": {

7
05_support_vector_machines.ipynb
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@ -15,7 +15,10 @@
"source": [
"<table align=\"left\">\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/05_support_vector_machines.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
" <a href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/05_support_vector_machines.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
" </td>\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://kaggle.com/kernels/welcome?src=https://github.com/ageron/handson-ml2/blob/add-kaggle-badge/05_support_vector_machines.ipynb\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" /></a>\n",
" </td>\n",
"</table>"
]
@ -1844,7 +1847,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.9"
"version": "3.7.10"
},
"nav_menu": {},
"toc": {

7
06_decision_trees.ipynb
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@ -20,7 +20,10 @@
"source": [
"<table align=\"left\">\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/06_decision_trees.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
" <a href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/06_decision_trees.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
" </td>\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://kaggle.com/kernels/welcome?src=https://github.com/ageron/handson-ml2/blob/add-kaggle-badge/06_decision_trees.ipynb\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" /></a>\n",
" </td>\n",
"</table>"
]
@ -729,7 +732,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.8"
"version": "3.7.10"
},
"nav_menu": {
"height": "309px",

7
07_ensemble_learning_and_random_forests.ipynb
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@ -20,7 +20,10 @@
"source": [
"<table align=\"left\">\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/07_ensemble_learning_and_random_forests.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
" <a href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/07_ensemble_learning_and_random_forests.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
" </td>\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://kaggle.com/kernels/welcome?src=https://github.com/ageron/handson-ml2/blob/add-kaggle-badge/07_ensemble_learning_and_random_forests.ipynb\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" /></a>\n",
" </td>\n",
"</table>"
]
@ -1402,7 +1405,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.9"
"version": "3.7.10"
},
"nav_menu": {
"height": "252px",

7
08_dimensionality_reduction.ipynb
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@ -15,7 +15,10 @@
"source": [
"<table align=\"left\">\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/08_dimensionality_reduction.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
" <a href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/08_dimensionality_reduction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
" </td>\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://kaggle.com/kernels/welcome?src=https://github.com/ageron/handson-ml2/blob/master/08_dimensionality_reduction.ipynb\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" /></a>\n",
" </td>\n",
"</table>"
]
@ -2259,7 +2262,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.9"
"version": "3.7.10"
}
},
"nbformat": 4,

7
09_unsupervised_learning.ipynb
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@ -15,7 +15,10 @@
"source": [
"<table align=\"left\">\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/09_unsupervised_learning.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
" <a href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/09_unsupervised_learning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
" </td>\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://kaggle.com/kernels/welcome?src=https://github.com/ageron/handson-ml2/blob/master/09_unsupervised_learning.ipynb\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" /></a>\n",
" </td>\n",
"</table>"
]
@ -3865,7 +3868,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.9"
"version": "3.7.10"
}
},
"nbformat": 4,

7
10_neural_nets_with_keras.ipynb
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@ -15,7 +15,10 @@
"source": [
"<table align=\"left\">\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/10_neural_nets_with_keras.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
" <a href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/10_neural_nets_with_keras.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
" </td>\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://kaggle.com/kernels/welcome?src=https://github.com/ageron/handson-ml2/blob/add-kaggle-badge/10_neural_nets_with_keras.ipynb\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" /></a>\n",
" </td>\n",
"</table>"
]
@ -2007,7 +2010,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.9"
"version": "3.7.10"
},
"nav_menu": {
"height": "264px",

7
11_training_deep_neural_networks.ipynb
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@ -20,7 +20,10 @@
"source": [
"<table align=\"left\">\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/11_training_deep_neural_networks.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
" <a href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/11_training_deep_neural_networks.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
" </td>\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://kaggle.com/kernels/welcome?src=https://github.com/ageron/handson-ml2/blob/add-kaggle-badge/11_training_deep_neural_networks.ipynb\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" /></a>\n",
" </td>\n",
"</table>"
]
@ -2706,7 +2709,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.9"
"version": "3.7.10"
},
"nav_menu": {
"height": "360px",

7
12_custom_models_and_training_with_tensorflow.ipynb
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@ -20,7 +20,10 @@
"source": [
"<table align=\"left\">\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/12_custom_models_and_training_with_tensorflow.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
" <a href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/12_custom_models_and_training_with_tensorflow.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
" </td>\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://kaggle.com/kernels/welcome?src=https://github.com/ageron/handson-ml2/blob/add-kaggle-badge/12_custom_models_and_training_with_tensorflow.ipynb\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" /></a>\n",
" </td>\n",
"</table>"
]
@ -3880,7 +3883,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.9"
"version": "3.7.10"
}
},
"nbformat": 4,

