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d02518d7c5a916840d084dcceb0a53ca3785c42d | 204,749 | ipynb | Jupyter Notebook | learning/ud170/lesson-1.ipynb | WL152/project-omega | 04445bc432c773c843e98d9ba09be7745e208aee | [
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d0251bdf6e19d3b0f11c1fdcdf6761bbcbfa563c | 43,281 | ipynb | Jupyter Notebook | 3_ML/src/ANN_feature_selection/.ipynb_checkpoints/Keras_FFN_multitask-checkpoint.ipynb | IBPA/MutationDB | eb1648e511cf6cc4a9e2cc72abafae4a8b20ac30 | [
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d02522b65bd61f622c0244e9bfff472944a14c58 | 850 | ipynb | Jupyter Notebook | cclhm0069/_build/jupyter_execute/mod4b/sem14.ipynb | ericbrasiln/intro-historia-digital | 5733dc55396beffeb916693c552fd4eb987472d0 | [
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d02540be05faa26ecc7f7187e0f71092d55c79a1 | 18,825 | ipynb | Jupyter Notebook | 01_Linear_Regression.ipynb | sarincr/Data-Analytics-with-PyTorch | 2e13164ce0b16897ddf0e93c6d19c8383a11b945 | [
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d025478c23538317bcb04a1349f710b067c26690 | 522,064 | ipynb | Jupyter Notebook | Model Comparison/Model_comparison.ipynb | asmolina/ML-project-fairness-aware-classification | 0c8d9d6e3d320a10b685388710fb680b0aaf3cae | [
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d02562ed2d085c138a990c1f5efc3179f182064c | 13,528 | ipynb | Jupyter Notebook | dictionary/dialect/pahang.ipynb | huseinzol05/Malay-Dataset | e27b7617c74395c86bb5ed9f3f194b3cac2f66f6 | [
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] | 51 | 2020-05-20T13:26:18.000Z | 2021-05-13T07:21:17.000Z | dictionary/dialect/pahang.ipynb | huseinzol05/Malay-Dataset | e27b7617c74395c86bb5ed9f3f194b3cac2f66f6 | [
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] | 3 | 2020-05-21T13:12:46.000Z | 2021-05-12T03:26:43.000Z | dictionary/dialect/pahang.ipynb | huseinzol05/Malaya-Dataset | c9c1917a6b1cab823aef5a73bd10e0fab0bff42d | [
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d0256441b14747ca8dffb4322c21c5836680fe9c | 678,580 | ipynb | Jupyter Notebook | Python_Stock/Portfolio_Strategies/Apple_Tesla_Split.ipynb | linusqzdeng/Stock_Analysis_For_Quant | de6232caed5328a2b1fa40bec1f45bbd822c88fa | [
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d0257854ca677253e85407b94878225459771021 | 750,741 | ipynb | Jupyter Notebook | ML-Predictions/.ipynb_checkpoints/Water-Level TSF - Copy (2)-checkpoint.ipynb | romilshah525/SIH-2019 | 35555a4826e097a4a1178e7a1a8580dc4f80a64e | [
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] | 6 | 2021-07-20T06:46:21.000Z | 2022-03-08T23:26:58.000Z | ML-Predictions/.ipynb_checkpoints/Water-Level TSF - Copy (2)-checkpoint.ipynb | romilshah525/Water.io | 35555a4826e097a4a1178e7a1a8580dc4f80a64e | [
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d02581b83a83a80c39d56cc597c59e4ac1b0803e | 8,760 | ipynb | Jupyter Notebook | Python_Basic_Assignments/Assignment_11.ipynb | dataqueenpend/-Assignments_fsDS_OneNeuron | 60ec0f1d357b738dd6c753254506a0a6f8241ceb | [
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d02588d73318038c341739e2c325be6b6b9a18f0 | 18,364 | ipynb | Jupyter Notebook | content/04. Not quite intelligent robots/04.2 Robot navigation using dead reckoning.ipynb | mmh352/tm129-robotics2020 | a73a00fcfcd7eb7857bc9b6ce28449e9e79a79bc | [
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d025923f45524e3a2695221cb7ebf18bf34ec741 | 1,712 | ipynb | Jupyter Notebook | DateTime.ipynb | helloprasanna/python | 1f218ddf84bc082dca5906833238389011ae344b | [
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"import datetime\ndob = '20110101'\n\n\ntoday = datetime.datetime.now()\nyyyy = int(dob[0:4])\nmm = int(dob[4:6])\ndd = int(dob[6:8])\ndob = datetime.datetime(yyyy,mm,dd)\nage_in_days = (today - dob).days\nage_in_years = age_in_days/365\nprint(int(age_in_years))\n\n",
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d025a2826feadb0833b80edfb314c69b919604f4 | 7,787 | ipynb | Jupyter Notebook | database_updater.ipynb | tlkh/reverse-image-search | df672ec5576916fd3c78c28ed6b2b28f68feed0a | [
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] | null | null | null | database_updater.ipynb | tlkh/reverse-image-search | df672ec5576916fd3c78c28ed6b2b28f68feed0a | [
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] | null | null | null | database_updater.ipynb | tlkh/reverse-image-search | df672ec5576916fd3c78c28ed6b2b28f68feed0a | [
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"## What this code does\nIn short, it is a reverse meme search, that identifies the source of the meme. It takes an image copypasta, extracts the individual *subimages* and compares it with a database of pictures (the database should be made up of copypastas, which is in TODO)\n\n### TODO\n\n### Clean... | [
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d025a2be6548d4737fd8e906e064257dd5d55f6c | 944,975 | ipynb | Jupyter Notebook | 3_Inference.ipynb | mohamed11981198/udacity-CVND-Image-Captioning | d7022de998e95ae3a93c7883aae6729f0f5de8fa | [
"MIT"
] | 1 | 2020-05-16T05:08:30.000Z | 2020-05-16T05:08:30.000Z | 3_Inference.ipynb | mohamed11981198/udacity-CVND-Image-Captioning | d7022de998e95ae3a93c7883aae6729f0f5de8fa | [
"MIT"
] | null | null | null | 3_Inference.ipynb | mohamed11981198/udacity-CVND-Image-Captioning | d7022de998e95ae3a93c7883aae6729f0f5de8fa | [
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"# Computer Vision Nanodegree\n\n## Project: Image Captioning\n\n---\n\nIn this notebook, you will use your trained model to generate captions for images in the test dataset.\n\nThis notebook **will be graded**. \n\nFeel free to use the links below to navigate the notebook:\n- [Step 1](#step1): Get D... | [
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d025c7be048c5717b19eefc1aeb8ae1933f29fba | 34,894 | ipynb | Jupyter Notebook | 1_microglia_segmentation/OGD_3_full_segmentation_pipeline-Copy1.ipynb | Nance-Lab/microFIBER | 5433b7d045a7f0e1611edc354c34e5963a25bb07 | [
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] | null | null | null | 1_microglia_segmentation/OGD_3_full_segmentation_pipeline-Copy1.ipynb | Nance-Lab/microFIBER | 5433b7d045a7f0e1611edc354c34e5963a25bb07 | [
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] | null | null | null | 1_microglia_segmentation/OGD_3_full_segmentation_pipeline-Copy1.ipynb | Nance-Lab/microFIBER | 5433b7d045a7f0e1611edc354c34e5963a25bb07 | [
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] | null | null | null | 36.047521 | 133 | 0.464492 | [
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"# Purpose: To run the full segmentation using the best scored method from 2_compare_auto_to_manual_threshold",
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d025db7c935b64b2e7be0da162fada983a3a2d17 | 869,948 | ipynb | Jupyter Notebook | renderer/rendergan_tf.ipynb | jjbits/RenderGAN | bf02f6d6fdb271599e2bf698fe920c704ce8c97a | [
"MIT"
] | 2 | 2020-08-26T22:39:45.000Z | 2021-05-02T11:52:08.000Z | renderer/rendergan_tf.ipynb | jjbits/RenderGAN | bf02f6d6fdb271599e2bf698fe920c704ce8c97a | [
"MIT"
] | null | null | null | renderer/rendergan_tf.ipynb | jjbits/RenderGAN | bf02f6d6fdb271599e2bf698fe920c704ce8c97a | [
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"_____no_output_____"
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"from __future__ import absolute_import, division, print_function, unicode_literals\n\ntry:\n # %tensorflow_version only exists in Colab.\n %tensorflow_version 2.x\nexcept Exception:\n pass\nimport tensorflow as t... | [
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d025dda576ceb330c702d13822e033439bec3b9d | 8,374 | ipynb | Jupyter Notebook | stats-279/SLU19 - Workflow/Exercise notebook.ipynb | hershaw/stats-279 | 4bcfaaace35563f3d17efcfa398da2b1d4ecc732 | [
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] | 12 | 2019-07-06T09:06:17.000Z | 2020-11-13T00:58:42.000Z | data-science-101/SLU19 - Workflow/Exercise notebook.ipynb | DareData/data-science-101 | 5ef71321dffe3b9b51c0d8c171c7b4a0550ecd3a | [
"MIT"
] | 29 | 2019-07-01T14:19:49.000Z | 2021-03-24T13:29:50.000Z | data-science-101/SLU19 - Workflow/Exercise notebook.ipynb | DareData/data-science-101 | 5ef71321dffe3b9b51c0d8c171c7b4a0550ecd3a | [
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] | 36 | 2019-07-05T15:53:35.000Z | 2021-07-04T04:18:02.000Z | 26.087227 | 226 | 0.566754 | [
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d025e50a95d00b4f5b87ae15a2a3d037f37db54c | 8,327 | ipynb | Jupyter Notebook | src/data/geoPandas_int.ipynb | dansmi-hub/mosquito-jsdm | a4787a9deae3afeb43030db3c1497aa880d34b1d | [
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d026089d8b8dcf8bd48f11093f51d39a08dd01f1 | 61,272 | ipynb | Jupyter Notebook | Assignment_3.ipynb | Sarbajit097/Assignment | 0594e55f49eea0e72706170f5456fa878282e069 | [
