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d01c0cd630250940fcd3f5a7075f0d09a6e1510a | 38,008 | ipynb | Jupyter Notebook | src/notebooks/255-percentage-stacked-area-chart.ipynb | nrslt/The-Python-Graph-Gallery | 55898de66070ae716c95442466783ee986576e7d | [
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d01c0d484ce04960fdd0338cd0bda4b3fa76f35f | 202,676 | ipynb | Jupyter Notebook | airbnb-rj-1/Data Treatment.ipynb | reneoctavio/analysis | e46eab42d5c3705e9c1dc9c1112be548153e794a | [
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"# Airbnb - Rio de Janeiro\n* Download [data](http://insideairbnb.com/get-the-data.html)\n* We downloaded `listings.csv` from all monthly dates available\n\n## Questions\n1. What was the price and supply behavior before and during the pandemic?\n2. Does a title in English or Portuguese impact the pric... | [
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d01c1091d8e8d7a085357003c5ec50bd34b206b1 | 59,548 | ipynb | Jupyter Notebook | lecture-04/lab.ipynb | LuxTheDude/modern-ai-course | 1cff6515f53354a0c2a6ae783ec116bfc3e06b74 | [
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"## To Do\n\nDownload a dataset from Domain\n\nConvert all string columns to unique integers ---> could use hashes\n",
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d01c420196f060f43a8738a0f3fe2e3f7f90bb4f | 449,830 | ipynb | Jupyter Notebook | Oreilly_Natural Language Processing with Python/ch01.ipynb | db12138/Online_Courses_and_Materials | 6a113056f4fd2667556942b3bcc9608bdf9c2968 | [
"MIT"
] | 1 | 2019-12-25T12:42:30.000Z | 2019-12-25T12:42:30.000Z | Oreilly_Natural Language Processing with Python/.ipynb_checkpoints/ch01-checkpoint.ipynb | db12138/Online_Courses_and_Materials | 6a113056f4fd2667556942b3bcc9608bdf9c2968 | [
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] | null | null | null | Oreilly_Natural Language Processing with Python/.ipynb_checkpoints/ch01-checkpoint.ipynb | db12138/Online_Courses_and_Materials | 6a113056f4fd2667556942b3bcc9608bdf9c2968 | [
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] | 3 | 2019-07-29T04:47:06.000Z | 2021-02-22T23:20:30.000Z | 161.229391 | 315,289 | 0.559113 | [
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d01c4ba72ec897fb463bc2949269425437eda8aa | 6,957 | ipynb | Jupyter Notebook | Assignments/HW_3/Pyspark.ipynb | soundreaper/DS-2.3-Data-Science-in-Production | e1cafb9188ab0c141688d8a34a5ad56b76a903ae | [
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d01c4ff48f9bfe6bf4b4a154a5751bedc04acbcc | 3,978 | ipynb | Jupyter Notebook | day8/warm-up-day-8-ex2-solution.ipynb | btardio/dataviz_warmups | 6eca22f9a722766941ddbf203a5d5a3044c09a48 | [
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] | null | null | null | day8/warm-up-day-8-ex2-solution.ipynb | btardio/dataviz_warmups | 6eca22f9a722766941ddbf203a5d5a3044c09a48 | [
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] | null | null | null | 26.171053 | 112 | 0.418552 | [
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d01c63fe42b19ef4d662aa29cf0722aba1e75a90 | 34,594 | ipynb | Jupyter Notebook | openmdao/docs/openmdao_book/features/core_features/working_with_components/distributed_components.ipynb | markleader/OpenMDAO | 0579ea9fa976706a18d56f167a171d474b5f993e | [
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d01c8207d079ae3a9fe28c732018862ee4168d48 | 55,037 | ipynb | Jupyter Notebook | module4-logistic-regression/LS_DS_214.ipynb | cedro-gasque/DS-Unit-2-Linear-Models | 48f654d5b9b6954bbd7ebd523c0f9479ce1b8ac1 | [
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"Lambda School Data Science\n\n*Unit 2, Sprint 1, Module 4*\n\n---\n\n# Logistic Regression\n- do train/validate/test split\n- begin with baselines for classification\n- express and explain the intuition and interpretation of Logistic Regression\n- use sklearn.linear_model.LogisticRegression to fit an... | [
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d01c905782e7d69d6d47044c0533a30eec32d7d8 | 7,432 | ipynb | Jupyter Notebook | torchbenchmark/models/pytorch_struct/notebooks/Unsupervised_CFG.ipynb | ramiro050/benchmark | 0fdd791d7b23b66a2f74e16349efc3add2b66aef | [
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d01c95a1d871718d1fb4562d08b7d73418180b99 | 25,230 | ipynb | Jupyter Notebook | LearningOnMarkedData/week2/sklearn.linear_model_part2.ipynb | ishatserka/MachineLearningAndDataAnalysisCoursera | e82e772df2f4aec162cb34ac6127df10d14a625a | [
