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d02a4b38a8f93841d03b9319e8f3ca561f476adf | 27,153 | ipynb | Jupyter Notebook | preprocessing/ExtractTerms.ipynb | tychen5/IR_TextMining | cbe60f48f1ae3f0ae84aebfa57697e7a744000ec | [
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d02a62b1d38444a360baf54ab040a154a6409533 | 5,529 | ipynb | Jupyter Notebook | viz/auto/3-hiveplot.ipynb | dhimmel/integrate | 93feba1765fbcd76fd79e22f25121f5399629148 | [
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d02a7597ae45b25bf0c4afceddf94638b3eda53c | 91,394 | ipynb | Jupyter Notebook | HW3/HW3.ipynb | jay-z007/Data-Science-Fundamentals | ffe04b4edc8bdc9115736e956577858ba8ba28a6 | [
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d02a886f61e87c8c50fd57b9b1c568e5c89885f4 | 11,562 | ipynb | Jupyter Notebook | Python/Exercises/Blatt-13.ipynb | BuserLukas/Logic | cc0447554cfa75b213a10a2db37ce82c42afb91d | [
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d02a97961440f90c91d5af9412b587126676bc4e | 14,874 | ipynb | Jupyter Notebook | 3. NLP/AZ/Text Classification/Models_Template.ipynb | AmirRazaMBA/TensorFlow-Certification | ec0990007cff6daf36beac6d00d95c81cdf80353 | [
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d02a9ad0e801949216c0a9617ff6e9eb1238be10 | 16,841 | ipynb | Jupyter Notebook | colabs/dv360_data_warehouse.ipynb | arbrown/starthinker | 1a14664fb1a8f2a757b100363ea8958833b7754c | [
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d02abd452549e2283a0198190281b6e263d22be9 | 410,419 | ipynb | Jupyter Notebook | notebooks/PythonTutorial.ipynb | jagar2/BMC | 884250645693ef828471fe1d132a093dc6df7593 | [
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d02abfc9cdccb9347a39f7c9d7a47546e5c2e5a9 | 2,768 | ipynb | Jupyter Notebook | python/Problem4.ipynb | ditekunov/ProjectEuler-research | e8f84388045cdc1391d1e363c7b55ff4f85be708 | [
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d02acba2b3ccea9c70da18a3a12285fdbfeef08b | 321,893 | ipynb | Jupyter Notebook | 3_2_preliminary_statistics_project_2.ipynb | cnktysz/qiskit-quantum-state-classifier | 41f63e1cea13e02dfeb7fc7721f8a218fed62678 | [
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d02ad73d6416b0e7d2b3c0eab95ccde0a3a09945 | 10,765 | ipynb | Jupyter Notebook | Projects/p2_continuous-control/.ipynb_checkpoints/DDPG_with_20_Agent-checkpoint.ipynb | Clara-YR/Udacity-DRL | 6736605f6cb47b7fac8ae950ea785f366758e0f1 | [
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d02ad9e24b3748683cb9b8ad60b09c81f062e549 | 60,793 | ipynb | Jupyter Notebook | Datascience_With_Python/Machine Learning/Algorithms/Optics Clustering Algorithm/optics_clustering_algorithm.ipynb | vishnupriya129/winter-of-contributing | 8632c74d0c2d55bb4fddee9d6faac30159f376e1 | [
"MIT"
] | 1,078 | 2021-09-05T09:44:33.000Z | 2022-03-27T01:16:02.000Z | Datascience_With_Python/Machine Learning/Algorithms/Optics Clustering Algorithm/optics_clustering_algorithm.ipynb | vishnupriya129/winter-of-contributing | 8632c74d0c2d55bb4fddee9d6faac30159f376e1 | [
"MIT"
] | 6,845 | 2021-09-05T12:49:50.000Z | 2022-03-12T16:41:13.000Z | Datascience_With_Python/Machine Learning/Algorithms/Optics Clustering Algorithm/optics_clustering_algorithm.ipynb | vishnupriya129/winter-of-contributing | 8632c74d0c2d55bb4fddee9d6faac30159f376e1 | [
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] | 2,629 | 2021-09-03T04:53:16.000Z | 2022-03-20T17:45:00.000Z | 160.403694 | 26,572 | 0.886007 | [
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d02adaed247be5cdea2c6bbe74a4047c33c877e7 | 659,831 | ipynb | Jupyter Notebook | Pokemon API.ipynb | kosmaspanag/api_work | 228a24a2532fb0a640897e25334b7882a00f1433 | [
