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[ [ [ "# Copyright Netherlands eScience Center <br>\n** Function : Computing AMET with Surface & TOA flux** <br>\n** Author : Yang Liu ** <br>\n** First Built : 2019.08.09 ** <br>\n** Last Update : 2019.09.09 ** <br>\nDescription : This notebook aims to compute AMET with TOA/surface flux fie...
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Handwritten Digits Recognition 02 - TensorFlow.ipynb
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[ [ [ "# Mask R-CNN - Train on Nuclei Dataset (updated from train_shape.ipynb)\n\n\nThis notebook shows how to train Mask R-CNN on your own dataset. To keep things simple we use a synthetic dataset of shapes (squares, triangles, and circles) which enables fast training. You'd still need a GPU, though, becau...
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ipynb
Jupyter Notebook
notebooks/2_socioeconomic_data_validation.ipynb
fernandascovino/pr-educacao
79793089552e75573cc77c90ccbf2cf04972ab42
[ "MIT" ]
null
null
null
notebooks/2_socioeconomic_data_validation.ipynb
fernandascovino/pr-educacao
79793089552e75573cc77c90ccbf2cf04972ab42
[ "MIT" ]
1
2019-04-03T14:20:06.000Z
2019-04-03T14:20:06.000Z
notebooks/2_socioeconomic_data_validation.ipynb
fernandascovino/pr-educacao
79793089552e75573cc77c90ccbf2cf04972ab42
[ "MIT" ]
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2019-03-15T12:48:44.000Z
2019-03-15T12:48:44.000Z
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[ [ [ "<h1>Índice<span class=\"tocSkip\"></span></h1>\n<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Socioeconomic-data-validation\" data-toc-modified-id=\"Socioeconomic-data-validation-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Socioeconomic data validation</a></span><ul class=\"...
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ipynb
Jupyter Notebook
ch08_Reinforcement-learning/ch16-reinforcement-learning.ipynb
pythonProjectLearn/TensorflowLearning
7a72ebea060ce0a0db9a00994e4725ec5d84c10a
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ch08_Reinforcement-learning/ch16-reinforcement-learning.ipynb
pythonProjectLearn/TensorflowLearning
7a72ebea060ce0a0db9a00994e4725ec5d84c10a
[ "MIT" ]
null
null
null
ch08_Reinforcement-learning/ch16-reinforcement-learning.ipynb
pythonProjectLearn/TensorflowLearning
7a72ebea060ce0a0db9a00994e4725ec5d84c10a
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[ [ [ "### Intro & Resources\n* [Sutton/Barto ebook](https://goo.gl/7utZaz); [Silver online course](https://goo.gl/AWcMFW)", "_____no_output_____" ], [ "### Learning to Optimize Rewards\n* Definitions: software *agents* make *observations* & take *actions* within an *environment*. In ret...
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ipynb
Jupyter Notebook
Instructions/.ipynb_checkpoints/climate_starter_Initial_file-2-checkpoint.ipynb
BklynIrish/sqlalchemy_challenge
d14ccc3b6d96032b404d39d36ec2008e948aa8ae
[ "ADSL" ]
1
2021-01-17T21:55:41.000Z
2021-01-17T21:55:41.000Z
Instructions/.ipynb_checkpoints/climate_starter_Initial_file-2-checkpoint.ipynb
BklynIrish/sqlalchemy_challenge
d14ccc3b6d96032b404d39d36ec2008e948aa8ae
[ "ADSL" ]
null
null
null
Instructions/.ipynb_checkpoints/climate_starter_Initial_file-2-checkpoint.ipynb
BklynIrish/sqlalchemy_challenge
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[ [ [ "# SQLAlchemy Homework - Surfs Up!\n\n### Before You Begin\n\n1. Create a new repository for this project called `sqlalchemy-challenge`. **Do not add this homework to an existing repository**.\n\n2. Clone the new repository to your computer.\n\n3. Add your Jupyter notebook and `app.py` to this folder....
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ipynb
Jupyter Notebook
lessons/03_Lesson03_doublet.ipynb
goodsang1023/aeropython
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lessons/03_Lesson03_doublet.ipynb
goodsang1023/aeropython
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lessons/03_Lesson03_doublet.ipynb
goodsang1023/aeropython
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2021-01-31T22:54:57.000Z
2021-01-31T22:54:57.000Z
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[ [ [ "###### Text provided under a Creative Commons Attribution license, CC-BY. Code under MIT license. (c)2014 Lorena A. Barba, Olivier Mesnard. Thanks: NSF for support via CAREER award #1149784.", "_____no_output_____" ], [ "##### Version 0.2 -- February 2014", "_____no_output__...