57
13_loading_and_preprocessing_data.ipynb
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@ -15,7 +15,10 @@
"source": [
"<table align=\"left\">\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/13_loading_and_preprocessing_data.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
" <a href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/13_loading_and_preprocessing_data.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
" </td>\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://kaggle.com/kernels/welcome?src=https://github.com/ageron/handson-ml2/blob/add-kaggle-badge/13_loading_and_preprocessing_data.ipynb\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" /></a>\n",
" </td>\n",
"</table>"
]
@ -44,18 +47,18 @@
"import sys\n",
"assert sys.version_info >= (3, 5)\n",
"\n",
"# Is this notebook running on Colab or Kaggle?\n",
"IS_COLAB = \"google.colab\" in sys.modules\n",
"IS_KAGGLE = \"kaggle_secrets\" in sys.modules\n",
"\n",
"if IS_COLAB or IS_KAGGLE:\n",
" !pip install -q -U tfx==0.21.2\n",
" print(\"You can safely ignore the package incompatibility errors.\")\n",
"\n",
"# Scikit-Learn ≥0.20 is required\n",
"import sklearn\n",
"assert sklearn.__version__ >= \"0.20\"\n",
"\n",
"try:\n",
" # %tensorflow_version only exists in Colab.\n",
" %tensorflow_version 2.x\n",
" !pip install -q -U tfx==0.21.2\n",
" print(\"You can safely ignore the package incompatibility errors.\")\n",
"except Exception:\n",
" pass\n",
"\n",
"# TensorFlow ≥2.0 is required\n",
"import tensorflow as tf\n",
"from tensorflow import keras\n",
@ -2609,7 +2612,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"We get about 73.7% accuracy on the validation set after just the first epoch, but after that the model makes no significant progress. We will do better in Chapter 16. For now the point is just to perform efficient preprocessing using `tf.data` and Keras preprocessing layers."
"We get about 73.5% accuracy on the validation set after just the first epoch, but after that the model makes no significant progress. We will do better in Chapter 16. For now the point is just to perform efficient preprocessing using `tf.data` and Keras preprocessing layers."
]
},
{
@ -2624,7 +2627,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"To compute the mean embedding for each review, and multiply it by the square root of the number of words in that review, we will need a little function:"
"To compute the mean embedding for each review, and multiply it by the square root of the number of words in that review, we will need a little function. For each sentence, this function needs to compute $M \\times \\sqrt N$, where $M$ is the mean of all the word embeddings in the sentence (excluding padding tokens), and $N$ is the number of words in the sentence (also excluding padding tokens). We can rewrite $M$ as $\\dfrac{S}{N}$, where $S$ is the sum of all word embeddings (it does not matter whether or not we include the padding tokens in this sum, since their representation is a zero vector). So the function must return $M \\times \\sqrt N = \\dfrac{S}{N} \\times \\sqrt N = \\dfrac{S}{\\sqrt N \\times \\sqrt N} \\times \\sqrt N= \\dfrac{S}{\\sqrt N}$."
]
},
{
@ -2637,7 +2640,7 @@
" not_pad = tf.math.count_nonzero(inputs, axis=-1)\n",
" n_words = tf.math.count_nonzero(not_pad, axis=-1, keepdims=True) \n",
" sqrt_n_words = tf.math.sqrt(tf.cast(n_words, tf.float32))\n",
" return tf.reduce_mean(inputs, axis=1) * sqrt_n_words\n",
" return tf.reduce_sum(inputs, axis=1) / sqrt_n_words\n",
"\n",
"another_example = tf.constant([[[1., 2., 3.], [4., 5., 0.], [0., 0., 0.]],\n",
" [[6., 0., 0.], [0., 0., 0.], [0., 0., 0.]]])\n",
@ -2648,7 +2651,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's check that this is correct. The first review contains 2 words (the last token is a zero vector, which represents the `<pad>` token). The second review contains 1 word. So we need to compute the mean embedding for each review, and multiply the first one by the square root of 2, and the second one by the square root of 1:"
"Let's check that this is correct. The first review contains 2 words (the last token is a zero vector, which represents the `<pad>` token). Let's compute the mean embedding for these 2 words, and multiply the result by the square root of 2:"
]
},
{
@ -2657,7 +2660,23 @@
"metadata": {},
"outputs": [],
"source": [
"tf.reduce_mean(another_example, axis=1) * tf.sqrt([[2.], [1.]])"
"tf.reduce_mean(another_example[0:1, :2], axis=1) * tf.sqrt(2.)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Looks good! Now let's check the second review, which contains just one word (we ignore the two padding tokens):"
]
},
{
"cell_type": "code",
"execution_count": 156,
"metadata": {},
"outputs": [],
"source": [
"tf.reduce_mean(another_example[1:2, :1], axis=1) * tf.sqrt(1.)"
]
},
{
@ -2669,7 +2688,7 @@
},
{
"cell_type": "code",
"execution_count": 156,
"execution_count": 157,
"metadata": {},
"outputs": [],
"source": [
@ -2696,7 +2715,7 @@
},
{
"cell_type": "code",
"execution_count": 157,
"execution_count": 158,
"metadata": {},
"outputs": [],
"source": [
@ -2721,7 +2740,7 @@
},
{
"cell_type": "code",
"execution_count": 158,
"execution_count": 159,
"metadata": {},
"outputs": [],
"source": [
@ -2733,7 +2752,7 @@
},
{
"cell_type": "code",
"execution_count": 159,
"execution_count": 160,
"metadata": {},
"outputs": [],
"source": [
@ -2766,7 +2785,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.9"
"version": "3.7.10"
},
"nav_menu": {
"height": "264px",