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] | null | null | null | LearningOnMarkedData/week2/sklearn.linear_model_part2.ipynb | ishatserka/MachineLearningAndDataAnalysisCoursera | e82e772df2f4aec162cb34ac6127df10d14a625a | [
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] | null | null | null | 50.159046 | 11,056 | 0.752517 | [
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d01c96edb67ed28e04de97fd908f6ded461990c4 | 58,737 | ipynb | Jupyter Notebook | Atividade 04.ipynb | Lucas-Otavio/MS211K-2s21 | 3828461e8fba2d30fce3fc4e45189b11a8007635 | [
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] | null | null | null | Atividade 04.ipynb | Lucas-Otavio/MS211K-2s21 | 3828461e8fba2d30fce3fc4e45189b11a8007635 | [
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] | null | null | null | Atividade 04.ipynb | Lucas-Otavio/MS211K-2s21 | 3828461e8fba2d30fce3fc4e45189b11a8007635 | [
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] | null | null | null | 51.478528 | 13,094 | 0.568483 | [
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d01cb1c614ee40ff3c971557d478e9fdce9bea13 | 3,179 | ipynb | Jupyter Notebook | 01_hello.ipynb | hannesloots/nbdev-tutorial | dd8bf2fc49de3486cc6cfa82d9d796a784e11eb0 | [
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d01cb914daa5ad93073bced2405a42b2fc714568 | 50,284 | ipynb | Jupyter Notebook | Assignment9- Dynamic.ipynb | bblank70/MSDS432 | f4cc8f42d1dfef8e5c42e92b9e356b43c2584052 | [
"MIT"
] | 1 | 2021-04-28T04:35:21.000Z | 2021-04-28T04:35:21.000Z | Assignment9- Dynamic.ipynb | bblank70/MSDS432 | f4cc8f42d1dfef8e5c42e92b9e356b43c2584052 | [
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] | null | null | null | Assignment9- Dynamic.ipynb | bblank70/MSDS432 | f4cc8f42d1dfef8e5c42e92b9e356b43c2584052 | [
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] | null | null | null | 159.126582 | 28,643 | 0.679441 | [
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d01cd77ba332fa91753a0feffde5081248ce2787 | 84,173 | ipynb | Jupyter Notebook | Chapter3/2019-03-03_AS_JJ_Chapter3-Part1.ipynb | alexanu/research | 31118d143f8720e4668a6d6ad7e5168fc2c244c5 | [
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] | null | null | null | Chapter3/2019-03-03_AS_JJ_Chapter3-Part1.ipynb | alexanu/research | 31118d143f8720e4668a6d6ad7e5168fc2c244c5 | [
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d01cee6ba5eb9a9e920816cc971048bcf937d8db | 3,635 | ipynb | Jupyter Notebook | playbook/tactics/initial-access/T1566.ipynb | haresudhan/The-AtomicPlaybook | 447b1d6bca7c3750c5a58112634f6bac31aff436 | [
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] | 8 | 2021-05-25T15:25:31.000Z | 2021-11-08T07:14:45.000Z | playbook/tactics/initial-access/T1566.ipynb | haresudhan/The-AtomicPlaybook | 447b1d6bca7c3750c5a58112634f6bac31aff436 | [
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] | 1 | 2021-08-23T17:38:02.000Z | 2021-10-12T06:58:19.000Z | playbook/tactics/initial-access/T1566.ipynb | haresudhan/The-AtomicPlaybook | 447b1d6bca7c3750c5a58112634f6bac31aff436 | [
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] | 2 | 2021-05-29T20:24:24.000Z | 2021-08-05T23:44:12.000Z | 69.903846 | 1,207 | 0.748831 | [
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"# T1566 - Phishing\nAdversaries may send phishing messages to elicit sensitive information and/or gain access to victim systems. All forms of phishing are electronically delivered social engineering. Phishing can be targeted, known as spearphishing. In spearphishing, a specific individual, company, o... | [
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] | 1 | 2021-09-29T08:21:56.000Z | 2021-09-29T08:21:56.000Z | 22,170 | 22,170 | 0.5682 | [
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d01d01d80a35e57154554dd399c3d8f781043c10 | 124,777 | ipynb | Jupyter Notebook | Section-08-Discretisation/08.01-Equal-width-discretisation.ipynb | cym3509/FeatureEngineering | 8237dbdec803f5bf91543466cff011108fb2c935 | [
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"## Discretisation\n\nDiscretisation is the process of transforming continuous variables into discrete variables by creating a set of contiguous intervals that span the range of the variable's values. Discretisation is also called **binning**, where bin is an alternative name for interval.\n\n\n### Di... | [
