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d01f3e74f9141a60e17ac6ef2b9782818c5c9555 | 248,147 | ipynb | Jupyter Notebook | Fall2020/Demos/Linear Regression Housing Demo.ipynb | yul091/MLClass | 6df8ccdbedfb01e448211cbe948147876a2a3057 | [
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d01f4bb18a8cd39d3e64c36128366f2c2375e46f | 26,408 | ipynb | Jupyter Notebook | misc/improving.ipynb | IGARDS/ranklib | 1acd8c0bd4d4045b55e6c5bd6cbb2fbe080c7479 | [
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d01f4dd1a9aeb0a9a6800e04ef2b2097a5fda0f8 | 734,101 | ipynb | Jupyter Notebook | factory/gmvrfit_reduce_to_gmvpfit_example.ipynb | llondon6/koalas | bc778ba492027b3d40f9c92ef44da5949d0e43c7 | [
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d01f54e45316959314a5732f6c110253b0b101f8 | 13,969 | ipynb | Jupyter Notebook | notebooks/JSON database.ipynb | ecoinvent/brightway2 | 78303c1efaf40dd94089929a8b0c3a9b59736733 | [
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d01f5e05f1656b11a2f4ad6676891fe7d52f4840 | 331,974 | ipynb | Jupyter Notebook | .ipynb_checkpoints/Annulus_Simple_Matplotlib-checkpoint.ipynb | brettavedisian/Liquid-Crystals | c7c6eaec594e0de8966408264ca7ee06c2fdb5d3 | [
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d01f69beda578642dfbf5071389bb928ec455970 | 64,179 | ipynb | Jupyter Notebook | Handwritten Digits Recognition 02 - TensorFlow.ipynb | kevin-linps/Handwritten-digits-recognition | 631145b7dee6c57e6288249cdc4d5d14daa3b789 | [
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d01fb33a077706b84ce5e40b190916400e3af244 | 39,037 | ipynb | Jupyter Notebook | uci/uci.ipynb | DPautoGAN/DPAutoGAN | 40b72fd59cb7cbca7b544ada30c9e78731465692 | [
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d01fbe9e1f4f84ee5abd12bb72f3b79d00f39853 | 18,142 | ipynb | Jupyter Notebook | average_radiosonde.ipynb | franzihe/Haukeliseter_16_17 | 91a50a34ce6084dfaae455df8fea336ace877873 | [
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d01fd76698305d30500dfd5d5d4ceb03669694df | 129,068 | ipynb | Jupyter Notebook | Transparent Model Interpretability.ipynb | iamollas/Informatics-Cafe-XAI-IML-Tutorial | 56e6320b65bf207b90308b5f21214600e7f65fb6 | [
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d01fddbe6591231dd76fa146cd9a89e28f52e9cc | 14,154 | ipynb | Jupyter Notebook | fashion.ipynb | rajeevak40/Course_AWS_Certified_Machine_Learning | 4debbf43d7a0a358395060cf2289393607554a1f | [
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d01fe8fbc589303ab3dac7ae4d122afec4e330d6 | 157,848 | ipynb | Jupyter Notebook | d2l/tensorflow/chapter_linear-networks/linear-regression-scratch.ipynb | nilesh-patil/dive-into-deeplearning | d1d20885652e640a92cd82dfb6627fcdb519c51d | [
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d02024e3e1087479b65cb447ef2569e3beb5fa81 | 776,366 | ipynb | Jupyter Notebook | train_nuclei.ipynb | xumm94/2018_data_science_bowl | 9f7a6b60b7c1e933c30acd8abbdeeb7bd869a3f6 | [
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d0202e7d55aed5ff965378766b7f24c84fd14dac | 939,574 | ipynb | Jupyter Notebook | notebooks/2_socioeconomic_data_validation.ipynb | fernandascovino/pr-educacao | 79793089552e75573cc77c90ccbf2cf04972ab42 | [
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d02030905b0a5851e858754be5eae74b2d72687a | 46,320 | ipynb | Jupyter Notebook | ch08_Reinforcement-learning/ch16-reinforcement-learning.ipynb | pythonProjectLearn/TensorflowLearning | 7a72ebea060ce0a0db9a00994e4725ec5d84c10a | [
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d02044f8849a68d81ca808d58efdc3127540f7a6 | 242,267 | ipynb | Jupyter Notebook | Instructions/.ipynb_checkpoints/climate_starter_Initial_file-2-checkpoint.ipynb | BklynIrish/sqlalchemy_challenge | d14ccc3b6d96032b404d39d36ec2008e948aa8ae | [