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] | null | null | null | Pokemon API.ipynb | kosmaspanag/api_work | 228a24a2532fb0a640897e25334b7882a00f1433 | [
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] | null | null | null | Pokemon API.ipynb | kosmaspanag/api_work | 228a24a2532fb0a640897e25334b7882a00f1433 | [
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d02ae1e05cc1166900401c1bc6f5ee08df591cc6 | 126,246 | ipynb | Jupyter Notebook | square_roots_intro.ipynb | izzetmert/lang_calc_2017 | 88e96dc7f1aab3533922a6fc9867b3b6194b4d82 | [
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] | null | null | null | square_roots_intro.ipynb | izzetmert/lang_calc_2017 | 88e96dc7f1aab3533922a6fc9867b3b6194b4d82 | [
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] | null | null | null | 87.066207 | 18,280 | 0.836771 | [
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d02af0ceb15a0318111b4b35fd3a755c34b94b71 | 27,434 | ipynb | Jupyter Notebook | Mathematics/Statistics/Statistics and Probability Python Notebooks/Computational and Inferential Thinking - The Foundations of Data Science (book)/Notebooks - by chapter/9. Randomness and Probabiltities/5. Finding_Probabilities.ipynb | okara83/Becoming-a-Data-Scientist | f09a15f7f239b96b77a2f080c403b2f3e95c9650 | [
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] | null | null | null | Mathematics/Statistics/Statistics and Probability Python Notebooks/Computational and Inferential Thinking - The Foundations of Data Science (book)/Notebooks - by chapter/9. Randomness and Probabiltities/5. Finding_Probabilities.ipynb | okara83/Becoming-a-Data-Scientist | f09a15f7f239b96b77a2f080c403b2f3e95c9650 | [
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] | 2 | 2022-02-09T15:41:33.000Z | 2022-02-11T07:47:40.000Z | 60.560706 | 11,612 | 0.721149 | [
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d02af2050475e2ef04e8f962b643092c6e372c26 | 45,785 | ipynb | Jupyter Notebook | Chapter7.ipynb | yangzhou95/notes | dcf70daf5cd3817a5f0aae3ec61530f457881b38 | [
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d02af894e132fc438d9d94b4f00fbc8d4bbafcc4 | 10,902 | ipynb | Jupyter Notebook | Stock_Algorithms/Multiple_Linear_Regression_with_Normalize_Data.ipynb | NTForked-ML/Deep-Learning-Machine-Learning-Stock | 8a137972d967423c7102a33ba639bd0d5d21a0e9 | [
"MIT"
] | 569 | 2019-02-06T16:35:19.000Z | 2022-03-31T03:45:28.000Z | Stock_Algorithms/Multiple_Linear_Regression_with_Normalize_Data.ipynb | crazyguitar/Deep-Learning-Machine-Learning-Stock | 99b4f30c3315806e8098327544d3d8cccfea8d65 | [
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] | 5 | 2021-02-27T07:03:58.000Z | 2022-03-31T14:09:41.000Z | Stock_Algorithms/Multiple_Linear_Regression_with_Normalize_Data.ipynb | ysdede/Deep-Learning-Machine-Learning-Stock | 2e3794efab3276b6bc389c8b38615540d4e2b144 | [
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] | 174 | 2019-05-23T11:46:54.000Z | 2022-03-31T04:44:38.000Z | 29.227882 | 116 | 0.394606 | [
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d02af9728ce0b2572e16a58a20992aaf0c5056d3 | 19,840 | ipynb | Jupyter Notebook | Chapter07/HandsOn-04_Transfer_with_IMDB_full_model.ipynb | wikibook/transfer-learning | 6085109673b819b99f100e379ff0b3afb8f87870 | [
"MIT"
] | 19 | 2019-08-02T07:51:40.000Z | 2021-10-05T12:55:08.000Z | Chapter07/HandsOn-04_Transfer_with_IMDB_full_model.ipynb | 0jipy/handson-Code | cdb71c11f42b311223e1caab4468cc85ea6031ed | [
"MIT"