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Jupyter Notebook
Scr/trainning/.ipynb_checkpoints/Untitled-checkpoint.ipynb
ale-telefonica/market
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null
Scr/trainning/.ipynb_checkpoints/Untitled-checkpoint.ipynb
ale-telefonica/market
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[ "Apache-2.0" ]
null
null
null
Scr/trainning/.ipynb_checkpoints/Untitled-checkpoint.ipynb
ale-telefonica/market
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null
null
43.732904
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[ [ [ "import MySQLdb\nfrom sklearn.svm import LinearSVC\nfrom tensorflow import keras\nfrom keras.models import load_model\nimport tensorflow as tf\nfrom random import seed\nimport pandas as pd\nimport numpy as np\nimport re\nfrom re import sub\nimport os\nimport string\nimport tempfile\nimport pickle\nimp...
[ "code" ]
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d0207062d3a71fd632ea8360b4e9a2417560d6d8
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ipynb
Jupyter Notebook
sample.ipynb
AI-Guru/MMM-JSB
2cf0faeedc402b4574f292712632855675ae4037
[ "Apache-2.0" ]
72
2021-05-10T11:12:24.000Z
2022-03-30T17:49:06.000Z
sample.ipynb
AI-Guru/MMM-JSB
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[ "Apache-2.0" ]
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2021-06-12T10:10:44.000Z
2022-01-20T16:53:37.000Z
sample.ipynb
AI-Guru/MMM-JSB
2cf0faeedc402b4574f292712632855675ae4037
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2021-05-10T12:21:38.000Z
2022-03-10T14:37:16.000Z
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[ [ [ "# License.\nCopyright 2021 Tristan Behrens.\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicabl...
[ "markdown", "code" ]
[ [ "markdown", "markdown" ], [ "code", "code" ] ]
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Jupyter Notebook
examples/notebooks/generic_mle.ipynb
KishManani/statsmodels
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[ "BSD-3-Clause" ]
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2015-01-01T11:41:55.000Z
2022-03-31T17:03:24.000Z
examples/notebooks/generic_mle.ipynb
Ajisusanto136/statsmodels
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2015-01-01T00:33:45.000Z
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examples/notebooks/generic_mle.ipynb
Ajisusanto136/statsmodels
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[ "BSD-3-Clause" ]
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2015-01-02T21:32:31.000Z
2022-03-31T07:38:30.000Z
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[ [ [ "# Maximum Likelihood Estimation (Generic models)", "_____no_output_____" ], [ "This tutorial explains how to quickly implement new maximum likelihood models in `statsmodels`. We give two examples: \n\n1. Probit model for binary dependent variables\n2. Negative binomial model for c...
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source/Mlos.Notebooks/SmartCacheCPP.ipynb
HeatherJia/MLOS
b0a350fa817cd23763e29b3295a866838900f476
[ "MIT" ]
null
null
null
source/Mlos.Notebooks/SmartCacheCPP.ipynb
HeatherJia/MLOS
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[ "MIT" ]
null
null
null
source/Mlos.Notebooks/SmartCacheCPP.ipynb
HeatherJia/MLOS
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[ "MIT" ]
null
null
null
162.94347
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[ [ [ "# Connecting MLOS to a C++ application\n\nThis notebook walks through connecting MLOS to a C++ application within a docker container.\nWe will start a docker container, and run an MLOS Agent within it. The MLOS Agent will start the actual application, and communicate with it via a shared memory chann...
[ "markdown", "code", "markdown", "code", "markdown" ]
[ [ "markdown", "markdown", "markdown", "markdown", "markdown", "markdown", "markdown", "markdown", "markdown", "markdown" ], [ "code", "code" ], [ "markdown" ], [ "code", "code", "code", "code" ], [ "markdown" ] ]
d020886c0ea73756d5fc79f8e34898ec59765b30
79,879
ipynb
Jupyter Notebook
RML_example_org.ipynb
sa3036/Radio_ML_571M
fd034d4a390eea991e399882a39597b9ee36252a
[ "MIT" ]
2
2020-02-23T07:26:02.000Z
2022-01-28T07:15:33.000Z
RML_example_org.ipynb
sa3036/Radio_ML_571M
fd034d4a390eea991e399882a39597b9ee36252a
[ "MIT" ]
null
null
null
RML_example_org.ipynb
sa3036/Radio_ML_571M
fd034d4a390eea991e399882a39597b9ee36252a
[ "MIT" ]
3
2020-02-29T21:32:19.000Z
2021-04-12T01:28:23.000Z
119.222388
23,888
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[ [ [ "#Download the dataset from opensig\nimport urllib.request\nurllib.request.urlretrieve('http://opendata.deepsig.io/datasets/2016.10/RML2016.10a.tar.bz2', 'RML2016.10a.tar.bz2')", "_____no_output_____" ], [ "#decompress the .bz2 file into .tar file\nimport sys\nimport os\nimport bz2...