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d0204d6ccf48d75c61cfe995f80acf3f22e84879 | 532,545 | ipynb | Jupyter Notebook | lessons/03_Lesson03_doublet.ipynb | goodsang1023/aeropython | dbb780745fc93f3d6fd173b7b55ba23cda3c1d8d | [
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] | 1 | 2021-01-31T22:54:57.000Z | 2021-01-31T22:54:57.000Z | 702.565963 | 230,320 | 0.948861 | [
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"###### Text provided under a Creative Commons Attribution license, CC-BY. Code under MIT license. (c)2014 Lorena A. Barba, Olivier Mesnard. Thanks: NSF for support via CAREER award #1149784.",
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d02054182c741f31b3c1fb1e9f7c693fbc9a0294 | 108,720 | ipynb | Jupyter Notebook | Scr/trainning/.ipynb_checkpoints/Untitled-checkpoint.ipynb | ale-telefonica/market | bf086065ee13d06981bee212c043ba308c1261e8 | [
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"import MySQLdb\nfrom sklearn.svm import LinearSVC\nfrom tensorflow import keras\nfrom keras.models import load_model\nimport tensorflow as tf\nfrom random import seed\nimport pandas as pd\nimport numpy as np\nimport re\nfrom re import sub\nimport os\nimport string\nimport tempfile\nimport pickle\nimp... | [
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d0207062d3a71fd632ea8360b4e9a2417560d6d8 | 3,670 | ipynb | Jupyter Notebook | sample.ipynb | AI-Guru/MMM-JSB | 2cf0faeedc402b4574f292712632855675ae4037 | [
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d020748f3703be79b4f4363621d29ad666d16585 | 14,139 | ipynb | Jupyter Notebook | examples/notebooks/generic_mle.ipynb | KishManani/statsmodels | 300b6fba90c65c8e94b4f83e04f7ae1b0ceeac2e | [
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d02080362be91dc8902c5894338ef02c51236dcc | 83,590 | ipynb | Jupyter Notebook | source/Mlos.Notebooks/SmartCacheCPP.ipynb | HeatherJia/MLOS | b0a350fa817cd23763e29b3295a866838900f476 | [
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] | null | null | null | source/Mlos.Notebooks/SmartCacheCPP.ipynb | HeatherJia/MLOS | b0a350fa817cd23763e29b3295a866838900f476 | [
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"# Connecting MLOS to a C++ application\n\nThis notebook walks through connecting MLOS to a C++ application within a docker container.\nWe will start a docker container, and run an MLOS Agent within it. The MLOS Agent will start the actual application, and communicate with it via a shared memory chann... | [
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d020886c0ea73756d5fc79f8e34898ec59765b30 | 79,879 | ipynb | Jupyter Notebook | RML_example_org.ipynb | sa3036/Radio_ML_571M | fd034d4a390eea991e399882a39597b9ee36252a | [
"MIT"
] | 2 | 2020-02-23T07:26:02.000Z | 2022-01-28T07:15:33.000Z | RML_example_org.ipynb | sa3036/Radio_ML_571M | fd034d4a390eea991e399882a39597b9ee36252a | [
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] | null | null | null | RML_example_org.ipynb | sa3036/Radio_ML_571M | fd034d4a390eea991e399882a39597b9ee36252a | [
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d0208ea794343d2d6cac264eb0aadfe16c420f37 | 2,952 | ipynb | Jupyter Notebook | 100days/day 03 - next permutation.ipynb | gopala-kr/ds-notebooks | bc35430ecdd851f2ceab8f2437eec4d77cb59423 | [
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] | 5 | 2018-02-23T22:08:28.000Z | 2020-08-19T08:31:47.000Z | 20.081633 | 64 | 0.392954 | [
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d020a55eca1e01c7f22e441e5ad6124ad968bc14 | 14,429 | ipynb | Jupyter Notebook | fairness.ipynb | ravikirankb/machine-learning-tutorial | 064937059ab7945d2c08ccdc839ca799f61bd1aa | [