] | 2 | 2019-10-23T07:19:25.000Z | 2020-05-19T07:00:31.000Z | Chapter07/HandsOn-04_Transfer_with_IMDB_full_model.ipynb | 0jipy/handson-Code | cdb71c11f42b311223e1caab4468cc85ea6031ed | [
"MIT"
] | 17 | 2019-11-16T22:52:16.000Z | 2021-12-02T03:41:51.000Z | 33.972603 | 248 | 0.544657 | [
[
[
"# Chapter 7. 텍스트 문서의 범주화 - (4) IMDB 전체 데이터로 전이학습\n\n- 앞선 전이학습 실습과는 달리, IMDB 영화리뷰 데이터셋 전체를 사용하며 문장 수는 10개 -> 20개로 조정한다\n- IMDB 영화 리뷰 데이터를 다운로드 받아 data 디렉토리에 압축 해제한다\n - 다운로드 : http://ai.stanford.edu/~amaas/data/sentiment/\n - 저장경로 : data/aclImdb",
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d02b02c28f3416910ab509e551bf11af6ba502e1 | 3,801 | ipynb | Jupyter Notebook | 3.Statistical_NLP/2_chatbot.ipynb | bonigarcia/nlp-examples | 4e7e3c06814d8fed9bd509759664a7af0a9eb8a7 | [
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d02b0af90077c6c2011e1edc5afcf3f060c3caaa | 15,835 | ipynb | Jupyter Notebook | Mission to mars/mission_to_mars.ipynb | DeLeon27/web-scraping-challenge | 5f46fff099b4f9ba2103f240e15001ae4a58956c | [
"ADSL"
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[
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d02b14e3027ed3c2544a20524607510934b9fd30 | 14,201 | ipynb | Jupyter Notebook | .ipynb_checkpoints/lgbm-optuna-cross-validate-checkpoint.ipynb | luigisaetta/bike-sharing-forecast | a76059d33aa8a6c3f0b742d4c22b14477c5653df | [
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"MIT"
] | null | null | null | .ipynb_checkpoints/lgbm-optuna-cross-validate-checkpoint.ipynb | luigisaetta/bike-sharing-forecast | a76059d33aa8a6c3f0b742d4c22b14477c5653df | [
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] | null | null | null | 31.142544 | 258 | 0.488416 | [
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d02b164239616a766821da2108845cad83753639 | 12,028 | ipynb | Jupyter Notebook | _notebooks/2020-07-11-Tuesday-Wonderland-Fidel-Huancas.ipynb | jazzcoffeestuff/blog | 7ec7c4a7b9ef565429e1db720ad43312b9a54f62 | [
"Apache-2.0"
] | null | null | null | _notebooks/2020-07-11-Tuesday-Wonderland-Fidel-Huancas.ipynb | jazzcoffeestuff/blog | 7ec7c4a7b9ef565429e1db720ad43312b9a54f62 | [
"Apache-2.0"
] | null | null | null | _notebooks/2020-07-11-Tuesday-Wonderland-Fidel-Huancas.ipynb | jazzcoffeestuff/blog | 7ec7c4a7b9ef565429e1db720ad43312b9a54f62 | [
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[
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d02b1c324ce01001ca940dbbb990fd220449d85b | 9,319 | ipynb | Jupyter Notebook | Corpus_Making/test_excel.ipynb | UWPRG/BETO2020 | 55b5b329395da79047e9083232101d15af9f2c49 | [
"MIT"
] | 4 | 2020-03-04T21:08:11.000Z | 2020-10-28T11:28:00.000Z | Corpus_Making/test_excel.ipynb | UWPRG/BETO2020 | 55b5b329395da79047e9083232101d15af9f2c49 | [
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] | 6 | 2019-04-15T16:51:16.000Z | 2019-11-13T02:45:53.000Z | 29.678344 | 652 | 0.452731 | [
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d02b2111c6c253e319da6bed149a8056da502b47 | 190,872 | ipynb | Jupyter Notebook | 01_Simple_Linear_Model.ipynb | Asciotti/TensorFlow-Tutorials | 7f67b593473f218544db6e46518b172fdabe20ca | [
"MIT"
] | null | null | null | 01_Simple_Linear_Model.ipynb | Asciotti/TensorFlow-Tutorials | 7f67b593473f218544db6e46518b172fdabe20ca | [
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] | null | null | null | 01_Simple_Linear_Model.ipynb | Asciotti/TensorFlow-Tutorials | 7f67b593473f218544db6e46518b172fdabe20ca | [
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[