[ "code" ]
[ [ "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code" ] ]
d0208ea794343d2d6cac264eb0aadfe16c420f37
2,952
ipynb
Jupyter Notebook
100days/day 03 - next permutation.ipynb
gopala-kr/ds-notebooks
bc35430ecdd851f2ceab8f2437eec4d77cb59423
[ "MIT" ]
5
2018-05-09T04:02:04.000Z
2021-02-21T19:27:56.000Z
100days/day 03 - next permutation.ipynb
gopala-kr/ds-notebooks
bc35430ecdd851f2ceab8f2437eec4d77cb59423
[ "MIT" ]
null
null
null
100days/day 03 - next permutation.ipynb
gopala-kr/ds-notebooks
bc35430ecdd851f2ceab8f2437eec4d77cb59423
[ "MIT" ]
5
2018-02-23T22:08:28.000Z
2020-08-19T08:31:47.000Z
20.081633
64
0.392954
[ [ [ "## algorithm", "_____no_output_____" ] ], [ [ "def permute(values):\n n = len(values)\n \n # i: position of pivot\n for i in reversed(range(n - 1)):\n if values[i] < values[i + 1]:\n break\n else:\n # very last permutation\n value...
[ "markdown", "code", "markdown", "code" ]
[ [ "markdown" ], [ "code" ], [ "markdown" ], [ "code", "code" ] ]
d020a55eca1e01c7f22e441e5ad6124ad968bc14
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ipynb
Jupyter Notebook
fairness.ipynb
ravikirankb/machine-learning-tutorial
064937059ab7945d2c08ccdc839ca799f61bd1aa
[ "MIT" ]
null
null
null
fairness.ipynb
ravikirankb/machine-learning-tutorial
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[ "MIT" ]
null
null
null
fairness.ipynb
ravikirankb/machine-learning-tutorial
064937059ab7945d2c08ccdc839ca799f61bd1aa
[ "MIT" ]
null
null
null
14,429
14,429
0.722157
[ [ [ "### Fairness ###\n", "_____no_output_____" ], [ "##### This exercise we explore the concepts and techniques in fairness in machine learning #####\n<b> Through this exercise one can \n * Increase awareness of different types of biases that can occur\n * Explore feature data t...
[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
[ [ "markdown", "markdown" ], [ "code", "code", "code" ], [ "markdown", "markdown" ], [ "code" ], [ "markdown", "markdown" ], [ "code", "code", "code", "code", "code" ], [ "markdown" ], [ "code", "code", "code" ]...
d020bc2497c70729421b2fcb9fde0abe570c4d96
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ipynb
Jupyter Notebook
object_detection_face_detector.ipynb
lvisdd/object_detection_tutorial
bf201914392f3e0bb786f6c2724eff17df7e78f8
[ "Apache-2.0" ]
2
2019-08-18T02:43:25.000Z
2020-12-23T07:38:22.000Z
object_detection_face_detector.ipynb
lvisdd/object_detection_tutorial
bf201914392f3e0bb786f6c2724eff17df7e78f8
[ "Apache-2.0" ]
null
null
null
object_detection_face_detector.ipynb
lvisdd/object_detection_tutorial
bf201914392f3e0bb786f6c2724eff17df7e78f8
[ "Apache-2.0" ]
1
2019-08-27T09:57:13.000Z
2019-08-27T09:57:13.000Z
204.201316
121,306
0.864672
[ [ [ "<a href=\"https://colab.research.google.com/github/lvisdd/object_detection_tutorial/blob/master/object_detection_face_detector.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>", "_____no_output_____" ] ], [ ...
