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d00e42c3682f74b38a0f25b45172a5bdbc43942f | 27,895 | ipynb | Jupyter Notebook | experiments/entities-search-engine/1. load data from sparql.ipynb | TheScienceMuseum/heritage-connector | c8f0970edfe2c43560949fe46f4d4415f4f8bc0b | [
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] | 15 | 2020-03-19T09:13:02.000Z | 2022-03-29T16:53:53.000Z | experiments/entities-search-engine/1. load data from sparql.ipynb | TheScienceMuseum/heritage-connector | c8f0970edfe2c43560949fe46f4d4415f4f8bc0b | [
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] | 311 | 2020-06-11T10:14:06.000Z | 2021-12-03T16:56:11.000Z | experiments/entities-search-engine/1. load data from sparql.ipynb | TheScienceMuseum/heritage-connector | c8f0970edfe2c43560949fe46f4d4415f4f8bc0b | [
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] | 1 | 2021-11-20T18:48:43.000Z | 2021-11-20T18:48:43.000Z | 109.392157 | 5,026 | 0.705646 | [
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d00e4ad5d073217bad815536744fb968af87bff6 | 263,316 | ipynb | Jupyter Notebook | plot_figure4_first_half.ipynb | COMP6248-Reproducability-Challenge/hypergradient-descent-reproduction | e54bc17cd5f681a115607f6babd326d30466f8a6 | [
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] | 1 | 2019-06-11T18:29:51.000Z | 2019-06-11T18:29:51.000Z | 968.073529 | 91,002 | 0.934782 | [
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d00e5261a5f317159a02a4d2025f4b2f6f643b44 | 642,483 | ipynb | Jupyter Notebook | _ipynb/SchnallSupplement.ipynb | simkovic/simkovic.github.io | 98d0e30f5547894ee11925548a4627ad7c28fa68 | [
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d00e56cda28e7500114e1f5d94a8babfba988fcc | 119,050 | ipynb | Jupyter Notebook | Hackerearth-Predict_condition_and_insurance_amount/train_models.ipynb | chiranjeet14/ML_Projects | 6347348b0d7b3a4dbf3e07ece20a7ff39b6bf85d | [
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] | null | null | null | Hackerearth-Predict_condition_and_insurance_amount/train_models.ipynb | chiranjeet14/ML_Projects | 6347348b0d7b3a4dbf3e07ece20a7ff39b6bf85d | [
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d00e598776ad6e007c754034eb81a3fe6a5e86ee | 690,476 | ipynb | Jupyter Notebook | traffic_sign_classifier_LeNet_enhanced_trainingdataset_HLS.ipynb | nguyenrobot/Traffic-Sign-Recognition-with-Keras-Tensorflow | 327e567304f3c3da4d541ef53af4c837ffb3dbc9 | [
"MIT"
] | 2 | 2020-12-14T01:27:48.000Z | 2021-01-26T03:34:38.000Z | traffic_sign_classifier_LeNet_enhanced_trainingdataset_HLS.ipynb | nguyenrobot/Traffic-Sign-Recognition-with-Keras-Tensorflow | 327e567304f3c3da4d541ef53af4c837ffb3dbc9 | [
"MIT"
] | 1 | 2022-01-09T11:31:08.000Z | 2022-01-09T11:31:08.000Z | traffic_sign_classifier_LeNet_enhanced_trainingdataset_HLS.ipynb | v-thiennp12/Traffic-Sign-Recognition-with-Keras-Tensorflow | 327e567304f3c3da4d541ef53af4c837ffb3dbc9 | [
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] | 1 | 2021-03-30T18:29:45.000Z | 2021-03-30T18:29:45.000Z | 397.968876 | 157,008 | 0.929776 | [
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d00e5b81389e6d21862a5eb9a75ef6d5b9cbeb5c | 731,862 | ipynb | Jupyter Notebook | .ipynb_checkpoints/Nucleotide metabolism-checkpoint.ipynb | polybiome/PolyEnzyme | 2e1e22685d13d6a570e6c73c3a7c16991cab24ce | [
