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# + [markdown] tags=["remove_cell"]
# # Местная реальн... | 60.451613 | 1,174 |
c795501e5f7b7dbf439f5c6386f1bf7f55105b7f | py | python | FinalProject_Taxis.ipynb | Snehlata25/DataMiningFinalProject | ['MIT'] | # ---
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# ## Check missing data
#... | 57.942801 | 7,663 |
00d41f63eafd11724836cb5e4cbb90f1be8e02ab | py | python | chapter_8/8_5_NMT/8_5_NMT_scheduled_sampling.ipynb | tedpark/nlp-with-pytorch | ['Apache-2.0'] | # ---
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# *아래 링크를 통해 이 노트북을 주... | 36.527731 | 1,018 |
7437d4f11b505d2c033e02b907509c9e520882a8 | py | python | ml_workflows/ml.ipynb | ronaldokun/datacamp | ['MIT'] | # ---
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import os
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from sklearn.ensemble i... | 61.675 | 2,706 |
d838e8f974e7e4cf27308d4d31c3e02c87f4a820 | py | python | Phase_1/ds-sql2-main/sql.ipynb | clareadunne/ds-east-042621-lectures | ['MIT'] | # ---
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# <h1>Table of Contents<span c... | 30.830703 | 6,108 |
15f7fc75453fa38073e96255ab3b94ea5ddb8f41 | py | python | prediction/multitask/fine-tuning/function documentation generation/ruby/small_model.ipynb | victory-hash/CodeTrans | ['MIT'] | # ---
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# <a href="https://colab... | 54.367347 | 1,594 |
00516cd54e93f4aef4084eeaaba783a5a94f79ec | py | python | quant_finance_lectures/Lecture28-Market-Impact-Models.ipynb | jonrtaylor/quant-finance-lectures | ['CC-BY-4.0'] | # ---
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# <img alt="QuantRocket logo" src="https://www.quantro... | 43.639115 | 1,167 |
228e3881b920dafb7400affd13565c2d9a68a827 | py | python | Automate the Boring Stuff with Python Ch10.ipynb | pgaods/Automate-the-Boring-Stuff-with-Python | ['MIT'] | # ---
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# In this chapter we study issues related to debugging... | 116.259259 | 1,248 |
8f14ac09a097bac974c2f4009fd66a622a9fd535 | py | python | notebooks/drug-efficacy/main.ipynb | shrikant9793/notebooks | ['Apache-2.0'] | # ---
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# # Interactive hypothesis testing
#
# If you need to ... | 88.62037 | 2,600 |
57a64df41aaa9ca5408c90fee3ff804cdf7bef30 | py | python | MNIST.ipynb | Fifth-marauder/Kaggle | ['Apache-2.0'] | # ---
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# <a href="https://colab... | 62.957265 | 7,233 |
9254ad1f912d8e741076a2d5aa892c9aa8988fb5 | py | python | notebooks/official/migration/UJ3 Vertex SDK Custom Image Classification with custom training container.ipynb | nayaknishant/vertex-ai-samples | ['Apache-2.0'] | # ---
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# + id="copyright"
# Copyright 2021 Google LLC
#
# Licensed under the Apache ... | 42.228414 | 2,092 |
8f36d228d8e0402e973c83cc65c8b06384aa3504 | py | python | irm/rex_cmnist.ipynb | tngym/fastai | ['Apache-2.0'] | # ---
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# <a href="https://colab... | 38.849398 | 1,324 |
1a5a2c758c175edb14d82e56036ef24374ef27e4 | py | python | JavaScripts/Image/ReduceRegion.ipynb | OIEIEIO/earthengine-py-notebooks | ['MIT'] | # ---
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# ... | 49.15534 | 1,023 |
a6fb994475a2c5309275dd167e9f84f6555daaac | py | python | task3_circuit_transpilation/task3_main.ipynb | Mohamed-ShehabEldin/Circuit-Transpilation-and-Generative-Modeling-QOSF-Task-1-3 | ['Apache-2.0'] | # ---
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# <h1 align="center">Quantum circuit tranpilation into... | 38.846868 | 1,165 |
a667c21ffefb3997f16992905363d6c825170b98 | py | python | 3. Landmark Detection and Tracking.ipynb | JSchuurmans/P3_Implement_SLAM | ['MIT'] | # ---
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# # Project 3: Implement SLAM
