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"# 09 Strain Gage\n\nThis is one of the most commonly used sensor. It is used in many transducers. Its fundamental operating principle is fairly easy to understand and it will be the purpose of this lecture. \n\nA strain gage is essentially a thin wire that is wrapped on film of plastic. \n<img src... | [
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"# spinner_thread.py\nimport threading \nimport itertools\nimport time\nimport sys\n\nclass Signal:\n go = True\n\ndef spin(msg, signal):\n write, flush = sys.stdout.write, sys.std... | [
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d000491b7c790e6ee107777a67eb83691ed8c106 | 4,243 | ipynb | Jupyter Notebook | Sessions/Problem-1.ipynb | Yunika-Bajracharya/pybasics | e04a014b70262ef9905fef5720f58a6f0acc0fda | [
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"## Problem 1\n---\n\n#### The solution should try to use all the python constructs\n\n- Conditionals and Loops\n- Functions\n- Classes\n\n#### and datastructures as possible\n\n- List\n- Tuple\n- Dictionary\n- Set",
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d0004ddbda5277669a00c9cb8161daa5a9dbecdb | 3,548 | ipynb | Jupyter Notebook | filePreprocessing.ipynb | zinccat/WeiboTextClassification | ec3729450f1aa0cfa2657cac955334cfae565047 | [
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"### 原始数据处理程序",
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d0005af9eace679454187ce22b2c411130a19e72 | 1,217 | ipynb | Jupyter Notebook | Access Environment variable.ipynb | shkhaider2015/PIAIC-QUARTER-2 | 2b6ef1c8d75f9f52b9da8e735751f5f80c76b227 | [
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d00070e01aa3101ac81e3c3f48915570e8611db3 | 5,451 | ipynb | Jupyter Notebook | stemming.ipynb | Ganeshatmuri/NaturalLanguageProcessing | 491d5bc50559c7a09e0b541a96c4314c20b80927 | [
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"paragraph = \"\"\"I have three visions for India. In 3000 years of our history, people from all over \n the world have come and invaded us, ca... | [
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d00076bca8d2b781f0ba8adff988c49a32fc6928 | 9,146 | ipynb | Jupyter Notebook | jupyter/onnxruntime/machine_learning_with_ONNXRuntime.ipynb | raghav-deepsource/djl | 8d774578a51b298d2ddeb1a898ddd5a157b7f0bd | [
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"# Classification on Iris dataset with sklearn and DJL\n\nIn this notebook, you will try to use a pre-trained sklearn model to run on DJL for a general classification task. The model was trained with [Iris flower dataset](https://en.wikipedia.org/wiki/Iris_flower_data_set).\n\n## Background \n\n### Ir... | [
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d00080cae9b7a28ebc8ef5ae33eb9e79b8f215bf | 5,019 | ipynb | Jupyter Notebook | Algorithms/landsat_radiance.ipynb | OIEIEIO/earthengine-py-notebooks | 5d6c5cdec0c73bf02020ee17d42c9e30d633349f | [
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d0008b5894090e9887e8ce1ff35481414c1bb8d4 | 22,698 | ipynb | Jupyter Notebook | cp2/cp2_method0.ipynb | jet-code/multivariable-control-systems | 81b57d51a4dfc92964f989794f71d525af0359ff | [
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d0009d5398ddafa5402c0865af43fc2773a267cc | 190,653 | ipynb | Jupyter Notebook | exercise_2.ipynb | deepak223098/Computer_Vision_Example | d477c1ef04f5e6eb58f078da03efce7a2c63f88b | [
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d000a660b0e9fa13fa75686a8147c7afbccbc039 | 34,254 | ipynb | Jupyter Notebook | Untitled1.ipynb | archit120/lingatagger | cb3d0e262900dba1fd1ead0a37fad531e37cff9f | [
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d000adb69592d40c5cafd8a3c20112357e08b63f | 295,491 | ipynb | Jupyter Notebook | parse_results_with_visualization/Hyper_params_visualization.ipynb | HenryNebula/Personalization_Final_Project | 5d18a8628bed2dfd2894b9d2f33c1e9a5df27ecc | [
"MIT"
] | 1 | 2020-11-03T18:02:15.000Z | 2020-11-03T18:02:15.000Z | parse_results_with_visualization/Hyper_params_visualization.ipynb | HenryNebula/Personalization_Final_Project | 5d18a8628bed2dfd2894b9d2f33c1e9a5df27ecc | [
"MIT"
] | null | null | null | parse_results_with_visualization/Hyper_params_visualization.ipynb | HenryNebula/Personalization_Final_Project | 5d18a8628bed2dfd2894b9d2f33c1e9a5df27ecc | [
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] | 1 | 2020-03-22T01:01:21.000Z | 2020-03-22T01:01:21.000Z | 286.884466 | 40,604 | 0.920891 | [
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d000b22fa28b30bab7e79443b600a0d64a9732ce | 5,071 | ipynb | Jupyter Notebook | import_file_checker_result.ipynb | acdh-oeaw/4dpuzzle | 7856bbd82c7dfa8da1d5f1ad40593219a35b3cfe | [
"MIT"