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d02b257045d8d09b9c594db2f4a88592a1279106 | 36,335 | ipynb | Jupyter Notebook | preprocessing/Untitled1.ipynb | SensesProject/regional-dutch | 09d2f7fc8e550a78da93f378691c717af8223210 | [
"0BSD"
] | null | null | null | preprocessing/Untitled1.ipynb | SensesProject/regional-dutch | 09d2f7fc8e550a78da93f378691c717af8223210 | [
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d02b369a91cd5775e3ce0eeb2ed88e0dc781baf6 | 1,047,827 | ipynb | Jupyter Notebook | Modelling trend life cycles in scientific research.ipynb | etattershall/trend-lifecycles | fd1b0ff57fb50808a865be9359a16c856fd37819 | [
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] | null | null | null | Modelling trend life cycles in scientific research.ipynb | etattershall/trend-lifecycles | fd1b0ff57fb50808a865be9359a16c856fd37819 | [
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d02c9b4eee9a3971bb9111039b70a8c5c2a140d9 | 294,490 | ipynb | Jupyter Notebook | prelim-model/2D TVD - Pa profile.ipynb | UBC-MOAD/pa-th-simple | bafb8dc6d281556f01233a32342a53bad8af392c | [
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d02cb374729f3dd4572d27770f515498220dddf9 | 5,103 | ipynb | Jupyter Notebook | notebooks/Exploring Json.ipynb | BillmanH/exoplanets | 92656bf8c917c6e07d91f82a7cd0b75679ffa680 | [
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d02d0029bad697b3958428cc561b083ba2b4ded3 | 587,536 | ipynb | Jupyter Notebook | topocode2.ipynb | pangeo-data/pangeo-rema | 271e573ca977001a1978c936b00c139b8262e865 | [
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d02d1b0a5ae75885d2eb4d0df89d40c14a42b62a | 15,280 | ipynb | Jupyter Notebook | fundamentals/src/notebooks/040_pipelines.ipynb | konabuta/fta-azure-machine-learning | 70da95e7a4c9b3e42db61bb0f69eda8e07c28eee | [
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d02d1b8258688297dd5f45d601777cca6f0d0880 | 41,285 | ipynb | Jupyter Notebook | Exercise3/Exercise3/local_feature_matching.ipynb | danikhani/CV1-2020 | 80b77776763dbd30f68bc2966e51e7ad592a0373 | [
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d02d2469f89df85a688b94b19325a89f2108b58c | 14,800 | ipynb | Jupyter Notebook | 2/trainGPR.ipynb | aghaeijo/Prediction-of-the-equivalent-sandgrain-height | cc249653c72ba3d8eea011a81e53ab2d3c62c747 | [
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d02d3823593ba041b92c33fece73233079ed3a28 | 23,078 | ipynb | Jupyter Notebook | notebooks/004_fingerprints/999_fetch_sitealign_features.ipynb | volkamerlab/kissim_app | 83ba9af39ec38f0dcae7c7c65dc5a31c3ee367d4 | [
"MIT"
] | 6 | 2021-02-19T20:01:57.000Z | 2022-02-03T04:25:25.000Z | notebooks/004_fingerprints/999_fetch_sitealign_features.ipynb | volkamerlab/kissim_app | 83ba9af39ec38f0dcae7c7c65dc5a31c3ee367d4 | [
"MIT"
] | 39 | 2020-12-16T09:19:13.000Z | 2021-12-11T09:17:01.000Z | notebooks/004_fingerprints/999_fetch_sitealign_features.ipynb | volkamerlab/kissim_app | 83ba9af39ec38f0dcae7c7c65dc5a31c3ee367d4 | [
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] | 1 | 2022-02-03T04:25:26.000Z | 2022-02-03T04:25:26.000Z | 29.511509 | 378 | 0.353713 | [
[
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"# SiteAlign features\n\nWe read the SiteAlign features from the respective [paper](https://onlinelibrary.wiley.com/doi/full/10.1002/prot.21858) and [SI table](https://onlinelibrary.wiley.com/action/downloadSupplement?doi=10.1002%2Fprot.21858&file=prot21858-SupplementaryTable.pdf) to verify `kissim`'s... | [
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d02d3cba361439fcb466b45432e194ac62fc8da8 | 10,389 | ipynb | Jupyter Notebook | _notebooks/2022-2-1-NYSE-data-analysis.ipynb | saaleh2/ALHODAIF-Portfolio | 913437ee7e05f3e4cac90ba4eef7ff202c313e3c | [