20
14_deep_computer_vision_with_cnns.ipynb
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@ -20,7 +20,10 @@
"source": [
"<table align=\"left\">\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/14_deep_computer_vision_with_cnns.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
" <a href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/14_deep_computer_vision_with_cnns.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
" </td>\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://kaggle.com/kernels/welcome?src=https://github.com/ageron/handson-ml2/blob/master/14_deep_computer_vision_with_cnns.ipynb\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" /></a>\n",
" </td>\n",
"</table>"
]
@ -49,17 +52,14 @@
"import sys\n",
"assert sys.version_info >= (3, 5)\n",
"\n",
"# Is this notebook running on Colab or Kaggle?\n",
"IS_COLAB = \"google.colab\" in sys.modules\n",
"IS_KAGGLE = \"kaggle_secrets\" in sys.modules\n",
"\n",
"# Scikit-Learn ≥0.20 is required\n",
"import sklearn\n",
"assert sklearn.__version__ >= \"0.20\"\n",
"\n",
"try:\n",
" # %tensorflow_version only exists in Colab.\n",
" %tensorflow_version 2.x\n",
" IS_COLAB = True\n",
"except Exception:\n",
" IS_COLAB = False\n",
"\n",
"# TensorFlow ≥2.0 is required\n",
"import tensorflow as tf\n",
"from tensorflow import keras\n",
@ -69,6 +69,8 @@
" print(\"No GPU was detected. CNNs can be very slow without a GPU.\")\n",
" if IS_COLAB:\n",
" print(\"Go to Runtime > Change runtime and select a GPU hardware accelerator.\")\n",
" if IS_KAGGLE:\n",
" print(\"Go to Settings > Accelerator and select GPU.\")\n",
"\n",
"# Common imports\n",
"import numpy as np\n",
@ -1366,7 +1368,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.9"
"version": "3.7.10"
},
"nav_menu": {},
"toc": {

20
15_processing_sequences_using_rnns_and_cnns.ipynb
View File

@ -20,7 +20,10 @@
"source": [
"<table align=\"left\">\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/15_processing_sequences_using_rnns_and_cnns.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
" <a href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/15_processing_sequences_using_rnns_and_cnns.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
" </td>\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://kaggle.com/kernels/welcome?src=https://github.com/ageron/handson-ml2/blob/master/15_processing_sequences_using_rnns_and_cnns.ipynb\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" /></a>\n",
" </td>\n",
"</table>"
]
@ -49,17 +52,14 @@
"import sys\n",
"assert sys.version_info >= (3, 5)\n",
"\n",
"# Is this notebook running on Colab or Kaggle?\n",
"IS_COLAB = \"google.colab\" in sys.modules\n",
"IS_KAGGLE = \"kaggle_secrets\" in sys.modules\n",
"\n",
"# Scikit-Learn ≥0.20 is required\n",
"import sklearn\n",
"assert sklearn.__version__ >= \"0.20\"\n",
"\n",
"try:\n",
" # %tensorflow_version only exists in Colab.\n",
" %tensorflow_version 2.x\n",
" IS_COLAB = True\n",
"except Exception:\n",
" IS_COLAB = False\n",
"\n",
"# TensorFlow ≥2.0 is required\n",
"import tensorflow as tf\n",
"from tensorflow import keras\n",
@ -69,6 +69,8 @@
" print(\"No GPU was detected. LSTMs and CNNs can be very slow without a GPU.\")\n",
" if IS_COLAB:\n",
" print(\"Go to Runtime > Change runtime and select a GPU hardware accelerator.\")\n",
" if IS_KAGGLE:\n",
" print(\"Go to Settings > Accelerator and select GPU.\")\n",
"\n",
"# Common imports\n",
"import numpy as np\n",
@ -2018,7 +2020,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.9"
"version": "3.7.10"
},
"nav_menu": {},
"toc": {