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d01d2ecd4bd595fb0947f289dd35fb299c66a89f | 18,244 | ipynb | Jupyter Notebook | homework/hw0/hw0.ipynb | aare1981/mcis6273_f21_datamining | 0bc0843712a05f05b9fae0b25a43391f11ba4f5c | [
"CC0-1.0"
] | 1 | 2021-08-31T23:49:23.000Z | 2021-08-31T23:49:23.000Z | homework/hw0/hw0.ipynb | aare1981/mcis6273_f21_datamining | 0bc0843712a05f05b9fae0b25a43391f11ba4f5c | [
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] | null | null | null | homework/hw0/hw0.ipynb | aare1981/mcis6273_f21_datamining | 0bc0843712a05f05b9fae0b25a43391f11ba4f5c | [
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] | 18 | 2021-08-22T20:43:46.000Z | 2021-12-15T08:19:44.000Z | 18,244 | 18,244 | 0.718209 | [
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"# MCIS6273 Data Mining (Prof. Maull) / Fall 2021 / HW0\n\n**This assignment is worth up to 20 POINTS to your grade total if you complete it on time.**\n\n| Points <br/>Possible | Due Date | Time Commitment <br/>(estimated) |\n|:---------------:|:--------:|:---------------:|\n| 20 | Wednesday, Sep 1 @... | [
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d01d3b492a4f59193788b65055f9bf9557e1948e | 931,614 | ipynb | Jupyter Notebook | numerical5.ipynb | fatginger1024/NumericalMethods | c4886d60da6fef8da9d0350bbc808cbd163d4623 | [
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] | null | null | null | numerical5.ipynb | fatginger1024/NumericalMethods | c4886d60da6fef8da9d0350bbc808cbd163d4623 | [
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] | null | null | null | numerical5.ipynb | fatginger1024/NumericalMethods | c4886d60da6fef8da9d0350bbc808cbd163d4623 | [
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] | null | null | null | 735.291239 | 139,344 | 0.935494 | [
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d01d3e308754b00f4b5b92d3d5404db84eb447db | 117,434 | ipynb | Jupyter Notebook | section_robot/ideal_robot9.ipynb | MasahiroOgawa/LNPR_BOOK_CODES | 112e9ce1b1312d77651c5958d44dbcd2ba225c19 | [
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"MIT"
] | 3 | 2018-11-07T04:33:13.000Z | 2018-12-31T01:35:16.000Z | section_robot/ideal_robot9.ipynb | MasahiroOgawa/LNPR_BOOK_CODES | 112e9ce1b1312d77651c5958d44dbcd2ba225c19 | [
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] | 116 | 2019-04-18T08:35:53.000Z | 2022-03-24T05:17:46.000Z | 110.47413 | 72,767 | 0.778488 | [
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d01d5402657e153fe23430e75c236d684d82137f | 80,885 | ipynb | Jupyter Notebook | MVA/TMVA_tutorial_classification_tmva_app.py.nbconvert.ipynb | LailinXu/hepstat-tutorial | 201b21c980bb29a9b81608b832475b3c356c8523 | [
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d01d73a62806e2bbe3b3507f32e3b7de8fa211a0 | 5,615 | ipynb | Jupyter Notebook | Colab Notebooks/competition/valuelabs_ml_hiring_challenge.ipynb | ankschoubey/notes | e8f86e90ceb93282073c1760bedcfbb8ad35a1df | [
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] | null | null | null | Colab Notebooks/competition/valuelabs_ml_hiring_challenge.ipynb | ankschoubey/notes | e8f86e90ceb93282073c1760bedcfbb8ad35a1df | [
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] | null | null | null | 5,615 | 5,615 | 0.648976 | [
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[
"from google.colab import drive\ndrive.mount('/content/drive')",
"_____no_output_____"
],
[
"from fastai import *\nfrom fastai.datasets import *\nfrom fastai.text import *\nimport pandas as pd\nfrom tqdm import tqdm",
"_____no_output_____"
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d01d810ce7ea79cc82ee3af11c4f2cbea93e89fe | 622,772 | ipynb | Jupyter Notebook | Notebooks/RadarCOVID-Report/Daily/RadarCOVID-Report-2020-11-07.ipynb | pvieito/Radar-STATS | 9ff991a4db776259bc749a823ee6f0b0c0d38108 | [
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] | 9 | 2020-10-14T16:58:32.000Z | 2021-10-05T12:01:56.000Z | Notebooks/RadarCOVID-Report/Daily/RadarCOVID-Report-2020-11-07.ipynb | pvieito/Radar-STATS | 9ff991a4db776259bc749a823ee6f0b0c0d38108 | [