#
# ---
#
# ## Project... | 55.983607 | 3,030 |
74b1230fa110bb5bc9d5c23cdddca4b5fac397e9 | py | python | Datasets/usgs_nlcd.ipynb | c11/earthengine-py-notebooks | ['MIT'] | # ---
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# ... | 30.445714 | 1,023 |
58fec8c07363a6a08a77adad2899f8a827804af8 | py | python | cnn_mnist.ipynb | LuposX/AndroidAppDigitRecognizer | ['MIT'] | # ---
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# + id="N9nKihCnMcM8" colab_type="code" colab={"base_uri": "https://localhost... | 81.326984 | 13,685 |
92e32a9603888f5367522215f37ecb7d5dabd381 | py | python | jupyter/topic03_decision_trees_knn/topic3_trees_knn.ipynb | ivan-magda/mlcourse_open_homeworks | ['MIT'] | # ---
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# <center>
# <img src="../../img/ods_stickers.... | 72.952064 | 1,143 |
e8427684d0f7138593c3698170a07fe61ed66380 | py | python | tutorials/W0D3_LinearAlgebra/W0D3_Tutorial3.ipynb | sjbabdi/course-content | ['CC-BY-4.0', 'BSD-3-Clause'] | # ---
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926b28286d8d963a33208d8d9c32b06d93e3fa33 | py | python | code/TCN_CAN_Data.ipynb | mehrotrasan16/CS581-CAN-DO-Project | ['MIT'] | # ---
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# <a href="https://colab... | 195.415445 | 160,646 |
22fdaa3f1b429c654d26e294d661960dbaffc1f5 | py | python | tutorials/W0D5_Statistics/W0D5_Tutorial2.ipynb | DianaMosquera/course-content | ['CC-BY-4.0', 'BSD-3-Clause'] | # ---
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#... | 56.382189 | 14,386 |
d81ac9ba1e1c43c8404da44b5d7841b0d0f44c5a | py | python | Week_2/NBB_NLP_Theory.ipynb | nikbearbrown/INFO_6210 | ['MIT'] | # ---
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# ## Text Mining and Natural Language Processing
#
# I... | 74.939655 | 1,153 |
7481564d3111b135f445ee6694ef5441ebea8248 | py | python | bertmodel.ipynb | anitha67/100DaysofMLCode | ['MIT'] | # ---
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# <a href="https://colab... | 37.628289 | 1,229 |
94f53e9584357a231ba096d7116a37fec29a9692 | py | python | brusselator_hopf.ipynb | cemozen/emergence_of_limit_cycle_dynamics | ['MIT'] | # ---
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# # Hopf Bifurcation: The Emergence of Limit-cycle Dyn... | 48.190883 | 1,313 |
4ac05a070371b437eae3b9698a89e3cbee69a056 | py | python | tensorflow/models/samples/core/get_started/eager.ipynb | Sioxas/python | ['MIT'] | # ---
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# ##### Copyright 2018 The... | 58.080068 | 1,042 |
1ae97492f6a90baf3469fa3229ececee33361fb3 | py | python | notebooks/biocoding_2021_pythonlab_03.ipynb | JasonJWilliamsNY/biocoding-2021-notebooks | ['Unlicense'] | # ---
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# # Review of String work, and moving on to lists
# L... | 49.899048 | 9,737 |
8fb3b397de5b60295dd996d3b51e6bf6fad1ed25 | py | python | Test_25_02_2021.ipynb | bouraouia/Corso_fuzzy_2021 | ['MIT'] | # ---
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# <a href="https://colab... | 40.774869 | 1,038 |
5b789be4825049cd8bc9e9562ee6c5305980d389 | py | python | bayesian_linear_regression.ipynb | stutun1/Bayesian-Approach-for-Different-Applications | ['Apache-2.0'] | # ---
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# # Bayesian regression with linear basis function mod... | 49.718033 | 1,055 |
4ac09fe981e530d7978ef9ac7effbbcd77535ce2 | py | python | TrOCR/Evaluating_TrOCR_base_handwritten_on_the_IAM_test_set.ipynb | FrancescoSaverioZuppichini/Transformers-Tutorials | ['MIT'] | # ---
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# <a href="https://colab... | 61.986301 | 2,530 |
8f71a3b9d30febbafe2c719de9611fc7690dbf54 | py | python | understanding-i3d.ipynb | song-william/kinetics-i3d | ['Apache-2.0'] | # ---
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# # Interpreting Video Classification Models
# Willia... | 59.404172 | 1,598 |
2f74e3a2be60793f53b7fbe7792f5cf5e05003aa | py | python | Sesion_08_AlgoritmosProbabilistas.ipynb | carlosalvarezh/Analisis-de-Algoritmos | ['MIT'] | # ---