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] | null | null | null | fairness.ipynb | ravikirankb/machine-learning-tutorial | 064937059ab7945d2c08ccdc839ca799f61bd1aa | [
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d020bc2497c70729421b2fcb9fde0abe570c4d96 | 155,193 | ipynb | Jupyter Notebook | object_detection_face_detector.ipynb | lvisdd/object_detection_tutorial | bf201914392f3e0bb786f6c2724eff17df7e78f8 | [
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] | 2 | 2019-08-18T02:43:25.000Z | 2020-12-23T07:38:22.000Z | object_detection_face_detector.ipynb | lvisdd/object_detection_tutorial | bf201914392f3e0bb786f6c2724eff17df7e78f8 | [
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] | null | null | null | object_detection_face_detector.ipynb | lvisdd/object_detection_tutorial | bf201914392f3e0bb786f6c2724eff17df7e78f8 | [
"Apache-2.0"
] | 1 | 2019-08-27T09:57:13.000Z | 2019-08-27T09:57:13.000Z | 204.201316 | 121,306 | 0.864672 | [
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d020c008258d93f9003722b2f6464169e94f20b5 | 16,517 | ipynb | Jupyter Notebook | day3.ipynb | msse-2021-bootcamp/team2-project | 3915fd811be09e79d7ea5c9a368d7849ef5b629b | [
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d020f5b1bd9f0c29260cc53abfe09763db1a4ae0 | 11,492 | ipynb | Jupyter Notebook | Project/Starbucks/.ipynb_checkpoints/Starbucks-checkpoint.ipynb | kundan7kumar/Machine-Learning | 8b62b68324713007c967a6120a0f48498992ce2f | [
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d020fdc9f7e8f6b6a7b5309d973617e62cceaf2a | 8,662 | ipynb | Jupyter Notebook | docs/source/examples/Widget Basics.ipynb | akhand1111/ipywidgets | a6228df8a24079bd4f8b6c1645b31e1c00218535 | [
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d021093cdd43dfa1411099f7c9b3cfe8e5f5dd11 | 16,994 | ipynb | Jupyter Notebook | 08_AfterAcceptance/06_KNN/knn.ipynb | yazdipour/DM17 | bcde44df990938723c843801c1333cbcf4e5bd76 | [
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d0216fd701d7db487674b85843bac74588acde0e | 33,197 | ipynb | Jupyter Notebook | lectures/02-functions.ipynb | sir-rois/mipt-python | da5f1861e17a31da4a2930a423f4dc0ce434bef0 | [
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d02182b56fc7b86aa031f44046e3fb6e7ae4aeaa | 137,027 | ipynb | Jupyter Notebook | jupyter/Chapter05/coherent_detector.ipynb | miltondsantos/software | d27375257b0260cad901837612fbca0174134229 | [
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d0219a2b3767e15b15af171547c2a5daf04208cb | 2,514 | ipynb | Jupyter Notebook | JupyterNotebooks/Labs/Lab 2.ipynb | WolfyVST/CMPT-220L-203-22S | 200cc519c0d177fc71d6c945328e35f6ce907c47 | [
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d021b4846093e2685a6b202bb31a23b6f87f8a65 | 36,793 | ipynb | Jupyter Notebook | Assignment_TaskC_Streaming_Application.ipynb | tonbao30/Parallel-dataprocessing-simulation | 2674ad83009be73af719e0a837970e45857b7517 | [
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d021bf8d23dd16f44565e18a49eb899ef3998f2e | 205,313 | ipynb | Jupyter Notebook | text-summarization-attention-mechanism.ipynb | buddhadeb33/Text-Summarization-Attention-Mechanism | e8ab5f81ec6d2f57238de3102f28bbe9f68a05be | [
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d021c58f5bce572203c05790d8e2e616371510a2 | 7,455 | ipynb | Jupyter Notebook | 0.15/_downloads/plot_brainstorm_phantom_ctf.ipynb | drammock/mne-tools.github.io | 5d3a104d174255644d8d5335f58036e32695e85d | [
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d021d0bfb33f8b1bf0764771946ba8d83c9d389d | 78,352 | ipynb | Jupyter Notebook | Complex_Systems.ipynb | davidgmiguez/julia_notebooks | b395fac8f73bf8d9d366d6354a561c722f37ce66 | [