"MIT"
] | null | null | null | .ipynb_checkpoints/Nucleotide metabolism-checkpoint.ipynb | polybiome/PolyEnzyme | 2e1e22685d13d6a570e6c73c3a7c16991cab24ce | [
"MIT"
] | null | null | null | .ipynb_checkpoints/Nucleotide metabolism-checkpoint.ipynb | polybiome/PolyEnzyme | 2e1e22685d13d6a570e6c73c3a7c16991cab24ce | [
"MIT"
] | null | null | null | 1,234.168634 | 193,991 | 0.548765 | [
[
[
"import escher\nimport escher.urls\nimport cobra\nimport cobra.test\nimport json\nimport os\nfrom IPython.display import HTML\nfrom copy import deepcopy",
"_____no_output_____"
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d00e6798546dcbadb9d9821cef6e8108a866c7ec | 15,288 | ipynb | Jupyter Notebook | Object Oriented Programming.ipynb | ramsvijay/basic_datatype_python | 2aa0db44a839ccc8d12fdd392e1a054dae4c1d30 | [
"MIT"
] | null | null | null | Object Oriented Programming.ipynb | ramsvijay/basic_datatype_python | 2aa0db44a839ccc8d12fdd392e1a054dae4c1d30 | [
"MIT"
] | null | null | null | Object Oriented Programming.ipynb | ramsvijay/basic_datatype_python | 2aa0db44a839ccc8d12fdd392e1a054dae4c1d30 | [
"MIT"
] | null | null | null | 19.675676 | 111 | 0.445055 | [
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d00e681d61071342ae7dd3806ccf61a9be66c5cb | 21,352 | ipynb | Jupyter Notebook | lessons/misc/quantum-computing/grovers-algorthim-2-qubits.ipynb | UAAppComp/studyGroup | 642eb769cb2abdce5de3f2f10dd12164ac0dd052 | [
"Apache-2.0"
] | 105 | 2015-06-22T15:23:19.000Z | 2022-03-30T12:20:09.000Z | lessons/misc/quantum-computing/grovers-algorthim-2-qubits.ipynb | UAAppComp/studyGroup | 642eb769cb2abdce5de3f2f10dd12164ac0dd052 | [
"Apache-2.0"
] | 314 | 2015-06-18T22:10:34.000Z | 2022-02-09T16:47:52.000Z | lessons/misc/quantum-computing/grovers-algorthim-2-qubits.ipynb | UAAppComp/studyGroup | 642eb769cb2abdce5de3f2f10dd12164ac0dd052 | [
"Apache-2.0"
] | 142 | 2015-06-18T22:11:53.000Z | 2022-02-03T16:14:43.000Z | 53.783375 | 4,264 | 0.736043 | [
[
[
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"_____no_output_____"
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],
[
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d00e6b7a8018e76d445b5f990b4731001148647b | 16,052 | ipynb | Jupyter Notebook | Visualization/image_color_ramp.ipynb | pberezina/earthengine-py-notebooks | 4cbe3c52bcc9ed3f1337bf097aa5799442991a5e | [
"MIT"
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] | null | null | null | 80.663317 | 9,052 | 0.810304 | [
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d00e73bfdc6bcf44cf90ef04cf7ed87037b621b8 | 6,373 | ipynb | Jupyter Notebook | TESI-IFPI-2018-master/activity02/question03.ipynb | danieldsf/ads-activities | 3da8168f685dfdbe146e20023fbdaefd46c21b54 | [
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d00e9188dfb27ae19d7df0a23d52b95beadc4303 | 84,244 | ipynb | Jupyter Notebook | 05.04-Feature-Engineering.ipynb | sebaspee/intro_machine_learning | b58cdfb6ab3e685753f3228e4f5b4f652d2da2a3 | [
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] | 1 | 2018-09-07T22:03:18.000Z | 2018-09-07T22:03:18.000Z | 05.04-Feature-Engineering.ipynb | sebaspee/intro_machine_learning | b58cdfb6ab3e685753f3228e4f5b4f652d2da2a3 | [
"MIT"