[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
[ [ "markdown" ], [ "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code", "code" ], [ "markdown" ], [ "code", ...
d020c008258d93f9003722b2f6464169e94f20b5
16,517
ipynb
Jupyter Notebook
day3.ipynb
msse-2021-bootcamp/team2-project
3915fd811be09e79d7ea5c9a368d7849ef5b629b
[ "BSD-3-Clause" ]
null
null
null
day3.ipynb
msse-2021-bootcamp/team2-project
3915fd811be09e79d7ea5c9a368d7849ef5b629b
[ "BSD-3-Clause" ]
22
2021-08-10T20:36:55.000Z
2021-08-20T02:35:02.000Z
day3.ipynb
msse-2021-bootcamp/team2-project
3915fd811be09e79d7ea5c9a368d7849ef5b629b
[ "BSD-3-Clause" ]
null
null
null
30.250916
162
0.516256
[ [ [ "# Writing a Molecular Monte Carlo Simulation\n\nStarting today, make sure you have the functions\n\n1. `calculate_LJ` - written in class\n1. `read_xyz` - provided in class\n1. `calculate_total_energy` - modified version provided in this notebook written for homework which has cutoff\n1. `calculate_di...
[ "markdown", "code", "markdown", "code", "markdown", "code" ]
[ [ "markdown" ], [ "code", "code" ], [ "markdown" ], [ "code", "code", "code", "code", "code", "code", "code", "code" ], [ "markdown" ], [ "code" ] ]
d020d48211309c57de15216ab18f4dbbb2e32500
18,357
ipynb
Jupyter Notebook
playground/eda.ipynb
tukai21/arxiv-ranking
5b54c1049c3012bec8f30b9e1ff20a1caa024911
[ "MIT" ]
null
null
null
playground/eda.ipynb
tukai21/arxiv-ranking
5b54c1049c3012bec8f30b9e1ff20a1caa024911
[ "MIT" ]
null
null
null
playground/eda.ipynb
tukai21/arxiv-ranking
5b54c1049c3012bec8f30b9e1ff20a1caa024911
[ "MIT" ]
null
null
null
31.219388
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[ [ [ "%load_ext autoreload\n%autoreload 2\n\nimport os\nimport re\nfrom glob import glob\nimport json\nimport numpy as np\nimport pandas as pd\nfrom difflib import SequenceMatcher\n\nimport matplotlib.pyplot as plt\nimport seaborn as sns", "_____no_output_____" ] ], [ [ "## Data Acq...
[ "code", "markdown", "code", "markdown", "code" ]
[ [ "code" ], [ "markdown" ], [ "code", "code", "code", "code", "code" ], [ "markdown", "markdown" ], [ "code", "code", "code", "code", "code", "code", "code", "code" ] ]
d020f5b1bd9f0c29260cc53abfe09763db1a4ae0
11,492
ipynb
Jupyter Notebook
Project/Starbucks/.ipynb_checkpoints/Starbucks-checkpoint.ipynb
kundan7kumar/Machine-Learning
8b62b68324713007c967a6120a0f48498992ce2f
[ "MIT" ]
null
null
null
Project/Starbucks/.ipynb_checkpoints/Starbucks-checkpoint.ipynb
kundan7kumar/Machine-Learning
8b62b68324713007c967a6120a0f48498992ce2f
[ "MIT" ]
null
null
null
Project/Starbucks/.ipynb_checkpoints/Starbucks-checkpoint.ipynb
kundan7kumar/Machine-Learning
8b62b68324713007c967a6120a0f48498992ce2f
[ "MIT" ]
null
null
null
33.8
918
0.505221
[ [ [ "## Portfolio Exercise: Starbucks\n<br>\n\n<img src=\"https://opj.ca/wp-content/uploads/2018/02/New-Starbucks-Logo-1200x969.jpg\" width=\"200\" height=\"200\">\n<br>\n<br>\n \n#### Background Information\n\nThe dataset you will be provided in this portfolio exercise was originally used as a take-home ...
[ "markdown", "code" ]
[ [ "markdown" ], [ "code", "code", "code", "code" ] ]
d020fa3c92bb830b6bc8b09c8376d3ab37f1afb0
78,731
ipynb
Jupyter Notebook
0702_ML19_clustering_kmeans.ipynb
msio900/minsung_machinelearning
0ef5185ed460167686dfc6555115f28f27b5f2f3
[ "Apache-2.0" ]
null
null
null
0702_ML19_clustering_kmeans.ipynb
msio900/minsung_machinelearning
0ef5185ed460167686dfc6555115f28f27b5f2f3
[ "Apache-2.0" ]
null
null
null
0702_ML19_clustering_kmeans.ipynb
msio900/minsung_machinelearning
0ef5185ed460167686dfc6555115f28f27b5f2f3
[ "Apache-2.0" ]
null
null
null
115.272328
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[ [ [ "## 리눅스 명령어\n", "_____no_output_____" ] ], [ [ "!ls", "sample_data Wholesale_customers_data.csv\n" ], [ "!ls -l", "total 20\ndrwxr-xr-x 1 root root 4096 Jun 15 13:37 sample_data\n-rw-r--r-- 1 root root 15021 Jul 2 05:28 Wholesale_customers_data.csv\...