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d0260cab1b0e583b5bf98590f3e1771ea82c46e9 | 8,283 | ipynb | Jupyter Notebook | examples/menpo.model.linear.PCAModel.ipynb | ikassi/menpo | ca702fc814a1ad50b27c44c6544ba364d3aa7e31 | [
"BSD-3-Clause"
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] | null | null | null | examples/menpo.model.linear.PCAModel.ipynb | ikassi/menpo | ca702fc814a1ad50b27c44c6544ba364d3aa7e31 | [
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d026177d8ac1d8b5ba74224a126d1fb7c95f3023 | 12,926 | ipynb | Jupyter Notebook | Loop_Statement.ipynb | kathleenmei/CPEN-21A-ECE-2-1 | 5f0437e6322f1f988819075bf2ff89267eb96a56 | [
"Apache-2.0"
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] | null | null | null | Loop_Statement.ipynb | kathleenmei/CPEN-21A-ECE-2-1 | 5f0437e6322f1f988819075bf2ff89267eb96a56 | [
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d026333d7ddf09a0df79969f3bcc890b2555c138 | 45,737 | ipynb | Jupyter Notebook | codes/labs_lecture07/lab01_mlp/.ipynb_checkpoints/mlp_exercise-checkpoint.ipynb | wesleyjtann/Deep-learning-course-CE7454-2018 | ec29057f5fd741359b99392ae08b6574c8d4882a | [
"MIT"
] | 2 | 2019-11-11T08:37:14.000Z | 2021-02-16T02:21:57.000Z | codes/labs_lecture07/lab01_mlp/mlp_exercise.ipynb | wesleyjtann/Deep-learning-course-CE7454-2018 | ec29057f5fd741359b99392ae08b6574c8d4882a | [
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] | null | null | null | codes/labs_lecture07/lab01_mlp/mlp_exercise.ipynb | wesleyjtann/Deep-learning-course-CE7454-2018 | ec29057f5fd741359b99392ae08b6574c8d4882a | [
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] | null | null | null | 155.568027 | 33,776 | 0.897851 | [
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[
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d026451c642a17bd4a921731ea60d7a5d4102ed9 | 18,613 | ipynb | Jupyter Notebook | docs/source/examples/rossmann/tensorflow.ipynb | lgardenhire/NVTabular | 225352cd2f95d6409c630b655f36f1a3e859ede7 | [
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] | null | null | null | docs/source/examples/rossmann/tensorflow.ipynb | lgardenhire/NVTabular | 225352cd2f95d6409c630b655f36f1a3e859ede7 | [
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d0264ad9006e244f99aea97cc3dd62ada8e7e6f6 | 1,101 | ipynb | Jupyter Notebook | hisim/inputs/loadprofiles/electrical-load_2-smart-appliances/process_data.ipynb | guptakaran55/HiSim | 3574c2719b194b4e7d5ec68513d62af34c08ce85 | [
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] | 12 | 2021-10-05T11:38:24.000Z | 2022-03-25T09:56:08.000Z | hisim/inputs/loadprofiles/electrical-load_2-smart-appliances/process_data.ipynb | guptakaran55/HiSim | 3574c2719b194b4e7d5ec68513d62af34c08ce85 | [
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] | 6 | 2021-10-06T13:27:55.000Z | 2022-03-10T12:55:15.000Z | hisim/inputs/loadprofiles/electrical-load_2-smart-appliances/process_data.ipynb | guptakaran55/HiSim | 3574c2719b194b4e7d5ec68513d62af34c08ce85 | [
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] | 4 | 2022-02-21T19:00:50.000Z | 2022-03-22T11:01:38.000Z | 18.982759 | 96 | 0.541326 | [
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d0268595013b88d8e32177838e57b1bdf1531363 | 13,691 | ipynb | Jupyter Notebook | Recommending System for Pictures - 4th place @ Yandex ML Competition.ipynb | dremovd/pictures-recommendation-yandex-ml-2019 | 339e9407a5718fbef74f9aa7986b51904c0e8d42 | [
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] | 4 | 2019-06-11T17:34:32.000Z | 2020-04-19T02:02:32.000Z | Recommending System for Pictures - 4th place @ Yandex ML Competition.ipynb | dremovd/pictures-recommendation-yandex-ml-2019 | 339e9407a5718fbef74f9aa7986b51904c0e8d42 | [
"MIT"
] | null | null | null | Recommending System for Pictures - 4th place @ Yandex ML Competition.ipynb | dremovd/pictures-recommendation-yandex-ml-2019 | 339e9407a5718fbef74f9aa7986b51904c0e8d42 | [
"MIT"
] | 1 | 2020-11-23T08:48:28.000Z | 2020-11-23T08:48:28.000Z | 24.189046 | 138 | 0.523702 | [
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d02691eeb9161ccab8d5a3c0f7dd13c8a556aac7 | 544,241 | ipynb | Jupyter Notebook | R_Advertisement_Prediction.ipynb | joymuli10/Advertising-Prediction-R | 90fc8f21081166f4eb34701aacbe86a50b9dd2a9 | [
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d026cb201d6586b1b323cff0ee2d9de997288ec4 | 4,795 | ipynb | Jupyter Notebook | jupyter/.ipynb_checkpoints/Untitled-checkpoint.ipynb | sasano8/magnet-migrade | b5669b34a6a3b845df8df96dfedaf967df6b88e2 | [
"MIT"
] | null | null | null | jupyter/.ipynb_checkpoints/Untitled-checkpoint.ipynb | sasano8/magnet-migrade | b5669b34a6a3b845df8df96dfedaf967df6b88e2 | [
"MIT"
] | 4 | 2021-03-24T23:38:22.000Z | 2021-03-31T07:24:30.000Z | jupyter/.ipynb_checkpoints/Untitled-checkpoint.ipynb | sasano8/magnet-migrade | b5669b34a6a3b845df8df96dfedaf967df6b88e2 | [
"MIT"
] | null | null | null | 25.236842 | 113 | 0.475287 | [
[
[
"!pip install pydantic",
"Requirement already satisfied: pydantic in /usr/local/lib/python3.8/dist-packages (1.6.1)\n\u001b[33mWARNING: You are using pip version 20.2.2; however, version 20.2.4 is available.\nYou should consider upgrading via the '/usr/bin/python3 -m pip install --upgrade pip' c... | [
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d026df3c7114b94cc3be1435ec3a079065ef68b2 | 118,583 | ipynb | Jupyter Notebook | notebooks/predict.ipynb | maxdel/span_ae | 76dde200a68fdca30bfe312fb9e47328f3212577 | [
"MIT"
] | 1 | 2019-11-27T10:55:06.000Z | 2019-11-27T10:55:06.000Z | notebooks/predict.ipynb | maxdel/span_ae | 76dde200a68fdca30bfe312fb9e47328f3212577 | [
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] | null | null | null | notebooks/predict.ipynb | maxdel/span_ae | 76dde200a68fdca30bfe312fb9e47328f3212577 | [
"MIT"
] | null | null | null | 241.022358 | 32,184 | 0.916177 | [
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[
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"_____no_output_____"
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],
[
[
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"_____no_output_____"
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d026e34a0ea41412fbdd766d5f9aacb24c19bf62 | 10,024 | ipynb | Jupyter Notebook | Crypto/imposter/imposter_writeup.ipynb | NeSE-Team/XNUCA2020Qualifier | c5cff6a63d968de53483e39fc314c0fd63f49d6c | [
"Apache-2.0"
] | 76 | 2020-11-01T05:47:46.000Z | 2022-03-21T05:56:34.000Z | Crypto/imposter/imposter_writeup.ipynb | NeSE-Team/XNUCA2020Qualifier | c5cff6a63d968de53483e39fc314c0fd63f49d6c | [
"Apache-2.0"
] | 2 | 2020-11-07T10:45:06.000Z | 2021-01-19T03:09:59.000Z | Crypto/imposter/imposter_writeup.ipynb | NeSE-Team/XNUCA2020Qualifier | c5cff6a63d968de53483e39fc314c0fd63f49d6c | [
"Apache-2.0"
] | 5 | 2020-11-01T16:27:29.000Z | 2022-01-18T08:17:15.000Z | 42.295359 | 511 | 0.589585 | [
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[
"This challenge implements an instantiation of OTR based on AES block cipher with modified version 1.0. OTR, which stands for Offset Two-Round, is a blockcipher mode of operation to realize an authenticated encryption with associated data (see [[1]](#1)). AES-OTR algorithm is a campaign of CAESAR comp... | [
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d026f3e4d6c336693446265b87d2fae00ea0174e | 7,036 | ipynb | Jupyter Notebook | Sessions/Session14/Day4/MJH_hackday.ipynb | hopkins9942/LSSTC-DSFP-Sessions | 6856ea7345a2fc2f0bf2ee920168444887c100ca | [
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"MIT"
] | null | null | null | Sessions/Session14/Day4/MJH_hackday.ipynb | hopkins9942/LSSTC-DSFP-Sessions | 6856ea7345a2fc2f0bf2ee920168444887c100ca | [
"MIT"
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"import os\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n%matplotlib inline\nimport torch\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.ensemble import IsolationForest\n",
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d026f4e06cec45f321b1c06d6eac850f67f585b3 | 15,649 | ipynb | Jupyter Notebook | docs/src/man/VC_Examples.ipynb | OpenMendel/QuasiCopula.jl | 3addff703c63b158fcadff3ca52575b3b486b321 | [
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] | null | null | null | docs/src/man/VC_Examples.ipynb | OpenMendel/QuasiCopula.jl | 3addff703c63b158fcadff3ca52575b3b486b321 | [
"MIT"
] | 1 | 2022-03-17T20:44:08.000Z | 2022-03-17T20:44:08.000Z | docs/src/man/VC_Examples.ipynb | OpenMendel/QuasiCopula.jl | 3addff703c63b158fcadff3ca52575b3b486b321 | [
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d026f5e659ec15b5e5d4f9c452e48dc8cd05c91a | 33,461 | ipynb | Jupyter Notebook | notebooks/sentiment_analysis/textblob.ipynb | ClaasM/streamed-sentiment-topic-intent | 76f6e8686ab629391fd714228547ed1de097466c | [
"MIT"
] | null | null | null | notebooks/sentiment_analysis/textblob.ipynb | ClaasM/streamed-sentiment-topic-intent | 76f6e8686ab629391fd714228547ed1de097466c | [
