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] | null | null | null | Bloque 3 - Machine Learning/02_No supervisado/PCA/01_Ejemplo_PCA_teoria.ipynb | franciscocanon-thebridge/bootcamp_thebridge_PTSep20 | e81cbdcb9254fa46a3925f41c583748e25b459c0 | [
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e7fd70c980628ddecd51cbbfef81a490c1f26b4a | 31,936 | ipynb | Jupyter Notebook | A failed attempt in Data Cleaning for the Asheville Dataset/Clustering - Asheville.ipynb | shilpiBose29/CIS519-Project | e2ce2f3c7e6e313d262d049721c8314ef5595dfa | [
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e7fd71f17afa253100724a89968aece3f4e9f6e9 | 139,699 | ipynb | Jupyter Notebook | hackathons/lunar_image_classification/02_image_augmentation.ipynb | amitbcp/machine_learning_with_Scikit_Learn_and_TensorFlow | 37dda063e316503d53ac45f3b104a5cf1aaa4d78 | [
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] | 11 | 2019-12-19T08:55:52.000Z | 2021-10-01T13:07:13.000Z | hackathons/lunar_image_classification/02_image_augmentation.ipynb | amitbcp/sckit-learn-examples | 0c26f9178a0cf96ff79faf3b9b250dd5b8f6c49a | [
"MIT"
] | 5 | 2019-10-09T01:41:19.000Z | 2022-02-10T00:19:01.000Z | hackathons/lunar_image_classification/02_image_augmentation.ipynb | amitbcp/sckit-learn-examples | 0c26f9178a0cf96ff79faf3b9b250dd5b8f6c49a | [
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] | 7 | 2019-10-08T06:10:14.000Z | 2020-12-01T07:49:21.000Z | 101.157857 | 54,406 | 0.809469 | [
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e7fdb1bef1a2069e5b2677e47d4efb63b2093782 | 1,325 | ipynb | Jupyter Notebook | python/OCW/6.00/Simple Fibonacci.ipynb | chadwhiting/algorithms-python | 39910a1845f6385455c87e439daff3217c6bafb8 | [
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e7fdbe9dccfabae6ca68f5c111732affe8cbcc61 | 1,974 | ipynb | Jupyter Notebook | connection.ipynb | Db2-DTE-POC/Db2-Click-To-Containerize-Lab | e0c7f380bdbd2aa382cc5899ea327ed1ae63b331 | [
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e7fdc7d916351de165d7f6a896336f93a6780734 | 38,120 | ipynb | Jupyter Notebook | docs/tutorials/hello_many_worlds.ipynb | sarvex/tensorflow-quantum | 09ba5d35b082d8229458522471a0c1ca8b77198d | [
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"##### Copyright 2020 The TensorFlow Authors.",
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e7fdd01e23358499fa214c21e64b6db78395b8c5 | 35,576 | ipynb | Jupyter Notebook | examples/Untitled1.ipynb | iamtxena/coherent-state-discrimination | 88a347d08730b2436264da8737940412847e60bf | [
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"import numpy as np\nimport strawberryfields as sf\nimport random\nfrom typing import List\nimport tensorflow as tf",
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e7fdd9e521749df84ab8b7450fb4670fe4858a08 | 71,665 | ipynb | Jupyter Notebook | nn.ipynb | LaudateCorpus1/pytorch_notebooks | a5eee7c71d34d94b3711fe24474ee5b6b4b3056f | [
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e7fde3312083e913375e385783481cc33f67a64b | 341,376 | ipynb | Jupyter Notebook | examples/reinforcement_learning/hiv_simulator.ipynb | yoshavit/whynot | e33e56bae377b65fe87feac5c6246ae38f4586e8 | [
"MIT"
] | 376 | 2020-03-20T20:09:16.000Z | 2022-03-29T09:53:33.000Z | examples/reinforcement_learning/hiv_simulator.ipynb | yoshavit/whynot | e33e56bae377b65fe87feac5c6246ae38f4586e8 | [
"MIT"
] | 5 | 2020-04-20T10:19:34.000Z | 2021-11-03T09:36:28.000Z | examples/reinforcement_learning/hiv_simulator.ipynb | yoshavit/whynot | e33e56bae377b65fe87feac5c6246ae38f4586e8 | [
"MIT"
] | 41 | 2020-03-20T23:14:38.000Z | 2022-03-09T06:02:01.000Z | 1,009.988166 | 235,444 | 0.95268 | [
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"# Policy Gradients on HIV Simulator\n\nAn example of using WhyNot for reinforcement learning. WhyNot presents a unified interface with the [OpenAI gym](https://github.com/openai/gym), which makes it easy to run sequential decision making experiments on simulators in WhyNot.\n\nIn this notebook we com... | [