"Apache-2.0"
] | null | null | null | _notebooks/2022-2-1-NYSE-data-analysis.ipynb | saaleh2/ALHODAIF-Portfolio | 913437ee7e05f3e4cac90ba4eef7ff202c313e3c | [
"Apache-2.0"
] | 1 | 2022-01-10T07:29:45.000Z | 2022-01-10T07:30:18.000Z | _notebooks/2022-2-1-NYSE-data-analysis.ipynb | saaleh2/ALHODAIF-Portfolio | 913437ee7e05f3e4cac90ba4eef7ff202c313e3c | [
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d02d3fefdc4ee0a2dd54e20010727f768e317d5a | 8,081 | ipynb | Jupyter Notebook | ads_classification_sklearn.ipynb | jaywoong/test_machinelearning | fa6a204133fda5382db433d3b4149a4c794e5ba7 | [
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d02d5af63757f56a166455a26890c226ffb090f5 | 15,055 | ipynb | Jupyter Notebook | notebooks/preprocessing/02_matrix.ipynb | sgg10/games_seeker | c9b7723586e79baffb5dc9f6ddb88f541da416a7 | [
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d02d6f1a8db2b9af61f9c599fcb3423749691989 | 7,616 | ipynb | Jupyter Notebook | notebooks/depTFIDFModel-Test.ipynb | BigBossAnwer/STS-Pipeline | 952d2c577dd4b8a66c99b80a24589a98e20c2e60 | [
"MIT"
] | null | null | null | notebooks/depTFIDFModel-Test.ipynb | BigBossAnwer/STS-Pipeline | 952d2c577dd4b8a66c99b80a24589a98e20c2e60 | [
"MIT"
] | null | null | null | notebooks/depTFIDFModel-Test.ipynb | BigBossAnwer/STS-Pipeline | 952d2c577dd4b8a66c99b80a24589a98e20c2e60 | [
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d02d89ba9d61ccaf8480a0915fc0f0622feb6c57 | 944 | ipynb | Jupyter Notebook | 01_Babynames.ipynb | cathimeister/spiced-w1-babynames | c1157d045f1f2b7d3a4eb5fd804da894e1ed56b6 | [
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] | 1 | 2019-03-01T08:50:08.000Z | 2019-03-01T08:50:08.000Z | 01_Babynames.ipynb | cathimeister/spiced-w1-babynames | c1157d045f1f2b7d3a4eb5fd804da894e1ed56b6 | [
"MIT"
] | null | null | null | 01_Babynames.ipynb | cathimeister/spiced-w1-babynames | c1157d045f1f2b7d3a4eb5fd804da894e1ed56b6 | [
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] | null | null | null | 17.481481 | 98 | 0.523305 | [
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d02d8cd0c7085f43f47b5b3b16aa0b2e14154572 | 446,520 | ipynb | Jupyter Notebook | gan-fashion-mnist/notebook.ipynb | Tiendil/public-jupyter-notebooks | 1681ca44d5805608cd3782ca1d793b6bad44f57b | [
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"BSD-3-Clause"
] | 1 | 2021-07-24T13:15:23.000Z | 2021-07-24T13:15:23.000Z | gan-fashion-mnist/notebook.ipynb | Tiendil/public-jupyter-notebooks | 1681ca44d5805608cd3782ca1d793b6bad44f57b | [
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d02d946411668ff61da1ba509cd880d8be727b19 | 2,321 | ipynb | Jupyter Notebook | test/dist-correct/exam_40/test-exam.ipynb | chrispyles/jexam | ebe83b170f51c5820e0c93955824c3798922f097 | [
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d02da62b648b970370ef8a950afca72de2aa1271 | 28,688 | ipynb | Jupyter Notebook | auto_mmd_vmd_run_v1_02.ipynb | CrazyReason/mmd_auto_motion_colab | ba7b2f85d11b453c14c28bf3258b63f4950d1045 | [
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] | 6 | 2020-04-01T16:08:24.000Z | 2022-03-11T05:08:35.000Z | auto_mmd_vmd_run_v1_02.ipynb | CrazyReason/mmd_auto_motion_colab | ba7b2f85d11b453c14c28bf3258b63f4950d1045 | [
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] | 2 | 2020-06-12T16:03:10.000Z | 2021-09-11T05:45:00.000Z | 42.188235 | 405 | 0.501115 | [
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d02da98d663e0fe3a5f19a337dd44c913316bebd | 421,197 | ipynb | Jupyter Notebook | nbs/dl1/00_notebook_tutorial.ipynb | jwdinius/course-v3 | 188214a51ce1f92bb348ebe7b2fd85b1b61fbe02 | [