] | null | null | null | import_file_checker_result.ipynb | acdh-oeaw/4dpuzzle | 7856bbd82c7dfa8da1d5f1ad40593219a35b3cfe | [
"MIT"
] | 6 | 2020-06-05T18:32:02.000Z | 2022-02-10T07:22:24.000Z | import_file_checker_result.ipynb | acdh-oeaw/4dpuzzle | 7856bbd82c7dfa8da1d5f1ad40593219a35b3cfe | [
"MIT"
] | 1 | 2020-06-30T13:52:41.000Z | 2020-06-30T13:52:41.000Z | 22.842342 | 128 | 0.521199 | [
[
[
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d000b6b92fe17366a6da83e58577a3929840b135 | 585,062 | ipynb | Jupyter Notebook | src/VotingClassifier/.ipynb_checkpoints/knn-checkpoint.ipynb | joaquinfontela/Machine-Learning | 733284fe82e6c128358fe2e7721d887e2683da9f | [
"MIT"
] | null | null | null | src/VotingClassifier/.ipynb_checkpoints/knn-checkpoint.ipynb | joaquinfontela/Machine-Learning | 733284fe82e6c128358fe2e7721d887e2683da9f | [
"MIT"
] | null | null | null | src/VotingClassifier/.ipynb_checkpoints/knn-checkpoint.ipynb | joaquinfontela/Machine-Learning | 733284fe82e6c128358fe2e7721d887e2683da9f | [
"MIT"
] | 1 | 2021-07-30T20:53:53.000Z | 2021-07-30T20:53:53.000Z | 62.04263 | 1,432 | 0.546662 | [
[
[
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"_____no_output_____"
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[
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"_____no_output_____"
],
[
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d000c817d7ae0508e04ac21d6d2f4e30cbc917d4 | 146,114 | ipynb | Jupyter Notebook | Python_Stock/Time_Series_Forecasting/Stock_Forecasting_Prophet_Uncertainty_Trend.ipynb | LastAncientOne/Stock_Analysis_For_Quant | b21d01d0fdd0098e454147942d9b07979ab315ad | [
"MIT"
] | 962 | 2019-07-17T09:57:41.000Z | 2022-03-29T01:55:20.000Z | Python_Stock/Time_Series_Forecasting/Stock_Forecasting_Prophet_Uncertainty_Trend.ipynb | j0el/Stock_Analysis_For_Quant | 8088fb0f6a1b1edeead6ae152fa4275e3d6dd746 | [
"MIT"
] | 5 | 2020-04-29T16:54:30.000Z | 2022-02-10T02:57:30.000Z | Python_Stock/Time_Series_Forecasting/Stock_Forecasting_Prophet_Uncertainty_Trend.ipynb | j0el/Stock_Analysis_For_Quant | 8088fb0f6a1b1edeead6ae152fa4275e3d6dd746 | [
"MIT"
] | 286 | 2019-08-04T10:37:58.000Z | 2022-03-28T06:31:56.000Z | 363.467662 | 81,973 | 0.905321 | [
[
[
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],
[
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"_____no_output_____"
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],
[
[
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] |
d000dbbbe2638cb9586ae564f2c03042b309708f | 7,478 | ipynb | Jupyter Notebook | delfin/Example - Delfin.ipynb | Open-Dataplatform/examples | 197e83308bef83b09a32fea898f16c8cd9c84acb | [
"MIT"
] | null | null | null | delfin/Example - Delfin.ipynb | Open-Dataplatform/examples | 197e83308bef83b09a32fea898f16c8cd9c84acb | [
"MIT"
] | null | null | null | delfin/Example - Delfin.ipynb | Open-Dataplatform/examples | 197e83308bef83b09a32fea898f16c8cd9c84acb | [
"MIT"
] | null | null | null | 26.424028 | 141 | 0.514175 | [
[
[
"# Delfin",
"_____no_output_____"
],
[
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"_____no_output_____"
]
],
[
[
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"_____no_output_____"
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],
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d000df8fd6872f4ebeceecda2014c8ed69838b8d | 49,315 | ipynb | Jupyter Notebook | 09_Time_Series/Apple_Stock/Exercises-with-solutions-code.ipynb | nat-bautista/tts-pandas-exercise | dd288b691e1789801b76675fed581c854adfaa26 | [
"BSD-3-Clause"
] | null | null | null | 09_Time_Series/Apple_Stock/Exercises-with-solutions-code.ipynb | nat-bautista/tts-pandas-exercise | dd288b691e1789801b76675fed581c854adfaa26 | [
"BSD-3-Clause"
] | null | null | null | 09_Time_Series/Apple_Stock/Exercises-with-solutions-code.ipynb | nat-bautista/tts-pandas-exercise | dd288b691e1789801b76675fed581c854adfaa26 | [
"BSD-3-Clause"
] | null | null | null | 70.956835 | 31,322 | 0.743303 | [
[
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],
[
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d000ef77a4fe5d63f756fa5540228432b5512788 | 13,227 | ipynb | Jupyter Notebook | Colab RDP/Colab RDP.ipynb | Apon77/Colab-Hacks | 3493aa482b7420b8f7c2d236308dd3568254860c | [
"MIT"
] | null | null | null | Colab RDP/Colab RDP.ipynb | Apon77/Colab-Hacks | 3493aa482b7420b8f7c2d236308dd3568254860c | [
"MIT"
] | null | null | null | Colab RDP/Colab RDP.ipynb | Apon77/Colab-Hacks | 3493aa482b7420b8f7c2d236308dd3568254860c | [
"MIT"
] | 2 | 2021-02-24T20:42:46.000Z | 2021-04-22T01:14:30.000Z | 37.791429 | 644 | 0.482422 | [
[
[
"<a href=\"https://colab.research.google.com/github/PradyumnaKrishna/Colab-Hacks/blob/RDP-v2/Colab%20RDP/Colab%20RDP.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>",
"_____no_output_____"