"MIT"
] | 8 | 2020-03-24T15:33:52.000Z | 2022-03-11T23:16:16.000Z | notebooks/sentiment_analysis/textblob.ipynb | ClaasM/Bachelors-Thesis | 76f6e8686ab629391fd714228547ed1de097466c | [
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] | null | null | null | 221.596026 | 29,524 | 0.905054 | [
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"# Testing different SA methods 4/5\n## Textblob\n",
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],
[
[
"import csv\nimport re\nimport random\n\nfrom textblob import TextBlob\n\n# Ugly hackery, but necessary: stackoverflow.com/questions/4383571/importing-files-from-different-folder\nimport sys\nsy... | [
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d02706cbf2067692ba72c9634f9cc1435b55f07c | 31,530 | ipynb | Jupyter Notebook | Some_strings_and_regex_operation_in_Python.ipynb | LanguegeEngineering/demo-igor-skorzybot | d056757b65d87d4691e39655c122dbd4d7941a02 | [
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] | null | null | null | Some_strings_and_regex_operation_in_Python.ipynb | LanguegeEngineering/demo-igor-skorzybot | d056757b65d87d4691e39655c122dbd4d7941a02 | [
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d0270cb19510138627ba78c9a9765757b15cdc77 | 30,455 | ipynb | Jupyter Notebook | .ipynb_checkpoints/05.1 Generating Text in the Style of an Example Text-checkpoint.ipynb | WillKoehrsen/deep_learning_cookbook | 6127041766367575135e3dfc2c00600153d238d2 | [
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] | 11 | 2018-10-11T09:44:48.000Z | 2021-09-28T09:22:38.000Z | .ipynb_checkpoints/05.1 Generating Text in the Style of an Example Text-checkpoint.ipynb | WillKoehrsen/deep_learning_cookbook | 6127041766367575135e3dfc2c00600153d238d2 | [
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"Apache-2.0"
] | 17 | 2018-10-04T04:56:56.000Z | 2022-01-25T15:00:09.000Z | 33.39364 | 144 | 0.446331 | [
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d027103293a2aa22126ad2bf23384f474ac63428 | 194,494 | ipynb | Jupyter Notebook | render_demo.ipynb | frankhome61/nerf | 2756d244a978efed478753e9fe700f38e7544df8 | [
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] | null | null | null | render_demo.ipynb | frankhome61/nerf | 2756d244a978efed478753e9fe700f38e7544df8 | [
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[
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d02714fb1e7527756a971551ad2df31dbc80204f | 509,276 | ipynb | Jupyter Notebook | DCGAN_V2.ipynb | SAKARA96/Offspring-Face-Generator | b249fbed98441b68ce991dc1ad63b8ebbaadb585 | [
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] | null | null | null | DCGAN_V2.ipynb | SAKARA96/Offspring-Face-Generator | b249fbed98441b68ce991dc1ad63b8ebbaadb585 | [
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] | null | null | null | DCGAN_V2.ipynb | SAKARA96/Offspring-Face-Generator | b249fbed98441b68ce991dc1ad63b8ebbaadb585 | [
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] | null | null | null | 46.726856 | 25,168 | 0.626972 | [
[
[
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"True\n3.7.5\n"
],
[
"import os\nimport numpy as np\nfrom os import listdir\nfrom PIL import Image\nimport time\nimport tensorflow as tf\nfrom tensorflow.ke... | [
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d0272bb5933732522f97c6c48377049a2ddb4905 | 1,712 | ipynb | Jupyter Notebook | docs/_src/3.Theory/04.graph-simplification.ipynb | snystrom/Mycelia | 67a978ec2de3c53fced46b98adbdfa2c4ca82889 | [
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] | 27 | 2021-06-24T17:53:36.000Z | 2022-03-05T19:26:01.000Z | docs/_src/3.Theory/04.graph-simplification.ipynb | snystrom/Mycelia | 67a978ec2de3c53fced46b98adbdfa2c4ca82889 | [
"MIT"
] | 1 | 2022-01-08T14:45:20.000Z | 2022-01-08T14:45:20.000Z | 34.24 | 88 | 0.663551 | [
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d0272de970b9aff6a278cbf226ebe388310bd87f | 10,988 | ipynb | Jupyter Notebook | aws_marketplace/using_algorithms/amazon_demo_product/Using_Algorithm_Arn_From_AWS_Marketplace.ipynb | Amirosimani/amazon-sagemaker-examples | bc35e7a9da9e2258e77f98098254c2a8e308041a | [
"Apache-2.0"
] | 2,610 | 2020-10-01T14:14:53.000Z | 2022-03-31T18:02:31.000Z | aws_marketplace/using_algorithms/amazon_demo_product/Using_Algorithm_Arn_From_AWS_Marketplace.ipynb | Amirosimani/amazon-sagemaker-examples | bc35e7a9da9e2258e77f98098254c2a8e308041a | [
"Apache-2.0"
] | 1,959 | 2020-09-30T20:22:42.000Z | 2022-03-31T23:58:37.000Z | aws_marketplace/using_algorithms/amazon_demo_product/Using_Algorithm_Arn_From_AWS_Marketplace.ipynb | Amirosimani/amazon-sagemaker-examples | bc35e7a9da9e2258e77f98098254c2a8e308041a | [
"Apache-2.0"
] | 2,052 | 2020-09-30T22:11:46.000Z | 2022-03-31T23:02:51.000Z | 30.269972 | 327 | 0.610666 | [
[
[
"# AWS Marketplace Product Usage Demonstration - Algorithms\n\n## Using Algorithm ARN with Amazon SageMaker APIs\n\nThis sample notebook demonstrates two new functionalities added to Amazon SageMaker:\n1. Using an Algorithm ARN to run training jobs and use that result for inference\n2. Using an AWS Ma... | [
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d0273109250ac5476a0af13f6e062b70f7c14afa | 18,731 | ipynb | Jupyter Notebook | examples/notebooks/statespace_structural_harvey_jaeger.ipynb | yarikoptic/statsmodels | f990cb1a1ef0c9883c9394444e6f9d027efabec6 | [
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"# Detrending, Stylized Facts and the Business Cycle\n\nIn an influential article, Harvey and Jaeger (1993) described the use of unobserved components models (also known as \"structural time series models\") to derive stylized facts of the business cycle.\n\nTheir paper begins:\n\n \"Establishing t... | [
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d0274cae8108a3c472d32c456c56908aa7f0a963 | 40,207 | ipynb | Jupyter Notebook | 01.Neural-networks-Deep-learning/Week2/Logistic Regression as a Neural Network/Logistic Regression with a Neural Network mindset v3.ipynb | navicester/deeplearning.ai-Assignments | fb3bdbf1b436b4399b16961782f5f2baa0274c87 | [
"MIT"
] | 2 | 2017-05-18T06:22:35.000Z | 2017-05-18T07:04:19.000Z | 01.Neural-networks-Deep-learning/Week2/Logistic Regression as a Neural Network/Logistic Regression with a Neural Network mindset v3.ipynb | navicester/deeplearning.ai-Assignments | fb3bdbf1b436b4399b16961782f5f2baa0274c87 | [
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] | null | null | null | 01.Neural-networks-Deep-learning/Week2/Logistic Regression as a Neural Network/Logistic Regression with a Neural Network mindset v3.ipynb | navicester/deeplearning.ai-Assignments | fb3bdbf1b436b4399b16961782f5f2baa0274c87 | [
"MIT"
] | 2 | 2018-05-24T19:31:14.000Z | 2018-06-08T04:33:52.000Z | 35.487202 | 434 | 0.545427 | [
[
[
"# Logistic Regression with a Neural Network mindset\n\nWelcome to your first (required) programming assignment! You will build a logistic regression classifier to recognize cats. This assignment will step you through how to do this with a Neural Network mindset, and so will also hone your intuitions... | [
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d0275d0833323c254dff3115db03772ac385813d | 7,708 | ipynb | Jupyter Notebook | src/5 Scrap.ipynb | galbiati/mnk-cleaning-analysis | d8e8b13b7a2c6431e453430588fa85fd694b3373 | [
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] | null | null | null | src/5 Scrap.ipynb | galbiati/mnk-cleaning-analysis | d8e8b13b7a2c6431e453430588fa85fd694b3373 | [
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] | null | null | null | 40.356021 | 147 | 0.495978 | [
[
[
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[
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d02760f9d1f93fcbe9a0c3528be0a844e2812445 | 4,351 | ipynb | Jupyter Notebook | S01 - Bootcamp and Binary Classification/SLU10 - Metrics for Regression/Example Notebook.ipynb | LDSSA/batch4-students | c0547ee0cf10645a0244336c976b304cff2f2000 | [
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] | 19 | 2020-06-10T09:24:18.000Z | 2022-01-25T15:19:29.000Z | S01 - Bootcamp and Binary Classification/SLU10 - Metrics for Regression/Example Notebook.ipynb | LDSSA/batch4-students | c0547ee0cf10645a0244336c976b304cff2f2000 | [
"MIT"
] | 25 | 2020-05-16T14:25:41.000Z | 2022-03-12T00:41:55.000Z | S01 - Bootcamp and Binary Classification/SLU10 - Metrics for Regression/Example Notebook.ipynb | LDSSA/batch4-students | c0547ee0cf10645a0244336c976b304cff2f2000 | [
"MIT"
] | 9 | 2020-08-04T22:08:14.000Z | 2021-12-16T17:24:30.000Z | 21.121359 | 191 | 0.505401 | [
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"# SLU10 - Metrics for regression: Example Notebook\n\nIn this notebook [some regression validation metrics offered by scikit-learn](http://scikit-learn.org/stable/modules/model_evaluation.html#common-cases-predefined-values) are presented.",