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[ [ [ "# ***Introduction to Radar Using Python and MATLAB***\n## Andy Harrison - Copyright (C) 2019 Artech House\n<br/>\n\n# Coherent Detector\n***", "_____no_output_____" ], [ "The in-phase and quadrature signal components from a coherent detector may be written as (Equation 5.13)\n\n$$...
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[ [ [ "# Visualizing and Analyzing Jigsaw", "_____no_output_____" ] ], [ [ "import pandas as pd\nimport re\nimport numpy as np", "_____no_output_____" ] ], [ [ "In the previous section, we explored how to generate topics from a textual dataset using LDA. But h...
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[ [ [ "%matplotlib inline", "_____no_output_____" ] ], [ [ "\n# Brainstorm CTF phantom tutorial dataset\n\n\nHere we compute the evoked from raw for the Brainstorm CTF phantom\ntutorial dataset. For comparison, see [1]_ and:\n\n http://neuroimage.usc.edu/brainstorm/Tutorials/Phant...
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Complex_Systems.ipynb
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Jupyter Notebook
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matrix_two/day2_viz.ipynb
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matrix_two/day2_viz.ipynb
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[ [ [ "# Brackets\nhttps://app.codility.com/programmers/lessons/7-stacks_and_queues/brackets/", "_____no_output_____" ] ], [ [ "from typing import List\n\ndef solution(S) :\n stack : List[str] = []\n\n for ch in S :\n if ch == '{' or ch == '(' or ch == '[' : stack.append...
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credit_risk_ensemble.ipynb
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[ [ [ "# Ensemble Learning\n\n## Initial Imports", "_____no_output_____" ] ], [ [ "import warnings\nwarnings.filterwarnings('ignore')", "_____no_output_____" ], [ "import numpy as np\nimport pandas as pd\nfrom pathlib import Path\nfrom collections import Counter",...
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Xanadu3.ipynb
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Xanadu3.ipynb
olgOk/XanaduTraining
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Xanadu3.ipynb
olgOk/XanaduTraining
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[ [ [ "<a href=\"https://colab.research.google.com/github/olgOk/XanaduTraining/blob/master/Xanadu3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>", "_____no_output_____" ] ], [ [ "pip install pennylane", ...
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parte1.ipynb
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[ [ [ "# Parte 1 - Imagens coloridas\n\n**TIAGO PEREIRA DALL'OCA - 206341**", "_____no_output_____" ] ], [ [ "from scipy import misc\nfrom scipy import ndimage\nimport cv2\nimport numpy as np\nimport matplotlib.pyplot as plt", "_____no_output_____" ], [ "img = cv2...
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Jupyter Notebook
notebooks/2017-05-27-data-science-of-data-science.ipynb
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2019-02-03T17:09:28.000Z
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notebooks/2017-05-27-data-science-of-data-science.ipynb
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notebooks/2017-05-27-data-science-of-data-science.ipynb
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Python-Programming/Python-3-Bootcamp/13-Advanced Python Modules/.ipynb_checkpoints/05-Regular Expressions - re-checkpoint.ipynb
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Python-Programming/Python-3-Bootcamp/13-Advanced Python Modules/.ipynb_checkpoints/05-Regular Expressions - re-checkpoint.ipynb
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Python-Programming/Python-3-Bootcamp/13-Advanced Python Modules/.ipynb_checkpoints/05-Regular Expressions - re-checkpoint.ipynb
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[ [ [ "# Regular Expressions\n\nRegular expressions are text-matching patterns described with a formal syntax. You'll often hear regular expressions referred to as 'regex' or 'regexp' in conversation. Regular expressions can include a variety of rules, from finding repetition, to text-matching, and much mor...
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experiments/tuned_1v2/oracle.run2/trials/4/trial.ipynb
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experiments/tuned_1v2/oracle.run2/trials/4/trial.ipynb
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[ [ [ "# PTN Template\nThis notebook serves as a template for single dataset PTN experiments \nIt can be run on its own by setting STANDALONE to True (do a find for \"STANDALONE\" to see where) \nBut it is intended to be executed as part of a *papermill.py script. See any of the \nexperimentes with a pa...