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d000f1ce0f008b8f64f705810da78b9e62f26064 | 63,150 | ipynb | Jupyter Notebook | scanpy_cellphonedb.ipynb | stefanpeidli/cellphonedb | c638935d7fc36e0c3156a1a8c26d2e0108b2bf0e | [
"MIT"
] | null | null | null | scanpy_cellphonedb.ipynb | stefanpeidli/cellphonedb | c638935d7fc36e0c3156a1a8c26d2e0108b2bf0e | [
"MIT"
] | null | null | null | scanpy_cellphonedb.ipynb | stefanpeidli/cellphonedb | c638935d7fc36e0c3156a1a8c26d2e0108b2bf0e | [
"MIT"
] | 1 | 2021-02-03T16:25:06.000Z | 2021-02-03T16:25:06.000Z | 45.398994 | 212 | 0.357846 | [
[
[
"from IPython.core.display import display, HTML\ndisplay(HTML(\"<style>.container { width:90% !important; }</style>\"))\n%matplotlib inline\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as pl\nimport scanpy as sc\n\nimport cellphonedb as cphdb\n\n# Original API works for python ... | [
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d000fa08beeccd71f734c998515820b69c9a44b2 | 35,623 | ipynb | Jupyter Notebook | isis/notebooks/crop_eis.ipynb | gknorman/ISIS3 | 4800a8047626a864e163cc74055ba60008c105f7 | [
"CC0-1.0"
] | 134 | 2018-01-18T00:16:24.000Z | 2022-03-24T03:53:33.000Z | isis/notebooks/crop_eis.ipynb | gknorman/ISIS3 | 4800a8047626a864e163cc74055ba60008c105f7 | [
"CC0-1.0"
] | 3,825 | 2017-12-11T21:27:34.000Z | 2022-03-31T21:45:20.000Z | isis/notebooks/crop_eis.ipynb | jessemapel/ISIS3 | bd43b627378c4009c6aaae8537ba472dbefb2152 | [
"CC0-1.0"
] | 164 | 2017-11-30T21:15:44.000Z | 2022-03-23T10:22:29.000Z | 30.291667 | 5,000 | 0.4318 | [
[
[
"from xml.dom import expatbuilder\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport struct\nimport os\n",
"_____no_output_____"
],
[
"# should be in the same directory as corresponding xml and csv\neis_filename = '/example/path/to/eis_image_file.dat'",
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d00105d05d5f1e74386cacf08350b830039167fb | 165,020 | ipynb | Jupyter Notebook | unsupervised ML crypto.ipynb | dmtiblin/UR-Unsupervised-Machine-Learning-Challenge | f9ffbe1113c21f93f454d629802fe0ec881ec85f | [
"ADSL"
] | null | null | null | unsupervised ML crypto.ipynb | dmtiblin/UR-Unsupervised-Machine-Learning-Challenge | f9ffbe1113c21f93f454d629802fe0ec881ec85f | [
"ADSL"
] | null | null | null | unsupervised ML crypto.ipynb | dmtiblin/UR-Unsupervised-Machine-Learning-Challenge | f9ffbe1113c21f93f454d629802fe0ec881ec85f | [
"ADSL"
] | null | null | null | 77.076133 | 40,052 | 0.693201 | [
[
[
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],
[
[
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"_____no_output_____"
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[
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d00112763ed80ce3a31e09059a2396453622c85e | 96,811 | ipynb | Jupyter Notebook | CTR Prediction/RS_Kaggle_Catboost.ipynb | amitdamri/Recommendation-Systems-Course | f8d096918b688b80c0a9acb6df3db2abe8fd8813 | [
"MIT"
] | 2 | 2021-08-23T19:15:43.000Z | 2021-11-16T13:20:04.000Z | CTR Prediction/RS_Kaggle_Catboost.ipynb | amitdamri/Recommendation-Systems-Course | f8d096918b688b80c0a9acb6df3db2abe8fd8813 | [
"MIT"
] | null | null | null | CTR Prediction/RS_Kaggle_Catboost.ipynb | amitdamri/Recommendation-Systems-Course | f8d096918b688b80c0a9acb6df3db2abe8fd8813 | [
"MIT"
] | null | null | null | 41.80095 | 2,808 | 0.403601 | [
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[
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"_____no_output_____"
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d001138d46ac9adce3ad006c5b68ae3c9b8221ce | 161,559 | ipynb | Jupyter Notebook | Randomized Optimization/NQueens.ipynb | cindynyoumsigit/MachineLearning | 383fb849ac1e98c2e96e7f0f241ba57fb99ed956 | [
"Apache-2.0"
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d0014f81a811c43d11fb4bd3fe7dfee63df9e993 | 43,495 | ipynb | Jupyter Notebook | docs/nb/simplemixing_class.ipynb | IceCubeOpenSource/USSR | d96158cb835245c40e5fc57239c6038c87b3ac01 | [
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d001aa363a9fa0a5c61ece6012b44540e4d5a4c5 | 265,027 | ipynb | Jupyter Notebook | o3_so2_upb/estacion_upb_data_processing_03.ipynb | fega/arduair-calibration | 8dbcbb947fc964ab248974234c053ebde9869213 | [
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] | null | null | null | o3_so2_upb/estacion_upb_data_processing_03.ipynb | fega/arduair-calibration | 8dbcbb947fc964ab248974234c053ebde9869213 | [
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"## Analisis de O3 y SO2 arduair vs estacion universidad pontificia bolivariana\nSe compararon los resultados generados por el equipo arduair y la estacion de calidad de aire propiedad de la universidad pontificia bolivariana seccional bucaramanga\n\nCabe resaltar que durante la ejecucion de las prueb... | [