55
16_nlp_with_rnns_and_attention.ipynb
View File

@ -20,7 +20,10 @@
"source": [
"<table align=\"left\">\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/16_nlp_with_rnns_and_attention.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
" <a href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/16_nlp_with_rnns_and_attention.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
" </td>\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://kaggle.com/kernels/welcome?src=https://github.com/ageron/handson-ml2/blob/add-kaggle-badge/16_nlp_with_rnns_and_attention.ipynb\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" /></a>\n",
" </td>\n",
"</table>"
]
@ -49,19 +52,18 @@
"import sys\n",
"assert sys.version_info >= (3, 5)\n",
"\n",
"# Is this notebook running on Colab or Kaggle?\n",
"IS_COLAB = \"google.colab\" in sys.modules\n",
"IS_KAGGLE = \"kaggle_secrets\" in sys.modules\n",
"\n",
"if IS_COLAB:\n",
" !pip install -q -U tensorflow-addons\n",
" !pip install -q -U transformers\n",
"\n",
"# Scikit-Learn ≥0.20 is required\n",
"import sklearn\n",
"assert sklearn.__version__ >= \"0.20\"\n",
"\n",
"try:\n",
" # %tensorflow_version only exists in Colab.\n",
" %tensorflow_version 2.x\n",
" !pip install -q -U tensorflow-addons\n",
" !pip install -q -U transformers\n",
" IS_COLAB = True\n",
"except Exception:\n",
" IS_COLAB = False\n",
"\n",
"# TensorFlow ≥2.0 is required\n",
"import tensorflow as tf\n",
"from tensorflow import keras\n",
@ -71,6 +73,8 @@
" print(\"No GPU was detected. LSTMs and CNNs can be very slow without a GPU.\")\n",
" if IS_COLAB:\n",
" print(\"Go to Runtime > Change runtime and select a GPU hardware accelerator.\")\n",
" if IS_KAGGLE:\n",
" print(\"Go to Settings > Accelerator and select GPU.\")\n",
"\n",
"# Common imports\n",
"import numpy as np\n",
@ -233,6 +237,13 @@
"dataset = tf.data.Dataset.from_tensor_slices(encoded[:train_size])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Note**: in previous versions of this code, we used `dataset.repeat()` now to make the dataset \"infinite\", and later in the notebook we set the `steps_per_epoch` argument when calling the `model.fit()` method. This was needed to work around some TensorFlow bugs. However, since these bugs have now been fixed, we can simplify the code: no need for `dataset.repeat()` or `steps_per_epoch` anymore."
]
},
{
"cell_type": "code",
"execution_count": 11,
@ -241,7 +252,7 @@
"source": [
"n_steps = 100\n",
"window_length = n_steps + 1 # target = input shifted 1 character ahead\n",
"dataset = dataset.repeat().window(window_length, shift=1, drop_remainder=True)"
"dataset = dataset.window(window_length, shift=1, drop_remainder=True)"
]
},
{
@ -341,8 +352,7 @@
" activation=\"softmax\"))\n",
"])\n",
"model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=\"adam\")\n",
"history = model.fit(dataset, steps_per_epoch=train_size // batch_size,\n",
" epochs=10)"
"history = model.fit(dataset, epochs=10)"
]
},
{
@ -484,7 +494,7 @@
"dataset = tf.data.Dataset.from_tensor_slices(encoded[:train_size])\n",
"dataset = dataset.window(window_length, shift=n_steps, drop_remainder=True)\n",
"dataset = dataset.flat_map(lambda window: window.batch(window_length))\n",
"dataset = dataset.repeat().batch(1)\n",
"dataset = dataset.batch(1)\n",
"dataset = dataset.map(lambda windows: (windows[:, :-1], windows[:, 1:]))\n",
"dataset = dataset.map(\n",
" lambda X_batch, Y_batch: (tf.one_hot(X_batch, depth=max_id), Y_batch))\n",
@ -506,7 +516,7 @@
" dataset = dataset.flat_map(lambda window: window.batch(window_length))\n",
" datasets.append(dataset)\n",
"dataset = tf.data.Dataset.zip(tuple(datasets)).map(lambda *windows: tf.stack(windows))\n",
"dataset = dataset.repeat().map(lambda windows: (windows[:, :-1], windows[:, 1:]))\n",
"dataset = dataset.map(lambda windows: (windows[:, :-1], windows[:, 1:]))\n",
"dataset = dataset.map(\n",
" lambda X_batch, Y_batch: (tf.one_hot(X_batch, depth=max_id), Y_batch))\n",
"dataset = dataset.prefetch(1)"
@ -556,8 +566,7 @@
"outputs": [],
"source": [
"model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=\"adam\")\n",
"steps_per_epoch = train_size // batch_size // n_steps\n",
"history = model.fit(dataset, steps_per_epoch=steps_per_epoch, epochs=50,\n",
"history = model.fit(dataset, epochs=50,\n",
" callbacks=[ResetStatesCallback()])"
]
},
@ -833,7 +842,7 @@
"def encode_words(X_batch, y_batch):\n",
" return table.lookup(X_batch), y_batch\n",
"\n",
"train_set = datasets[\"train\"].repeat().batch(32).map(preprocess)\n",
"train_set = datasets[\"train\"].batch(32).map(preprocess)\n",
"train_set = train_set.map(encode_words).prefetch(1)"
]
},
@ -864,7 +873,7 @@
" keras.layers.Dense(1, activation=\"sigmoid\")\n",
"])\n",
"model.compile(loss=\"binary_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])\n",
"history = model.fit(train_set, steps_per_epoch=train_size // 32, epochs=5)"
"history = model.fit(train_set, epochs=5)"
]
},
{
@ -890,7 +899,7 @@
"outputs = keras.layers.Dense(1, activation=\"sigmoid\")(z)\n",
"model = keras.models.Model(inputs=[inputs], outputs=[outputs])\n",
"model.compile(loss=\"binary_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])\n",
"history = model.fit(train_set, steps_per_epoch=train_size // 32, epochs=5)"
"history = model.fit(train_set, epochs=5)"
]
},
{
@ -959,8 +968,8 @@
"datasets, info = tfds.load(\"imdb_reviews\", as_supervised=True, with_info=True)\n",
"train_size = info.splits[\"train\"].num_examples\n",
"batch_size = 32\n",
"train_set = datasets[\"train\"].repeat().batch(batch_size).prefetch(1)\n",
"history = model.fit(train_set, steps_per_epoch=train_size // batch_size, epochs=5)"
"train_set = datasets[\"train\"].batch(batch_size).prefetch(1)\n",
"history = model.fit(train_set, epochs=5)"
]
},
{
@ -2748,7 +2757,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.9"
"version": "3.7.10"
},
"nav_menu": {},
"toc": {