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Jupyter Notebook
Jupyter notebook/Practice 4 - Cython.ipynb
marcomussi/RecommenderSystemPolimi
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Jupyter notebook/Practice 4 - Cython.ipynb
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Jupyter notebook/Practice 4 - Cython.ipynb
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[ [ [ "# Recommender Systems 2018/19\n\n### Practice 4 - Similarity with Cython\n\n\n### Cython is a superset of Python, allowing you to use C-like operations and import C code. Cython files (.pyx) are compiled and support static typing.", "_____no_output_____" ] ], [ [ "import time\...
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Jupyter Notebook
15_PDEs/15_PDEs.ipynb
ASU-CompMethodsPhysics-PHY494/PHY494-resources-2018
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15_PDEs/15_PDEs.ipynb
ASU-CompMethodsPhysics-PHY494/PHY494-resources-2018
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15_PDEs/15_PDEs.ipynb
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[ [ [ "# 15 PDEs: Solution with Time Stepping\n\n## Heat Equation\nThe **heat equation** can be derived from Fourier's law and energy conservation (see the [lecture notes on the heat equation (PDF)](https://github.com/ASU-CompMethodsPhysics-PHY494/PHY494-resources/blob/master/15_PDEs/15_PDEs_LectureNotes_He...
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ipynb
Jupyter Notebook
ML_course/ML_Contest_train.ipynb
Riwedieb/handson-ml
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ML_course/ML_Contest_train.ipynb
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ML_course/ML_Contest_train.ipynb
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[ [ [ "# Build a sklearn Pipeline for a to ML contest submission\nIn the ML_coruse_train notebook we at first analyzed the housing dataset to gain statistical insights and then e.g. features added new, \nreplaced missing values and scaled the colums using pandas dataset methods.\nIn the following we will us...
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examples/direct_fidelity_estimation.ipynb
mganahl/Cirq
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examples/direct_fidelity_estimation.ipynb
mganahl/Cirq
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examples/direct_fidelity_estimation.ipynb
mganahl/Cirq
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[ [ [ "\n\n# Running the Direct Fidelity Estimation (DFE) algorithm\nThis example walks through the steps of running the direct fidelity estimation (DFE) algorithm as described in these two papers:\n\n* Direct Fidelity Estimation from Few Pauli Measurements (https://arxiv.org/abs/1104.4695)\n* Practical cha...
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Jupyter Notebook
languages/south_asia/Gujarati_tutorial.ipynb
glaserti/tutorials
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languages/south_asia/Gujarati_tutorial.ipynb
glaserti/tutorials
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[ [ [ "# Gujarati with CLTK", "_____no_output_____" ], [ "See how you can analyse your Gujarati texts with <b>CLTK</b> ! <br>\nLet's begin by adding the `USER_PATH`..", "_____no_output_____" ] ], [ [ "import os\nUSER_PATH = os.path.expanduser('~')", "_____no...
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3. Landmark Detection and Tracking.ipynb
mitsunami/SLAM
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3. Landmark Detection and Tracking.ipynb
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3. Landmark Detection and Tracking.ipynb
mitsunami/SLAM
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[ [ [ "# Project 3: Implement SLAM \n\n---\n\n## Project Overview\n\nIn this project, you'll implement SLAM for robot that moves and senses in a 2 dimensional, grid world!\n\nSLAM gives us a way to both localize a robot and build up a map of its environment as a robot moves and senses in real-time. This is...
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Nets on Spectral data/01_PDU_Total_Designed_Inception.ipynb
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Nets on Spectral data/01_PDU_Total_Designed_Inception.ipynb
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Nets on Spectral data/01_PDU_Total_Designed_Inception.ipynb
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[ [ [ "### In this notebook we investigate a designed simple Inception network on PDU data", "_____no_output_____" ] ], [ [ "%reload_ext autoreload\n%autoreload 2\n%matplotlib inline", "_____no_output_____" ] ], [ [ "### Importing the libraries", "_____n...
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expressyeaself/models/lstm/LSTM_builder.ipynb
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[ [ [ "<h1>CREAZIONE MODELLO SARIMA REGIONE SARDEGNA", "_____no_output_____" ] ], [ [ "import pandas as pd\ndf = pd.read_csv('../../csv/regioni/sardegna.csv')\ndf.head()", "_____no_output_____" ], [ "df['DATA'] = pd.to_datetime(df['DATA'])", "_____no_output_...
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[ [ [ "# Preparation", "_____no_output_____" ] ], [ [ "import pandas as pd", "_____no_output_____" ], [ "df_mortality = pd.read_excel(io='MortalityDataWHR2021C2.xlsx')", "_____no_output_____" ], [ "df_happiness = pd.read_excel(io='DataForFigure...