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d021d550b0599d907d8fb7798c9a992c49369ca5 | 174,864 | ipynb | Jupyter Notebook | matrix_two/day2_viz.ipynb | mattzajac/dw_matrix | 16763c44f6c46fc06d0a4a10b5467cc6f0eeaa92 | [
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d021d6389574c61647d2b662f7f9d280195d89a7 | 5,490 | ipynb | Jupyter Notebook | codility-lessons/7 Stacks and Queues.ipynb | stanislawbartkowski/learnml | 1b87c3d433b38a86f85d6e9588cc5de54375bbba | [
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d021f38cc7bbc3fa3fa32b416732f96d8d886c63 | 33,649 | ipynb | Jupyter Notebook | credit_risk_ensemble.ipynb | THaoV1001/Classification-Homework | ce3d0800104504911eeb5a56639c14fac20e637e | [
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d022065303ea0097200b2cf3ef960cf4c0691919 | 53,192 | ipynb | Jupyter Notebook | Xanadu3.ipynb | olgOk/XanaduTraining | 1e4af1091117b219d7a504226a45a1065e010b26 | [
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"MIT"
] | null | null | null | Xanadu3.ipynb | olgOk/XanaduTraining | 1e4af1091117b219d7a504226a45a1065e010b26 | [
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d02209f9fd745eb214f001cd1a9b2ba0015ea9b5 | 305,154 | ipynb | Jupyter Notebook | parte1.ipynb | tiagodalloca/mc920-trabalho1 | 644e1a7e383a6fd934fcaec15e5de2d5d52c3a4d | [
"MIT"
] | null | null | null | parte1.ipynb | tiagodalloca/mc920-trabalho1 | 644e1a7e383a6fd934fcaec15e5de2d5d52c3a4d | [
"MIT"
] | null | null | null | parte1.ipynb | tiagodalloca/mc920-trabalho1 | 644e1a7e383a6fd934fcaec15e5de2d5d52c3a4d | [
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d0221d3e5b90db8e540f6b05e8a0532131e2d35d | 46,176 | ipynb | Jupyter Notebook | notebooks/2017-05-27-data-science-of-data-science.ipynb | daniel-acuna/daniel-acuna.github.io | f3dec9f84b594a8d1afdac89b7553b8269e0e230 | [
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"BSD-3-Clause"
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d02222a7741bbcbc70431c5d8c4e65ba6687c1b1 | 20,062 | ipynb | Jupyter Notebook | Python-Programming/Python-3-Bootcamp/13-Advanced Python Modules/.ipynb_checkpoints/05-Regular Expressions - re-checkpoint.ipynb | vivekparasharr/Learn-Programming | 1ae07ef5143bff3c504978e1d375698820f59af0 | [
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d0223de02f6fdaa6529a4970d2ba0ecc8d5df39d | 237,044 | ipynb | Jupyter Notebook | experiments/tuned_1v2/oracle.run2/trials/4/trial.ipynb | stevester94/csc500-notebooks | 4c1b04c537fe233a75bed82913d9d84985a89177 | [
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d02241d313de8b4ee050e90fc3a6311f6fc7ae14 | 49,141 | ipynb | Jupyter Notebook | Jupyter notebook/Practice 4 - Cython.ipynb | marcomussi/RecommenderSystemPolimi | ce45b1eee2231abe1a844697648e94b98dadabea | [
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d022595d6a51a74ac9d53ea19e3e3063161a32ad | 669,918 | ipynb | Jupyter Notebook | 15_PDEs/15_PDEs.ipynb | ASU-CompMethodsPhysics-PHY494/PHY494-resources-2018 | 635a6678569406e11865c8a583a56f4a3cf2bdc4 | [
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"# 15 PDEs: Solution with Time Stepping\n\n## Heat Equation\nThe **heat equation** can be derived from Fourier's law and energy conservation (see the [lecture notes on the heat equation (PDF)](https://github.com/ASU-CompMethodsPhysics-PHY494/PHY494-resources/blob/master/15_PDEs/15_PDEs_LectureNotes_He... | [
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d0225c9bb0eb873370c1c779409d7026474ae20f | 47,292 | ipynb | Jupyter Notebook | ML_course/ML_Contest_train.ipynb | Riwedieb/handson-ml | 76ffe3b41732c76e3487aaabe38719075cd712d1 | [
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d0225db7b41c0f2d8fdb96885904c07732daeb9b | 8,111 | ipynb | Jupyter Notebook | examples/direct_fidelity_estimation.ipynb | mganahl/Cirq | f2bf60f31ad247a68589d7c29263a6765fc3f791 | [