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d001ace14ccebd1da740b49bc211312a9f784676 | 3,445 | ipynb | Jupyter Notebook | examples/discovery v1 configuration tasks .ipynb | SeptBlast/python-sdk | 8ba86b8abbff7cd020303b877d730130696ea21d | [
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"## Accessing TerraClimate data with the Planetary Computer STAC API\n\n[TerraClimate](http://www.climatologylab.org/terraclimate.html) is a dataset of monthly climate and climatic water balance for global terrestrial surfaces from 1958-2019. These data provide important inputs for ecological and hydr... | [
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d001cb3360293f2ae9e91ec462e9e3bfafdf84f4 | 12,880 | ipynb | Jupyter Notebook | notebooks/automl-classification-Force-text-dnn.ipynb | konabuta/AutoML-Pipeline | 4cb9a3ccc15d482f0b4d1fcacd53ff81f28b14be | [
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d001e1dab2b894cab4d4d2c83c742e1185d5b2cb | 10,010 | ipynb | Jupyter Notebook | examples/notebook/contrib/steel.ipynb | MaximilianAzendorf/wasm-or-tools | f16c3efc13ad5d41c7a65338434ea88ed908c398 | [
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d001e40a54bddb838a8e4e457fafea447a6e0f3a | 14,298 | ipynb | Jupyter Notebook | _drafts/linear-optimization/.ipynb_checkpoints/Linear Optimization-checkpoint.ipynb | evjrob/everettsprojects.com | 95b22907bd9f8b4aa2e3df510c2c263267a3775e | [
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d001efd8179f1c0b37fcf3b8a9cfb25aae925b34 | 14,560 | ipynb | Jupyter Notebook | data/read_data.ipynb | sannatti/softcifar | 6d93cc6732b8487a4369960dcfaa6cc8f1f65164 | [
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d002161c8340e231abaa9ca4c9f4b2c0350581f8 | 117,329 | ipynb | Jupyter Notebook | breast_linearsvm.ipynb | baopuzi/Breast_Cancer_Detection | 6b27ee4958c6eda4388830f316cca2fe343748ca | [
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d002337a342addb363e0327a0e1d6d4c1e3a0c98 | 15,072 | ipynb | Jupyter Notebook | docs/Supplementary-Materials/01-Spark-SQL.ipynb | ymei9/Big-Data-Analytics-for-Business | fba226e86a47ff188562655ce23b7af79781948a | [
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d002549fdfb1e35d86637b3c9d97a47a8f614a9f | 17,252 | ipynb | Jupyter Notebook | .ipynb_checkpoints/LaneDetect-checkpoint.ipynb | Eng-Mo/CarND-Advanced-Lane-Lines | 1fc98e892f22ecdae81e1b02b10335be5eabcd88 | [
"MIT"
] | null | null | null | .ipynb_checkpoints/LaneDetect-checkpoint.ipynb | Eng-Mo/CarND-Advanced-Lane-Lines | 1fc98e892f22ecdae81e1b02b10335be5eabcd88 | [
"MIT"
] | null | null | null | .ipynb_checkpoints/LaneDetect-checkpoint.ipynb | Eng-Mo/CarND-Advanced-Lane-Lines | 1fc98e892f22ecdae81e1b02b10335be5eabcd88 | [
"MIT"
] | 1 | 2020-04-21T10:50:43.000Z | 2020-04-21T10:50:43.000Z | 38.508929 | 161 | 0.546719 | [
[
[
"import numpy as np\nimport cv2\nimport matplotlib\nimport matplotlib.pyplot as plt\nimport matplotlib as mpimg\nimport numpy as np\nfrom IPython.display import HTML\nimport os, sys\nimport glob\nimport moviepy\nfrom moviepy.editor import VideoFileClip\nfrom moviepy.editor import * \nfrom IPython impo... | [
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d0026c3e195defcec74621cf3f60c1e4f5892723 | 7,576 | ipynb | Jupyter Notebook | lect13_NumPy/2021_DPO_13_2_heroku.ipynb | weqrwer/Python_DPO_2021_fall | 8558ed2c1a744638f693ad036cfafccd1a05f392 | [
"MIT"
] | 3 | 2022-02-19T17:20:33.000Z | 2022-03-02T11:35:56.000Z | lect13_NumPy/2021_DPO_13_2_heroku.ipynb | weqrwer/Python_DPO_2021_fall | 8558ed2c1a744638f693ad036cfafccd1a05f392 | [
"MIT"
] | null | null | null | lect13_NumPy/2021_DPO_13_2_heroku.ipynb | weqrwer/Python_DPO_2021_fall | 8558ed2c1a744638f693ad036cfafccd1a05f392 | [
"MIT"
] | 8 | 2021-09-16T10:28:30.000Z | 2021-11-24T06:20:09.000Z | 25.0033 | 135 | 0.556494 | [
[
[
"## Как выложить бота на HEROKU\n\n*Подготовил Ян Пиле*",
"_____no_output_____"
],
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"Сразу оговоримся, что мы на heroku выкладываем\n\n**echo-Бота в телеграме, написанного с помощью библиотеки [pyTelegramBotAPI](https://github.com/eternnoir/pyTelegramBotAPI)**.\n\nА взаимодействие... | [
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d00271ef6c25cb9f8943f0f3640f0c6209e44e85 | 29,784 | ipynb | Jupyter Notebook | Python for Finance - Code Files/83 Computing Alpha, Beta, and R Squared in Python/Python 2/Computing Alpha, Beta, and R Squared in Python - Solution.ipynb | siddharthjain1611/Python_for_Finance_Investment_Fundamentals-and-Data-Analytics | f2f1e22f2d578c59f833f8f3c8b4523d91286e9e | [
"MIT"
] | 3 | 2020-03-24T12:58:37.000Z | 2020-08-03T17:22:35.000Z | Python for Finance - Code Files/83 Computing Alpha, Beta, and R Squared in Python/Python 2/Computing Alpha, Beta, and R Squared in Python - Solution.ipynb | siddharthjain1611/Python_for_Finance_Investment_Fundamentals-and-Data-Analytics | f2f1e22f2d578c59f833f8f3c8b4523d91286e9e | [