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d01d9263b00a168712a3e8e33cb4609a3d2f239f | 4,683 | ipynb | Jupyter Notebook | notebooks/custom-conversions.ipynb | openscm/openscm-units | fcada9ec83e155e4b80f120d2294053a42f1e8e7 | [
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] | 4 | 2020-10-30T10:50:29.000Z | 2021-11-03T22:14:27.000Z | docs/source/notebooks/custom-conversions.ipynb | openscm-project/openscm-units | f97fac6c1ac00dd507e23d55949d8d45b07bf9a8 | [
"BSD-3-Clause"
] | 33 | 2020-03-26T05:36:55.000Z | 2022-02-08T09:28:25.000Z | docs/source/notebooks/custom-conversions.ipynb | openscm-project/openscm-units | f97fac6c1ac00dd507e23d55949d8d45b07bf9a8 | [
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"# NBVAL_IGNORE_OUTPUT\nimport traceback\n\nimport pandas as pd\n\nfrom openscm_units import ScmUnitRegistry",
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d01d9edfd0691244266b87d31df294699f62f5a1 | 20,862 | ipynb | Jupyter Notebook | playground-tmqa-1.ipynb | RomainClaret/mse.thesis.code | 11837100c438d376a90392018ed69fff067c8ddf | [
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] | null | null | null | playground-tmqa-1.ipynb | RomainClaret/mse.thesis.code | 11837100c438d376a90392018ed69fff067c8ddf | [
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] | null | null | null | 112.16129 | 8,170 | 0.62041 | [
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d01da1941442457dcf8bdf6456201f8ffd6e3870 | 331,625 | ipynb | Jupyter Notebook | PotencialEletrico/Quest10Graph.ipynb | BrunoZimmer/TeoriaEletromagnetica | e035c5ff72eb82930fb7b640d2cd65a2ce3fc9a4 | [
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"MIT"
] | null | null | null | PotencialEletrico/Quest10Graph.ipynb | BrunoZimmer/TeoriaEletromagnetica | e035c5ff72eb82930fb7b640d2cd65a2ce3fc9a4 | [
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] | null | null | null | 1,305.610236 | 174,203 | 0.82729 | [
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d01da5fd4d09f03b097fd3644de18d4b79b432d6 | 4,499 | ipynb | Jupyter Notebook | testing/loss.ipynb | kamildar/cyclegan | e3ab1d7987aff9080ef9063d0005bfc97f80c32c | [
"MIT"
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] | null | null | null | testing/loss.ipynb | kamildar/cyclegan | e3ab1d7987aff9080ef9063d0005bfc97f80c32c | [
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[
[
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"_____no_output_____"
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[
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d01db1aa74ee13756b8c810f9f2241d779b941cf | 50,197 | ipynb | Jupyter Notebook | plot_noise_results.ipynb | Seanny123/rnn-comparison | fab64ed929e8a5838d5e82a3697e5c1bab92c664 | [
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] | 6 | 2016-11-05T16:05:45.000Z | 2020-07-27T13:28:05.000Z | plot_noise_results.ipynb | Seanny123/rnn-comparison | fab64ed929e8a5838d5e82a3697e5c1bab92c664 | [
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] | null | null | null | plot_noise_results.ipynb | Seanny123/rnn-comparison | fab64ed929e8a5838d5e82a3697e5c1bab92c664 | [
"MIT"
] | 1 | 2019-11-19T05:21:40.000Z | 2019-11-19T05:21:40.000Z | 193.065385 | 22,492 | 0.904297 | [
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d01dc121ea4c1d3b4d87f32afbecb9e480d3d004 | 80,578 | ipynb | Jupyter Notebook | 01_rl_introduction__markov_decision_process/2_tower_of_hanoi_intro.ipynb | loftiskg/rl-course | 27cd62fbb9573a535acc56279ab5786f20549b6d | [
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] | null | null | null | 01_rl_introduction__markov_decision_process/2_tower_of_hanoi_intro.ipynb | loftiskg/rl-course | 27cd62fbb9573a535acc56279ab5786f20549b6d | [
"Apache-2.0"
] | null | null | null | 01_rl_introduction__markov_decision_process/2_tower_of_hanoi_intro.ipynb | loftiskg/rl-course | 27cd62fbb9573a535acc56279ab5786f20549b6d | [
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[
[
"<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Tower-of-Hanoi\" data-toc-modified-id=\"Tower-of-Hanoi-1\">Tower of Hanoi</a></span></li><li><span><a href=\"#Learning-Outcomes\" data-toc-modified-id=\"Learning-Outcomes-2\">Lear... | [
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d01dc55c8219cd36a3553dc8854b04b04f3d08b0 | 25,863 | ipynb | Jupyter Notebook | thesis_code/auto_detection.ipynb | hhodac/keras-yolo3 | bd9f1595d271c4c5d340796b352b0254c9e63322 | [