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e7fde5e669ce74ad52f23ecb606e46dc2773e01e | 29,618 | ipynb | Jupyter Notebook | _notebooks/2020-02-20-test.ipynb | hasanaliyev/notes | 02cf7a80d01ab7b0c939113ee8617ef2ad836999 | [
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"# Testing Fastpages Notebook Blog Post\n> A tutorial of fastpages for Jupyter notebooks.\n\n- toc: true \n- badges: true\n- comments: true\n- categories: [jupyter]\n- image: images/chart-preview.png",
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e7fdf20a46a9095768299e65dcd1ac3ba514d5b1 | 165,793 | ipynb | Jupyter Notebook | module3-databackedassertions/LS_DS_113_Making_Data_backed_Assertions_Assignment.ipynb | davidanagy/DS-Unit-1-Sprint-1-Dealing-With-Data | 67daffde8ef4d794fd92189e7057af14aaf78fde | [
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e7fdf5e5e7dbbac71e389cd1a1f62c596facd891 | 188,547 | ipynb | Jupyter Notebook | Week -12/TED Talk - Correlation - Comments/Correlation - Comments.ipynb | AshishJangra27/Data-Science-Specialization | 638e490fdd00e57fa8cdc7fcbc946307babd6d0a | [
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e7fe2a57bea6774a7b5406b04e5cbf848d4280ef | 136,797 | ipynb | Jupyter Notebook | CodeSprints/multicollinearity_methods.ipynb | jjessamy/EnvDatSci2021 | fbcbcbf394c790bb52079a64d7aece1ea8cd7310 | [
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"MIT"
] | null | null | null | CodeSprints/multicollinearity_methods.ipynb | jjessamy/EnvDatSci2021 | fbcbcbf394c790bb52079a64d7aece1ea8cd7310 | [
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] | null | null | null | 117.826873 | 78,444 | 0.841649 | [
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"## Multicollinearity and Regression Analysis\nIn this tutorial, we will be using a spatial dataset of county-level election and demographic statistics for the United States. This time, we'll explore different methods to diagnose and account for multicollinearity in our data. Specifically, we'll calcu... | [
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e7fe2bf423f8d636a396c0d884e651182a16cbef | 33,795 | ipynb | Jupyter Notebook | docs/test/testinnsending/.ipynb_checkpoints/person-enk-med-vedlegg-2021-checkpoint.ipynb | Skatteetaten/skattemelding | 3ab6ff9ea6cbabd8745904a840ae118844e3a357 | [
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e7fe46c24e7713bd38ce1c1b945c2a8cd4c1124c | 7,270 | ipynb | Jupyter Notebook | notebooks/Full Run.ipynb | joelmpiper/ga_project | 2caa34de771970429112149479539eda1d796e57 | [
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] | 1 | 2019-06-02T03:55:59.000Z | 2019-06-02T03:55:59.000Z | 28.735178 | 334 | 0.573728 | [
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e7fe4dbdce7d1032e12bf7fc7be5a22f24c69374 | 2,730 | ipynb | Jupyter Notebook | _notebooks/2020-03-02-jupyter-notebook.ipynb | fabge/snippets | eba330edc270990c5a4eb7bc5e96ae4f748b0026 | [
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"# \"Jupyter notebook\"\n> \"Setup and snippets for a smooth jupyter notebook experience\"\n- toc: False\n- branch: master\n- categories: [code snippets, jupyter, python]",
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e7fe616e3645d2f0a82b542a465cba174c66858e | 37,378 | ipynb | Jupyter Notebook | pyfund/Cap10/Notebooks/DSA-Python-Cap10-Lab04.ipynb | guimaraesalves/Data-Science-Academy | c24247cbf643a5a7c037013e4a122389aa523ac3 | [
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] | null | null | null | pyfund/Cap10/Notebooks/DSA-Python-Cap10-Lab04.ipynb | guimaraesalves/Data-Science-Academy | c24247cbf643a5a7c037013e4a122389aa523ac3 | [
"MIT"
] | null | null | null | pyfund/Cap10/Notebooks/DSA-Python-Cap10-Lab04.ipynb | guimaraesalves/Data-Science-Academy | c24247cbf643a5a7c037013e4a122389aa523ac3 | [
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] | null | null | null | 112.924471 | 14,188 | 0.854861 | [
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e7fe6b95ebc9680a8a8b4da8ccfe3075942b1543 | 182,454 | ipynb | Jupyter Notebook | Superposition/Superposition.ipynb | djjacqmin/QuantumKatas | 4ebaaa5705aacfd2ad443411eee26b6d5d1c3472 | [