"MIT"
] | null | null | null | Python for Finance - Code Files/83 Computing Alpha, Beta, and R Squared in Python/Python 2/Computing Alpha, Beta, and R Squared in Python - Solution.ipynb | siddharthjain1611/Python_for_Finance_Investment_Fundamentals-and-Data-Analytics | f2f1e22f2d578c59f833f8f3c8b4523d91286e9e | [
"MIT"
] | 1 | 2021-10-19T23:59:37.000Z | 2021-10-19T23:59:37.000Z | 58.514735 | 8,992 | 0.727001 | [
[
[
"## Computing Alpha, Beta, and R Squared in Python ",
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],
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"*Suggested Answers follow (usually there are multiple ways to solve a problem in Python).*",
"_____no_output_____"
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"*Running a Regression in Python - continued:*",
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d0027ad3b4f0aacb9dd8f9d1e33562baa9f49a38 | 63,120 | ipynb | Jupyter Notebook | Kaggle_Challenge_Assignment_Submission5.ipynb | JimKing100/DS-Unit-2-Kaggle-Challenge | d1a705987c5a4df8b3ab74daab453754b77045cc | [
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] | null | null | null | Kaggle_Challenge_Assignment_Submission5.ipynb | JimKing100/DS-Unit-2-Kaggle-Challenge | d1a705987c5a4df8b3ab74daab453754b77045cc | [
"MIT"
] | null | null | null | Kaggle_Challenge_Assignment_Submission5.ipynb | JimKing100/DS-Unit-2-Kaggle-Challenge | d1a705987c5a4df8b3ab74daab453754b77045cc | [
"MIT"
] | null | null | null | 50.821256 | 280 | 0.488926 | [
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[
"<a href=\"https://colab.research.google.com/github/JimKing100/DS-Unit-2-Kaggle-Challenge/blob/master/Kaggle_Challenge_Assignment_Submission5.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>",
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d0027f0c7d75bd8bb29a639ddc1b5f2c87a4cb6c | 2,897 | ipynb | Jupyter Notebook | Simple Interest, compound interest.ipynb | nankris/Puzzles-Questions- | 5044f84054e94ba9420c6239fd2170eed7007d00 | [
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] | null | null | null | Simple Interest, compound interest.ipynb | nankris/Puzzles-Questions- | 5044f84054e94ba9420c6239fd2170eed7007d00 | [
"Apache-2.0"
] | null | null | null | Simple Interest, compound interest.ipynb | nankris/Puzzles-Questions- | 5044f84054e94ba9420c6239fd2170eed7007d00 | [
"Apache-2.0"
] | null | null | null | 17.993789 | 45 | 0.480152 | [
[
[
"#simple interest = ptr/100\n#p=principle amount\n#t is time (units of time)\n#r is rate (percent of interest)\np=int(input(\"principle amount\"))\nt=int(input(\"units of time\"))\nr=int(input(\"percent of interest\"))",
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],
... | [
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d00283e186c87119f70280c9f6b55acf172e7287 | 612,682 | ipynb | Jupyter Notebook | Combining-Thresholds.ipynb | joshrwhite/CarND-LaneLines-P1 | dbb2dbf9e3569b85c2613524dcedcaf5e3d54e84 | [
"MIT"
] | null | null | null | Combining-Thresholds.ipynb | joshrwhite/CarND-LaneLines-P1 | dbb2dbf9e3569b85c2613524dcedcaf5e3d54e84 | [
"MIT"
] | null | null | null | Combining-Thresholds.ipynb | joshrwhite/CarND-LaneLines-P1 | dbb2dbf9e3569b85c2613524dcedcaf5e3d54e84 | [
"MIT"
] | null | null | null | 4,505.014706 | 607,340 | 0.961143 | [
[
[
"import numpy as np\nimport cv2\nimport matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\nimport pickle\n\n# Read in an image\nimage = mpimg.imread('signs_vehicles_xygrad.png')\n\ndef abs_sobel_thresh(img, orient='x', sobel_kernel=3, thresh=(0, 255)):\n # Apply the following steps to img\... | [
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d0029a295b258a0e9387ce34994b69142f1dd639 | 297,382 | ipynb | Jupyter Notebook | simulations.ipynb | diozaka/eitest | b2c37ad93e7760673a2f46279f913bd03440a8f2 | [
"MIT"
] | 2 | 2020-05-21T11:53:20.000Z | 2020-11-01T06:12:49.000Z | simulations.ipynb | diozaka/eitest | b2c37ad93e7760673a2f46279f913bd03440a8f2 | [
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] | null | null | null | simulations.ipynb | diozaka/eitest | b2c37ad93e7760673a2f46279f913bd03440a8f2 | [
"MIT"
] | null | null | null | 219.30826 | 30,184 | 0.896709 | [
[
[
"import numpy as np\nimport matplotlib.pyplot as plt\nimport numba\nfrom tqdm import tqdm\n\nimport eitest",
"_____no_output_____"
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[
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"# Data generators",
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],
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d002b497b780d100d2744ba4e601610df2c25117 | 569,608 | ipynb | Jupyter Notebook | examples/Top 25 AY Graph w Roster and Team Logo Data.ipynb | AccidentalGuru/nflfastpy | c16dcc13dc91c6a051f39ea8c28962a789322762 | [