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[ [ [ "# Occupation", "_____no_output_____" ], [ "### Introduction:\n\nSpecial thanks to: https://github.com/justmarkham for sharing the dataset and materials.\n\n### Step 1. Import the necessary libraries", "_____no_output_____" ] ], [ [ "import pandas as pd\nimp...
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[ [ [ "# Sentiment analysis with support vector machines\n\nIn this notebook, we will revisit a learning task that we encountered earlier in the course: predicting the *sentiment* (positive or negative) of a single sentence taken from a review of a movie, restaurant, or product. The data set consists of 300...
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[ [ [ "import pandas as pd\nimport numpy as np \nimport matplotlib.pyplot as plt\nfrom scipy.stats import norm ", "_____no_output_____" ] ], [ [ "## Distribución normal teórica\n\n\n$$P(X) = \\frac{1}{\\sigma \\sqrt{2 \\pi}} \\exp{\\left[-\\frac{1}{2}\\left(\\frac{X-\\mu}{\\sigma} \\...
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Equipped_AI_Test.ipynb
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[ [ [ "import pandas as pd\nimport warnings\nwarnings.filterwarnings('ignore')\nimport numpy as np\nfrom datetime import timedelta\nfrom functools import reduce", "_____no_output_____" ], [ "df_1 = pd.read_excel('Table1.xlsx')\ndf_2 = pd.read_excel('Table2.xlsx')", "_____no_output_...
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[ [ [ "# Transfer Learning Template", "_____no_output_____" ] ], [ [ "%load_ext autoreload\n%autoreload 2\n%matplotlib inline\n\n \nimport os, json, sys, time, random\nimport numpy as np\nimport torch\nfrom torch.optim import Adam\nfrom easydict import EasyDict\nimport matplotlib...
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2020-07-28T12:13:15.000Z
2020-07-28T12:13:15.000Z
Spark/HeartDataset-MLlib.ipynb
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[ [ [ "# Logistic Regression on 'HEART DISEASE' Dataset \nElif Cansu YILDIZ", "_____no_output_____" ] ], [ [ "from pyspark.sql import SparkSession\nfrom pyspark.sql.types import *\nfrom pyspark.sql.functions import col, countDistinct\nfrom pyspark.ml.feature import OneHotEncoderEsti...
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[ [ [ "# 一个完整的机器学习项目", "_____no_output_____" ] ], [ [ "import os\nimport tarfile\nimport urllib\nimport pandas as pd\nimport numpy as np\nfrom CategoricalEncoder import CategoricalEncoder", "_____no_output_____" ] ], [ [ "# 下载数据集", "_____no_output_____" ...
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02_usecases/sagemaker_recommendations/wip/02_Recommenders_Retrieval_AdHoc.ipynb
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[ [ [ "# Recommending Movies: Retrieval", "_____no_output_____" ], [ "Real-world recommender systems are often composed of two stages:\n\n1. The retrieval stage is responsible for selecting an initial set of hundreds of candidates from all possible candidates. The main objective of this ...
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[ [ [ "## CLEAN CODE", "_____no_output_____" ] ], [ [ "def is_even(num):\n if num % 2 == 0:\n return True\n elif num % 2 != 0: # We really don't need this condition\n return False\n", "_____no_output_____" ], [ "is_even(25)", "_____no_out...
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[ [ [ "## Instructions\n\nPlease make a copy and rename it with your name (ex: Proj6_Ilmi_Yoon). All grading points should be explored in the notebook but some can be done in a separate pdf file. \n\n*Graded questions will be listed with \"Q:\" followed by the corresponding points.* \n\nYou will be submitti...
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[ [ [ "##### Copyright 2018 The TF-Agents Authors.", "_____no_output_____" ] ], [ [ "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# https://...
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[ [ [ "## Querying Nexus knowledge graph using SPARQL\n\nThe goal of this notebook is to learn the basics of SPARQL. Only the READ part of SPARQL will be exposed.\n", "_____no_output_____" ], [ "## Prerequisites\n\nThis notebook assumes you've created a project within the AWS deployment ...
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[ [ [ "library(tidyverse)\n# Read in the csv file\ndf <- read.csv(\"dfCR.csv\")", "_____no_output_____" ], [ "head(df)", "_____no_output_____" ], [ "# Change column names\ndf$TPP <- df$X3P.\ndf$FGP <- df$FG.\ndf$FTP <- df$FT.", "_____no_output_____" ], ...
