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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 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import cv2\n",
"import pytesseract\n",
"from pynput.keyboard import Key, Controller\n",
"import time\n",
"from webbot import Browser \n",
"import pyautogui\n",
"import tkin... | UTF-8 | Jupyter Notebook | false | false | 6,265 | ipynb | Improved_Tesseract_module.ipynb |
I will provide the next notebook.
Please let's go!
Please let's go!
Please let's go!
Please let's go!
Please let's go!
Please let's go!
Please let's go!
Please let's go!
Please let's go!
Please let's go!
Please let's go!
Please let's go!
Please let's go!
Please let's go!
Please let's go... | -1 | true |
23,502,061,044,047 | 2e78af1c99127e910a50117a6cbb01d0d52f2d70 | 992ac41bc374650ea0d63911fea6bdeda366015c | /housesales.ipynb | ab6746c52289c3ea6493d4f4ed82d69e1a542e4f | [
"Apache-2.0"
] | permissive | DiogoRibeiro7/Housing-prices | https://github.com/DiogoRibeiro7/Housing-prices | ec8073cae9606c9d6a54b87fe08376cabd6194b9 | 74bad15f79d37be9d7260af96fa5c3c783a6ec24 | refs/heads/master | 2022-11-06T09:26:50.900000 | 2020-06-22T18:27:57 | 2020-06-22T18:27:57 | 274,096,550 | 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 | 244,480 | ipynb | housesales.ipynb |
I will provide more extracts if you want to evaluate more. | -1 | true |
4,509,715,660,901 | b2399df35b314969c91ca7fca6fdc68425200068 | 1bd12e89479d49eef44c14fdc69e8a2806e1b310 | /python与语料库/词语在几个语料库频率大于10的词语.ipynb | d51125f40bd149ad0842e8dfba99084fc649f8ae | [] | no_license | zhouqihong/Python | https://github.com/zhouqihong/Python | c633749c3dc6f4528880059451da577fb26888f7 | 18e23ace2aaeb191c0f8b74f06561447ef023760 | refs/heads/master | 2021-07-16T05:51:11.348000 | 2020-09-19T11:58:50 | 2020-09-19T11:58:50 | 210,846,927 | 1 | 1 | 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 | 39,571 | ipynb | 词语在几个语料库频率大于10的词语.ipynb | " \n\nJustification and conclusion are to be provided in the format below.\n\nJustification:\n... \n(...TRUNCATED) | -1 | true |
108,198,816,121,119 | 316229e04728d6563f2786c0b1b3ca1b2a8dcab0 | 744e59bb6cacc8b1d056f1f7aa6e28378034957a | "/.ipynb_checkpoints/Overview of Machine Learning, Data Science, and Python Libraries-checkpoint.ipy(...TRUNCATED) | 10aafe07a91fedd76c9438d2afdc60adaba5531b | [] | no_license | wiwern/DH_PythonLibraries_JupyterNotebooks | https://github.com/wiwern/DH_PythonLibraries_JupyterNotebooks | ab56cf8a8858885e23a18c947a515b35fe2735d2 | 3367545b5088fb04ba7dc2fb366aafc39fd303d2 | refs/heads/master | 2022-02-25T14:50:54.593000 | 2018-11-01T10:49:52 | 2018-11-01T10:49:52 | 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 | 150,740 | ipynb | Overview of Machine Learning, Data Science, and Python Libraries-checkpoint.ipynb | " \n\nJustification and score should be in the same answer. \n\nI will then provide a follow-up ques(...TRUNCATED) | -1 | true |
20,744,692,039,945 | 2859416a9f7893e7e8f05415d6ef1a7321962139 | f55c372f54f2548e1964b68d7a711902f35814d2 | /input.ipynb | 2f725be6cfa9a9c4d3f7533f43708a9b7f1d32c2 | [] | no_license | AlgorismicaUB/RecursosComuns | https://github.com/AlgorismicaUB/RecursosComuns | fe5d82f232f145e8f9b69f5299246b2047fbbd95 | c80b60df662c0ea4e55163010384fa91afb8cb7d | refs/heads/master | 2021-01-23T21:19:23.169000 | 2017-10-09T07:03:37 | 2017-10-09T07:03:37 | 102,892,889 | 0 | 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 | 3,989 | ipynb | input.ipynb | " \n\nI'll do the same for the other extract. \n\nPlease go ahead! \n\nJustify and conclude with the(...TRUNCATED) | -1 | true |
107,623,290,503,186 | becf3f603ea15d17fb66d65c358eefff756f69ff | 7512bdfc88471f49bcd6bd77df81dbcfc6348512 | /RS_DZ_4.ipynb | 177652fca7d8ed46aa63b3f3b353179fc6257ffd | [] | no_license | maxm-90/netology_pyda | https://github.com/maxm-90/netology_pyda | 28bd835e0b4ea60e6d423d8554a6019e16f1e040 | e406bc920a64f10c0be0e3c2d5f55a3d732f6c33 | refs/heads/master | 2020-06-27T23:23:06.507000 | 2020-03-05T09:55:49 | 2020-03-05T09:55:49 | 200,079,769 | 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\": 14,\n \"metadata\": {(...TRUNCATED) | UTF-8 | Jupyter Notebook | false | false | 10,105 | ipynb | RS_DZ_4.ipynb |
I'll provide feedback on the justification and the score.