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d02dd3b19800f54efc24c25461799fe88bd339f0 | 375,897 | ipynb | Jupyter Notebook | P1.ipynb | MohamedHeshamMustafa/CarND-LaneLines-P1 | e609a998b6feaa433072d33c85026dd42faed090 | [
"MIT"
] | null | null | null | P1.ipynb | MohamedHeshamMustafa/CarND-LaneLines-P1 | e609a998b6feaa433072d33c85026dd42faed090 | [
"MIT"
] | null | null | null | P1.ipynb | MohamedHeshamMustafa/CarND-LaneLines-P1 | e609a998b6feaa433072d33c85026dd42faed090 | [
"MIT"
] | null | null | null | 367.805284 | 115,036 | 0.928842 | [
[
[
"# Self-Driving Car Engineer Nanodegree\n\n\n## Project: **Finding Lane Lines on the Road** \n***\nIn this project, you will use the tools you learned about in the lesson to identify lane lines on the road. You can develop your pipeline on a series of individual images, and later apply the result to ... | [
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d02de5b7e03a9cbed136420152b0319ff2c7ff72 | 845,027 | ipynb | Jupyter Notebook | Week4/AdvML_week4_ex2.ipynb | mikkokotola/AdvancedMachineLearning | 574e82d4104ac04f1cb9889beb5be7d122bd0d01 | [
"MIT"
] | 1 | 2020-03-18T08:51:44.000Z | 2020-03-18T08:51:44.000Z | Week4/AdvML_week4_ex2.ipynb | mikkokotola/AdvancedMachineLearning | 574e82d4104ac04f1cb9889beb5be7d122bd0d01 | [
"MIT"
] | null | null | null | Week4/AdvML_week4_ex2.ipynb | mikkokotola/AdvancedMachineLearning | 574e82d4104ac04f1cb9889beb5be7d122bd0d01 | [
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] | null | null | null | 2,600.083077 | 256,780 | 0.961881 | [
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"## Advanced Course in Machine Learning\n## Week 4\n## Exercise 2 / Probabilistic PCA\n\nimport numpy as np\nimport scipy\nimport pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport matplotlib.animation as animation\nfrom numpy import linalg as LA\n\nsns.set_style(\"darkgrid\"... | [
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d02deeaec567b87ef29c33226a3114420f0f8583 | 25,904 | ipynb | Jupyter Notebook | .ipynb_checkpoints/Labelling-checkpoint.ipynb | Pramadita/Analisis-Sentimen-Bansos-Random-Forest | ae1b80acd8597905c0d542fcff4628ba558d9b83 | [
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] | null | null | null | .ipynb_checkpoints/Labelling-checkpoint.ipynb | Pramadita/Analisis-Sentimen-Bansos-Random-Forest | ae1b80acd8597905c0d542fcff4628ba558d9b83 | [
"MIT"
] | null | null | null | .ipynb_checkpoints/Labelling-checkpoint.ipynb | Pramadita/Analisis-Sentimen-Bansos-Random-Forest | ae1b80acd8597905c0d542fcff4628ba558d9b83 | [
"MIT"
] | null | null | null | 43.979626 | 1,649 | 0.491044 | [
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[
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],
[
"data = pd.read_csv('Dataset/All-Pra & Pasca ND Clean for translate 5.csv',sep=\";\")#nama file data\ndata = data.astype({'Tweet' : 'string'})\ndata = data... | [
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d02dff2150000ff2339051356625cb9998b1d557 | 1,497 | ipynb | Jupyter Notebook | if-condition.ipynb | cj-asimov12/python_ds | b063e1047addc337af451566d93e3851615b4ef2 | [
"MIT"
] | null | null | null | if-condition.ipynb | cj-asimov12/python_ds | b063e1047addc337af451566d93e3851615b4ef2 | [
"MIT"
] | null | null | null | if-condition.ipynb | cj-asimov12/python_ds | b063e1047addc337af451566d93e3851615b4ef2 | [
"MIT"
] | null | null | null | 19.96 | 108 | 0.492986 | [