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e7fe76d9f4fa37c6c098223b73c135e31c5f53d3 | 184,963 | ipynb | Jupyter Notebook | Project Bike Sharing/Bay_Area_Bike_Share_Analysis.ipynb | afshimono/data_analyst_nanodegree | 8a047abe3770fbd2865c078ecaa121ce096189c2 | [
"MIT"
] | null | null | null | Project Bike Sharing/Bay_Area_Bike_Share_Analysis.ipynb | afshimono/data_analyst_nanodegree | 8a047abe3770fbd2865c078ecaa121ce096189c2 | [
"MIT"
] | null | null | null | Project Bike Sharing/Bay_Area_Bike_Share_Analysis.ipynb | afshimono/data_analyst_nanodegree | 8a047abe3770fbd2865c078ecaa121ce096189c2 | [
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] | null | null | null | 146.796032 | 14,038 | 0.841893 | [
[
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"# Bay Area Bike Share Analysis\n\n## Introduction\n\n> **Tip**: Quoted sections like this will provide helpful instructions on how to navigate and use an iPython notebook.\n\n[Bay Area Bike Share](http://www.bayareabikeshare.com/) is a company that provides on-demand bike rentals for customers in San... | [
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e7fe7ae8f0fa3d7880d849ee86507e94306714a8 | 46,552 | ipynb | Jupyter Notebook | Projects/upper_sco_age/apsidal_motion_age.ipynb | gfeiden/Notebook | daf211f5c059d4b11dab0b57b05ec05918f430b2 | [
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] | null | null | null | Projects/upper_sco_age/apsidal_motion_age.ipynb | gfeiden/Notebook | daf211f5c059d4b11dab0b57b05ec05918f430b2 | [
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] | null | null | null | 117.853165 | 33,234 | 0.85543 | [
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"# Apsidal Motion Age for HD 144548\n\nHere, I am attempting to derive an age for the triple eclipsing hierarchical triple HD 144548 (Upper Scoripus member) based on the observed orbital precession (apsidal motion) of the inner binary system's orbit about the tertiary companion (star A). A value for t... | [
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e7fe7d2c51fa1259359ec1a38f67c1a6b2fb9cb4 | 869,440 | ipynb | Jupyter Notebook | covid_regression.ipynb | faisal004/covid-predicton-model | 03b08ace8bb250178ca09b55b9bd37c972b84dce | [
"MIT"
] | null | null | null | covid_regression.ipynb | faisal004/covid-predicton-model | 03b08ace8bb250178ca09b55b9bd37c972b84dce | [
"MIT"
] | null | null | null | covid_regression.ipynb | faisal004/covid-predicton-model | 03b08ace8bb250178ca09b55b9bd37c972b84dce | [
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] | null | null | null | 229.706737 | 255,964 | 0.864134 | [
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"#this project is created by faisal husian and faizal khan in covid pandemic, linear regression fits best for our data set rather than decision tree.\n\nimport pandas as pd",
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e7fe87b8997c7a5ba3e33a7662ae43f4b98323ea | 706,504 | ipynb | Jupyter Notebook | CORD19_Analysis.ipynb | davidcdupuis/CORD-19 | a158886544b9919f3d23fd803c47721f81cf683a | [
"MIT"
] | null | null | null | CORD19_Analysis.ipynb | davidcdupuis/CORD-19 | a158886544b9919f3d23fd803c47721f81cf683a | [
"MIT"
] | null | null | null | CORD19_Analysis.ipynb | davidcdupuis/CORD-19 | a158886544b9919f3d23fd803c47721f81cf683a | [
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] | null | null | null | 80.623531 | 33,336 | 0.641835 | [
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e7fe92d19970c867f5c590c7d3631bd0e4901d9e | 20,736 | ipynb | Jupyter Notebook | Named_Entity_Recognition.ipynb | David-Woroniuk/Data_Driven_Investor | ae429fb29a26f584523e663bf30768a002dda326 | [
"MIT"
] | 14 | 2020-09-24T15:38:08.000Z | 2022-02-03T17:43:54.000Z | Named_Entity_Recognition.ipynb | David-Woroniuk/Data_Driven_Investor | ae429fb29a26f584523e663bf30768a002dda326 | [
"MIT"
] | 2 | 2020-10-20T11:46:57.000Z | 2021-02-25T15:30:39.000Z | Named_Entity_Recognition.ipynb | David-Woroniuk/Data_Driven_Investor | ae429fb29a26f584523e663bf30768a002dda326 | [
"MIT"
] | 6 | 2020-10-12T09:29:47.000Z | 2021-07-28T16:59:39.000Z | 47.342466 | 191 | 0.491802 | [
