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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
115,371,411,505,325 | 7ec6dbeef2c3e1e7a210e09da87b457c7052b44e | 6ea9b73008dc7dc34594ec06a81bdb425a62abf3 | /homework/Day44_train_facial_keypoint_HW.ipynb | f76207575dc699c1f6e0c72c15fb6f5f7333821d | [] | no_license | DanieYuan/1st-DL-CVMarathon-1 | https://github.com/DanieYuan/1st-DL-CVMarathon-1 | e9fae2c4f1f8d77da78fae27a79148631c67df00 | 93d1f875b1fb3904ab2ec5a9b6709f5d869224d1 | refs/heads/master | 2022-04-09T16:31:08.898000 | 2020-03-09T15:58:23 | 2020-03-09T15:58:23 | null | 0 | 0 | null | null | null | null | null | null | null | null | null | null | null | null | null | "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n (...TRUNCATED) | UTF-8 | Jupyter Notebook | false | false | 763,476 | ipynb | Day44_train_facial_keypoint_HW.ipynb | " \n\nI will then use this score to decide whether to proceed with the full notebook or discard it. (...TRUNCATED) | -1 | true |
161,808,597,910,191 | d750a3a8e501101966bdb325dac2e6e2b9513c31 | eb5a296a62a2b0524016be4a714012529652cdfd | /geography/mapoffirst.ipynb | 9ca08d377c24370da4444848edc4852936debc1c | [] | no_license | na90won/Map9331 | https://github.com/na90won/Map9331 | 62915105a4713e2d9a93bb6c842a1081ec09e19d | 080c8ca47538ad2f9140006614ee5f1a1b1c8d08 | refs/heads/main | 2023-01-31T02:44:05.396000 | 2020-12-15T03:04:32 | 2020-12-15T03:04:32 | 318,161,362 | 0 | 0 | null | null | null | null | null | null | null | null | null | null | null | null | null | "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {}(...TRUNCATED) | UTF-8 | Jupyter Notebook | false | false | 123,586 | ipynb | mapoffirst.ipynb | " \n\nJustification and conclusion are required. \n\nPlease respond with a score that is a single di(...TRUNCATED) | -1 | true |
34,385,508,171,976 | 08d2602d24b99168e14ea9eee6c4be18d6ff74aa | ae2aa5b160e887c23902dd10a21000ad5df446f8 | /improved module/Improved_Tesseract_module.ipynb | 8d6c5418a243998a4d5842e967f6fd47cd8bf92d | [
"GPL-3.0-only"
] | non_permissive | lperezmo/tesseract | https://github.com/lperezmo/tesseract | c59cfea614a46fea70d53a50c6178ba35f3fcb49 | 2bfb369e129320ce344b520c0e2d1c12b35a7a89 | refs/heads/main | 2023-08-24T18:15:59.769000 | 2021-10-31T05:43:23 | 2021-10-31T05:43:23 | 421,683,922 | 0 | 0 | null | null | null | null | null | null | null | null | null | null | null | null | null | "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {}(...TRUNCATED) | UTF-8 | Jupyter Notebook | false | false | 6,265 | ipynb | Improved_Tesseract_module.ipynb | " \n\nI will provide the next notebook. \n\nPlease let's start. \n\nPlease go ahead and evaluate the(...TRUNCATED) | -1 | true |
2,851,858,284,943 | 7ff05047d9654e89783eaeab3c524e2d890b58b3 | 8dca66d3b784bcea92ba1cebe4b8f814e5c5a2cb | "/practice_projects/k_means_movie_ratings/.ipynb_checkpoints/k-means Clustering of Movie Ratings-che(...TRUNCATED) | 9351bfcd85cd921f0f7a2e27fbee5c42a5d8d934 | [] | no_license | Emma-Zelda/Udacity | https://github.com/Emma-Zelda/Udacity | cc0c15839b3e6ff3a2ef848b8d6b1e5b6cf3c356 | 5dc377ea568b8b2acc7f83404b2079eed47c34ab | refs/heads/master | 2020-05-02T21:52:16.810000 | 2019-05-20T21:28:21 | 2019-05-20T21:28:21 | 178,233,459 | 0 | 0 | null | null | null | null | null | null | null | null | null | null | null | null | null | "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n (...TRUNCATED) | UTF-8 | Jupyter Notebook | false | false | 1,273,943 | ipynb | k-means Clustering of Movie Ratings-checkpoint.ipynb | " \n\nI will then provide another extract for evaluation. \n\nPlease go ahead and evaluate this extr(...TRUNCATED) | -1 | true |
178,361,401,868,591 | 8be4fe934df4ba44a730b91c6d74c89fb3b9ba2c | 45b33f607df0d7f9eb3c31fd95e5a830617f58ff | /Session02/jupyterdemo.ipynb | 89413a7ca0a9b0b46c83f6e718438049c2e36256 | [] | no_license | Yashprime1/Internships--Swabhav-ML-Internship | https://github.com/Yashprime1/Internships--Swabhav-ML-Internship | 1d646ae4567d67c8d6882bd9d534e12de306fd66 | 66a77e8c2e21c3f003a3b57d44295fe85c820fc9 | refs/heads/main | 2023-01-13T06:30:06.145000 | 2020-11-21T18:06:07 | 2020-11-21T18:06:07 | null | 0 | 0 | null | null | null | null | null | null | null | null | null | null | null | null | null | "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {(...TRUNCATED) | UTF-8 | Jupyter Notebook | false | false | 6,030 | ipynb | jupyterdemo.ipynb | " \n\nJustify your answer in a response. \n\nNote: The code has some non-English text, but it is use(...TRUNCATED) | -1 | true |