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d0226a500ed6c8e8c6b03c08a9fcfefb1ef7026a | 10,066 | ipynb | Jupyter Notebook | languages/south_asia/Gujarati_tutorial.ipynb | glaserti/tutorials | fb56a58bbe2e0ae338b01a9528cecc9b652df7cc | [
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] | 44 | 2017-03-18T09:30:50.000Z | 2022-02-04T00:05:34.000Z | languages/south_asia/Gujarati_tutorial.ipynb | glaserti/tutorials | fb56a58bbe2e0ae338b01a9528cecc9b652df7cc | [
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d0226aed22f893f8ef1c0c3fb0cbc5335ebcdb7f | 93,932 | ipynb | Jupyter Notebook | 3. Landmark Detection and Tracking.ipynb | mitsunami/SLAM | 9aa5f35dbe4b110acb0625efb833ca6532c6d108 | [
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] | null | null | null | 3. Landmark Detection and Tracking.ipynb | mitsunami/SLAM | 9aa5f35dbe4b110acb0625efb833ca6532c6d108 | [
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"# Project 3: Implement SLAM \n\n---\n\n## Project Overview\n\nIn this project, you'll implement SLAM for robot that moves and senses in a 2 dimensional, grid world!\n\nSLAM gives us a way to both localize a robot and build up a map of its environment as a robot moves and senses in real-time. This is... | [
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d0226f90661049b017f88d274cbf51df7ed8fa52 | 36,457 | ipynb | Jupyter Notebook | Nets on Spectral data/01_PDU_Total_Designed_Inception.ipynb | Saman689/Weed-sensing-basics | 25355b20af94432fbe43969cc21fcbf402d01972 | [
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] | null | null | null | Nets on Spectral data/01_PDU_Total_Designed_Inception.ipynb | Saman689/Weed-sensing-basics | 25355b20af94432fbe43969cc21fcbf402d01972 | [
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d022858ab38d5aa60a93f1a29210f329f36f7527 | 331,787 | ipynb | Jupyter Notebook | expressyeaself/models/lstm/LSTM_builder.ipynb | yeastpro/expressYeaself | e7a94176f84c6b501b5ea4d76c5f82592af168ed | [
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d0228891397648a1c2fc6ba056b6a5cb9e3c47a9 | 19,847 | ipynb | Jupyter Notebook | Woodgreen_Data_Science_&_Python_Nov_2021_Week_3.ipynb | tjido/woodgreen | 24e6a999e096c3c520aec5d10e8628401c3a848a | [
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d0228e0ab13d5f1668dc3354744ca667c417e75c | 409,821 | ipynb | Jupyter Notebook | Modulo 4 - Analisi per regioni/regioni/Sardegna/SARDEGNA - SARIMA mensile.ipynb | SofiaBlack/Towards-a-software-to-measure-the-impact-of-the-COVID-19-outbreak-on-Italian-deaths | c418eba90dc07f58633e7e4cd2719c46f0a6b202 | [
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] | 3 | 2021-04-02T21:54:52.000Z | 2021-04-13T14:24:29.000Z | Modulo 4 - Analisi per regioni/regioni/Sardegna/SARDEGNA - SARIMA mensile.ipynb | SofiaBlack/COVID-19_deaths_analysis | c418eba90dc07f58633e7e4cd2719c46f0a6b202 | [
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] | null | null | null | Modulo 4 - Analisi per regioni/regioni/Sardegna/SARDEGNA - SARIMA mensile.ipynb | SofiaBlack/COVID-19_deaths_analysis | c418eba90dc07f58633e7e4cd2719c46f0a6b202 | [
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d022a4d0df6edb6965f07dcfa466eb7849bb928d | 52,652 | ipynb | Jupyter Notebook | #01. Data Tables & Basic Concepts of Programming/Untitled.ipynb | gabisintope/machine-learning-program | f693371937e19a5d2d6b9e26bfc8063f6724c970 | [
"MIT"
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] | null | null | null | #01. Data Tables & Basic Concepts of Programming/Untitled.ipynb | gabisintope/machine-learning-program | f693371937e19a5d2d6b9e26bfc8063f6724c970 | [
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] | null | null | null | 36.81958 | 102 | 0.349218 | [