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d0276af4d8d9914f47cdd3e1f7ea0e8c3c423f3c | 19,757 | ipynb | Jupyter Notebook | book/_build/jupyter_execute/pandas/23-Kaggle Submission.ipynb | hossainlab/dsnotes | fee64e157f45724bba1f49ad1b186dcaaf1e6c02 | [
"CC0-1.0"
] | null | null | null | book/_build/jupyter_execute/pandas/23-Kaggle Submission.ipynb | hossainlab/dsnotes | fee64e157f45724bba1f49ad1b186dcaaf1e6c02 | [
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] | null | null | null | book/_build/jupyter_execute/pandas/23-Kaggle Submission.ipynb | hossainlab/dsnotes | fee64e157f45724bba1f49ad1b186dcaaf1e6c02 | [
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d02774b2431cb71880ef4dc59f72e8946508413f | 23,902 | ipynb | Jupyter Notebook | Jupyter/stockOther.ipynb | minplemon/stockThird | e32c202c95ba19fe2db97f6e5dd175ac64ee1996 | [
"MIT"
] | 6 | 2020-03-10T14:54:22.000Z | 2021-11-28T11:49:06.000Z | Jupyter/stockOther.ipynb | minplemon/stockThird | e32c202c95ba19fe2db97f6e5dd175ac64ee1996 | [
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] | null | null | null | Jupyter/stockOther.ipynb | minplemon/stockThird | e32c202c95ba19fe2db97f6e5dd175ac64ee1996 | [
"MIT"
] | 5 | 2019-06-25T09:49:53.000Z | 2020-03-01T11:56:32.000Z | 36.772308 | 122 | 0.354322 | [
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[
"from jqdatasdk import *\nauth('18620668927', 'minpeng123')",
"_____no_output_____"
],
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"#记录由上市公司年报、中报、一季报、三季报统计出的分红转增情况\nq = query(finance.STK_XR_XD).filter(finance.STK_XR_XD.report_date >= '2019-01-01').limit(10)\nfinance.run_query(q)",
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d027851fa16810ff380fd888efc70ca8a7fcf92e | 10,110 | ipynb | Jupyter Notebook | Diabetes Dataset/Experiment with Features/062_BloodPressure, SkinThickness, Insulin, BMI and DiabetesPedigreeFunction .ipynb | AnkitaxPriya/Diabetes-Prediction | 2a68fc067019dde8eda31ebb91436746abc4e98e | [
"MIT"
] | null | null | null | Diabetes Dataset/Experiment with Features/062_BloodPressure, SkinThickness, Insulin, BMI and DiabetesPedigreeFunction .ipynb | AnkitaxPriya/Diabetes-Prediction | 2a68fc067019dde8eda31ebb91436746abc4e98e | [
"MIT"
] | null | null | null | Diabetes Dataset/Experiment with Features/062_BloodPressure, SkinThickness, Insulin, BMI and DiabetesPedigreeFunction .ipynb | AnkitaxPriya/Diabetes-Prediction | 2a68fc067019dde8eda31ebb91436746abc4e98e | [
"MIT"
] | null | null | null | 27.69863 | 93 | 0.357864 | [
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[
"# Import the required libraries\nimport warnings\nwarnings.filterwarnings('ignore')\n\nimport pandas as pd\nimport numpy as np\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport joblib\n%matplotlib inline\n\nfrom sklearn.linear_model import LogisticRegression",
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d0278553588dd7f8314c321803f36496e9b6f6fd | 2,300 | ipynb | Jupyter Notebook | notebooks/meanshift.ipynb | JLCaraveo/sklearn-projects-Platzi | d2556dd90479a9057bd78face993fefd8ad47a5f | [
"MIT"
] | null | null | null | notebooks/meanshift.ipynb | JLCaraveo/sklearn-projects-Platzi | d2556dd90479a9057bd78face993fefd8ad47a5f | [
"MIT"
] | null | null | null | notebooks/meanshift.ipynb | JLCaraveo/sklearn-projects-Platzi | d2556dd90479a9057bd78face993fefd8ad47a5f | [
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] | null | null | null | 28.04878 | 85 | 0.574783 | [
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"import pandas as pd\n\nfrom sklearn.cluster import MeanShift",
"_____no_output_____"
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"df_candies = pd.read_csv('../data/raw/candy.csv')\n\nx = df_candies.drop('competitorname', axis=1)\n\nmeanshift = MeanShift().fit(x)\nprint(meanshift.labels_)\nprint('_'*64)\nprint(meanshift... | [
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d02796466cba49831e90efd9b05214df8021d6b1 | 515,187 | ipynb | Jupyter Notebook | Neural Networks and Deep Learning/Week3/Planar_data_classification_with_one_hidden_layer.ipynb | sounok1234/Deeplearning_Projects | 707cc101de6ba14c06186a829aed7ae54b21dab4 | [
"MIT"
] | null | null | null | Neural Networks and Deep Learning/Week3/Planar_data_classification_with_one_hidden_layer.ipynb | sounok1234/Deeplearning_Projects | 707cc101de6ba14c06186a829aed7ae54b21dab4 | [
"MIT"
] | null | null | null | Neural Networks and Deep Learning/Week3/Planar_data_classification_with_one_hidden_layer.ipynb | sounok1234/Deeplearning_Projects | 707cc101de6ba14c06186a829aed7ae54b21dab4 | [
"MIT"
] | null | null | null | 279.385575 | 322,660 | 0.915365 | [
[
[
"# Planar data classification with one hidden layer\n\nWelcome to your week 3 programming assignment! It's time to build your first neural network, which will have one hidden layer. Now, you'll notice a big difference between this model and the one you implemented previously using logistic regression.... | [
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d0279dda138d7ae3deec64a3a5334dcc2e319c3d | 36,096 | ipynb | Jupyter Notebook | loc_clust_stripe_segmentation.ipynb | YuTian8328/flow-based-clustering | da293edbfac058f5908fc0ab057d3097f0becc47 | [
"MIT"
] | null | null | null | loc_clust_stripe_segmentation.ipynb | YuTian8328/flow-based-clustering | da293edbfac058f5908fc0ab057d3097f0becc47 | [
"MIT"
] | null | null | null | loc_clust_stripe_segmentation.ipynb | YuTian8328/flow-based-clustering | da293edbfac058f5908fc0ab057d3097f0becc47 | [
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] | null | null | null | 53.475556 | 7,084 | 0.752438 | [
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"import numpy as np\nimport datetime\n\n\nimport matplotlib.pyplot as plt\nfrom PIL import Image\n\nfrom scipy.sparse import csr_matrix\nimport matplotlib.pyplot as plt\n\nfrom sklearn.cluster import KMeans\n\nfrom numpy.linalg import norm\nfrom sklearn.feature_extraction import image\nimport warnings... | [
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d027b1c892d498010316ee8523329537452ac06a | 1,648 | ipynb | Jupyter Notebook | Chapter01/.ipynb_checkpoints/13 Jumping between frames in video file-checkpoint.ipynb | PCJimmmy/OpenCV-3-Computer-Vision-with-Python-Cookbook | 08be606384e3439183599c147291901d80fc8310 | [
"MIT"
] | 1 | 2019-08-18T03:53:01.000Z | 2019-08-18T03:53:01.000Z | Chapter01/.ipynb_checkpoints/13 Jumping between frames in video file-checkpoint.ipynb | HardToMake/OpenCV-3-Computer-Vision-with-Python-Cookbook | 325dc921cb89bcfa029241e8ca5644b343be53b0 | [
"MIT"
] | 1 | 2020-06-29T06:25:37.000Z | 2020-06-29T06:25:37.000Z | Chapter01/.ipynb_checkpoints/13 Jumping between frames in video file-checkpoint.ipynb | HardToMake/OpenCV-3-Computer-Vision-with-Python-Cookbook | 325dc921cb89bcfa029241e8ca5644b343be53b0 | [
"MIT"
] | 2 | 2019-08-12T01:02:07.000Z | 2021-02-18T15:02:45.000Z | 21.973333 | 65 | 0.534587 | [
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"import cv2\n\ncapture = cv2.VideoCapture('../data/drop.avi')\nframe_count = capture.get(cv2.CAP_PROP_FRAME_COUNT)\nprint('Frame count:', frame_count)\n\nprint('Position:', capture.get(cv2.CAP_PROP_POS_FRAMES))\n_, frame = capture.read()\ncv2.imshow('frame0', frame)\n\nprint('Position:', capture.get(c... | [
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d027c6708e27ee5d934bde790c253e3e8b02fd52 | 490,784 | ipynb | Jupyter Notebook | project1/.ipynb_checkpoints/Ex2_Eve_Rahbe_235549-checkpoint.ipynb | antoine-alleon/Biological_Modelling_Neural_Network_python_exercises | b266654975a37033ac22e29197e7930ccc3ccfc6 | [
"MIT"
] | null | null | null | project1/.ipynb_checkpoints/Ex2_Eve_Rahbe_235549-checkpoint.ipynb | antoine-alleon/Biological_Modelling_Neural_Network_python_exercises | b266654975a37033ac22e29197e7930ccc3ccfc6 | [
"MIT"
] | null | null | null | project1/.ipynb_checkpoints/Ex2_Eve_Rahbe_235549-checkpoint.ipynb | antoine-alleon/Biological_Modelling_Neural_Network_python_exercises | b266654975a37033ac22e29197e7930ccc3ccfc6 | [
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] | null | null | null | 360.605437 | 42,872 | 0.924924 | [
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d027cb77007abd480fcf50ca872329564434b924 | 9,298 | ipynb | Jupyter Notebook | azure/train_pipeline.ipynb | fashourr/covid-cxr | 0cf126b047ec47e0e94151741f5a8267bded9f14 | [
"MIT"
] | 122 | 2020-03-27T14:56:07.000Z | 2022-03-09T09:50:02.000Z | azure/train_pipeline.ipynb | fashourr/covid-cxr | 0cf126b047ec47e0e94151741f5a8267bded9f14 | [
"MIT"
] | 9 | 2020-03-30T19:35:49.000Z | 2021-08-07T15:15:37.000Z | azure/train_pipeline.ipynb | fashourr/covid-cxr | 0cf126b047ec47e0e94151741f5a8267bded9f14 | [
"MIT"
] | 63 | 2020-04-01T17:00:17.000Z | 2022-03-25T07:03:42.000Z | 39.565957 | 169 | 0.595827 | [
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d027f22288e1ecb488a2bf9021eb82a99b2ec57e | 46,425 | ipynb | Jupyter Notebook | Jupyter/SIT742P01A-Python.ipynb | jilliant/sit742 | 71d996c5735ebc4e084e55d21f775c3612eca9a5 | [