"MIT"
] | null | null | null | examples/Top 25 AY Graph w Roster and Team Logo Data.ipynb | AccidentalGuru/nflfastpy | c16dcc13dc91c6a051f39ea8c28962a789322762 | [
"MIT"
] | null | null | null | examples/Top 25 AY Graph w Roster and Team Logo Data.ipynb | AccidentalGuru/nflfastpy | c16dcc13dc91c6a051f39ea8c28962a789322762 | [
"MIT"
] | null | null | null | 1,600.022472 | 546,150 | 0.956005 | [
[
[
"<a href=\"https://colab.research.google.com/github/AccidentalGuru/nflfastpy/blob/master/examples/Top%2025%20AY%20Graph%20w%20Roster%20and%20Team%20Logo%20Data.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>",
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d002bc0e0081d73349f836a6e32db713d13f5fa2 | 948,447 | ipynb | Jupyter Notebook | Analyses/The Evolution of The Russian Comedy_Verse_Features.ipynb | innawendell/European_Comedy | f9f6bf2844878503bccb9de2efe549ffc9c7df6b | [
"MIT"
] | null | null | null | Analyses/The Evolution of The Russian Comedy_Verse_Features.ipynb | innawendell/European_Comedy | f9f6bf2844878503bccb9de2efe549ffc9c7df6b | [
"MIT"
] | null | null | null | Analyses/The Evolution of The Russian Comedy_Verse_Features.ipynb | innawendell/European_Comedy | f9f6bf2844878503bccb9de2efe549ffc9c7df6b | [
"MIT"
] | null | null | null | 383.056139 | 157,244 | 0.922239 | [
[
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"## The Analysis of The Evolution of The Russian Comedy. Part 3.",
"_____no_output_____"
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d002c5802863aebf588359e2d82fe7676ce02717 | 54,374 | ipynb | Jupyter Notebook | Utils/dowhy/docs/source/example_notebooks/lalonde_pandas_api.ipynb | maliha93/Fairness-Analysis-Code | acf13c6e7993704fc627249fe4ada44d8b616264 | [
"MIT"
] | 2,904 | 2019-05-07T08:09:33.000Z | 2022-03-31T18:28:41.000Z | Utils/dowhy/docs/source/example_notebooks/lalonde_pandas_api.ipynb | maliha93/Fairness-Analysis-Code | acf13c6e7993704fc627249fe4ada44d8b616264 | [
"MIT"
] | 238 | 2019-05-11T02:57:22.000Z | 2022-03-31T23:47:18.000Z | Utils/dowhy/docs/source/example_notebooks/lalonde_pandas_api.ipynb | maliha93/Fairness-Analysis-Code | acf13c6e7993704fc627249fe4ada44d8b616264 | [
"MIT"
] | 527 | 2019-05-08T16:23:45.000Z | 2022-03-30T21:02:41.000Z | 54.103483 | 7,392 | 0.660775 | [
[
[
"# Lalonde Pandas API Example\nby Adam Kelleher",
"_____no_output_____"
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[
"We'll run through a quick example using the high-level Python API for the DoSampler. The DoSampler is different from most classic causal effect estimators. Instead of estimating statistics under intervent... | [
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d002d1911d9b6ad53cf0adca340ece83cdf4c874 | 81,309 | ipynb | Jupyter Notebook | use_case/01_intro_tutorial.ipynb | elekt/datenguide-python | 2f764c0a56500a95bf1829684ad96cdcae571037 | [
"MIT"
] | 1 | 2020-07-15T17:06:43.000Z | 2020-07-15T17:06:43.000Z | use_case/01_intro_tutorial.ipynb | elekt/datenguide-python | 2f764c0a56500a95bf1829684ad96cdcae571037 | [
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] | null | null | null | use_case/01_intro_tutorial.ipynb | elekt/datenguide-python | 2f764c0a56500a95bf1829684ad96cdcae571037 | [
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] | null | null | null | 34.540782 | 269 | 0.409241 | [
[
[
"# Welcome to the Datenguide Python Package\n\nWithin this notebook the functionality of the package will be explained and demonstrated with examples.\n\n### Topics\n\n- Import\n- get region IDs\n- get statstic IDs\n- get the data\n - for single regions\n - for multiple regions",
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d002e156d97ada6b864f67260dc27a30bd6903b6 | 10,991 | ipynb | Jupyter Notebook | dadosBQ_Pandas.ipynb | DrumondVilela/ProjetoFinal | 61887cfdc4284083237e82f168be4f78732d394f | [
"MIT"
] | null | null | null | dadosBQ_Pandas.ipynb | DrumondVilela/ProjetoFinal | 61887cfdc4284083237e82f168be4f78732d394f | [
"MIT"
] | null | null | null | dadosBQ_Pandas.ipynb | DrumondVilela/ProjetoFinal | 61887cfdc4284083237e82f168be4f78732d394f | [
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] | null | null | null | 28.400517 | 152 | 0.472386 | [
[
[
"pip install pandera",
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[
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[
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d002e9694740606d508ae0ab95f60cdabad6c231 | 1,949 | ipynb | Jupyter Notebook | python-challenges/Challenges/.ipynb_checkpoints/Invert-binary-tree-checkpoint.ipynb | coopersec/research-learning | da65e7999f0f9948c85dc2a74d15b25dbc3f7108 | [
"MIT"
] | 1 | 2022-02-10T23:59:46.000Z | 2022-02-10T23:59:46.000Z | python-challenges/Challenges/Invert-binary-tree.ipynb | coopersec/research-learning | da65e7999f0f9948c85dc2a74d15b25dbc3f7108 | [