] | 9 | 2018-08-31T22:13:59.000Z | 2019-09-28T16:14:24.000Z | 93.293466 | 30,588 | 0.837947 | [
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d00e99de383f5fb306540ec8d80ba2e5c712c61a | 52,646 | ipynb | Jupyter Notebook | VGG16_CIFAR10.ipynb | UdbhavPrasad072300/CPS803_Final_Project | 8ccf14705026c315fde87bc1e2728e27cf8cf5ff | [
"MIT"
] | 2 | 2021-09-10T01:01:38.000Z | 2021-09-20T17:15:06.000Z | VGG16_CIFAR10.ipynb | UdbhavPrasad072300/CPS803_Final_Project | 8ccf14705026c315fde87bc1e2728e27cf8cf5ff | [
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] | null | null | null | VGG16_CIFAR10.ipynb | UdbhavPrasad072300/CPS803_Final_Project | 8ccf14705026c315fde87bc1e2728e27cf8cf5ff | [
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] | null | null | null | 87.597338 | 16,556 | 0.751396 | [
[
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[
"import torch \nimport torch.nn as nn\n\nfrom src.data.dataset import get_dataloader\n\nimport torchvision.transforms as transforms\n\nimport numpy as np\nimport matplotlib.pyplot as plt",
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d00ee5dbde43cd3d5af4ecf610aa3f3dec799d12 | 241,408 | ipynb | Jupyter Notebook | p2_sl_finding_donors/p2_sl_finding_donors.ipynb | superkley/udacity-mlnd | 2038110e6bebad6e4290441cf4da618059a02a04 | [
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d00f151aa9061242e04714e878e85ed5b5067975 | 99,400 | ipynb | Jupyter Notebook | notebooks/StructuralT1.ipynb | jhconning/DevII | fc9c9e91dae79eea3e55beb67b2de01d8c375b04 | [
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d00f3c747872b4a0f4a5987a69c25479305ef0d3 | 1,935 | ipynb | Jupyter Notebook | README.ipynb | jfdahl/Advent-of-Code-2019 | 3e5100e77eddc09f361c07a3c860a0fd97ecaa78 | [
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d00f45ac1c19a706c9177a247573b0a345e5aaa7 | 8,089 | ipynb | Jupyter Notebook | JNotebooks/feats-CC-hand-conv.ipynb | rmsouza01/ML101 | ca2e5d25ab3dc26079719a3755dc78827935e42e | [
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d0101d7f29ceee849f6a59f20b8679a73efb447e | 2,926 | ipynb | Jupyter Notebook | sec04-1_LabIntro/My_Lab_Diagram.ipynb | codered-by-ec-council/Network-Automation-in-Python | c8efba489d22db4e488b0c4c1145517bdb629b22 | [
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d01025db39607a9e679b182448f26919f592ea40 | 6,117 | ipynb | Jupyter Notebook | P33_Test_Trained_Model/Test_Trained_Model.ipynb | Chia-Feng/Pytorch_Learning | 3954294ce825076d75adf550be999890dceac9d7 | [
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d01049078cdab202f77bd4c8b4329a56f4331778 | 185,914 | ipynb | Jupyter Notebook | SupervisedLearning/Problem2_SupervisedLearning_Gaiceanu.ipynb | TheodoraG/FRTN65 | dc3849ed449ac7a7889014d759469f7744801306 | [
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d0105412e27d0ed232116837433cdc6244d5dea3 | 145,967 | ipynb | Jupyter Notebook | demo/IHaskell.ipynb | rpglover64/IHaskell | 4968a2838e9fce7559903520f0cb3c7028fdbeae | [
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d010598126498ec894fb28fd1d12a823070b7c5c | 8,247 | ipynb | Jupyter Notebook | concepts/lifesupport/vasopressors_inotropes.ipynb | AKI-Group-ukmuenster/AmsterdamUMCdb | 5541e908181526e184ce17d78c0da371ea2b9d00 | [