"MIT"
] | 1 | 2019-03-08T05:58:59.000Z | 2019-03-08T05:58:59.000Z | Jupyter/SIT742P01A-Python.ipynb | jilliant/sit742 | 71d996c5735ebc4e084e55d21f775c3612eca9a5 | [
"MIT"
] | null | null | null | Jupyter/SIT742P01A-Python.ipynb | jilliant/sit742 | 71d996c5735ebc4e084e55d21f775c3612eca9a5 | [
"MIT"
] | 1 | 2022-03-09T14:44:13.000Z | 2022-03-09T14:44:13.000Z | 57.670807 | 4,125 | 0.550415 | [
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"# SIT742: Modern Data Science \n**(Week 01: Programming Python)**\n\n---\n- Materials in this module include resources collected from various open-source online repositories.\n- You are free to use, change and distribute this package.\n- If you found any issue/bug for this document, please submit an ... | [
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d0280b6471f2285f6be59dcd2e8bc7a438cd2fe5 | 527,755 | ipynb | Jupyter Notebook | 00. DynamicProgramming/05. General Equilibrium.ipynb | JMSundram/ConsumptionSavingNotebooks | 338a8cecbe0043ebb4983c2fe0164599cd2a4fc0 | [
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] | 1 | 2019-06-03T18:33:44.000Z | 2019-07-02T13:51:21.000Z | 00. DynamicProgramming/05. General Equilibrium.ipynb | JMSundram/ConsumptionSavingNotebooks | 338a8cecbe0043ebb4983c2fe0164599cd2a4fc0 | [
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] | 34 | 2019-02-26T19:27:37.000Z | 2021-12-27T09:34:04.000Z | 277.619674 | 131,424 | 0.917566 | [
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[
"# General Equilibrium",
"_____no_output_____"
],
[
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d0280bb7ebf666cc2c79aedcf989ddce331103b2 | 18,683 | ipynb | Jupyter Notebook | LeNet-Lab.ipynb | dumebi/-Udasity-CarND-LeNet-Lab | a02a97373b10efe1ee18ae3115f5e4ed9934512f | [
"MIT"
] | null | null | null | LeNet-Lab.ipynb | dumebi/-Udasity-CarND-LeNet-Lab | a02a97373b10efe1ee18ae3115f5e4ed9934512f | [
"MIT"
] | null | null | null | LeNet-Lab.ipynb | dumebi/-Udasity-CarND-LeNet-Lab | a02a97373b10efe1ee18ae3115f5e4ed9934512f | [
"MIT"
] | null | null | null | 34.406998 | 2,100 | 0.599101 | [
[
[
"# LeNet Lab\n\nSource: Yan LeCun",
"_____no_output_____"
],
[
"## Load Data\n\nLoad the MNIST data, which comes pre-loaded with TensorFlow.\n\nYou do not need to modify this section.",
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d0280f2f545220cf9bd8028e67c6265c7ed56436 | 322,995 | ipynb | Jupyter Notebook | Threshold Investigation.ipynb | mwcotton/DAGmetrics | 974a1d6781e041e59edb081659184dc34153f3d8 | [
"MIT"
] | 1 | 2020-11-07T21:11:56.000Z | 2020-11-07T21:11:56.000Z | Threshold Investigation.ipynb | mwcotton/DAGmetrics | 974a1d6781e041e59edb081659184dc34153f3d8 | [
"MIT"
] | null | null | null | Threshold Investigation.ipynb | mwcotton/DAGmetrics | 974a1d6781e041e59edb081659184dc34153f3d8 | [
"MIT"
] | null | null | null | 146.019439 | 37,416 | 0.839954 | [
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"import numpy as np\nimport scipy as sp\nimport scipy.interpolate\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport scipy.stats\nimport scipy.optimize\n\nfrom scipy.optimize import curve_fit \n\nimport minkowskitools as mt",
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d02816856d1dda0c5693e22c70f12a2f228d42ca | 174,448 | ipynb | Jupyter Notebook | Prace_domowe/Praca_domowa3/Grupa1/EljasiakBartlomiej/pd3.ipynb | niladrem/2020L-WUM | ddccedd900e41de196612c517227e1348c7195df | [
"Apache-2.0"
] | null | null | null | Prace_domowe/Praca_domowa3/Grupa1/EljasiakBartlomiej/pd3.ipynb | niladrem/2020L-WUM | ddccedd900e41de196612c517227e1348c7195df | [
"Apache-2.0"
] | null | null | null | Prace_domowe/Praca_domowa3/Grupa1/EljasiakBartlomiej/pd3.ipynb | niladrem/2020L-WUM | ddccedd900e41de196612c517227e1348c7195df | [
"Apache-2.0"
] | 1 | 2020-06-01T23:23:16.000Z | 2020-06-01T23:23:16.000Z | 57.36534 | 64,072 | 0.686187 | [
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d0282d05845ae8f98d6d4a69a5f04f48d963139f | 8,309 | ipynb | Jupyter Notebook | graphs_trees/tree_dfs/dfs_challenge.ipynb | hanbf/interactive-coding-challenges | 1676ac16c987e35eeb4be6ab57a3c10ed9b71b8b | [
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] | null | null | null | graphs_trees/tree_dfs/dfs_challenge.ipynb | hanbf/interactive-coding-challenges | 1676ac16c987e35eeb4be6ab57a3c10ed9b71b8b | [
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[
"This notebook was prepared by [Donne Martin](https://github.com/donnemartin). Source and license info is on [GitHub](https://github.com/donnemartin/interactive-coding-challenges).",
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"# Challenge Notebook",
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d0282f93ebb8e50de46e6e988c967cc9bc3c6d35 | 220,983 | ipynb | Jupyter Notebook | chapter14_generative-adversarial-networks/gan-intro.ipynb | vishaalkapoor/mxnet-the-straight-dope | 8b69042bae8bb9ab2fa73a357ab4cc0111a9b92e | [
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] | 2,796 | 2017-07-12T06:23:19.000Z | 2022-02-19T16:38:09.000Z | chapter14_generative-adversarial-networks/gan-intro.ipynb | m2rik/mxnet-the-straight-dope | b524c70401e9fb62cb2af411cee3abe2e344bace | [
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"# Generative Adversarial Networks\n\n\nThroughout most of this book, we've talked about how to make predictions.\nIn some form or another, we used deep neural networks learned mappings from data points to labels.\nThis kind of learning is called discriminative learning,\nas in, we'd like to be able t... | [
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d0284e8bc5b94550d3a2f08bfd196c58361b0982 | 55,684 | ipynb | Jupyter Notebook | notebooks/module05_01_cross_validation.ipynb | lottieandrews/CS345 | 0316f5a72c7b1c616f3ff692a38ad48044b50746 | [
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] | 9 | 2020-08-26T20:24:25.000Z | 2022-02-06T21:17:04.000Z | notebooks/module05_01_cross_validation.ipynb | lottieandrews/CS345 | 0316f5a72c7b1c616f3ff692a38ad48044b50746 | [
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] | null | null | null | notebooks/module05_01_cross_validation.ipynb | lottieandrews/CS345 | 0316f5a72c7b1c616f3ff692a38ad48044b50746 | [
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] | 17 | 2020-08-25T19:13:26.000Z | 2021-05-06T21:59:16.000Z | 70.308081 | 8,248 | 0.794824 | [
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"*This notebook is part of course materials for CS 345: Machine Learning Foundations and Practice at Colorado State University.\nOriginal versions were created by Asa Ben-Hur.\nThe content is availabe [on GitHub](https://github.com/asabenhur/CS345).*\n\n*The text is released under the [CC BY-SA licen... | [
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d028530fe628db56c8f7d65ca7865b4389f247ce | 18,255 | ipynb | Jupyter Notebook | data_analysis/3.3_anomaly_detection/anomaly_detection.ipynb | camille-vanhoffelen/modern-ML-engineer | 1ee62260beac1e0eeca1fd77a1eeadb48a0065b9 | [
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] | 10 | 2020-07-24T17:33:09.000Z | 2022-01-29T13:47:06.000Z | data_analysis/3.3_anomaly_detection/anomaly_detection.ipynb | pandeyankit83/modern-ML-engineer | 1ee62260beac1e0eeca1fd77a1eeadb48a0065b9 | [
"CC-BY-4.0"
] | null | null | null | data_analysis/3.3_anomaly_detection/anomaly_detection.ipynb | pandeyankit83/modern-ML-engineer | 1ee62260beac1e0eeca1fd77a1eeadb48a0065b9 | [
"CC-BY-4.0"
] | 2 | 2020-08-07T13:15:36.000Z | 2021-12-14T17:57:59.000Z | 43.882212 | 821 | 0.646289 | [
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"# Lecture 3.3: Anomaly Detection\n\n[**Lecture Slides**](https://docs.google.com/presentation/d/1_0Z5Pc5yHA8MyEBE8Fedq44a-DcNPoQM1WhJN93p-TI/edit?usp=sharing)\n\nThis lecture, we are going to use gaussian distributions to detect anomalies in our emoji faces dataset\n\n**Learning goals:**\n\n- Introdu... | [
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d02864a9715a23a804ee382bba43e9187727f856 | 804,768 | ipynb | Jupyter Notebook | models/densenet121_144_128_hlcp_no_img_aug.ipynb | pujandave25/chexpert | 87bf49b7a2500c1edbaf27e224042f1c271f6941 | [
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] | 1 | 2021-05-14T11:13:55.000Z | 2021-05-14T11:13:55.000Z | models/densenet121_144_128_hlcp_no_img_aug.ipynb | pujandave25/chexpert | 87bf49b7a2500c1edbaf27e224042f1c271f6941 | [
"Apache-2.0"
] | null | null | null | models/densenet121_144_128_hlcp_no_img_aug.ipynb | pujandave25/chexpert | 87bf49b7a2500c1edbaf27e224042f1c271f6941 | [
"Apache-2.0"
] | 2 | 2021-05-09T19:01:40.000Z | 2021-05-12T09:37:17.000Z | 1,188.726736 | 435,472 | 0.949855 | [
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d0286a664c8681937df2ace0b975d61bde05c4bf | 226,774 | ipynb | Jupyter Notebook | Titanic.ipynb | hashmat3525/Titanic | 1a15bbf8afeff7aa522a916653984da4e92bfc8a | [