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"\"\"\"\n1. Input the values of a and b as 10 and 20 respectively. Now check if a is greater or b is greater\nusing if condition. Think about all the edge cases, and print the statements accordingly.\n\"\"\"",
"_____no_output_____"
],
[
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d02e0174c5e9cfcba31b3d3461129920fca9d203 | 252,516 | ipynb | Jupyter Notebook | Week2/Bayes Classifier.ipynb | yumengdong/GANs | 973291b913cbc8c8764670f70c2b6fc5682f9a6b | [
"MIT"
] | null | null | null | Week2/Bayes Classifier.ipynb | yumengdong/GANs | 973291b913cbc8c8764670f70c2b6fc5682f9a6b | [
"MIT"
] | null | null | null | Week2/Bayes Classifier.ipynb | yumengdong/GANs | 973291b913cbc8c8764670f70c2b6fc5682f9a6b | [
"MIT"
] | null | null | null | 329.655352 | 11,672 | 0.93261 | [
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],
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"import util\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.stats import multivariate_normal as mvn\n\n%matplotlib inline",
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d02e05fedc1cab9bbf7a51c221ec1408ef71f57a | 103,876 | ipynb | Jupyter Notebook | notebooks/KMeans_40_22800_v2.ipynb | KYHyeon/captcha-solver | 08567b25017339aeec8c51a4f420104f9abb8f9a | [
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] | 1 | 2020-12-15T12:52:51.000Z | 2020-12-15T12:52:51.000Z | notebooks/KMeans_40_22800_v2.ipynb | KYHyeon/captcha-solver | 08567b25017339aeec8c51a4f420104f9abb8f9a | [
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] | null | null | null | notebooks/KMeans_40_22800_v2.ipynb | KYHyeon/captcha-solver | 08567b25017339aeec8c51a4f420104f9abb8f9a | [
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d02e2a93b98d19e8fbab3ad65f4a92b0365af9ee | 6,436 | ipynb | Jupyter Notebook | Data Augumentation/extract_imgs.ipynb | vcaptainv/SinGan-Data-Augumentation | 09b0ef180bebb0ed31ab3dfd7e29a3d4ba684f97 | [
"MIT"
] | null | null | null | Data Augumentation/extract_imgs.ipynb | vcaptainv/SinGan-Data-Augumentation | 09b0ef180bebb0ed31ab3dfd7e29a3d4ba684f97 | [
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] | null | null | null | Data Augumentation/extract_imgs.ipynb | vcaptainv/SinGan-Data-Augumentation | 09b0ef180bebb0ed31ab3dfd7e29a3d4ba684f97 | [
"MIT"
] | null | null | null | 26.377049 | 112 | 0.511187 | [
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d02e381b652a29699932c288945e75b813f9f5de | 5,907 | ipynb | Jupyter Notebook | Big_Data_Integration_and_Processing/SoccerTweetAnalysis.ipynb | P7h/Coursera__Big_Data_Integration_and_Processing | 41a7f4bf548932ece71b15343c8fec4b4fcda5c9 | [
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] | 3 | 2017-03-18T20:41:20.000Z | 2019-05-03T18:22:01.000Z | Big_Data_Integration_and_Processing/SoccerTweetAnalysis.ipynb | P7h/Coursera__Big_Data_Integration_and_Processing | 41a7f4bf548932ece71b15343c8fec4b4fcda5c9 | [
"Apache-2.0"
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] | 3 | 2017-04-08T07:37:20.000Z | 2020-07-10T15:43:48.000Z | 23.347826 | 126 | 0.559506 | [
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[
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d02e40362f3f38a0cdcc8b61d62a6feb685f15c1 | 273,192 | ipynb | Jupyter Notebook | In-Class Projects/Project 8 - Working with OLS.ipynb | zacharyejohnson/ECON411 | b547a18f49480c10517166be5da3225c071ee9cf | [
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d02e42512a8fcf9c32683903115fd4e520cf82b9 | 467,271 | ipynb | Jupyter Notebook | examples/tutorial/patched.ipynb | natbusa/datalabframework | 77f1249f55c76f20f2ef6253c0af2f1943f36226 | [