20
17_autoencoders_and_gans.ipynb
View File

@ -20,7 +20,10 @@
"source": [
"<table align=\"left\">\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/17_autoencoders_and_gans.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
" <a href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/17_autoencoders_and_gans.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
" </td>\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://kaggle.com/kernels/welcome?src=https://github.com/ageron/handson-ml2/blob/master/17_autoencoders_and_gans.ipynb\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" /></a>\n",
" </td>\n",
"</table>"
]
@ -49,17 +52,14 @@
"import sys\n",
"assert sys.version_info >= (3, 5)\n",
"\n",
"# Is this notebook running on Colab or Kaggle?\n",
"IS_COLAB = \"google.colab\" in sys.modules\n",
"IS_KAGGLE = \"kaggle_secrets\" in sys.modules\n",
"\n",
"# Scikit-Learn ≥0.20 is required\n",
"import sklearn\n",
"assert sklearn.__version__ >= \"0.20\"\n",
"\n",
"try:\n",
" # %tensorflow_version only exists in Colab.\n",
" %tensorflow_version 2.x\n",
" IS_COLAB = True\n",
"except Exception:\n",
" IS_COLAB = False\n",
"\n",
"# TensorFlow ≥2.0 is required\n",
"import tensorflow as tf\n",
"from tensorflow import keras\n",
@ -69,6 +69,8 @@
" print(\"No GPU was detected. LSTMs and CNNs can be very slow without a GPU.\")\n",
" if IS_COLAB:\n",
" print(\"Go to Runtime > Change runtime and select a GPU hardware accelerator.\")\n",
" if IS_KAGGLE:\n",
" print(\"Go to Settings > Accelerator and select GPU.\")\n",
"\n",
"# Common imports\n",
"import numpy as np\n",
@ -1773,7 +1775,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.9"
"version": "3.7.10"
},
"nav_menu": {
"height": "381px",

