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(fix): Whitespace in import removed
1 parent 755b1e9 commit 57147ff

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+52
-40
lines changed

Diff for: ‎data_preprocessing.ipynb

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"from sklearn.model_selection import train_test_split\n",
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"import os\n",
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"\n",
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"% matplotlib inline"
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"%matplotlib inline"
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]
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},
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{
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"metadata": {
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"anaconda-cloud": {},
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"kernelspec": {
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"display_name": "Python [default]",
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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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": "3.6.4"
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"version": "3.6.9"
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}
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},
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"nbformat": 4,

Diff for: ‎decision_tree_classification.ipynb

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"from sklearn.model_selection import train_test_split\n",
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"np.random.seed(123)\n",
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"\n",
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"% matplotlib inline"
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"%matplotlib inline"
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]
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},
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{
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"metadata": {
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"anaconda-cloud": {},
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"kernelspec": {
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"display_name": "Python [conda root]",
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"display_name": "Python 3",
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"language": "python",
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"name": "conda-root-py"
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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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": "3.6.4"
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"version": "3.6.9"
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},
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"toc": {
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"nav_menu": {},

Diff for: ‎decision_tree_regression.ipynb

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"from sklearn.model_selection import train_test_split\n",
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"np.random.seed(123)\n",
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"\n",
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"% matplotlib inline"
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"%matplotlib inline"
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]
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},
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{
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"metadata": {
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"anaconda-cloud": {},
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"kernelspec": {
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"display_name": "Python [conda root]",
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"display_name": "Python 3",
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"language": "python",
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"name": "conda-root-py"
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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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": "3.6.4"
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"version": "3.6.9"
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},
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"toc": {
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"nav_menu": {},

Diff for: ‎image_preprocessing.ipynb

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"source": [
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"# How to preprocess an image dataset for machine learning\n",
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"\n",
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"** What is preprocessing? ** \n",
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"**What is preprocessing?** \n",
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"Preprocessing describes the process of cleaning and converting a 'raw' (i.e. unprocessed) dataset into a clean dataset.\n",
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"\n",
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"![alt text](figures/preprocessing.png)\n",
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"\n",
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"In this notebook we will look at an **image dataset**.\n",
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"\n",
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"** Possible preprocessing steps **\n",
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"**Possible preprocessing steps**\n",
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"\n",
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"As mentioned already, the preprocessing steps you will need for your dataset depend on the nature of the dataset and models you want to train. Possible preprocessing steps for images are:\n",
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"\n",
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": 1,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2018-06-17T02:20:48.589362Z",
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"start_time": "2018-06-17T02:20:48.563901Z"
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}
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},
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"outputs": [],
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"outputs": [
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{
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"ename": "ModuleNotFoundError",
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"evalue": "No module named 'skimage'",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m<ipython-input-1-265b6fb9420c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mskimage\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mio\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 11\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mskimage\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mimg_as_float\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mskimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mresize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrotate\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'skimage'"
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]
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}
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],
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"source": [
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"import glob\n",
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"import os\n",
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"from skimage.transform import resize, rotate\n",
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"from skimage.color import rgb2gray\n",
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"\n",
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"% matplotlib inline\n",
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"%matplotlib inline\n",
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"warnings.simplefilter('ignore')"
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]
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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": "3.6.5"
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"version": "3.6.9"
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},
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"toc": {
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"nav_menu": {},

Diff for: ‎k_nearest_neighbour.ipynb

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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"end_time": "2018-03-26T14:32:41.915819Z",
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"from sklearn.model_selection import train_test_split\n",
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"np.random.seed(123)\n",
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"\n",
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"% matplotlib inline"
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"%matplotlib inline"
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]
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"metadata": {
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"anaconda-cloud": {},
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"kernelspec": {
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"display_name": "Python [conda root]",
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"display_name": "Python 3",
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"language": "python",
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"name": "conda-root-py"
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"name": "python3"
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"codemirror_mode": {
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"name": "python",
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"nbconvert_exporter": "python",
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"version": "3.6.9"
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"toc": {
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"nav_menu": {},

Diff for: ‎kmeans.ipynb

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"cell_type": "code",
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"end_time": "2018-03-11T13:39:31.237035Z",
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"from sklearn.datasets import make_blobs\n",
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"np.random.seed(123)\n",
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"\n",
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"% matplotlib inline"
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"%matplotlib inline"
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"name": "python",
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"version": "3.6.9"
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"nav_menu": {},

Diff for: ‎linear_regression.ipynb

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"name": "python",
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"version": "3.6.9"
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Diff for: ‎logistic_regression.ipynb

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},
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"import matplotlib.pyplot as plt\n",
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"np.random.seed(123)\n",
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"\n",
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Diff for: ‎perceptron.ipynb

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"from sklearn.model_selection import train_test_split\n",
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Diff for: ‎simple_neural_net.ipynb

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"from sklearn.model_selection import train_test_split\n",
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Diff for: ‎softmax_regression.ipynb

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