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d022aa6f23c8161910edc62d1e2c3d014738626b | 6,066 | ipynb | Jupyter Notebook | kernel/Mermaid/_sc.ipynb | nufeng1999/Myjupyter-kernel | 7862ce8afae139d39ad2896f3e36a19b5df9923e | [
"MIT"
] | null | null | null | kernel/Mermaid/_sc.ipynb | nufeng1999/Myjupyter-kernel | 7862ce8afae139d39ad2896f3e36a19b5df9923e | [
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] | null | null | null | kernel/Mermaid/_sc.ipynb | nufeng1999/Myjupyter-kernel | 7862ce8afae139d39ad2896f3e36a19b5df9923e | [
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] | null | null | null | 36.542169 | 137 | 0.523244 | [
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d022b1d5a676fb9e8eb1553bd52e7492f26f4d34 | 4,550 | ipynb | Jupyter Notebook | 03_Grouping/Occupation/Exercise.ipynb | mtzupan/pandas_exercises | 3527cda51234e126ba5600ab9596e4bd4cca5d63 | [
"BSD-3-Clause"
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] | null | null | null | 03_Grouping/Occupation/Exercise.ipynb | mtzupan/pandas_exercises | 3527cda51234e126ba5600ab9596e4bd4cca5d63 | [
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d022c798fc4624392513cda9d6daa1ca7f820243 | 68,240 | ipynb | Jupyter Notebook | probability/probability-course/notebooks/[Clase9]Distribucion_normal.ipynb | Elkinmt19/data-science-dojo | 9e3d7ca8774474e1ad74138c7215ca3acdabf07c | [
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d022ec3565eccfd85c5d926e06eab6f9dfe6ab6e | 27,237 | ipynb | Jupyter Notebook | Equipped_AI_Test.ipynb | VAD3R-95/Hackathons_and_Interviews | 54b8f770e3af7012eea44f0c905d30cdc2a8fcb2 | [
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d022f1ea09394a71240b040ba27b4bd897f45876 | 232,515 | ipynb | Jupyter Notebook | experiments/tl_1v2/cores-oracle.run1.framed/trials/14/trial.ipynb | stevester94/csc500-notebooks | 4c1b04c537fe233a75bed82913d9d84985a89177 | [
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d0230d4e31ce4e6f4bd2c86515598c4727ce2b29 | 9,939 | ipynb | Jupyter Notebook | Spark/HeartDataset-MLlib.ipynb | elifcansuyildiz/MachineLearningNotebooks | a27b924948b82172be3d90d7edaf8fb60c6e22ca | [
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] | null | null | null | 32.480392 | 141 | 0.499346 | [
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d023473c65b21a45ca4175db20e6e3fbc18c0d90 | 181,151 | ipynb | Jupyter Notebook | sklearn-guide/chapter03/ml-3.ipynb | a630140621/machine-learning-course | 7fba4dd46fe458c8754c2fe7d64627ee98a89c42 | [
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d0234b84393f6273898e1ed97305af471971dfb7 | 46,962 | ipynb | Jupyter Notebook | 02_usecases/sagemaker_recommendations/wip/02_Recommenders_Retrieval_AdHoc.ipynb | MarcusFra/workshop | 83f16d41f5e10f9c23242066f77a14bb61ac78d7 | [
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d02373de98d97dca796f1591fbec9f93968be53f | 4,183 | ipynb | Jupyter Notebook | clean_code.ipynb | MaiaNgo/python-zerotomastery | 6b37021af531b9adc029f1dd20b4aa0be3c6a800 | [
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d023b8a82c989009828292c380c8ddce26c68761 | 26,223 | ipynb | Jupyter Notebook | docs/tutorials/6_reinforce_tutorial.ipynb | Zuu97/agents | 3299a62165027c7844e4574260dca6512e0369a0 | [
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d023c70bab97d347c794117f3a84dfd645fbf486 | 16,266 | ipynb | Jupyter Notebook | src/main/paradox/docs/tutorial/notebooks/Query_Sparql_View.ipynb | clifle/nexus | 53cbe57349c6267ccd3365e0879e7c9912268223 | [
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d023ccc3403c8915d180cbbadb5ac67f394a74fa | 19,621 | ipynb | Jupyter Notebook | Boosted Late-Fusion.ipynb | Sakina8/Multimodal-Classification2020 | 8753ab6be535e59b3b95c4a99eda7b97e4fc5461 | [