34
18_reinforcement_learning.ipynb
View File

@ -20,7 +20,10 @@
"source": [
"<table align=\"left\">\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/18_reinforcement_learning.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
" <a href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/18_reinforcement_learning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
" </td>\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://kaggle.com/kernels/welcome?src=https://github.com/ageron/handson-ml2/blob/add-kaggle-badge/18_reinforcement_learning.ipynb\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" /></a>\n",
" </td>\n",
"</table>"
]
@ -29,13 +32,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"# Setup"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Setup\n",
"First, let's import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures. We also check that Python 3.5 or later is installed (although Python 2.x may work, it is deprecated so we strongly recommend you use Python 3 instead), as well as Scikit-Learn ≥0.20 and TensorFlow ≥2.0."
]
},
@ -49,19 +46,18 @@
"import sys\n",
"assert sys.version_info >= (3, 5)\n",
"\n",
"# Is this notebook running on Colab or Kaggle?\n",
"IS_COLAB = \"google.colab\" in sys.modules\n",
"IS_KAGGLE = \"kaggle_secrets\" in sys.modules\n",
"\n",
"if IS_COLAB or IS_KAGGLE:\n",
" !apt update && apt install -y libpq-dev libsdl2-dev swig xorg-dev xvfb\n",
" !pip install -q -U tf-agents pyvirtualdisplay gym[atari,box2d]\n",
"\n",
"# Scikit-Learn ≥0.20 is required\n",
"import sklearn\n",
"assert sklearn.__version__ >= \"0.20\"\n",
"\n",
"try:\n",
" # %tensorflow_version only exists in Colab.\n",
" %tensorflow_version 2.x\n",
" !apt update && apt install -y libpq-dev libsdl2-dev swig xorg-dev xvfb\n",
" !pip install -q -U tf-agents pyvirtualdisplay gym[atari,box2d]\n",
" IS_COLAB = True\n",
"except Exception:\n",
" IS_COLAB = False\n",
"\n",
"# TensorFlow ≥2.0 is required\n",
"import tensorflow as tf\n",
"from tensorflow import keras\n",
@ -71,6 +67,8 @@
" print(\"No GPU was detected. CNNs can be very slow without a GPU.\")\n",
" if IS_COLAB:\n",
" print(\"Go to Runtime > Change runtime and select a GPU hardware accelerator.\")\n",
" if IS_KAGGLE:\n",
" print(\"Go to Settings > Accelerator and select GPU.\")\n",
"\n",
"# Common imports\n",
"import numpy as np\n",
@ -3223,7 +3221,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.9"
"version": "3.7.10"
}
},
"nbformat": 4,

