mirror of
https://github.com/gsi-upm/sitc
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202 lines
7.9 KiB
Plaintext
202 lines
7.9 KiB
Plaintext
{
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"cells": [
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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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"![](files/images/EscUpmPolit_p.gif \"UPM\")"
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]
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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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"# Course Notes for Learning Intelligent Systems"
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]
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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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"Department of Telematic Engineering Systems, Universidad Politécnica de Madrid, © 2016 Carlos A. Iglesias"
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]
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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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"## [Introduction to Machine Learning](2_0_0_Intro_ML.ipynb)"
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]
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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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"# Table of Contents\n",
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"\n",
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"* [Machine Learning](#Machine-Learning)\n",
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"* [Machine learning algorithms](#Machine-learning-algorithms)\n",
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"\t\t* [Supervised machine learning model](#Supervised-machine-learning-model)\n",
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"\t\t* [Unsupervised machine learning model](#Unsupervised-machine-learning-model)\n",
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"* [sklearn interface](#sklearn-interface)\n",
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"* [References](#References)"
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]
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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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"# Machine Learning"
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]
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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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"This is an introduction of general ideas about machine learning and the general interface of scikit-learn, taken from the [scikit-learn tutorial](http://www.astroml.org/sklearn_tutorial/general_concepts.html). \n",
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"\n",
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"You can skip it during the lab session and read it later,"
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]
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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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"## Machine learning algorithms"
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]
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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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"Machine learning algorithms are programs that learn a model from a dataset with the aim of making predictions or learning structures to organize the data.\n",
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"\n",
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"In scikit-learn, machine learning algorithms take as an input a *numpy* array (n_samples, n_features), where\n",
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"* **n_samples**: number of samples. Each sample is an item to process (i.e. classify). A sample can be a document, a picture, a sound, a video, a row in database or CSV file, or whatever you can describe with a fixed set of quantitative traits.\n",
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"* **n_features**: The number of features or distinct traits that can be used to describe each item in a quantitative manner.\n",
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"\n",
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"The number of features should be defined in advanced and it can be very high dimensional (e.g. millions of features) with most of them being zeros for a given sample. In this case we may use (scipy.sparse) sparse matrices instead of (numpy) arrays so as to make the data fit in memory.\n",
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"\n",
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"The first step in machine learning is **identifying the relevant features** from the input data, and the second step is **extracting the features** from the input data. \n",
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"\n",
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"[Machine learning algorithms](http://machinelearningmastery.com/a-tour-of-machine-learning-algorithms/) can be classified according to learning style into:\n",
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"* **Supervised learning**: input data (training dataset) has a known label or result. Example problems are classification and regression. A model is prepared through a training process where it is required to make predictions and is corrected when those predictions are wrong. The training process continues until the model achieves a desired level of accuracy on the training data.\n",
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"* **Unsupervised learning**: input data is not labeled. A model is prepared by deducing structures present in the input data. This may be to extract general rules. Example problems are clustering, dimensionality reduction and association rule learning.\n",
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"* **Semi-supervised learning**:i nput data is a mixture of labeled and unlabeled examples. There is a desired prediction problem but the model must learn the structures to organize the data as well as make predictions. Example problems are classification and regression."
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]
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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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"### Supervised machine learning model"
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]
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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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"In *supervised machine learning models*, the machine learning algorithm takes as an input a training dataset, composed of feature vectors and labels, and produces a predictive model which is used for make prediction on new data.\n",
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"![](files/images/plot_ML_flow_chart_1.png)"
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]
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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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"### Unsupervised machine learning model"
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]
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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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"In *unsupervised machine learning models*, the machine learning model algorithm takes as an input the feature vectors and produces a predictive model that is used to fit its parameters so as to best summarize regularities found in the data.\n",
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"![](files/images/plot_ML_flow_chart_3.png)"
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]
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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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"## sklearn interface"
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]
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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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"scikit-learn has a uniform interface for all the estimators, some methods are only available is the estimator is supervised or unsupervised:\n",
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"\n",
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"* Available in *all estimators*:\n",
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" * **model.fit()**: fit training data. For supervised learning applications, this accepts two arguments: the data X and the labels y (e.g. model.fit(X, y)). For unsupervised learning applications, this accepts only a single argument, the data X (e.g. model.fit(X)).\n",
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"\n",
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"* Available in *supervised estimators*:\n",
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" * **model.predict()**: given a trained model, predict the label of a new set of data. This method accepts one argument, the new data X_new (e.g. model.predict(X_new)), and returns the learned label for each object in the array.\n",
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" * **model.predict_proba()**: For classification problems, some estimators also provide this method, which returns the probability that a new observation has each categorical label. In this case, the label with the highest probability is returned by model.predict().\n",
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"\n",
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"* Available in *unsupervised estimators*:\n",
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" * **model.transform()**: given an unsupervised model, transform new data into the new basis. This also accepts one argument X_new, and returns the new representation of the data based on the unsupervised model.\n",
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" * **model.fit_transform()**: some estimators implement this method, which performs a fit and a transform on the same input data.\n",
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"\n",
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"\n",
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"![](files/images/plot_ML_flow_chart_2.png)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"collapsed": false
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},
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"source": [
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"## References"
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]
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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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"* [General concepts of machine learning with scikit-learn](http://www.astroml.org/sklearn_tutorial/general_concepts.html)\n",
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"* [A Tour of Machine Learning Algorithms](http://machinelearningmastery.com/a-tour-of-machine-learning-algorithms/)"
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]
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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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"### Licence"
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]
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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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"The notebook is freely licensed under under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/). \n",
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"\n",
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"© 2016 Carlos A. Iglesias, Universidad Politécnica de Madrid."
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]
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}
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],
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"metadata": {
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"kernelspec": {
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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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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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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": "3.5.1"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}
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