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"# install the FedTools package:\n!pip install FedTools\n\n# install chart studio (Plotly):\n!pip install chart-studio\n\n# import pandas and numpy for data wrangling:\nimport pandas as pd\nimport numpy as np\n\n# from FedTools, import the MonetrayPolicyCommittee module to download statements:\nfrom F... | [
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e7fe9487ae8159203014b328afa4238b4c1d6084 | 123,618 | ipynb | Jupyter Notebook | DataOrganize_ver5.ipynb | SiyangJ/Epidemiology | f7115c08ade1f3b6525dea9b26beea0e9fadd9cd | [
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] | null | null | null | DataOrganize_ver5.ipynb | SiyangJ/Epidemiology | f7115c08ade1f3b6525dea9b26beea0e9fadd9cd | [
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] | null | null | null | DataOrganize_ver5.ipynb | SiyangJ/Epidemiology | f7115c08ade1f3b6525dea9b26beea0e9fadd9cd | [
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] | null | null | null | 35.098807 | 106 | 0.319573 | [
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e7fe9921716f6d81456f9a59fa8021207f2d4a92 | 97,107 | ipynb | Jupyter Notebook | python/trajectory-following/plots.ipynb | be2rlab/exoskeleton_ros | deffd353ec17c1a31de31273b4fd3e92391a82de | [
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] | null | null | null | python/trajectory-following/plots.ipynb | be2rlab/exoskeleton_ros | deffd353ec17c1a31de31273b4fd3e92391a82de | [
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] | null | null | null | python/trajectory-following/plots.ipynb | be2rlab/exoskeleton_ros | deffd353ec17c1a31de31273b4fd3e92391a82de | [
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] | null | null | null | 416.76824 | 27,056 | 0.93605 | [
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"%matplotlib inline\nimport numpy as np\nimport matplotlib.pyplot as plt\nwith open('data.txt','r') as f:\n data = f.read().split()\ni = 0\nplt_x = []\nplt_y = []\nplt_ros_x = []\nplt_ros_y = []\nplt_f = []\nplt_r = []\nplt_ros_f = []\nplt_ros_r = []\nplt_t = []\nend = len(data)%9\nt0 = float(data[... | [
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e7fec074fa122809def31fb1c4681dc3aa50785b | 551,547 | ipynb | Jupyter Notebook | Lesson 3: Introduction to Neural Networks/4 GradientDescent.ipynb | makeithappenlois/Udacity-AI | c82c7abaf179730740fecae76d388b14cbb3917c | [
"MIT"
] | 2 | 2020-01-03T17:36:15.000Z | 2020-01-13T18:24:14.000Z | Lesson 3: Introduction to Neural Networks/4 GradientDescent.ipynb | makeithappenlois/Udacity-AI | c82c7abaf179730740fecae76d388b14cbb3917c | [
"MIT"
] | null | null | null | Lesson 3: Introduction to Neural Networks/4 GradientDescent.ipynb | makeithappenlois/Udacity-AI | c82c7abaf179730740fecae76d388b14cbb3917c | [
"MIT"
] | 2 | 2020-01-03T16:30:16.000Z | 2020-01-08T15:03:14.000Z | 45.398551 | 104,184 | 0.659418 | [
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"# Implementing the Gradient Descent Algorithm\n\nIn this lab, we'll implement the basic functions of the Gradient Descent algorithm to find the boundary in a small dataset. First, we'll start with some functions that will help us plot and visualize the data.",
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"MIT"
] | null | null | null | combine-monitoring-and-reporting-mprns-and-gprns/README.ipynb | Rebeccacachia/projects | 4f8bebce1c0b4ba2d032aba658b38c3de9effb32 | [
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e7fee190429ebb4a0209aa72bf1a6d1c285610b7 | 773,708 | ipynb | Jupyter Notebook | 09-Civil_Air_Patrol_and_georegistration.ipynb | bwsi-remote-sensing-2020/12-Civil_Air_Patrol_and_Georegistration | 79ff884456d302bcf105bc8da1cd4b4f49285430 | [
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e7fef0687093065e9c194eacb1ca4c29dd0a24a4 | 25,956 | ipynb | Jupyter Notebook | temp/prepper_old.ipynb | esturdivant-usgs/geomorph-working-files | bd8d5391714ad0d8243580b6278c21ba419d83c5 | [