"MIT"
] | null | null | null | thesis_code/auto_detection.ipynb | hhodac/keras-yolo3 | bd9f1595d271c4c5d340796b352b0254c9e63322 | [
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] | null | null | null | thesis_code/auto_detection.ipynb | hhodac/keras-yolo3 | bd9f1595d271c4c5d340796b352b0254c9e63322 | [
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] | null | null | null | 49.641075 | 3,403 | 0.536017 | [
[
[
"# Auto detection to main + 4 cropped images\n**Pipeline:**\n\n1. Load cropped image csv file\n2. Apply prediction\n3. Save prediction result back to csv file\n* pred_value\n* pred_cat\n* pred_bbox",
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d01dc94860ba7cd3912d213a050f30213666f5f4 | 18,280 | ipynb | Jupyter Notebook | paper_experiments_work_log/speaker_recognition.ipynb | cgnorthcutt/EgoCom-Dataset | 4f17ea5447e6990071dbab4936cc3e713551a3f4 | [
"MIT"
] | 36 | 2020-11-05T20:30:18.000Z | 2021-12-07T04:35:35.000Z | paper_experiments_work_log/speaker_recognition.ipynb | cgnorthcutt/EgoCom-Dataset | 4f17ea5447e6990071dbab4936cc3e713551a3f4 | [
"MIT"
] | 2 | 2020-11-07T21:39:41.000Z | 2020-11-07T21:45:06.000Z | paper_experiments_work_log/speaker_recognition.ipynb | cgnorthcutt/EgoCom-Dataset | 4f17ea5447e6990071dbab4936cc3e713551a3f4 | [
"MIT"
] | 5 | 2020-11-07T20:46:15.000Z | 2021-11-06T14:05:46.000Z | 38.083333 | 1,177 | 0.580963 | [
[
[
"from egocom import audio\nfrom egocom.multi_array_alignment import gaussian_kernel\nfrom egocom.transcription import async_srt_format_timestamp\nfrom scipy.io import wavfile\nimport os\nimport numpy as np\nimport pandas as pd\nfrom sklearn.metrics import accuracy_score\nfrom egocom.transcription impo... | [
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d01dd1d7ec7f0b13e20998a703611cf0fd94532e | 20,954 | ipynb | Jupyter Notebook | wandb/run-20211017_221447-2snzs8gh/tmp/code/_session_history.ipynb | Programmer-RD-AI/Intel-Image-Classification-V2 | 4be91f82c5ec699d6ea91ca068167a72d4e63723 | [
"Apache-2.0"
] | null | null | null | wandb/run-20211017_221447-2snzs8gh/tmp/code/_session_history.ipynb | Programmer-RD-AI/Intel-Image-Classification-V2 | 4be91f82c5ec699d6ea91ca068167a72d4e63723 | [
"Apache-2.0"
] | null | null | null | wandb/run-20211017_221447-2snzs8gh/tmp/code/_session_history.ipynb | Programmer-RD-AI/Intel-Image-Classification-V2 | 4be91f82c5ec699d6ea91ca068167a72d4e63723 | [
"Apache-2.0"
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[
[
"from torchvision.models import *\nimport wandb\nfrom sklearn.model_selection import train_test_split\nimport os,cv2\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom torch.nn import *\nimport torch,torchvision\nfrom tqdm import tqdm\ndevice = 'cuda'\nPROJECT_NAME = 'Intel-Image-Classificatio... | [
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d01e6673f28a5ed622f563542b3af13485088507 | 8,471 | ipynb | Jupyter Notebook | notebooks/keras/train.ipynb | Petr-By/qtpyvis | 0b9a151ee6b9a56b486c2bece9c1f03414629efc | [
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d01e6f9ac48e76695054e553bb419552c2dc56d8 | 439,450 | ipynb | Jupyter Notebook | R5.Bayesian_Regression/Bayesian_regression_professor.ipynb | ML4DS/ML4all | 7336489dcb87d2412ad62b5b972d69c98c361752 | [
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] | 27 | 2016-11-30T17:34:00.000Z | 2022-03-23T23:11:48.000Z | R5.Bayesian_Regression/Bayesian_regression_professor.ipynb | ML4DS/ML4all | 7336489dcb87d2412ad62b5b972d69c98c361752 | [
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] | 14 | 2016-11-30T17:34:18.000Z | 2021-09-15T09:53:32.000Z | 263.77551 | 88,512 | 0.910074 | [
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d01e73ff3c22b0145af3b1e9a789f5d248ed8c82 | 424,178 | ipynb | Jupyter Notebook | study_roadmaps/3_image_processing_deep_learning_roadmap/3_deep_learning_advanced/1_Blocks in Deep Learning Networks/3) Resnet V2 Block (Type - 1).ipynb | arijitgupta42/monk_v1 | bc7c57ad342f96fc779ac8db530d6ee614d093c0 | [
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d01e7aa39a28d592a321f4077c48786e3ccf3e7f | 46,430 | ipynb | Jupyter Notebook | Tutorial-GRHD_Equations-Cartesian.ipynb | rhaas80/nrpytutorial | 4398cd6b5a071c8fb8b2b584a01f07a4591dd5f4 | [