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] | 3 | 2019-10-30T08:56:35.000Z | 2019-10-31T08:50:52.000Z | Titanic.ipynb | hashmat3525/Titanic | 1a15bbf8afeff7aa522a916653984da4e92bfc8a | [
"MIT"
] | null | null | null | Titanic.ipynb | hashmat3525/Titanic | 1a15bbf8afeff7aa522a916653984da4e92bfc8a | [
"MIT"
] | 1 | 2019-10-31T08:51:10.000Z | 2019-10-31T08:51:10.000Z | 113.217174 | 60,121 | 0.739318 | [
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"import numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn import preprocessing\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn import svm\nfrom s... | [
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d0287996d84ee72a3e442149d0f9b2efaea53c0c | 449,053 | ipynb | Jupyter Notebook | 07.02-Gaussian-transformation-sklearn.ipynb | sri-spirited/feature-engineering-for-ml | 607c376cf92efd0ca9cc0f4f4959f639f793dedc | [
"BSD-3-Clause"
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"BSD-3-Clause"
] | null | null | null | 07.02-Gaussian-transformation-sklearn.ipynb | sri-spirited/feature-engineering-for-ml | 607c376cf92efd0ca9cc0f4f4959f639f793dedc | [
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] | null | null | null | 487.042299 | 77,357 | 0.941145 | [
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"## Gaussian Transformation with Scikit-learn\n\nScikit-learn has recently released transformers to do Gaussian mappings as they call the variable transformations. The PowerTransformer allows to do Box-Cox and Yeo-Johnson transformation. With the FunctionTransformer, we can specify any function we wan... | [
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d0288eacfb4079d91fa8e3187893fe6983ec98a0 | 33,476 | ipynb | Jupyter Notebook | EDA/news_stat.ipynb | pcrete/stock_prediction_using_contextual_information | de77fa09bf25b184821093539debb5aa8056a862 | [
"Apache-2.0"
] | null | null | null | EDA/news_stat.ipynb | pcrete/stock_prediction_using_contextual_information | de77fa09bf25b184821093539debb5aa8056a862 | [
"Apache-2.0"
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] | null | null | null | 34.36961 | 2,398 | 0.453638 | [
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"from IPython.core.display import display, HTML\ndisplay(HTML(\"<style>.container { width:95% !important; }</style>\"))\n\nfrom jupyterthemes import jtplot\njtplot.style()\n\nfrom plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\ninit_notebook_mode(connected=True)\n\nimport os\... | [
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d028905b3c3af922b2af68abdecdef151ddd49a5 | 10,880 | ipynb | Jupyter Notebook | content/lessons/05/Class-Coding-Lab/CCL-Iterations.ipynb | MahopacHS/spring2019-rizzenM | 2860b3338c8f452e45aac04f04388a417b2cf506 | [
"MIT"
] | null | null | null | content/lessons/05/Class-Coding-Lab/CCL-Iterations.ipynb | MahopacHS/spring2019-rizzenM | 2860b3338c8f452e45aac04f04388a417b2cf506 | [
"MIT"
] | null | null | null | content/lessons/05/Class-Coding-Lab/CCL-Iterations.ipynb | MahopacHS/spring2019-rizzenM | 2860b3338c8f452e45aac04f04388a417b2cf506 | [
"MIT"
] | null | null | null | 52.307692 | 1,199 | 0.583732 | [
[
[
"# In-Class Coding Lab: Iterations\n\nThe goals of this lab are to help you to understand:\n\n- How loops work.\n- The difference between definite and indefinite loops, and when to use each.\n- How to build an indefinite loop with complex exit conditions.\n- How to create a program from a complex idea... | [
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d028a007b4fd192febb7bfbba4e944a70b272b83 | 1,919 | ipynb | Jupyter Notebook | notebooks/data_processing_numeric.ipynb | hirogen317/chamomile | 6a185cc74ae2c54832f9bdc04f77b22d70bc9dc0 | [
"Apache-2.0"
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"Apache-2.0"
] | 8 | 2020-03-31T11:24:43.000Z | 2022-03-12T00:24:08.000Z | notebooks/data_processing_numeric.ipynb | hirogen317/chamomile | 6a185cc74ae2c54832f9bdc04f77b22d70bc9dc0 | [
"Apache-2.0"
] | null | null | null | 22.845238 | 132 | 0.503908 | [
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d028a6e9608ebdfd0601eba4f484b95f82d659e6 | 3,509 | ipynb | Jupyter Notebook | debug/pytorch/max_mem_allocated.ipynb | stas00/fastai-misc | e7e8c18ed798f91b2e026c667f795f45992608b8 | [
"Apache-2.0"
] | 1 | 2018-06-01T17:39:59.000Z | 2018-06-01T17:39:59.000Z | debug/pytorch/max_mem_allocated.ipynb | stas00/fastai-misc | e7e8c18ed798f91b2e026c667f795f45992608b8 | [
"Apache-2.0"
] | null | null | null | debug/pytorch/max_mem_allocated.ipynb | stas00/fastai-misc | e7e8c18ed798f91b2e026c667f795f45992608b8 | [
"Apache-2.0"
] | null | null | null | 27.414063 | 118 | 0.54403 | [
[
[
"import torch \nimport pynvml\npynvml = pynvml\npynvml.nvmlInit()\nnvml_preload = 0\nnvml_prev = 0\npytorch_prev = 0\ndef nvml_used():\n handle = pynvml.nvmlDeviceGetHandleByIndex(torch.cuda.current_device())\n info = pynvml.nvmlDeviceGetMemoryInfo(handle)\n return b2mb(info.used)\ndef b2mb... | [
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d028af63c6518bd29539551388ee5ca3c12722c1 | 160,721 | ipynb | Jupyter Notebook | chapter08.ipynb | fKVzGecnXYhM/nlp100 | 8ea3b8e0e2904ce4ccdfdbecb11ea1b28596a791 | [
"MIT"
] | 26 | 2020-06-08T02:12:42.000Z | 2022-02-21T01:41:01.000Z | chapter08.ipynb | fKVzGecnXYhM/nlp100 | 8ea3b8e0e2904ce4ccdfdbecb11ea1b28596a791 | [
"MIT"
] | null | null | null | chapter08.ipynb | fKVzGecnXYhM/nlp100 | 8ea3b8e0e2904ce4ccdfdbecb11ea1b28596a791 | [
"MIT"
] | 8 | 2020-05-23T05:49:50.000Z | 2021-11-17T08:43:50.000Z | 90.905543 | 42,722 | 0.750941 | [
[
[
"# 第8章: ニューラルネット\n第6章で取り組んだニュース記事のカテゴリ分類を題材として，ニューラルネットワークでカテゴリ分類モデルを実装する．なお，この章ではPyTorch, TensorFlow, Chainerなどの機械学習プラットフォームを活用せよ．",
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"## 70. 単語ベクトルの和による特徴量\n***\n問題50で構築した学習データ，検証データ，評価データを行列・ベクトルに変換したい．例えば，学習データについて，すべての事例$x_i$の特徴ベクトル$\\boldsymbol{x}_i$... | [
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d028b24ee4e8fea5c9702ca2aee3a468bcda3385 | 1,267 | ipynb | Jupyter Notebook | .ipynb_checkpoints/images-checkpoint.ipynb | Ke-Chi-Chang/prerequisite_python | de64c6f31c3bcbf33cb814c7a69755c8112ede64 | [
"MIT"
] | null | null | null | .ipynb_checkpoints/images-checkpoint.ipynb | Ke-Chi-Chang/prerequisite_python | de64c6f31c3bcbf33cb814c7a69755c8112ede64 | [
"MIT"
] | null | null | null | .ipynb_checkpoints/images-checkpoint.ipynb | Ke-Chi-Chang/prerequisite_python | de64c6f31c3bcbf33cb814c7a69755c8112ede64 | [
"MIT"
] | null | null | null | 17.121622 | 54 | 0.494081 | [
[
[
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],
[
"img_path = pathlib.Path('./archi_1920x1080.jpg')",
"_____no_output_____"
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[
"img = cv2.imread(str(img_path))\nprint(img.shape)\ncv2.imshow()",
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d028b549147fa4e141085ab0f1632b4bd59d9570 | 14,726 | ipynb | Jupyter Notebook | recordsearch/2-Analyse-a-series.ipynb | GLAM-Workbench/glam-workbench-presentations | 036c7380a41a7dc34b5b523af8e83514a2247e6d | [
"MIT"
] | 8 | 2018-04-16T06:48:24.000Z | 2018-07-04T23:45:44.000Z | RecordSearch/2-Analyse-a-series.ipynb | GLAM-Workbench/ozglam-workbench | 3406d098f74e941a0533d860a98492ffe9bc5476 | [
"MIT"
] | 7 | 2020-11-18T21:24:35.000Z | 2022-03-11T23:27:57.000Z | recordsearch/2-Analyse-a-series.ipynb | GLAM-Workbench/glam-workbench-presentations | 036c7380a41a7dc34b5b523af8e83514a2247e6d | [
"MIT"
] | 3 | 2018-10-18T09:35:14.000Z | 2019-11-20T01:50:34.000Z | 28.931238 | 387 | 0.577142 | [
[
[
"# Analyse a series",
"_____no_output_____"
],
[
"<div class=\"alert alert-block alert-warning\">\n <b>Under construction</b>\n</div>",
"_____no_output_____"
]
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d028c00837702eb9ac32592e9df14867d99ade9a | 5,671 | ipynb | Jupyter Notebook | python/modules/jupyter/Pyopenssl.ipynb | HHW-zhou/snippets | f1e5c1fd361e1b86e072a1c01ca8707ac0b9d68b | [
"Apache-2.0"
] | null | null | null | python/modules/jupyter/Pyopenssl.ipynb | HHW-zhou/snippets | f1e5c1fd361e1b86e072a1c01ca8707ac0b9d68b | [
"Apache-2.0"
] | null | null | null | python/modules/jupyter/Pyopenssl.ipynb | HHW-zhou/snippets | f1e5c1fd361e1b86e072a1c01ca8707ac0b9d68b | [
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] | null | null | null | 31.159341 | 946 | 0.54858 | [
[
[
"## Pyopenssl\n[官方文档](https://www.pyopenssl.org/)",
"_____no_output_____"
],
[
"### 使用openssl生成私钥和公钥\n[参考资料](https://blog.csdn.net/huanhuanq1209/article/details/80899017)\n> openssl \n> genrsa -out private.pem 1024 \n> rsa -in public.pem -pubout -out rsa_public_key.pem",
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d028cfa6b15cf11867cfd85841fa6a640b545b34 | 18,185 | ipynb | Jupyter Notebook | starter_code/.ipynb_checkpoints/VacationPy-checkpoint.ipynb | jackaloppy/python-api-challenge | 541e25e01dbf76ee42f14f0f4a529d9d51be30bc | [
"ADSL"
] | null | null | null | starter_code/.ipynb_checkpoints/VacationPy-checkpoint.ipynb | jackaloppy/python-api-challenge | 541e25e01dbf76ee42f14f0f4a529d9d51be30bc | [