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# <h1... | 44.579208 | 4,716 |
92bbe491e115a36464df8145c9424e91b1aad458 | py | python | notebook-projects/Diabetes - Linear Regression.ipynb | philip-papasavvas/ml_sandbox | ['MIT'] | # ---
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# <h1>Table of Contents<span c... | 48.2 | 1,534 |
c52560aece7923e0291a01ad502e45459c3dea26 | py | python | DIabetes/Diabetes_Model.ipynb | Coding-Ghostman/Diabetes-Prediction | ['MIT'] | # ---
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# # Description for Modules
# pandas-> read ... | 102.170068 | 7,619 |
747fd49c227402f111c8c46ec0e0a79b566aa425 | py | python | notebook/LSTM_Stock_prediction_single_days.ipynb | toanquachp/dl_stock_prediction | ['Apache-2.0'] | # ---
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# # Upload data
# + id="4... | 60.370717 | 7,663 |
586bb1a070b00da7b6b5ed73662b676fa4f28588 | py | python | tutorials/Image/05_conditional_operations.ipynb | Preejababu/geemap | ['MIT'] | # ---
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0084fa975569e1a18d3d4da40579e84afc6e26cf | py | python | Probability Distributions/Python/Gaussian (Normal).ipynb | PennNGG/Quantitative-Neuroscience | ['Apache-2.0'] | # ---
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c78f967c5c533c2dcc6049779c8db40ccba13f0f | py | python | Projects/Misc - Machine Learning/Models/Untitled1.ipynb | sanjivch/MyML | ['MIT'] | # ---
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import pandas as pd
vizag = pd.read_html("https://app... | 338 | 6,151 |
741cf808c065ab8ac928eb9c34799944262252fe | py | python | tutorials/W3D3_NetworkCausality/student/W3D3_Tutorial4.ipynb | erlichlab/course-content | ['CC-BY-4.0'] | # ---
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# <a href="https://colab... | 119.254887 | 59,202 |
74f8e8600c19f0e69dd1734011aee9d171a84dfd | py | python | src/1 Basic Statistics.ipynb | WormLabCaltech/mprsq | ['MIT'] | # ---
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74cd4169c783417c4e059b037ccdf75dc9ff7a45 | py | python | tutorials/W1D1_ModelTypes/W1D1_Tutorial2.ipynb | hanhou/course-content | ['CC-BY-4.0'] | # ---
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# <h1>Table of Contents<span c... | 50.332623 | 1,592 |
57f221d46ba5e68a5b3d7f642ca53737c925ecd9 | py | python | examples/socio_econ_cmf_micro.ipynb | arita37/causeinfer | ['MIT'] | # ---
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# **Center for Microfinance Dataset**
#
# A dataset on... | 45.121118 | 2,544 |
00b33f2146c8ce028cb000f03eab5365810ff7ab | py | python | Case_2_Model_2.ipynb | JoeValval/neural-networks-forhealth-technology-applications | ['MIT'] | # ---
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# <h1>Table of Contents<span class="tocSkip"></span></... | 35.996951 | 1,037 |
5b8d68d8d3fb7787c84daf34bb0b4238a61350f0 | py | python | sphinx/datascience/source/causal-inference.ipynb | oneoffcoder/books | ['CC-BY-4.0'] | # ---
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# # Causal Inference
#
# Let's learn about causal infe... | 35.32626 | 1,075 |
744831aedf1ff7cf71a9baa8e81b50c456eff733 | py | python | Learning_ML/ML_Techniques/Model_Evaluation_Sklearn.ipynb | oke-aditya/Machine_Learning | ['MIT'] | # ---
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# # Model Evaluation Techn... | 209.747783 | 164,602 |
e7c0943acf5dda9e336126d8431236c72138cdfd | py | python | huggingface_t5_6_3.ipynb | skywalker00001/Conterfactual-Reasoning-Project | ['MIT'] | # ---
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# <a href="https://colab... | 36.657183 | 1,342 |
2e050f1fad5ce1a551185131ffbb111ac259edb2 | py | python | tutorials/nlp/Zero_Shot_Intent_Recognition.ipynb | sudhakarsingh27/NeMo | ['Apache-2.0'] | # ---
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# + colab... | 49.501475 | 1,212 |