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d02e5067791542b8e09c6e4fdb8bc70a91921a78 | 5,250 | ipynb | Jupyter Notebook | notebooks/model_decision_making.ipynb | larahabashy/capstone-diabetes | 021e5c18ebf366e953444eea15833036b62b42d0 | [
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d02e76b9c6da2b8e677d432034b1b411b68629a1 | 575,966 | ipynb | Jupyter Notebook | Indexer_for_Santa.ipynb | taniokah/where-is-santa- | caa511ebccd3ab6921c01710c23cf47d45f2f125 | [
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] | null | null | null | Indexer_for_Santa.ipynb | taniokah/where-is-santa- | caa511ebccd3ab6921c01710c23cf47d45f2f125 | [
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d02e86dfe1a1305e5def4677e1a3fb7ced06a822 | 39,024 | ipynb | Jupyter Notebook | multi_epoch-max-duration-Autumnal.ipynb | Niu-LIU/Canopus | 751967cfcbc0047b714152e14586cabf9c359ad9 | [
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d02e8d9fe1fec1cd4e7a63a672c330803538ff40 | 4,843 | ipynb | Jupyter Notebook | scripts/human_eval/prepare_qualitative_block.ipynb | edorado93/Writing-editing-Network | 9898666d5be2c0d2bd84903af3a0e6fa93823953 | [
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] | 1 | 2020-08-28T00:43:26.000Z | 2020-08-28T00:43:26.000Z | scripts/human_eval/prepare_qualitative_block.ipynb | edorado93/Writing-editing-Network | 9898666d5be2c0d2bd84903af3a0e6fa93823953 | [
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d02e90203b3be535b0c087275b3cc4a630b30007 | 87,301 | ipynb | Jupyter Notebook | notebooks/test_sunpy_1.0.0.ipynb | MSKirk/MachineLearning | 14e19244441aeef1f28e24e3b3f63b659b80087e | [
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d02eac645b6d9edef9812d6d2d1cea240f6ab7e7 | 27,457 | ipynb | Jupyter Notebook | CMC/.ipynb_checkpoints/BCR_CMC_test-checkpoint.ipynb | wtwt5237/Benisse | 2c7e569ff7f1d15d883576dd9487612e5ed1077f | [
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d02ed16e59e5c19b70bbc3aa815cbdfc8577bbc4 | 25,301 | ipynb | Jupyter Notebook | content/en/docs/components/pipelines/sdk/build-pipeline.ipynb | droctothorpe/website | cb78f24d663f50aa13ef1846962ac6d3cba20b7c | [
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d02edbaeaac510d581d7c5092d9f32d163498dd6 | 40,113 | ipynb | Jupyter Notebook | tutorials/old_generation_notebooks/colab/6- Sarcasm Classifiers (TF-IDF).ipynb | fcivardi/spark-nlp-workshop | aedb1f5d93577c81bc3dd0da5e46e02586941541 | [
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d02ee05735d8bcfae1cbbf3c750efa51cc3d91eb | 205,856 | ipynb | Jupyter Notebook | HW_03_LSTM.ipynb | RamSaw/NLP | 01d135b14430c178ca61341e22b7dadd07662625 | [
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d02ee3805d264f9350212b9368ff767af4041eb3 | 4,902 | ipynb | Jupyter Notebook | ejercicios/D1_E2_callbacks_SOLUCION.ipynb | lcmencia/penguin-tf-workshop | b4491c6a587fe80c15f98527b13b91822f760e6b | [
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"MIT"
] | null | null | null | ejercicios/D1_E2_callbacks_SOLUCION.ipynb | lcmencia/penguin-tf-workshop | b4491c6a587fe80c15f98527b13b91822f760e6b | [
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] | 6 | 2020-01-21T22:35:53.000Z | 2020-01-28T15:47:44.000Z | 26.074468 | 249 | 0.573643 | [
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d02eeaee296e0064e63be51986016ee77e52ba77 | 96,902 | ipynb | Jupyter Notebook | .ipynb_checkpoints/Mission_to_Mars-checkpoint.ipynb | danelle1126/web-scraping-challenge | 8937448f5a0b6e57ee89099395c64d6787197f5e | [
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"ADSL"
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