"MIT"
] | null | null | null | python-challenges/Challenges/Invert-binary-tree.ipynb | coopersec/research-learning | da65e7999f0f9948c85dc2a74d15b25dbc3f7108 | [
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] | null | null | null | 22.929412 | 97 | 0.471524 | [
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"# Definition for a binary tree node.\nclass Node:\n def __init__(self, data, val=0, left=None, right=None):\n self.data = data\n self.val = val\n self.left = left\n self.right = right\nclass Solution:\n def invertTree(self, root: Node) -> Node:\n if root is No... | [
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d002ec87edea457693b0b71f1c924cd62b4f7937 | 5,646 | ipynb | Jupyter Notebook | notebooks/ch03.ipynb | jkurdys/ThinkPython2 | 7bdbe11f6ef62eac29ee7d06170bd734a061cb0b | [
"MIT"
] | null | null | null | notebooks/ch03.ipynb | jkurdys/ThinkPython2 | 7bdbe11f6ef62eac29ee7d06170bd734a061cb0b | [
"MIT"
] | null | null | null | notebooks/ch03.ipynb | jkurdys/ThinkPython2 | 7bdbe11f6ef62eac29ee7d06170bd734a061cb0b | [
"MIT"
] | null | null | null | 18.45098 | 80 | 0.374956 | [
[
[
"def repeat_lyrics():\n print_lyrics()\n print_lyrics()",
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],
[
"def print_lyrics():\n print('hi')\n print('how do you do?')\n ",
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[
"repeat_lyrics()",
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d002f51b520dfb6f7f7c8f13e0401f22dc925760 | 633,929 | ipynb | Jupyter Notebook | Chapter4_TheGreatestTheoremNeverTold/Ch4_LawOfLargeNumbers_PyMC3.ipynb | quantopian/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers | 11006cea89d6b2cbf4fc06173d717d3f08966f93 | [
"MIT"
] | 74 | 2016-07-22T19:03:32.000Z | 2022-03-24T04:23:28.000Z | Chapter4_TheGreatestTheoremNeverTold/Ch4_LawOfLargeNumbers_PyMC3.ipynb | noisyoscillator/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers | 11006cea89d6b2cbf4fc06173d717d3f08966f93 | [
"MIT"
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d0032d65102a1a810d0135cd280d208c57458812 | 14,124 | ipynb | Jupyter Notebook | notebooks/figure_supervised_comp.ipynb | priyaravichander/ganspace | fefc001fabf6986a98da4df3166fc31693a4c26b | [
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d00352c042e11023e04cd767f979253bf98e6a8d | 11,488 | ipynb | Jupyter Notebook | Vine_Review_Analysis.ipynb | ethiry99/HW16_Amazon_Vine_Analysis | efbd4d44125888472f833ff8c3304848796caa7a | [
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d00358ed9a2b3961036e248c0ed61a57653a1f75 | 538 | ipynb | Jupyter Notebook | Python/Scikit-learn.ipynb | JnuLi/DataScience | 3a0c5992a84c1f9633fe7b27c2252f5964cb3f8d | [
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[
[
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d003b29dce0beb4d9f782ffacfd931f35fe514a0 | 196,103 | ipynb | Jupyter Notebook | chapter09_numoptim/04_energy.ipynb | aaazzz640/cookbook-2nd-code | c0edeb78fe5a16e64d1210437470b00572211a82 | [
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d003e6e33eceedc5e5cddd6fb58381fa9aead533 | 6,852 | ipynb | Jupyter Notebook | Samples/src/Arithmetic/Adder Example.ipynb | fafel/Quantum | 630c4ab7de5422c69b0a629740d231819fa88a49 | [
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d003ef56f03dfdceaee65b794dacbe4d4ecf8265 | 48,437 | ipynb | Jupyter Notebook | Ch11_Optimization_Algorithms/11-9.ipynb | StevenJokess/d2l-en-read | 71b0f35971063b9fe5f21319b8072d61c9e5a298 | [
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] | 1 | 2021-05-05T13:54:26.000Z | 2021-05-05T13:54:26.000Z | 393.796748 | 22,832 | 0.613993 | [
[
[
"%matplotlib inline\nfrom d2l import torch as d2l\nimport torch",
"_____no_output_____"
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[
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d00405c26bbe9c7f2bef47f5af53566489e8e8a0 | 30,991 | ipynb | Jupyter Notebook | notebooks/.ipynb_checkpoints/Matrix-checkpoint.ipynb | raissabthibes/bmc | 840800fb94ea3bf188847d0771ca7197dfec68e3 | [
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] | null | null | null | 26.53339 | 1,019 | 0.498984 | [
[
[
"# Matrix\n\n> Marcos Duarte \n> Laboratory of Biomechanics and Motor Control ([http://demotu.org/](http://demotu.org/)) \n> Federal University of ABC, Brazil",
"_____no_output_____"
],
[
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d004070b4f7fc66b6d7f11146a3c0cefbaa72435 | 162,625 | ipynb | Jupyter Notebook | embedding_word_clusters2.ipynb | mzkhan2000/KG-Embeddings | a56cc9df706817e05346fb9a2083b87d4bd27380 | [
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d0040e8578098f6f8b611f8d9e09479a0aab9a68 | 103,332 | ipynb | Jupyter Notebook | DAY-12/DAY-12.ipynb | BhuvaneshHingal/LetsUpgrade-AI-ML | 63f7114d680b2738c9c40983996adafe55c0edd2 | [