26
19_training_and_deploying_at_scale.ipynb
View File

@ -20,7 +20,10 @@
"source": [
"<table align=\"left\">\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/19_training_and_deploying_at_scale.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
" <a href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/19_training_and_deploying_at_scale.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
" </td>\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://kaggle.com/kernels/welcome?src=https://github.com/ageron/handson-ml2/blob/add-kaggle-badge/19_training_and_deploying_at_scale.ipynb\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" /></a>\n",
" </td>\n",
"</table>"
]
@ -43,20 +46,19 @@
"import sys\n",
"assert sys.version_info >= (3, 5)\n",
"\n",
"# Scikit-Learn ≥0.20 is required\n",
"import sklearn\n",
"assert sklearn.__version__ >= \"0.20\"\n",
"# Is this notebook running on Colab or Kaggle?\n",
"IS_COLAB = \"google.colab\" in sys.modules\n",
"IS_KAGGLE = \"kaggle_secrets\" in sys.modules\n",
"\n",
"try:\n",
" # %tensorflow_version only exists in Colab.\n",
" %tensorflow_version 2.x\n",
"if IS_COLAB or IS_KAGGLE:\n",
" !echo \"deb http://storage.googleapis.com/tensorflow-serving-apt stable tensorflow-model-server tensorflow-model-server-universal\" > /etc/apt/sources.list.d/tensorflow-serving.list\n",
" !curl https://storage.googleapis.com/tensorflow-serving-apt/tensorflow-serving.release.pub.gpg | apt-key add -\n",
" !apt update && apt-get install -y tensorflow-model-server\n",
" !pip install -q -U tensorflow-serving-api\n",
" IS_COLAB = True\n",
"except Exception:\n",
" IS_COLAB = False\n",
"\n",
"# Scikit-Learn ≥0.20 is required\n",
"import sklearn\n",
"assert sklearn.__version__ >= \"0.20\"\n",
"\n",
"# TensorFlow ≥2.0 is required\n",
"import tensorflow as tf\n",
@ -67,6 +69,8 @@
" print(\"No GPU was detected. CNNs can be very slow without a GPU.\")\n",
" if IS_COLAB:\n",
" print(\"Go to Runtime > Change runtime and select a GPU hardware accelerator.\")\n",
" if IS_KAGGLE:\n",
" print(\"Go to Settings > Accelerator and select GPU.\")\n",
"\n",
"# Common imports\n",
"import numpy as np\n",
@ -1250,7 +1254,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.9"
"version": "3.7.10"
}
},
"nbformat": 4,

20
README.md
View File

@ -11,27 +11,23 @@ python. It contains the example code and solutions to the exercises in the secon
## Quick Start
### Want to play with these notebooks online without having to install anything?
Use any of the following services.
Use any of the following services (I recommended Colab or Kaggle, since they offer free GPUs and TPUs).
**WARNING**: Please be aware that these services provide temporary environments: anything you do will be deleted after a while, so make sure you download any data you care about.
**WARNING**: _Please be aware that these services provide temporary environments: anything you do will be deleted after a while, so make sure you download any data you care about._
* **Recommended**: open this repository in [Colaboratory](https://colab.research.google.com/github/ageron/handson-ml2/blob/master/):
<a href="https://colab.research.google.com/github/ageron/handson-ml2/blob/master/"><img src="https://colab.research.google.com/img/colab_favicon.ico" width="90" /></a>
* <a href="https://colab.research.google.com/github/ageron/handson-ml2/blob/master/" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
* Or open it in [Binder](https://mybinder.org/v2/gh/ageron/handson-ml2/master):
<a href="https://mybinder.org/v2/gh/ageron/handson-ml2/master"><img src="https://matthiasbussonnier.com/posts/img/binder_logo_128x128.png" width="90" /></a>
* <a href="https://homl.info/kaggle/"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open in Kaggle" /></a>
* _Note_: Most of the time, Binder starts up quickly and works great, but when handson-ml2 is updated, Binder creates a new environment from scratch, and this can take quite some time.
* <a href="https://mybinder.org/v2/gh/ageron/handson-ml2/HEAD?filepath=%2Findex.ipynb"><img src="https://mybinder.org/badge_logo.svg" alt="Launch binder" /></a>
* Or open it in [Deepnote](https://beta.deepnote.com/launch?template=data-science&url=https%3A//github.com/ageron/handson-ml2/blob/master/index.ipynb):
<a href="https://beta.deepnote.com/launch?template=data-science&url=https%3A//github.com/ageron/handson-ml2/blob/master/index.ipynb"><img src="https://www.deepnote.com/static/illustration.png" width="150" /></a>
* <a href="https://homl.info/deepnote/"><img src="https://deepnote.com/buttons/launch-in-deepnote-small.svg" alt="Launch in Deepnote" /></a>
### Just want to quickly look at some notebooks, without executing any code?
Browse this repository using [jupyter.org's notebook viewer](https://nbviewer.jupyter.org/github/ageron/handson-ml2/blob/master/index.ipynb):
<a href="https://nbviewer.jupyter.org/github/ageron/handson-ml2/blob/master/index.ipynb"><img src="https://jupyter.org/assets/nav_logo.svg" width="150" /></a>
* <a href="https://nbviewer.jupyter.org/github/ageron/handson-ml2/blob/master/index.ipynb"><img src="https://raw.githubusercontent.com/jupyter/design/master/logos/Badges/nbviewer_badge.svg" alt="Render nbviewer" /></a>
_Note_: [github.com's notebook viewer](index.ipynb) also works but it is slower and the math equations are not always displayed correctly.
* [github.com's notebook viewer](https://github.com/ageron/handson-ml2/blob/master/index.ipynb) also works but it's not ideal: it's slower, the math equations are not always displayed correctly, and large notebooks often fail to open.
### Want to run this project using a Docker image?
Read the [Docker instructions](https://github.com/ageron/handson-ml2/tree/master/docker).