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d01e7c762c0124364c4faa5621abbee1c291be21 | 200,286 | ipynb | Jupyter Notebook | committee103s5.ipynb | sashkarivkind/imagewalker | 999e1ae78cfe1512e1be894d9e7891a7d0c41233 | [
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d01e836d090df6e298bd255ad94914eff61faf28 | 9,516 | ipynb | Jupyter Notebook | Algorithms.ipynb | BeanHam/STA-663-Project | 64e4b5ea11b937b3b8ef9e200ce5db0b7785663e | [
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] | 1 | 2019-12-19T17:32:59.000Z | 2019-12-19T17:32:59.000Z | 38.370968 | 118 | 0.476986 | [
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d01e89bc3a5b15812cc76cd2a443a93cf73dfd31 | 409,334 | ipynb | Jupyter Notebook | Poster/2021-03-11_small_multiples_DF.ipynb | ph1001/Data_Visualisation_Project_Group_I | 6ae4ba6ede9a5b827c9a6c0617c206cc352c512b | [
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] | null | null | null | cn/examen/Examen - Proba practică.ipynb | GabrielMajeri/teme-fmi | b4d7a416a5ca71b76d66b9407ad2b8ee2af9301e | [
"MIT"
] | 59 | 2020-01-22T11:39:59.000Z | 2022-03-28T00:19:06.000Z | 206.849057 | 37,172 | 0.907025 | [
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d01e906e9e5e0dc8822aba81b67f96a6ffc56081 | 17,869 | ipynb | Jupyter Notebook | _notebooks/2020-12-26-Array-Visualiser.ipynb | logicatcore/scratchpad | 0460fff340a893a8245ba684a1b96825228e3d1d | [
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"# Input data representation as 2D array of 3D blocks\n> An easy way to represent input data to neural networks or any other machine learning algorithm in the form of 2D array of 3D-blocks\n\n- toc: false\n- branch: master\n- badges: true\n- comments: true\n- categories: [machine learning, jupyter, gr... | [
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d01e9e6e267f0396dc454a608390a983297b0dcc | 361,679 | ipynb | Jupyter Notebook | jupman-tests.ipynb | DavidLeoni/iep | c8b62d0e896003b58ab9cd45265bffce5c8fddeb | [
"Apache-2.0"
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"Apache-2.0"
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"Apache-2.0"
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d01ea93a3506ed2be6aa8ff31aa58d658b6dfc07 | 142,768 | ipynb | Jupyter Notebook | bike-sharing-demand/bike-sharing-demand-rf.ipynb | jaepil-choi/Kaggle_bikeshare | 338705ae239b6c332276fc68c25ca202e4713d2f | [
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"MIT"
] | null | null | null | bike-sharing-demand/bike-sharing-demand-rf.ipynb | jaepil-choi/Kaggle_bikeshare | 338705ae239b6c332276fc68c25ca202e4713d2f | [
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d01eb4731f08d86ae6c2d1bc2281de260cd36515 | 18,541 | ipynb | Jupyter Notebook | code/model_his_her_tfidf_nmf.ipynb | my321/project4_econtwitter | 1ce705fea7da10b1954e2f6fc64f80ec1881bcc6 | [
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d01ec756903adcfddddb46e8049cecf6153b35b1 | 57,008 | ipynb | Jupyter Notebook | 00_Variables_to_Classes.ipynb | Zqs0527/geothermics | 8001c93cee3091e8d5e0d4dc3fabbf1463aa4c15 | [
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"# From Variables to Classes\n## A short Introduction \n\nPython - as any programming language - has many extensions and libraries at its disposal. Basically, there exist libraries for everything. \n\n<center>But what are **libraries**? </center> \n\nBasically, **libraries** are a collection of me... | [
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d01ec80d2418020e47cdc7f9a9320ec1fd867b06 | 66,459 | ipynb | Jupyter Notebook | Missions_to_Mars/mission_to_mars.ipynb | yawavi92/web-scraping-challenge | 9df99a4523cfd08231f9ce6802255858ad2ed098 | [
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"# Dependencies\nfrom bs4 import BeautifulSoup as bs\nimport requests\nimport pymongo\nfrom splinter import Browser\nfrom webdriver_manager.chrome import ChromeDriverManager\nimport pandas as pd",
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d01ed02ad24082dce65457234fa9842f894ef752 | 204,541 | ipynb | Jupyter Notebook | notebooks/14-mw-prediction-space.ipynb | mwegrzyn/volume-wise-language | ed24b11667e6b26d3ed09ce0aae383c26852821c | [
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] | 1 | 2019-11-19T13:48:33.000Z | 2019-11-19T13:48:33.000Z | notebooks/14-mw-prediction-space.ipynb | mwegrzyn/volume-wise-language | ed24b11667e6b26d3ed09ce0aae383c26852821c | [