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d023ccfec1814303c75ac783f19ff43185d83197 | 153,928 | ipynb | Jupyter Notebook | data/ferch_fullyears.ipynb | carocamargo/ohw20-proj-pyxpcm | 301a36564167e22ab644f51ad1872c02bdcbbbb4 | [
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d023e55f92924c0e5581dc92bf545d8395ccefba | 20,698 | ipynb | Jupyter Notebook | notebooks/1-Using-ImageJ/Ops/stats/percentile.ipynb | sonjoonho/tutorials | 37a59e3c66e66303f66523d26bbb38e4bd140eaf | [
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d023f853b1ac57a0d1e3c003e84ee1c1a7d1770e | 4,397 | ipynb | Jupyter Notebook | notebooks/2020-05-15 tscan refactor.ipynb | danielsuo/toy_flood | 471d3c4091d86d4a00fbf910937d4e60fdaf79a1 | [
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d023fc5838f23bf41a3a3bd01dae6b2e34e24e90 | 10,756 | ipynb | Jupyter Notebook | Part 5 - Confidence Intervals and Analysis of Linear Regression Model.ipynb | yesman89/predicting-nba-games | 4d4c59fe82e8556fcc84627cf5da8f33ef9b251c | [
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d023fe55ff0f0e6b1ae8a6c6e25c6deadb570b32 | 21,368 | ipynb | Jupyter Notebook | notebooks/mog-eigval-dist.ipynb | LMescheder/TheNumericsOfGANs | 68d915fc01608e7f585af853a2aabeacbfa2d53f | [
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d0240247c03a25455cc36262a2d7cdb33e3b5360 | 32,782 | ipynb | Jupyter Notebook | TensorFlowIntro/.ipynb_checkpoints/TensorFlowIntroduction-checkpoint.ipynb | dschmoeller/03TrafficSignClassifierCNN | d5d7b638d94adb4d0156d353519598d6da276dfd | [
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d0240fdced14f4f68fd4fbcf17819cac2f847eae | 9,915 | ipynb | Jupyter Notebook | ipynbs/reshape_demo.ipynb | zbytes/fsqs-tips-tricks-notes | cb56832646f83f94cfec553d314e8fce8ed73b94 | [
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d0241044e5207c049561f415325a261625de66ba | 152,074 | ipynb | Jupyter Notebook | .ipynb_checkpoints/DSP-checkpoint.ipynb | Valentine-Efagene/Jupyter-Notebooks | 91a1d98354a270d214316eba21e4a435b3e17f5d | [
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d024137b8886b0019ea90b7ee9f066d653bd2551 | 12,163 | ipynb | Jupyter Notebook | modules.ipynb | LoicGrobol/python-im | 28cac9392d09be29fd9234b0e21466e5408a9252 | [
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"MIT"
] | 7 | 2018-09-19T06:47:18.000Z | 2018-12-12T12:03:29.000Z | 21.451499 | 237 | 0.541972 | [
[
[
"# langages de script – Python\n\n## Modules et packages\n\n### M1 Ingénierie Multilingue – INaLCO\n\nclement.plancq@ens.fr",
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"Les modules et les packages permettent d'ajouter des fonctionnalités à Python\n\nUn module est un fichier (```.py```) qui contie... | [
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d0241fc0158d3364a5ee185ab6b2d84ed8068f06 | 24,190 | ipynb | Jupyter Notebook | notebooks/LS333_DSPT6_Model_Demo.ipynb | DrewRust/DSPT6-Twitoff | c444c14441832051e767ab4d2c8c439cc56f0406 | [
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] | null | null | null | notebooks/LS333_DSPT6_Model_Demo.ipynb | DrewRust/DSPT6-Twitoff | c444c14441832051e767ab4d2c8c439cc56f0406 | [
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] | null | null | null | notebooks/LS333_DSPT6_Model_Demo.ipynb | DrewRust/DSPT6-Twitoff | c444c14441832051e767ab4d2c8c439cc56f0406 | [
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] | 6 | 2020-08-09T10:36:47.000Z | 2021-05-08T06:20:16.000Z | 55.228311 | 12,108 | 0.721042 | [
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"### DSPT6 - Adding Data Science to a Web Application\n\nThe purpose of this notebook is to demonstrate:\n- Simple online analysis of data from a user of the Twitoff app or an API\n- Train a more complicated offline model, and serialize the results for online use",