7
extra_autodiff.ipynb
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@ -20,7 +20,10 @@
"source": [
"<table align=\"left\">\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/extra_autodiff.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
" <a href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/extra_autodiff.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
" </td>\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://kaggle.com/kernels/welcome?src=https://github.com/ageron/handson-ml2/blob/master/extra_autodiff.ipynb\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" /></a>\n",
" </td>\n",
"</table>"
]
@ -909,7 +912,7 @@
"name": "python",
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"pygments_lexer": "ipython3",
"version": "3.7.9"
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},
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"height": "603px",

7
extra_gradient_descent_comparison.ipynb
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@ -20,7 +20,10 @@
"source": [
"<table align=\"left\">\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/extra_gradient_descent_comparison.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
" <a href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/extra_gradient_descent_comparison.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
" </td>\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://kaggle.com/kernels/welcome?src=https://github.com/ageron/handson-ml2/blob/master/extra_gradient_descent_comparison.ipynb\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" /></a>\n",
" </td>\n",
"</table>"
]
@ -268,7 +271,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.9"
"version": "3.7.10"
}
},
"nbformat": 4,

7
index.ipynb
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@ -12,7 +12,10 @@
"\n",
"<table align=\"left\">\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/index.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
" <a href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/index.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
" </td>\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://homl.info/kaggle/\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" /></a>\n",
" </td>\n",
"</table>"
]
@ -127,7 +130,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.9"
"version": "3.7.10"
},
"nav_menu": {},
"toc": {

7
math_differential_calculus.ipynb
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@ -19,7 +19,10 @@
"source": [
"<table align=\"left\">\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/math_differential_calculus.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
" <a href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/math_differential_calculus.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
" </td>\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://kaggle.com/kernels/welcome?src=https://github.com/ageron/handson-ml2/blob/master/math_differential_calculus.ipynb\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" /></a>\n",
" </td>\n",
"</table>"
]
@ -1780,7 +1783,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.1"
"version": "3.7.10"
},
"pycharm": {
"stem_cell": {

7
math_linear_algebra.ipynb
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@ -17,7 +17,10 @@
"source": [
"<table align=\"left\">\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/math_linear_algebra.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
" <a href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/math_linear_algebra.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
" </td>\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://kaggle.com/kernels/welcome?src=https://github.com/ageron/handson-ml2/blob/master/math_linear_algebra.ipynb\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" /></a>\n",
" </td>\n",
"</table>"
]
@ -3074,7 +3077,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.5"
"version": "3.7.10"
},
"toc": {
"toc_cell": false,

7
tools_matplotlib.ipynb
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@ -15,7 +15,10 @@
"source": [
"<table align=\"left\">\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/tools_matplotlib.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
" <a href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/tools_matplotlib.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
" </td>\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://kaggle.com/kernels/welcome?src=https://github.com/ageron/handson-ml2/blob/master/tools_matplotlib.ipynb\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" /></a>\n",
" </td>\n",
"</table>"
]
@ -1253,7 +1256,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.9"
"version": "3.7.10"
},
"toc": {
"toc_cell": true,

7
tools_numpy.ipynb
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@ -15,7 +15,10 @@
"source": [
"<table align=\"left\">\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/tools_numpy.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
" <a href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/tools_numpy.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
" </td>\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://kaggle.com/kernels/welcome?src=https://github.com/ageron/handson-ml2/blob/master/tools_numpy.ipynb\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" /></a>\n",
" </td>\n",
"</table>"
]
@ -2849,7 +2852,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.9"
"version": "3.7.10"
},
"toc": {
"toc_cell": false,

7
tools_pandas.ipynb
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@ -18,7 +18,10 @@
"source": [
"<table align=\"left\">\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/tools_pandas.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
" <a href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/tools_pandas.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
" </td>\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://kaggle.com/kernels/welcome?src=https://github.com/ageron/handson-ml2/blob/master/tools_pandas.ipynb\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" /></a>\n",
" </td>\n",
"</table>"
]
@ -2804,7 +2807,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.9"
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},
"toc": {
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