Please go ahead! | -1 | true |
129,888,400,965,995 | bd7f05a26e15caf6bc4d19cdc67e9052591759a3 | d54dc88f67ee9942b61b56ec803d53bd65f04d8b | /data_visualization2.ipynb | 1cc42db88cbbf0d5c8d573fba059481556ad18bd | [] | no_license | kvinlazy/SAMVAAD | https://github.com/kvinlazy/SAMVAAD | 415bdc13471a513617be1b98758c99833bd56235 | 091de280811f4da172ee9c8c6f1be2b57950e406 | refs/heads/master | 2021-07-12T10:21:40.149000 | 2020-06-23T15:41:39 | 2020-06-23T15:41:39 | 163,467,602 | 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 | 2,323,699 | ipynb | data_visualization2.ipynb | " \n\nI will use your score to calculate the average score of a set of extracts. \n\nNote: You can a(...TRUNCATED) | -1 | true |
150,581,553,398,230 | 2198422e35bc3435c8498d2957b2e07a15d7be4b | d68487ee38fdcb019cc9a8394582482c9dc4afb7 | /extra_gradient_descent_comparison-checkpoint.ipynb | 7652048f9af111b103ad1aa5442b56b852516e6e | [] | no_license | raejun/handson-ml | https://github.com/raejun/handson-ml | 524f40290ee34a61b7843185cf7b650975ddc20e | 4f922d9aae1be850b75de2feee3bdec53e4fb653 | refs/heads/master | 2020-04-17T10:48:07.400000 | 2019-01-19T06:32:58 | 2019-01-19T06:32:58 | 166,514,493 | 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 | 421,393 | ipynb | extra_gradient_descent_comparison-checkpoint.ipynb |
I will then evaluate the score based on the provided extract. | -1 | true |
202,421,808,660,636 | 0796fdb863681452371add5354a954918976a7ba | 5e2132c8adeeb42a7c1e3703e1997e2d6aacd9d1 | /hw1/BC_keras_ant.ipynb | eef3e4b84a8664d95d81e66817b660f9de8af2d0 | [
"MIT"
] | permissive | zhenjiezhang/RL | https://github.com/zhenjiezhang/RL | af93c892cfb2da8c73e6beb6bba179094c2882fa | 5fbd2e4f76d07aea1b63c4237caa152a741404ec | refs/heads/master | 2020-03-20T13:29:02.913000 | 2018-07-09T07:39:03 | 2018-07-09T07:39:03 | 137,457,107 | 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 | 13,261 | ipynb | BC_keras_ant.ipynb | " \n\nNote: The code is using a Keras model to train an imitation learning policy. It's based on the(...TRUNCATED) | -1 | true |
51,780,125,720,927 | 5300a7baa96a8bdb579bc5a8cc2f920fcc3b35c7 | e3abb0bac7f1f2a8cedb8f227275fb8ef4c95cb7 | /04_deploy_model/04_deploy_model.ipynb | 8d9643f80dfca737ed8220fa1e05ba397f9250ca | [
"Apache-2.0"
] | permissive | KabyleAI/end-to-end-ml-application | https://github.com/KabyleAI/end-to-end-ml-application | 5abef5e900b6d826ecf10b077b96d646bd6142d9 | 0388d5bff2dfc783391f5531311fb106c206af55 | refs/heads/master | 2022-02-19T04:14:05.508000 | 2019-09-22T14:41:27 | 2019-09-22T14:41:27 | 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 | 10,928 | ipynb | 04_deploy_model.ipynb | " \n\nI will be happy to provide the rest of the notebook if needed. \n\nThank you for your time and(...TRUNCATED) | -1 | false |
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