00991fd4cb10a3042166dbc0bf66f07bf9c10f72 | py | python | projects/fMRI/load_cichy_fMRI_MEG.ipynb | janeite/course-content | ['CC-BY-4.0', 'BSD-3-Clause'] | # ---
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# <a href="https://colab... | 601.057361 | 223,365 |
a6a631b7296c21ddfbaca5c643f853cfd0639815 | py | python | notebooks/04_model_select_and_optimize/tuning_spark_als.ipynb | imatiach-msft/Recommenders | ['MIT'] | # ---
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# <i>Copyright (c) Microsoft Corpo... | 43.249012 | 1,128 |
8fdb95b3cb1811db7d5564e778c9b80b8c89c86e | py | python | video_game_sales.ipynb | antonioravila/Analise-de-dados | ['MIT'] | # ---
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# <a href="https://colab... | 87.781925 | 25,693 |
1a852fabb95e3db182e199861eda245f0afff189 | py | python | Quantopian Notebooks/.ipynb_checkpoints/Cloned+from+%22Introduction+to+Research%22-checkpoint.ipynb | miaortizma/algoritmos-2018-01 | ['MIT'] | # ---
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# #Introduction to the Research Environment
#
# The re... | 38.84 | 1,122 |
8f814c5f3be4cd2eea54c33949e0bdbcfee7a618 | py | python | notebooks/02_Calibration_Likelihood.ipynb | JamesSample/enviro_mod_notes | ['MIT'] | # ---
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# %matplotlib inline
import matplotlib.pyplot as plt, ... | 103.526502 | 1,323 |
22db18bf11d24219536511931bc2252d4dff9183 | py | python | notebooks/Impacto dos tweets do MBL.ipynb | JoaoCarabetta/ideologia | ['MIT'] | # ---
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# <h1>Table of Contents<span c... | 35.332967 | 5,693 |
3d2fad6f5e15b5c69c94d5ff1a135b51e578f31c | py | python | .ipynb_checkpoints/Chapter 4 - Aggregate Supply, Technology, and Economic Growth-checkpoint.ipynb | jlcatonjr/Macroeconomics-Growth-and-Monetary-Equilibrium | ['MIT'] | # ---
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# # Aggregate Supply, Technology, and Economic Growth
... | 120.944297 | 1,154 |
c74308cba881148386a9aa2b527b80b4c78c91d5 | py | python | Lectures/W08-L15-InitializationNormalizationActivation.ipynb | anthonyjclark/cs152sp22 | ['MIT'] | # ---
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# %% [ma... | 31.76 | 1,554 |
921693c82c39e975e261ad12598cd107f94b5844 | py | python | docs/site/tutorials/walkthrough.ipynb | KawashimaHirotaka/Swift | ['CC-BY-4.0'] | # -*- coding: utf-8 -*-
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# <a target="_blank" href="https://www.tensorflow.org/swift/tutorials/walkthrough"><img src="https://www.tensorflow.org/images/tf_logo_32px.png" />View on TensorFlow.org</a>
# </... | 57.984615 | 1,042 |
15ee30fc6929f1eee4b29c09c75803245b0f6bad | py | python | The_Easy_Text_Analyzer.ipynb | haining-b/The_Easy_Text_Analyzer | ['CC0-1.0'] | # ---
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747d081ec33c33e86ae88fe1304ddafd8cb706e7 | py | python | 03-graph-classification-exercise.ipynb | hli8nova/pytorch-gnn-tutorial-odsc2021 | ['Apache-2.0'] | # ---
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# # Graph... | 145.949153 | 63,194 |
922a779d27fca1ec8e7a7773c5bd17d977e6ffc8 | py | python | Week_3_zoomcamp_revised.ipynb | 1985shree/Data-science-Zoomcamp-projects | ['MIT'] | # ---
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# <a href="https://colab... | 51.486553 | 7,349 |
2d4b512a2ea1151c185b8ac9621b80619d262e97 | py | python | Chapter03/25_Impact_of_building_a_deeper_neural_network.ipynb | aihill/Modern-Computer-Vision-with-PyTorch | ['MIT'] | # ---
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#... | 36.291022 | 1,306 |
1a167b0a2ce1e9396ee24dcb86a3a2f138b3f06f | py | python | lesson_notebooks/l19/sklearn_feature_importance_solution.ipynb | zhanghaitao1/ai_algorithms_trading-term2 | ['MIT'] | # ---
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# <h1>Table of Contents<span c... | 55.812081 | 4,339 |
00d393601b691da945ea6d993047fae618cfb9a1 | py | python | titanic/outstanding-case/.ipynb_checkpoints/a-statistical-analysis-ml-workflow-of-titanic-checkpoint.ipynb | paulsweet/Kaggle | ['MIT'] | # ---