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] | 1 | 2020-09-11T18:11:54.000Z | 2020-09-11T18:11:54.000Z | DAY-12/DAY-12.ipynb | BhuvaneshHingal/LetsUpgrade-AI-ML | 63f7114d680b2738c9c40983996adafe55c0edd2 | [
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] | 1 | 2020-07-22T19:47:15.000Z | 2020-07-22T19:47:15.000Z | 41.632554 | 7,496 | 0.459277 | [
[
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"_____no_output_____"
],
[
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d0041a77863352490141e03473f86483a40a1160 | 188,100 | ipynb | Jupyter Notebook | assignment2/ConvolutionalNetworks.ipynb | pranav-s/Stanford_CS234_CV_2017 | 9b0536812477dd0ea0e2dc4f063976a2e79148cc | [
"MIT"
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"MIT"
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"# Convolutional Networks\n\nSo far we have worked with deep fully-connected networks, using them to explore different optimization strategies and network architectures. Fully-connected networks are a good testbed for experimentation because they are very computationally efficient, but in practice all... | [
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d00420c554adf6b85194a495ea114ccdcbbd6599 | 549,303 | ipynb | Jupyter Notebook | Experiment1_Main/Components/One25/one25.ipynb | ttrogers/frigo-chen-rogers | ddc8808f21a89259df83a161ee72faf2487623d4 | [
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"_____no_output_____"
],
[
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"_____no_output_____"
]
],
[
[
"get.data <- dget(\"get_data.r\") #script to read data files\nget.pars <- dget(\"get_pars.r\") #script t... | [
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d004241124de9de95e3a3f380317afae282cf966 | 126,007 | ipynb | Jupyter Notebook | Notes/KerasExercise.ipynb | GrayLand119/GLColabNotes | b42729491c20af0d9fb44c3de66f8b2756cccf03 | [
"MIT"
] | null | null | null | Notes/KerasExercise.ipynb | GrayLand119/GLColabNotes | b42729491c20af0d9fb44c3de66f8b2756cccf03 | [
"MIT"
] | null | null | null | Notes/KerasExercise.ipynb | GrayLand119/GLColabNotes | b42729491c20af0d9fb44c3de66f8b2756cccf03 | [
"MIT"
] | null | null | null | 152.735758 | 38,972 | 0.878308 | [
[
[
"# About\n\n此笔记包含了以下内容:\n\n* keras 的基本使用\n* 组合特征\n* 制作dataset\n* 模型的存取(2种方式)\n* 添加检查点\n",
"_____no_output_____"
]
],
[
[
"import tensorflow as tf\nfrom tensorflow.keras import layers\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport math",
"_____no_output_____"... | [
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d0042eab5854b447de51a429a272d3a09f8991fe | 10,515 | ipynb | Jupyter Notebook | 2016/loris/day_1.ipynb | bbglab/adventofcode | 65b6d8331d10f229b59232882d60024b08d69294 | [
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"MIT"
] | null | null | null | 2016/loris/day_1.ipynb | bbglab/adventofcode | 65b6d8331d10f229b59232882d60024b08d69294 | [
"MIT"
] | 3 | 2016-12-02T09:20:42.000Z | 2021-12-01T13:31:07.000Z | 36.510417 | 277 | 0.463338 | [
[
[
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"_____no_output_____"
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[
[
"--- Day 1: No Time for a Taxicab ---\n\nSanta's sleigh uses a very high-precision clock to guide its movements, and the clock's oscillator is regulated by stars. Unfortunately, the stars have been stolen... by the Easter ... | [
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d004339a3b87926b5dad0ec695083c16c43f98ab | 4,990 | ipynb | Jupyter Notebook | Distance.ipynb | iscanegemen/Data-Science-with-Foursquare-API- | 8523509275a1abd6a2598746f86b2dfb33750b5a | [
"MIT"
] | 1 | 2020-11-26T17:33:28.000Z | 2020-11-26T17:33:28.000Z | Distance.ipynb | iscanegemen/Data-Science-with-Foursquare-API | 8523509275a1abd6a2598746f86b2dfb33750b5a | [
"MIT"
] | null | null | null | Distance.ipynb | iscanegemen/Data-Science-with-Foursquare-API | 8523509275a1abd6a2598746f86b2dfb33750b5a | [
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] | null | null | null | 39.92 | 1,137 | 0.593988 | [
[
[
"import pandas as pd\nimport math\n\n\n\ndf=pd.read_csv(r\"C:\\Users\\MONSTER\\Desktop\\newyorkcoffeewithdetails.csv\",error_bad_lines=False)\n\ndistance_dict = {}\n\nlat_input=float(input(\"Latitude : \")) # User's latitude and longitude\nlon_input=float(input(\"longitude : \"))\n\n\nfor i in range(l... | [
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d0044a2c29cd84a8d9336751d88f257bba7451b1 | 14,085 | ipynb | Jupyter Notebook | notebooks/beginner/notebooks/strings.ipynb | jordantcarlisle/learn-python3 | 53964f7d67d64af10233f91403e04bb4d9b1a566 | [
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] | null | null | null | 20.472384 | 224 | 0.496557 | [
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