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d0244ba52c853d20441d4f19f11f10f0abcaf65e | 25,937 | ipynb | Jupyter Notebook | project/Untitled.ipynb | HenryTingle/aae497-f19 | 29df124e31fcc1a1a8462211b1390e5a74f40cd9 | [
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d024b963de0a4d62a395fed2b7c90ea35cf91aca | 46,349 | ipynb | Jupyter Notebook | experiments/main_simulations/plot_bivariate_identifiability.ipynb | rflperry/sparse_shift | 7c0d68be21d56f706d1251b914d305786a4c9726 | [
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] | 2 | 2022-01-31T14:12:54.000Z | 2022-02-01T18:17:24.000Z | experiments/main_simulations/plot_bivariate_identifiability.ipynb | rflperry/sparse_shift | 7c0d68be21d56f706d1251b914d305786a4c9726 | [
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d024c356228288bec14fac92e4851446e6776f66 | 19,198 | ipynb | Jupyter Notebook | Batteries Included.ipynb | ThePoetCoder/Odds-and-Ends | fb287bfcf9eda3d4a06c44f83a6bddb4ef09c61f | [
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d024c6d5cc340a7aa889be2ccdc481fa8212c027 | 56,523 | ipynb | Jupyter Notebook | IBM Cloud/WML/notebooks/regression/xgboost_scikit_wrapper/Watson OpenScale and Watson ML Engine Regression.ipynb | arsuryan/watson-openscale-samples | 338e1e236d91baa10562ac6037eba91ca3e8a449 | [
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d024cc41eebc3782f7c9c834bd33a23def6d3b81 | 50,794 | ipynb | Jupyter Notebook | outlier_detection/training_outlier_detection.ipynb | felix-exel/kfserving-advanced | f75c5759c2ab1a5b0fba0ac0fda59f4e9062dfec | [
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d024d7301b88fc76d935836a20499b64fcd6a2c8 | 3,069 | ipynb | Jupyter Notebook | midterm-commands.ipynb | jstevenr/Spring-2018 | 239bd404ea4e19f6fdd3c09036d175f21c70d7af | [
"Apache-2.0"
] | null | null | null | midterm-commands.ipynb | jstevenr/Spring-2018 | 239bd404ea4e19f6fdd3c09036d175f21c70d7af | [
"Apache-2.0"
] | null | null | null | midterm-commands.ipynb | jstevenr/Spring-2018 | 239bd404ea4e19f6fdd3c09036d175f21c70d7af | [
"Apache-2.0"
] | null | null | null | 17.947368 | 63 | 0.424242 | [
[
[
"import pandas as pd\nimport numpy as pd",
"_____no_output_____"
],
[
"data = pd.Series([0.25,0.5,0.75,1.0],index=[2,5,3,7])\ndata",
"_____no_output_____"
],
[
"df = pd.DataFrame([[1,2],[3,4],[5,6]],\n columns = ['foo','bar'],\n i... | [
"code"
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[
"code",
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"code",
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"code",
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"code",
"code",
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"code"
]
] |
d024f264c1b25b0a02df0f2234dbda314f21b018 | 7,339 | ipynb | Jupyter Notebook | 06_Linear_Regression_Boston_House_Prices.ipynb | alzaia/keras_projects | 4e946b59b635b81300d55a8892175c34f186e011 | [
"MIT"
] | 1 | 2019-03-12T02:40:45.000Z | 2019-03-12T02:40:45.000Z | 06_Linear_Regression_Boston_House_Prices.ipynb | alzaia/keras_projects | 4e946b59b635b81300d55a8892175c34f186e011 | [
"MIT"
] | null | null | null | 06_Linear_Regression_Boston_House_Prices.ipynb | alzaia/keras_projects | 4e946b59b635b81300d55a8892175c34f186e011 | [
"MIT"
] | null | null | null | 25.306897 | 277 | 0.545715 | [
[
[
"### Linear regression on Boston house prices",
"_____no_output_____"
]
],
[
[
"from keras import models\nfrom keras import layers\nimport numpy as np\nimport matplotlib.pyplot as plt",
"_____no_output_____"
],
[
"# Download the data\nfrom keras.datasets imp... | [
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[
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[
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