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"# Visualize Counts for the three classes \n\nThe number of volume-wise predictions for each of the three classes can be visualized in a 2D-space (with two classes as the axes and the remained or class1-class2 as the value of the third class). Also, the percentage of volume-wise predictions can be sh... | [
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d01ed39cb8c6da6e3866fa2101c2d0d789d727d7 | 342,568 | ipynb | Jupyter Notebook | nbs/00_torch_core.ipynb | nigh8w0lf/fastai2 | 60e5b489a57b385f206fb85f372a5b4bf843ccc6 | [
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d01edb84f2931921b31ed12c04bc59a1692033ec | 26,512 | ipynb | Jupyter Notebook | v1-uvod/sc-siit-v1-cv-basics.ipynb | ftn-ai-lab/sc-2019-siit | 935980fa256ad25f6b4488d0235d0103ecadbf9d | [
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] | 7 | 2019-11-04T10:19:34.000Z | 2020-10-11T18:09:05.000Z | v1-uvod/sc-siit-v1-cv-basics.ipynb | ftn-ai-lab/sc-2019-siit | 935980fa256ad25f6b4488d0235d0103ecadbf9d | [
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] | 74 | 2019-11-26T16:43:53.000Z | 2020-07-06T18:05:52.000Z | v1-uvod/sc-siit-v1-cv-basics.ipynb | ftn-ai-lab/sc-2019-siit | 935980fa256ad25f6b4488d0235d0103ecadbf9d | [
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] | 4 | 2019-11-04T19:06:20.000Z | 2020-07-16T17:58:25.000Z | 31.827131 | 719 | 0.612025 | [
[
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"# Soft Computing\n\n## Vežba 1 - Digitalna slika, computer vision, OpenCV\n\n### OpenCV\n\nOpen source biblioteka namenjena oblasti računarske vizije (eng. computer vision). Dokumentacija dostupna <a href=\"https://opencv.org/\">ovde</a>.\n\n### matplotlib\n\nPlotting biblioteka za programski jezik P... | [
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d01eeb35694bd0dae461f98e757eb78cd17962ef | 159,504 | ipynb | Jupyter Notebook | paper_code/Beuzen_et_al_2019_code.ipynb | TomasBeuzen/BeuzenEtAl_2019_NHESS_GP_runup_model | ee2a3eae715619b0b7e264d01f968b178bb1587c | [
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] | 2 | 2019-11-23T03:22:03.000Z | 2022-02-04T00:38:13.000Z | paper_code/Beuzen_et_al_2019_code.ipynb | TomasBeuzen/BeuzenEtAl_GP_Paper | ee2a3eae715619b0b7e264d01f968b178bb1587c | [
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] | null | null | null | paper_code/Beuzen_et_al_2019_code.ipynb | TomasBeuzen/BeuzenEtAl_GP_Paper | ee2a3eae715619b0b7e264d01f968b178bb1587c | [
"MIT"
] | 1 | 2019-09-23T18:00:25.000Z | 2019-09-23T18:00:25.000Z | 239.855639 | 71,108 | 0.903256 | [
[
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"## <center>Ensemble models from machine learning: an example of wave runup and coastal dune erosion</center>\n### <center>Tomas Beuzen<sup>1</sup>, Evan B. Goldstein<sup>2</sup>, Kristen D. Splinter<sup>1</sup></center>\n<center><sup>1</sup>Water Research Laboratory, School of Civil and Environmental... | [
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d01ef20a85439b6956960e98c50c8e4fc4975cc2 | 22,318 | ipynb | Jupyter Notebook | NN using PyTorch.ipynb | Spurryag/PyTorch-Scholarship-Programme-Solutions | 360e15a833a204b3234d0410830661f27487c3f2 | [
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] | null | null | null | NN using PyTorch.ipynb | Spurryag/PyTorch-Scholarship-Programme-Solutions | 360e15a833a204b3234d0410830661f27487c3f2 | [
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] | null | null | null | 43.675147 | 4,964 | 0.633793 | [
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"import torch\nfrom torch import nn\nimport torch.nn.functional as F\nfrom torchvision import datasets, transforms\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n# Define a transform to normalize the data - change the range of values in the image [histogram stretch]\ntransform = transforms.Co... | [
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d01efc423c6f773aa7178495142b2e036267e6dc | 35,256 | ipynb | Jupyter Notebook | Resources/Generator/GenerateData.ipynb | Ryan-Malin/Wk19MachineLearning | 550c1a539718cab387f2f00d0e0415821873fcbb | [
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[
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"!wget https://LoanStats_2019Q1.csv.zip\n!wget https://LoanStats_2019Q2.csv.zip\n!wget https://LoanStats_2019Q3.csv.zip\n!wget https://LoanStats_2019Q4.csv.zip\n//LoanStats_2020Q1.csv.zip",