"ADSL"
] | null | null | null | starter_code/.ipynb_checkpoints/VacationPy-checkpoint.ipynb | jackaloppy/python-api-challenge | 541e25e01dbf76ee42f14f0f4a529d9d51be30bc | [
"ADSL"
] | null | null | null | 30.460637 | 160 | 0.396426 | [
[
[
"# VacationPy\n----\n\n#### Note\n* Keep an eye on your API usage. Use https://developers.google.com/maps/reporting/gmp-reporting as reference for how to monitor your usage and billing.\n\n* Instructions have been included for each segment. You do not have to follow them exactly, but they are included... | [
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d028d4782d1449d8e0563ac0576089c298ca3281 | 73,242 | ipynb | Jupyter Notebook | S01 - Bootcamp and Binary Classification/SLU07 - Regression with Linear Regression/Example notebook.ipynb | claury/sidecar-academy-batch2 | 874e5a31f739be9e9f328eb2a7b043976453a0f9 | [
"MIT"
] | 2 | 2022-02-04T17:40:04.000Z | 2022-03-26T18:03:12.000Z | S01 - Bootcamp and Binary Classification/SLU07 - Regression with Linear Regression/Example notebook.ipynb | claury/sidecar-academy-batch2 | 874e5a31f739be9e9f328eb2a7b043976453a0f9 | [
"MIT"
] | null | null | null | S01 - Bootcamp and Binary Classification/SLU07 - Regression with Linear Regression/Example notebook.ipynb | claury/sidecar-academy-batch2 | 874e5a31f739be9e9f328eb2a7b043976453a0f9 | [
"MIT"
] | 2 | 2021-10-30T16:20:13.000Z | 2021-11-25T12:09:31.000Z | 30.240297 | 9,968 | 0.530188 | [
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[
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d028d5160fb78d32a85cbadcbceccf3cbb5b3810 | 59,645 | ipynb | Jupyter Notebook | tensorflow/practice/recurrentnets/SimpleRNNECG5000.ipynb | lisuizhe/ml-algorithm | af0755869657b4085de44d4ec95b8c5269d9ac1a | [
"Apache-2.0"
] | 3 | 2019-04-21T06:04:20.000Z | 2019-04-26T00:03:14.000Z | tensorflow/practice/recurrentnets/SimpleRNNECG5000.ipynb | lisuizhe/ml-algorithm | af0755869657b4085de44d4ec95b8c5269d9ac1a | [
"Apache-2.0"
] | null | null | null | tensorflow/practice/recurrentnets/SimpleRNNECG5000.ipynb | lisuizhe/ml-algorithm | af0755869657b4085de44d4ec95b8c5269d9ac1a | [
"Apache-2.0"
] | null | null | null | 139.357477 | 44,804 | 0.857557 | [
[
[
"%matplotlib inline\n\nfrom scipy.io import arff\nimport numpy as np\n\n# download from http://timeseriesclassification.com/description.php?Dataset=ECG5000\ndataset_train, meta = arff.loadarff('./data/ECG5000/ECG5000_TRAIN.arff')\n\nds_train = np.asarray(dataset_train.tolist(), dtype=np.float32)\nx_da... | [
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d028dc3ead25183e65430330b4ba6d81548920f7 | 12,218 | ipynb | Jupyter Notebook | Cloud_Data_warehouse/.ipynb_checkpoints/create_infrastructure-checkpoint.ipynb | ManaliSharma/Data_Engineering_Projects | 4a6eb1be9bc3de36ca44bfd7b635b92a2adb76ae | [
"MIT"
] | 1 | 2021-07-28T02:33:13.000Z | 2021-07-28T02:33:13.000Z | Cloud_Data_warehouse/.ipynb_checkpoints/create_infrastructure-checkpoint.ipynb | ManaliSharma/Data_Engineering_Projects | 4a6eb1be9bc3de36ca44bfd7b635b92a2adb76ae | [
"MIT"
] | null | null | null | Cloud_Data_warehouse/.ipynb_checkpoints/create_infrastructure-checkpoint.ipynb | ManaliSharma/Data_Engineering_Projects | 4a6eb1be9bc3de36ca44bfd7b635b92a2adb76ae | [
"MIT"
] | null | null | null | 37.136778 | 177 | 0.534048 | [
[
[
"#importing libraries\nimport pandas as pd\nimport boto3\nimport json\nimport configparser\nfrom botocore.exceptions import ClientError\nimport psycopg2",
"_____no_output_____"
],
[
"\ndef config_parse_file():\n \"\"\"\n Parse the dwh.cfg configuration file\n :return:\n ... | [
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d028ddb9794ee5de75a48950f470954e5dd010bf | 25,286 | ipynb | Jupyter Notebook | notebooks/test_raw_gan_check.ipynb | Steve-Tod/3D_generation | 75817f91f4a69da06375b4401d4182e932102cd1 | [
"Apache-2.0"
] | null | null | null | notebooks/test_raw_gan_check.ipynb | Steve-Tod/3D_generation | 75817f91f4a69da06375b4401d4182e932102cd1 | [
"Apache-2.0"
] | null | null | null | notebooks/test_raw_gan_check.ipynb | Steve-Tod/3D_generation | 75817f91f4a69da06375b4401d4182e932102cd1 | [
"Apache-2.0"
] | 1 | 2019-07-24T03:35:27.000Z | 2019-07-24T03:35:27.000Z | 35.464236 | 158 | 0.484853 | [
[
[
"import os, sys\nos.environ['CUDA_VISIBLE_DEVICES'] = '2'\nsys.path.append('../')",
"_____no_output_____"
],
[
"import argparse, json\nfrom tqdm import tqdm_notebook as tqdm",
"_____no_output_____"
],
[
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d028fe22fa804a1bdcacb353d3af87090abf9241 | 17,457 | ipynb | Jupyter Notebook | util/imutil.ipynb | shoulderhu/azure-image-ipy | ed9bf5c5e4293b2516518e2f0434d35e97a27f11 | [
"MIT"
] | null | null | null | util/imutil.ipynb | shoulderhu/azure-image-ipy | ed9bf5c5e4293b2516518e2f0434d35e97a27f11 | [
"MIT"
] | null | null | null | util/imutil.ipynb | shoulderhu/azure-image-ipy | ed9bf5c5e4293b2516518e2f0434d35e97a27f11 | [
"MIT"
] | null | null | null | 41.075294 | 1,149 | 0.46583 | [
[
[
"import numpy as np\nimport matplotlib.pyplot as plt\nimport os\nimport scipy.ndimage as ndi\nimport skimage.filters as fl\nimport warnings",
"_____no_output_____"
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[
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d02914a2b75b59fde6aac11d2913d767cc6f597f | 28,705 | ipynb | Jupyter Notebook | Dataset Analysis.ipynb | nicolascarva/Nico-Carvajal-Winter-2022-Data-Science-Intern-Challenge | 58f3738a9821b51ef5a1a595633b5e46b0475ba4 | [
"MIT"
] | null | null | null | Dataset Analysis.ipynb | nicolascarva/Nico-Carvajal-Winter-2022-Data-Science-Intern-Challenge | 58f3738a9821b51ef5a1a595633b5e46b0475ba4 | [
"MIT"
] | null | null | null | Dataset Analysis.ipynb | nicolascarva/Nico-Carvajal-Winter-2022-Data-Science-Intern-Challenge | 58f3738a9821b51ef5a1a595633b5e46b0475ba4 | [
"MIT"
] | null | null | null | 30.96548 | 129 | 0.363595 | [
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[
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[
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d0291d1f2e0abff28b8ebe2da656f3ec7b60d570 | 420,384 | ipynb | Jupyter Notebook | style_transfer.ipynb | sbrml/style-transfer | b6fe8864f85d8d96a3f02283599a39ecd8dc1a66 | [
"MIT"
] | null | null | null | style_transfer.ipynb | sbrml/style-transfer | b6fe8864f85d8d96a3f02283599a39ecd8dc1a66 | [
"MIT"
] | 7 | 2021-06-08T22:41:57.000Z | 2022-03-12T00:51:20.000Z | style_transfer.ipynb | sbrml/style-transfer | b6fe8864f85d8d96a3f02283599a39ecd8dc1a66 | [
"MIT"
] | null | null | null | 1,387.405941 | 410,944 | 0.958367 | [
[
[
"import numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\n\nimport tensorflow as tf\n\nimport os\nfrom imageio import imwrite\n\nfrom tqdm import tqdm",
"_____no_output_____"
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[
"!ls imgs",
"Odo_bayeux_tapestry.png josh_is_bae.jpg\r\ngirl_o... | [
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d0292ee47523490972937f1dde9999a6e6b09378 | 4,480 | ipynb | Jupyter Notebook | notebooks/Complete-Python-Bootcamp-master/Methods.ipynb | sheldon-cheah/cppkernel | 212c81f34c2f144d605fc0be4a90327989ab7625 | [
"BSD-3-Clause"
] | 6 | 2017-09-28T12:38:00.000Z | 2020-07-15T04:41:07.000Z | notebooks/Complete-Python-Bootcamp-master/Methods.ipynb | sheldon-cheah/cppkernel | 212c81f34c2f144d605fc0be4a90327989ab7625 | [
"BSD-3-Clause"
] | 5 | 2016-08-25T06:06:12.000Z | 2016-11-26T18:57:20.000Z | notebooks/Complete-Python-Bootcamp-master/Methods.ipynb | sheldon-cheah/cppkernel | 212c81f34c2f144d605fc0be4a90327989ab7625 | [
"BSD-3-Clause"
] | 1 | 2019-11-05T05:29:25.000Z | 2019-11-05T05:29:25.000Z | 23.703704 | 307 | 0.560268 | [
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[
"#Methods\n\nWe've already seen a few example of methods when learning about Object and Data Structure Types in Python. Methods are essentially functions built into objects. Later on in the course we will learn about how to create our own objects and methods using Object Oriented Programming (OOP) and... | [
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d0295719c535d4e3d2a750dc7c0f9f93255adcfb | 4,502 | ipynb | Jupyter Notebook | tadsilweny_airplane_finder.ipynb | CHesseling/tadsilweny | d49acddc8358ac21171336c91e4ae9770b73cc19 | [
"MIT"
] | null | null | null | tadsilweny_airplane_finder.ipynb | CHesseling/tadsilweny | d49acddc8358ac21171336c91e4ae9770b73cc19 | [
"MIT"
] | null | null | null | tadsilweny_airplane_finder.ipynb | CHesseling/tadsilweny | d49acddc8358ac21171336c91e4ae9770b73cc19 | [
"MIT"
] | 1 | 2019-03-12T00:48:14.000Z | 2019-03-12T00:48:14.000Z | 22.17734 | 150 | 0.528654 | [
[
[
"# You need to install the OpenSkyAPI library\n# Get it here: https://github.com/openskynetwork/opensky-api",
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[