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] | 24 | 2019-11-19T04:23:39.000Z | 2022-01-20T09:52:03.000Z | 29.880435 | 151 | 0.475688 | [
[
[
"<img src=\"../../img/logo_amds.png\" alt=\"Logo\" style=\"width: 128px;\"/>\n\n# AmsterdamUMCdb - Freely Accessible ICU Database\n\nversion 1.0.2 March 2020 \nCopyright © 2003-2020 Amsterdam UMC - Amsterdam Medical Data Science",
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d0108f0bf340e898e4dbdfdd3c5c850793364945 | 43,385 | ipynb | Jupyter Notebook | notebooks/6.Incorporating_Related_Time_Series_dataset_to_your_Predictor.ipynb | ardilacarlosh/amazon-forecast-samples | 3a516e5b9b2af745546784178fcea115eda3008c | [
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] | null | null | null | notebooks/6.Incorporating_Related_Time_Series_dataset_to_your_Predictor.ipynb | ardilacarlosh/amazon-forecast-samples | 3a516e5b9b2af745546784178fcea115eda3008c | [
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] | null | null | null | notebooks/6.Incorporating_Related_Time_Series_dataset_to_your_Predictor.ipynb | ardilacarlosh/amazon-forecast-samples | 3a516e5b9b2af745546784178fcea115eda3008c | [
"MIT-0"
] | null | null | null | 31.257205 | 1,024 | 0.569575 | [
[
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"# Amazon Forecast: predicting time-series at scale\n\nForecasting is used in a variety of applications and business use cases: For example, retailers need to forecast the sales of their products to decide how much stock they need by location, Manufacturers need to estimate the number of parts require... | [
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d010a03c84b208f351e5fda95c1999b11c77618b | 13,465 | ipynb | Jupyter Notebook | notebooks/scratchpad.ipynb | nkrishnaswami/census-data-aggregator | 27bc8aae25473604aa48f26b8b038e366685d566 | [
"MIT"
] | null | null | null | notebooks/scratchpad.ipynb | nkrishnaswami/census-data-aggregator | 27bc8aae25473604aa48f26b8b038e366685d566 | [
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d010b6850d041922f384a75d39e8698b35702776 | 13,494 | ipynb | Jupyter Notebook | Cumulative_Projects/Wk 5 Reggie_Linear_Regression_Solution.ipynb | jfreeman812/Project_ZF | d71472e09cd19f006d758b7344b959f74b667824 | [
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d010bdf4a24b60fb17b4990af62360d887f42d35 | 24,482 | ipynb | Jupyter Notebook | examples/presentation.ipynb | jtpio/pixijs-jupyter | e690d96e177d34ddc4937b40a816f36fe427b00d | [
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d010cea376f26f4a79668b1e962fa5f3d8b1f69c | 20,549 | ipynb | Jupyter Notebook | semana_6/intro_regressao.ipynb | rocabrera/curso_ml | e61a15ba3b031540dfce39a2d587e3738446838d | [
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d010d3dd19432c4679651da1d99329e198bbad57 | 158,849 | ipynb | Jupyter Notebook | examples/3C279_spectrum.ipynb | volodymyrss/oda_api_benchmark | 91580ff5ac121352f901880eeecf3bfc12fb4c24 | [
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d010d5cc33b41e8e5da0a910ee6a3ea53556f9ba | 56,145 | ipynb | Jupyter Notebook | agreg/Lambda_Calcul_en_OCaml.ipynb | doc22940/notebooks-2 | 6a7bdec5ed2195005d64ca1f9eaf6613d68fb8ca | [
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d010db4605df3cc52b2d0a4c87e9606881fc1dc1 | 188,543 | ipynb | Jupyter Notebook | week8/Dimensionality Reduction and Clustering.ipynb | yixinouyang/dso-560-nlp-and-text-analytics | f1a322b6d4c9058880dcde5d623ebceaa164e7b4 | [