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d01eff1c77ec92e1ca9ce8e74245d31ad853b842 | 828,173 | ipynb | Jupyter Notebook | pretrained-model/tts/fastspeech2/export/fastspeech2-haqkiem.ipynb | ishine/malaya-speech | fd34afc7107af1656dff4b3201fa51dda54fde18 | [
"MIT"
] | 111 | 2020-08-31T04:58:54.000Z | 2022-03-29T15:44:18.000Z | pretrained-model/tts/fastspeech2/export/fastspeech2-haqkiem.ipynb | ishine/malaya-speech | fd34afc7107af1656dff4b3201fa51dda54fde18 | [
"MIT"
] | 14 | 2020-12-16T07:27:22.000Z | 2022-03-15T17:39:01.000Z | pretrained-model/tts/fastspeech2/export/fastspeech2-haqkiem.ipynb | ishine/malaya-speech | fd34afc7107af1656dff4b3201fa51dda54fde18 | [
"MIT"
] | 29 | 2021-02-09T08:57:15.000Z | 2022-03-12T14:09:19.000Z | 876.373545 | 134,136 | 0.953214 | [
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d01f037ba1cd050e72af183ec64f14b5fc1e0fda | 963,672 | ipynb | Jupyter Notebook | AppliedDataScienceWithPython/Week3.ipynb | MikeBeaulieu/coursework | 5f8ad9da8252b64992cea40d24615c63e31f7890 | [
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] | null | null | null | AppliedDataScienceWithPython/Week3.ipynb | MikeBeaulieu/coursework | 5f8ad9da8252b64992cea40d24615c63e31f7890 | [
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] | null | null | null | AppliedDataScienceWithPython/Week3.ipynb | MikeBeaulieu/coursework | 5f8ad9da8252b64992cea40d24615c63e31f7890 | [
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d01f03f308dc4687c9fea7b96b440cae8e4c9b38 | 32,373 | ipynb | Jupyter Notebook | custom-who-to-follow/exploration.ipynb | ericmbudd/twitter-data-viz | 55eebab346a0cac8f5eaec17f8b78361fa35738c | [
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] | null | null | null | custom-who-to-follow/exploration.ipynb | ericmbudd/twitter-data-viz | 55eebab346a0cac8f5eaec17f8b78361fa35738c | [
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d01f161cfbc2a56b79b31eccb9a93a39f55da3b1 | 5,151 | ipynb | Jupyter Notebook | Homework 2 Solutions.ipynb | newby-jay/MATH381-Fall2021-JupyterNotebooks | 9181fb6e154081de26fb267e0794a67f60ae11a0 | [
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"%pylab inline\n%config InlineBackend.figure_format = 'retina'\nfrom ipywidgets import interact\nimport scipy\nimport scipy.special",
"Populating the interactive namespace from numpy and matplotlib\n"
]
],
[
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"# Question #1\nAssume that $f(\\cdot)$ is an infinitely smooth and ... | [
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d01f172ad5f8f184c32d274bc7b3ea08fba35d55 | 128,905 | ipynb | Jupyter Notebook | examples/2-profiling-vision.ipynb | raymon-ai/raymon | e1b0370de1f1d55c06b3c78fd820b1c1fe65db68 | [
"MIT"
] | 21 | 2021-06-14T08:37:22.000Z | 2022-03-08T05:41:54.000Z | examples/2-profiling-vision.ipynb | raymon-ai/raymon | e1b0370de1f1d55c06b3c78fd820b1c1fe65db68 | [
"MIT"
] | 57 | 2021-01-30T08:45:13.000Z | 2022-02-21T16:15:00.000Z | examples/2-profiling-vision.ipynb | raymon-ai/raymon | e1b0370de1f1d55c06b3c78fd820b1c1fe65db68 | [
"MIT"
] | 1 | 2021-06-18T09:53:58.000Z | 2021-06-18T09:53:58.000Z | 429.683333 | 83,666 | 0.938676 | [
[
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"# Building and using data schemas for computer vision\nThis tutorial illustrates how to use raymon profiling to guard image quality in your production system. The image data is taken from [Kaggle](https://www.kaggle.com/ravirajsinh45/real-life-industrial-dataset-of-casting-product) and is courtesy of... | [
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d01f2146651afa938d0677d2193e88eca37f5931 | 93,049 | ipynb | Jupyter Notebook | clustering/k_means.ipynb | JVBravoo/Learning-Machine-Learning | 1ee58c1c74661450132f2af6a26066980b5f9cf2 | [
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] | null | null | null | 122.594203 | 22,344 | 0.834636 | [
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"pip install pandas",
"Requirement already satisfied: pandas in /usr/local/Cellar/jupyterlab/2.1.1/libexec/lib/python3.8/site-packages (1.0.3)\nRequirement already satisfied: numpy>=1.13.3 in /usr/local/Cellar/jupyterlab/2.1.1/libexec/lib/python3.8/site-packages (from pandas) (1.18.3)\nRequireme... | [
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