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d0295acaf88102e4362944791723fd452b4bcb0e | 1,964 | ipynb | Jupyter Notebook | Chapter 1 - Machine Learning Toolkit/Exercise 3 - Order of Execution.ipynb | doc-E-brown/Applied-Supervised-Learning-with-Python | f125cecde1af4f77017302c3393acf9c2415ce9a | [
"MIT"
] | 2 | 2021-06-08T18:00:07.000Z | 2021-10-08T06:31:38.000Z | Chapter 1 - Machine Learning Toolkit/Exercise 3 - Order of Execution.ipynb | TrainingByPackt/Applied-Supervised-Learning-with-Python | f125cecde1af4f77017302c3393acf9c2415ce9a | [
"MIT"
] | null | null | null | Chapter 1 - Machine Learning Toolkit/Exercise 3 - Order of Execution.ipynb | TrainingByPackt/Applied-Supervised-Learning-with-Python | f125cecde1af4f77017302c3393acf9c2415ce9a | [
"MIT"
] | 16 | 2019-06-04T22:22:17.000Z | 2022-01-02T06:43:44.000Z | 23.95122 | 297 | 0.523422 | [
[
[
"# Exercise 3: Order of Execution\n\nThis Jupyter notebook has been written to partner with Lesson 1 - Machine Learning Toolkit",
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d029788ebf15bd92da1e891d543437553ce22c78 | 17,357 | ipynb | Jupyter Notebook | task_7_SVMs.ipynb | knutzk/handson-ml | 3b80038e85e6ea0ac1dc1c4e068f563adca1d760 | [
"Apache-2.0"
] | 1 | 2020-05-01T11:20:53.000Z | 2020-05-01T11:20:53.000Z | task_7_SVMs.ipynb | knutzk/handson-ml | 3b80038e85e6ea0ac1dc1c4e068f563adca1d760 | [
"Apache-2.0"
] | null | null | null | task_7_SVMs.ipynb | knutzk/handson-ml | 3b80038e85e6ea0ac1dc1c4e068f563adca1d760 | [
"Apache-2.0"
] | null | null | null | 39.447727 | 523 | 0.586046 | [
[
[
"# Task 4: Support Vector Machines\n\n_All credit for the code examples of this notebook goes to the book \"Hands-On Machine Learning with Scikit-Learn & TensorFlow\" by A. Geron. Modifications were made and text was added by K. Zoch in preparation for the hands-on sessions._",
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d0297a8e9e5e6b2bb07a7d662643db6cd1351a8b | 10,412 | ipynb | Jupyter Notebook | courses/machine_learning/deepdive/05_review/3_tensorflow_dnn.ipynb | Glairly/introduction_to_tensorflow | aa0a44d9c428a6eb86d1f79d73f54c0861b6358d | [
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"# Create TensorFlow Deep Neural Network Model\n\n**Learning Objective**\n- Create a DNN model using the high-level Estimator API \n\n## Introduction\n\nWe'll begin by modeling our data using a Deep Neural Network. To achieve this we will use the high-level Estimator API in Tensorflow. Have a look at ... | [
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d029873db88b1ea4359504c4a5033f53b11ce879 | 20,682 | ipynb | Jupyter Notebook | notebooks/dem_comparison.ipynb | pat-schmitt/tutorials | 060d1cf83da31ae197f43f75be0cc111ed97186e | [
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d02996c71b61bb386bfe90f27b22b75e9bf1fa52 | 4,654 | ipynb | Jupyter Notebook | code/sagemaker_rcf.ipynb | tkeech1/aws_ml | 512fd8c8770cbf5128ce9e0c03655c3d3021776b | [
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d0299c6345b10b08764b6c77e22afb1ec6d3278c | 5,390 | ipynb | Jupyter Notebook | 00_index.ipynb | vtquang194/Big-Earth-data | d57191f1bff9d5c2fee2e69a97a7bdb8583ac665 | [
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d029a95c1231b8124a4ac31c833022163d3a1c74 | 92,057 | ipynb | Jupyter Notebook | notebooks/.ipynb_checkpoints/pycaret-final-checkpoint.ipynb | ChandrakanthNethi/predict-the-employee-attrition-rate-in-organizations | 3ddae6d20e1ae35b4efb4f32206f75b43a42407e | [
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] | null | null | null | notebooks/.ipynb_checkpoints/pycaret-final-checkpoint.ipynb | ChandrakanthNethi/predict-the-employee-attrition-rate-in-organizations | 3ddae6d20e1ae35b4efb4f32206f75b43a42407e | [
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d029ac20974d9cb7a89daa2bb85d96c4928708ff | 34,906 | ipynb | Jupyter Notebook | test.ipynb | sima97/unihobby | eb70be2d1e0f85ecd67b97f07ba49071c8a3aa1d | [
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] | null | null | null | test.ipynb | sima97/unihobby | eb70be2d1e0f85ecd67b97f07ba49071c8a3aa1d | [
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] | null | null | null | test.ipynb | sima97/unihobby | eb70be2d1e0f85ecd67b97f07ba49071c8a3aa1d | [
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] | null | null | null | 59.668376 | 2,024 | 0.582164 | [
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d029b32958aa6b1475db0c3e8e847d794f6d4ece | 9,894 | ipynb | Jupyter Notebook | tests/Test_binning.ipynb | ptyshevs/int_seq | 645e469ee86d0c807ae57f5bbedbab46c6da3675 | [
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] | 8 | 2018-11-18T20:08:38.000Z | 2020-09-12T08:28:35.000Z | tests/Test_binning.ipynb | ptyshevs/int_seq | 645e469ee86d0c807ae57f5bbedbab46c6da3675 | [
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] | 3 | 2020-01-28T22:32:24.000Z | 2020-03-31T00:39:08.000Z | tests/Test_binning.ipynb | ptyshevs/int_seq | 645e469ee86d0c807ae57f5bbedbab46c6da3675 | [
"MIT"
] | 1 | 2018-11-21T22:55:49.000Z | 2018-11-21T22:55:49.000Z | 24.25 | 238 | 0.537396 | [
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d029ba35490782c025f348d562f60290eb42b0c6 | 809,309 | ipynb | Jupyter Notebook | Repaso_algebra_LinealHeidy.ipynb | 1966hs/MujeresDigitales | 1360f017f0e18d27612ffc6c42cd2a84a2e7b925 | [
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d029c16ab01d4efeaa90c6ac2fb6ddb1c7892b87 | 5,355 | ipynb | Jupyter Notebook | AdventofCode2020/timings/Timing Day 1.ipynb | evan-freeman/puzzles | 234c77df026a6f3f39a57b47c47636d8008a8571 | [
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d029c4f14b630e689193f9659cfa0f670a250f35 | 17,798 | ipynb | Jupyter Notebook | notebooks/M6-ensemble_sol_01.ipynb | datagistips/scikit-learn-mooc | 9eb67c53173218b5cd3061712c827c6a663e425a | [
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] | 1 | 2021-07-14T09:41:21.000Z | 2021-07-14T09:41:21.000Z | notebooks/M6-ensemble_sol_01.ipynb | datagistips/scikit-learn-mooc | 9eb67c53173218b5cd3061712c827c6a663e425a | [
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] | null | null | null | notebooks/M6-ensemble_sol_01.ipynb | datagistips/scikit-learn-mooc | 9eb67c53173218b5cd3061712c827c6a663e425a | [
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d029c6f972bba00349e1cc8a4bd1a3d5a310069e | 36,669 | ipynb | Jupyter Notebook | lessons/Recommendations/1_Intro_to_Recommendations/4_Collaborative Filtering - Solution.ipynb | callezenwaka/DSND_Term2 | 6252cb75f9fbd61043b308a783b1d62cdd217001 | [
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] | 1,030 | 2018-07-03T19:09:50.000Z | 2022-03-25T05:48:57.000Z | lessons/Recommendations/1_Intro_to_Recommendations/4_Collaborative Filtering - Solution.ipynb | callezenwaka/DSND_Term2 | 6252cb75f9fbd61043b308a783b1d62cdd217001 | [
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] | 21 | 2018-09-20T14:36:04.000Z | 2021-10-11T18:25:31.000Z | lessons/Recommendations/1_Intro_to_Recommendations/4_Collaborative Filtering - Solution.ipynb | callezenwaka/DSND_Term2 | 6252cb75f9fbd61043b308a783b1d62cdd217001 | [
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] | 1,736 | 2018-06-27T19:33:46.000Z | 2022-03-28T17:52:33.000Z | 42.148276 | 507 | 0.585754 | [
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d029ca492680dfbfcdd0f6b95260793605262d01 | 43,126 | ipynb | Jupyter Notebook | Feature_Engineering_Toolkit_demo_features_v1.ipynb | jassimran/Feature-Engineering-Toolkit | 25609a192c1d1c7fb83c5a2c19439dcb776fbcc3 | [
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d029cf1a4352b34888de3488c6aea05fd620f367 | 18,024 | ipynb | Jupyter Notebook | nircam_jdox/nircam_grisms/figure4_sensitivity.ipynb | aliciacanipe/nircam_jdox | fa1c3381283bb08b870162d0dd3bc9d5e94561ea | [
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] | 1 | 2022-03-10T06:48:27.000Z | 2022-03-10T06:48:27.000Z | nircam_jdox/nircam_grisms/figure4_sensitivity.ipynb | aliciacanipe/nircam_jdox | fa1c3381283bb08b870162d0dd3bc9d5e94561ea | [
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] | 7 | 2019-04-05T16:30:32.000Z | 2019-05-02T16:30:26.000Z | nircam_jdox/nircam_grisms/figure4_sensitivity.ipynb | aliciacanipe/nircam_jdox | fa1c3381283bb08b870162d0dd3bc9d5e94561ea | [
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] | 3 | 2019-03-20T15:14:26.000Z | 2019-12-17T20:16:40.000Z | 38.76129 | 167 | 0.576343 | [
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d029e39e0fc4ef237aaa6e2bdd33933dd7b51872 | 147,157 | ipynb | Jupyter Notebook | image/2. Flower Classification with TPUs/kaggle/fast-pytorch-xla-for-tpu-with-multiprocessing.ipynb | nishchalnishant/Completed_Kaggle_competitions | fc920af79f09de642e1e590cdc281bfbf5a92db3 | [
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],
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"import os\nimport collections\nfrom datetime import datetime, timedelta\n\nos.environ[\"XRT_TPU_CONFIG\"] = \"tpu_wor... | [
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