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d0110876301904dc94bd2cfeb8d9ef5a1196953a | 14,425 | ipynb | Jupyter Notebook | 08_OSK.ipynb | seokyeongheo/slow_statistics | f8eb8f043420f247e197e0abfe39b5cc2d418a3a | [
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d01108dac7b72da5bd0e421abbfaf5cd547d8ce2 | 100,351 | ipynb | Jupyter Notebook | qiskit/circuit_basics.ipynb | jonhealy1/learning-quantum | b0a1bf109450f5b0dcca5c6f81228e6dcfd62ca5 | [
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d0110c3c7efbc1b93bb84ca519a4e9288d5aee47 | 11,559 | ipynb | Jupyter Notebook | Linear_algebra_Solutions.ipynb | suryasuresh06/cvg1 | e49a728e1dd5549e56b3ea93af581b96f861b3bc | [
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d01167309dcf91d67bcf58ddeea245b52923e32e | 3,021 | ipynb | Jupyter Notebook | practice 1-checkpoint.ipynb | adaobi15/adaobiCSC102 | 889419ead330a8b469e95cee7febba2d66635284 | [
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d01185baf3cebecc54735e16f3a9d821703c60a4 | 48,720 | ipynb | Jupyter Notebook | colors_overview.ipynb | MallikaKhullar/Stanford_CS224u | 36dc3394408c698a524c0d508923eb7934a862ce | [
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d0119003f614b6e03e9abb40aaffcef84589abc4 | 59,322 | ipynb | Jupyter Notebook | discrete dynamic programming.ipynb | keiikegami/DP | be00f528e8e0aeb62c9547a9b99f84e28bcf3940 | [
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] | 1 | 2020-02-28T00:15:33.000Z | 2020-02-28T00:15:33.000Z | 19.533419 | 1,790 | 0.474251 | [
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]
],
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[
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d011a6992c3ab9f49b2d89ca114ec6567b137f90 | 6,010 | ipynb | Jupyter Notebook | BisQue_Graphical_Annotations.ipynb | benlazarine/bisque-notebooks | b8d2d710262b6fc63d88f445081fe7536bf851e1 | [
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d011b3319b0fbf23f5b4fa20edb6c0baa71bee29 | 5,696 | ipynb | Jupyter Notebook | anaysis.ipynb | yao0510/Fake-EmoReact-2021 | d6d6facd46c1a31cbc70bfef8688715897b9470b | [
"MIT"
] | null | null | null | anaysis.ipynb | yao0510/Fake-EmoReact-2021 | d6d6facd46c1a31cbc70bfef8688715897b9470b | [
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] | null | null | null | 25.542601 | 128 | 0.535288 | [
[
[
"import pandas as pd\nimport json\nimport numpy as np\nfrom cleaner import *",
"_____no_output_____"
],
[
"# df_train = read_json('./processed_data/preprocess_train.json')\n# df_dev = read_json('./processed_data/preprocess_my_dev.json')\n# df_test = read_json('./processed_data/prep... | [
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d011c308a1530cf1273c44ca0837489c3ec6c822 | 4,951 | ipynb | Jupyter Notebook | ga1/GroupAssignment1_Template.ipynb | lfgberg/IST210 | 41f74e55e435f78cc34befdad67d46674c369dfb | [
"MIT"
] | null | null | null | ga1/GroupAssignment1_Template.ipynb | lfgberg/IST210 | 41f74e55e435f78cc34befdad67d46674c369dfb | [
"MIT"
] | null | null | null | ga1/GroupAssignment1_Template.ipynb | lfgberg/IST210 | 41f74e55e435f78cc34befdad67d46674c369dfb | [
"MIT"
] | null | null | null | 21.526087 | 246 | 0.524137 | [