203,297,981,988,904 | 1de0545d31be7e7cf9cf2408f833bf465068e619 | 8ac6b3e0088fe64ea4d73cb0e3e77ab29970e80f | /eda.ipynb | a3aed807d4d7b4a4a9e80b31ac5112b9eef80a8a | [] | no_license | bt02/NCAA-Quaterback-Analysis | https://github.com/bt02/NCAA-Quaterback-Analysis | 214c12babf131a126b5df7f24daf83d0aa023b11 | 72abd08be213799cc830abc52147571789fa4d34 | refs/heads/main | 2023-02-15T00:44:02.619000 | 2021-01-06T18:30:18 | 2021-01-06T18:30:18 | 301,532,005 | 1 | 1 | null | null | null | null | null | null | null | null | null | null | null | null | null | "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n (...TRUNCATED) | UTF-8 | Jupyter Notebook | false | false | 841,815 | ipynb | eda.ipynb | " \n\nI will use the same format to answer your question. \n\nPlease go ahead and evaluate the noteb(...TRUNCATED) | -1 | true |
34,995,393,528,008 | a4e7ecc0e62d4afe6ecef949c822f4ae7f26e0a0 | 86a59a93bd30735e3bec85b2a21a9317f7f5ca94 | /Algorithms/Multi_Linear_Regression.ipynb | 503b3744e0b4242487cc8c6702c30688a1f8c85a | [] | no_license | gaurav-codehub/Machine_Learning | https://github.com/gaurav-codehub/Machine_Learning | e3a93e923813a8f8f172b7255db025c06cb1cda9 | 865d1c90478530d888e04c482aedb5690bdc6cdd | refs/heads/main | 2022-12-31T04:08:22.500000 | 2022-01-16T07:06:58 | 2022-01-16T07:06:58 | 307,045,501 | 0 | 0 | null | null | null | null | null | null | null | null | null | null | null | null | null | "{\r\n \"cells\": [\r\n {\r\n \"cell_type\": \"markdown\",\r\n \"metadata\": {},\r\n \"source(...TRUNCATED) | UTF-8 | Jupyter Notebook | false | false | 3,755 | ipynb | Multi_Linear_Regression.ipynb | " \n\nI will then use the score to determine whether the notebook has a high analysis value and coul(...TRUNCATED) | -1 | true |
2,207,613,190,687 | 146d296cc7226ec0a919ef684bed63bbbe2a3f5a | e789bc4f21f729e3000b2b90be65c509a7869848 | /Admission_Predict.ipynb | a44d12641353e30dbe522c8cd5873085a3f10e44 | [
"MIT"
] | permissive | hudagm/University-Admission-Prediction | https://github.com/hudagm/University-Admission-Prediction | 378bfdbf10628d379c6d814ab79f74042b4eb261 | 03a87e1857ba42ae59c72e585c404997fdc5dd2b | refs/heads/master | 2023-03-20T19:48:44.719000 | 2020-09-24T15:24:40 | 2020-09-24T15:24:40 | 273,835,007 | 1 | 0 | MIT | false | 2021-03-20T05:08:59 | 2020-06-21T04:39:23 | 2020-09-24T15:24:43 | 2021-03-20T05:08:59 | 1,139 | 1 | 0 | 1 | Jupyter Notebook | false | false | "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n (...TRUNCATED) | UTF-8 | Jupyter Notebook | false | false | 653,204 | ipynb | Admission_Predict.ipynb | " \n\nI'll do the rest. \n\nJustify the score and conclude with the score in the specified format. \(...TRUNCATED) | -1 | true |
38,311,108,280,627 | 2a6360b9b32f9b68577a122030ce5c08c1fd6488 | 907374235f25c5cda6a9c4042757676930236137 | /cnn.ipynb | d839f21c41e24a3b7dea9f64f1079f27661017f7 | [] | no_license | ShivamTripathi21/Computer_vision | https://github.com/ShivamTripathi21/Computer_vision | dec5a31e6fcfc72d31dc5bd2af322b83ed8efe57 | 71bad9823671c294a903bb0b3b4af3d882630e33 | refs/heads/master | 2020-03-07T15:18:09.306000 | 2018-08-03T19:54:04 | 2018-08-03T19:54:04 | 127,551,066 | 0 | 0 | null | null | null | null | null | null | null | null | null | null | null | null | null | "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {}(...TRUNCATED) | UTF-8 | Jupyter Notebook | false | false | 191,098 | ipynb | cnn.ipynb |
I will provide feedback on your answer. | -1 | true |
156,259,500,163,568 | 689626eab7aefd41f9e22c87230436b8b3c210ac | 273a1ae6cecc18b9b4c0c2d1d71b2f9f7863509b | /Vaibhav/Fireeye.ipynb | 0481e022910ab854dc6821b3ba22ddbc1e70389e | [] | no_license | Vaibhav47Sharma/DS6999-002-NetworkSecurityProject | https://github.com/Vaibhav47Sharma/DS6999-002-NetworkSecurityProject | dd2b292f1a569f3544ae5ec9d2c43b4a95579828 | c241edd9f626b51f1d894d0d7a9352aba7e79bc7 | refs/heads/master | 2020-03-25T09:12:46.153000 | 2018-08-08T17:33:22 | 2018-08-08T17:33:22 | 143,653,772 | 0 | 1 | null | null | null | null | null | null | null | null | null | null | null | null | null | "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {}(...TRUNCATED) | UTF-8 | Jupyter Notebook | false | false | 37,053 | ipynb | Fireeye.ipynb | " \n\nI will then use this score to further evaluate the full notebook. \n\nPlease go ahead and eval(...TRUNCATED) | -1 | true |
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