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"# Pre-process input data for coastal variable extraction\n\nAuthor: Emily Sturdivant; esturdivant@usgs.gov\n\n***\n\nPre-process files to be used in extractor.ipynb (Extract barrier island metrics along transects). See the project [README](https://github.com/esturdivant-usgs/BI-geomorph-extraction/bl... | [
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e7ff09a12bcc69c80a8edb7d2a43f583cc9694ec | 9,573 | ipynb | Jupyter Notebook | notebooks/Python-in-2-days/D1_L2_IPython/06-IPython-And-Shell-Commands.ipynb | mohr110/ml_training | 562df9c9c0d939d190c503753d75dcbac89c8340 | [
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e7ff1db6e0a006934ea92f7f8b8e8ce2377e9f24 | 24,124 | ipynb | Jupyter Notebook | session/sentiment/fnet-base.ipynb | AetherPrior/malaya | 45d37b171dff9e92c5d30bd7260b282cd0912a7d | [
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e7ff2a8a6f55a78e65610e798ff630ddcb362c35 | 117,266 | ipynb | Jupyter Notebook | stock-time-series-using-gru.ipynb | 23subbhashit/stock-price-prdictions-IBM-using-GRU | 3f208d57ea9202e48af76c2d914c9dc97b7e38d1 | [
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e7ff37c70fa86d74765a69d07c26087cf2ce623a | 14,299 | ipynb | Jupyter Notebook | _notebooks/2021-07-02-ch1-data-model.ipynb | jjmachan/fluent-python | 5e1b43902f215ce54b9bda54a4de7555fe13bf6f | [
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] | 4 | 2020-08-17T16:49:06.000Z | 2022-02-14T06:45:29.000Z | 31.762376 | 370 | 0.572319 | [
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e7ffa5781abdc6720a6bdb4061c11cb051ae1351 | 13,198 | ipynb | Jupyter Notebook | lostanlen_waspaa2019_fig3.ipynb | BirdVox/lostanlen_waspaa2019 | 4b3daa5b5945683e28511e157dfab1db1644f80a | [
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e7ffa669fa84c3ddb87339155f56ce5ef5a2f19c | 168,803 | ipynb | Jupyter Notebook | convolutional-neural-networks/mnist-mlp/mnist_mlp_solution.ipynb | staskh/deep-learning-v2-pytorch | 6be44d048381c90dce1c309f24eb9024ed31d362 | [
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e7ffb3f56a80b5b79bddb0d710569cdb408f106e | 94,829 | ipynb | Jupyter Notebook | eunice012716/Week2/ch4/4.10/example_ch4_10.ipynb | eunice012716/Intern-Training | c3bbf42448a0b41e96d88569b6cfd57d78338716 | [
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e7ffc35225efb6340c6103d150d4f8b3220e13bb | 17,129 | ipynb | Jupyter Notebook | test_notebooks/Untitled.ipynb | clarka34/jupyter_scisheets_widget | b411b2b0a9692cd1252b8d8b9d6cebcb9c53c8e8 | [
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],
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e7ffd8f5306e72b714b742ae76237bddff3bc061 | 749,790 | ipynb | Jupyter Notebook | Notebooks/RadarCOVID-Report/Daily/RadarCOVID-Report-2021-06-29.ipynb | pvieito/Radar-STATS | 9ff991a4db776259bc749a823ee6f0b0c0d38108 | [
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] | 3 | 2020-09-27T07:39:26.000Z | 2020-10-02T07:48:56.000Z | 84.359811 | 140,708 | 0.726485 | [
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[
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e7fff3134ef38b7f5713f8b7b3f07357db3a9abe | 2,363 | ipynb | Jupyter Notebook | chapter01/exercise10.ipynb | soerenberg/probability-and-computing-exercises | e892852e598d44e55c30350027c5f6f94915da7b | [
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] | 1 | 2021-11-04T14:41:24.000Z | 2021-11-04T14:41:24.000Z | chapter01/exercise10.ipynb | soerenberg/probability-and-computing-exercises | e892852e598d44e55c30350027c5f6f94915da7b | [
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e7fff5bd0b20d24c1f2e46b37613d11c4cbd8766 | 663,653 | ipynb | Jupyter Notebook | Wrangle-act.ipynb | AbdElrhman-m/Wrangle-and-Analyze-Data | 4b2da6e216a6a90c86ebfd2344458213e389a230 | [
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] | 1 | 2019-04-12T18:56:59.000Z | 2019-04-12T18:56:59.000Z | Wrangle-act.ipynb | AbdElrhman-m/Wrangle-and-Analyze-Data | 4b2da6e216a6a90c86ebfd2344458213e389a230 | [
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"code",
"code",
"code"
],
[
"markdown"
],
[
"code",
"code",
"code",
"code",
"code",
"code",
"code"
],
... |
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