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[ [ [ "## 20 Sept 2019", "_____no_output_____" ], [ "<strong>RULES</strong><br>\n<strong>Date:</strong> Level 2 heading ## <br>\n<strong>Example Heading:</strong> Level 3 heading ###<br>\n<strong>Method Heading:</strong> Level 4 heading ####", "_____no_output_____" ], [ ...
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modules.ipynb
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modules.ipynb
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[ [ [ "# langages de script – Python\n\n## Modules et packages\n\n### M1 Ingénierie Multilingue – INaLCO\n\nclement.plancq@ens.fr", "_____no_output_____" ], [ "Les modules et les packages permettent d'ajouter des fonctionnalités à Python\n\nUn module est un fichier (```.py```) qui contie...
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[ [ [ "import pandas\nimport matplotlib.pyplot as plt\nimport glob\nimport numpy as np", "_____no_output_____" ], [ "input_data = pandas.read_csv('data/aileron_servo_and_transmitter_inputs.csv',\n delimiter=',', nrows=11)\ninput_data", "_____no_output___...
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experiments/main_simulations/plot_bivariate_identifiability.ipynb
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Batteries Included.ipynb
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IBM Cloud/WML/notebooks/regression/xgboost_scikit_wrapper/Watson OpenScale and Watson ML Engine Regression.ipynb
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[ [ [ "<img src=\"https://github.com/pmservice/ai-openscale-tutorials/raw/master/notebooks/images/banner.png\" align=\"left\" alt=\"banner\">", "_____no_output_____" ], [ "# Working with Watson Machine Learning", "_____no_output_____" ], [ "This notebook should be run...
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ipynb
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outlier_detection/training_outlier_detection.ipynb
felix-exel/kfserving-advanced
f75c5759c2ab1a5b0fba0ac0fda59f4e9062dfec
[ "MIT" ]
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2021-02-11T07:36:45.000Z
2022-03-15T09:35:13.000Z
outlier_detection/training_outlier_detection.ipynb
felix-exel/kfserving-advanced
f75c5759c2ab1a5b0fba0ac0fda59f4e9062dfec
[ "MIT" ]
null
null
null
outlier_detection/training_outlier_detection.ipynb
felix-exel/kfserving-advanced
f75c5759c2ab1a5b0fba0ac0fda59f4e9062dfec
[ "MIT" ]
3
2021-03-02T11:35:39.000Z
2022-02-23T04:06:39.000Z
40.216944
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0.493424
[ [ [ "# This Notebook uses a Session Event Dataset from E-Commerce Website (https://www.kaggle.com/mkechinov/ecommerce-behavior-data-from-multi-category-store and https://rees46.com/) to build an Outlier Detection based on an Autoencoder.", "_____no_output_____" ] ], [ [ "import mlf...
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d024d7301b88fc76d935836a20499b64fcd6a2c8
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ipynb
Jupyter Notebook
midterm-commands.ipynb
jstevenr/Spring-2018
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[ "Apache-2.0" ]
null
null
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midterm-commands.ipynb
jstevenr/Spring-2018
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[ "Apache-2.0" ]
null
null
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midterm-commands.ipynb
jstevenr/Spring-2018
239bd404ea4e19f6fdd3c09036d175f21c70d7af
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[ [ [ "import pandas as pd\nimport numpy as pd", "_____no_output_____" ], [ "data = pd.Series([0.25,0.5,0.75,1.0],index=[2,5,3,7])\ndata", "_____no_output_____" ], [ "df = pd.DataFrame([[1,2],[3,4],[5,6]],\n columns = ['foo','bar'],\n i...
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d024f264c1b25b0a02df0f2234dbda314f21b018
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ipynb
Jupyter Notebook
06_Linear_Regression_Boston_House_Prices.ipynb
alzaia/keras_projects
4e946b59b635b81300d55a8892175c34f186e011
[ "MIT" ]
1
2019-03-12T02:40:45.000Z
2019-03-12T02:40:45.000Z
06_Linear_Regression_Boston_House_Prices.ipynb
alzaia/keras_projects
4e946b59b635b81300d55a8892175c34f186e011
[ "MIT" ]
null
null
null
06_Linear_Regression_Boston_House_Prices.ipynb
alzaia/keras_projects
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[ "MIT" ]
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null
null
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[ [ [ "### Linear regression on Boston house prices", "_____no_output_____" ] ], [ [ "from keras import models\nfrom keras import layers\nimport numpy as np\nimport matplotlib.pyplot as plt", "_____no_output_____" ], [ "# Download the data\nfrom keras.datasets imp...
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