[
[
"# Illegal Airplane Dealership ",
"_____no_output_____"
],
[
"### what is your idea?",
"_____no_output_____"
],
[
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[
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"Apache-2.0"
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"## Gambling 101\n\nYou are participating in a lottery game. A deck of cards numbered from 1-50 is shuffled and 5 cards are drawn out and laid out. You are given a coin. For each card, you toss the coin and pick it up if it says heads, otherwise you don't pick it up. The sum of the cards is what you w... | [
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d0129886a14437458caf326efac5b852acd6172b | 231,823 | ipynb | Jupyter Notebook | Starter_Code/credit_risk_ensemble.ipynb | eriklarson33/HW_11-Risky_Business | 03f110e50a937c8474f9c0b20c1d05581f99b634 | [
"ADSL"
] | null | null | null | Starter_Code/credit_risk_ensemble.ipynb | eriklarson33/HW_11-Risky_Business | 03f110e50a937c8474f9c0b20c1d05581f99b634 | [
"ADSL"
] | null | null | null | Starter_Code/credit_risk_ensemble.ipynb | eriklarson33/HW_11-Risky_Business | 03f110e50a937c8474f9c0b20c1d05581f99b634 | [
"ADSL"
] | null | null | null | 45.760561 | 31,020 | 0.446082 | [
[
[
"# 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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d012a593594f291b2918ec7854f9a6f45b8861c1 | 48,159 | ipynb | Jupyter Notebook | Desafio 7 (enem-2)/main.ipynb | leosimoes/Codenation-AceleraDev-DataScience | 328a8248d09544e57e2b45ac9b2df29f858bcef8 | [
"MIT"
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"MIT"
] | null | null | null | Desafio 7 (enem-2)/main.ipynb | leosimoes/Codenation-AceleraDev-DataScience | 328a8248d09544e57e2b45ac9b2df29f858bcef8 | [
"MIT"
] | null | null | null | 35.2297 | 373 | 0.393405 | [
[
[
"# Codenation - Data Science\n<pre>Autor: Leonardo Simões</pre>\n\n## Desafio 7 - Descubra as melhores notas de matemática do ENEM 2016\n\nVocê deverá criar um modelo para prever a nota da prova de matemática de quem participou do ENEM 2016. Para isso, usará Python, Pandas, Sklearn e Regression.\n\n##... | [
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d012cd06e20f081da60c8d8a64b19347858b6549 | 634,707 | ipynb | Jupyter Notebook | 08 - The matplotlib Ecosystem/0802 - Pandas.ipynb | croach/mastering-matplotlib | fdc2aca87140a1927d9230ab61305cd742e200d5 | [
"MIT"
] | 137 | 2016-04-16T17:25:19.000Z | 2022-01-22T21:45:41.000Z | 08 - The matplotlib Ecosystem/0802 - Pandas.ipynb | croach/mastering-matplotlib | fdc2aca87140a1927d9230ab61305cd742e200d5 | [
"MIT"
] | 2 | 2016-04-09T19:27:49.000Z | 2018-04-15T15:35:17.000Z | 08 - The matplotlib Ecosystem/0802 - Pandas.ipynb | croach/mastering-matplotlib | fdc2aca87140a1927d9230ab61305cd742e200d5 | [
"MIT"
] | 76 | 2016-03-23T09:58:36.000Z | 2021-08-05T14:28:02.000Z | 1,590.744361 | 442,484 | 0.947242 | [
[
[
"## Introduction\n\nIf you've had any experience with the python scientific stack, you've probably come into contact with, or at least heard of, the [pandas][1] data analysis library. Before the introduction of pandas, if you were to ask anyone what language to learn as a budding data scientist, most ... | [
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