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008e18c765612c8daa131b1b538c355de905a2ad | py | python | activitysim/examples/example_mtc/notebooks/getting_started.ipynb | mattwigway/activitysim | ['BSD-3-Clause'] | # ---
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# #... | 54.924901 | 1,027 |
008c393c83acaaf39188a4c8065f44f3a611a289 | py | python | Data Science/Self_Driving_Car/Umbrella_Academy_INFO7390_Project/INFO7390_Notebooks/Basics_of_Convolutional_Neural_Network_&_Imitation_Learning.ipynb | RushabhNisher/Data | ['Unlicense'] | # ---
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# ![](https://mlnotebook.github.io/img/CNN/convSobel.g... | 62.742236 | 1,103 |
4aff172c4041a1b584b7ab40996a555abc367ddf | py | python | 12_Extraction_based_Question_Answering_using_BERT.ipynb | bensjx/ML_repo | ['MIT'] | # ---
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# + id="J3Dwlj-xEC4N" outputId="ea695bf1-40ba-4382-d34d-c5a1ab5e14ed" colab={... | 50.563107 | 1,018 |
4aefafa6d1ae81492c64bd4979aacec2aa65ae1a | py | python | secondaryPtychography.ipynb | tangchini/OPTI556 | ['MIT'] | # ---
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# <a href="https://colab... | 2,261.43662 | 157,289 |
dfa6e6217f78c58a28fa749bc89a2131997d518a | py | python | notebooks/Overview.ipynb | vinayya/nlp | ['Apache-2.0'] | # ---
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dfd6cb3ca2683ff04a669a57888f304057c22c1a | py | python | MNEPython/Afirstlook.ipynb | Lei-I-Zhang/FLUX | ['BSD-3-Clause'] | # ---
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# # A first look at the data
# ## Introdu... | 49.669903 | 1,112 |
c51c32b54202ed920116f53d12f92439c0ac7282 | py | python | notebooks/data_challenge_IMNN_x_DELFI_cosmo_demo.ipynb | tlmakinen/kosmo-kompress | ['Apache-2.0'] | # ---
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# #### From Quarks to... | 42.288913 | 1,292 |
220aaaaa1913450a3e2f8846ba79e45f944f4839 | py | python | notebooks/07_Autoencoders.ipynb | pligor/mnist-from-scratch | ['MIT'] | # ---
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# # Autoencoders
#
# In this no... | 80.19883 | 1,037 |
e8c0cfc5a8583877fa5953377e3c9e4a6d602437 | py | python | Tutorials/Keiko/glad_alert.ipynb | c11/earthengine-py-notebooks | ['MIT'] | # ---
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# <table class="ee-notebook-buttons" align="left">
# ... | 41.96732 | 1,023 |
e83adee06c3916ccde044caa88ef97c2173aeb7c | py | python | Neural_Network_Fundamentals/.ipynb_checkpoints/Draft_of_the_Tutorial-checkpoint.ipynb | romanarion/InformationSystemsWS1718 | ['MIT'] | # ---
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# # Neura... | 55.902208 | 1,125 |
8f62e43ea214b41b220109e068bae194a0f0018d | py | python | ImageCollection/convert_imagecollection_to_image.ipynb | mllzl/earthengine-py-notebooks | ['MIT'] | # ---
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# ... | 52.211111 | 1,023 |
009a69b592c3b2509a98fb53b9f47e177987c87d | py | python | 06/CS480_Assignment_6.ipynb | AbhishekD10/cs480student | ['MIT'] | # ---
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# <a href="https://colab... | 173.390306 | 55,708 |
d81caf5665dac211bd3371e1b0681c1249b42689 | py | python | NOG-NLG dummy codes/46_Deep_conversational_answers_dummy_code.ipynb | Cezanne-ai/project-2021 | ['Apache-2.0'] | # ---
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# There are 3 types of books that our model ... | 3,156.5 | 87,418 |
1a2bc50fa2da65b737c24a0bd03b5e7220855af8 | py | python | 3. Landmark Detection and Tracking.ipynb | HarshitaDPoojary/simultaneous-localization-and-mapping | ['MIT'] | # ---
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# # Project 3: Implement SLAM
#
# ---
#
# ## Project... | 54.967185 | 3,030 |
572e49ec7284a6109a1a82ea294756a4e6b3d760 | py | python | Classification/Adaptive Boosting/AdaBoostClassifier_MinMaxScaler.ipynb | mohityogesh44/ds-seed | ['Apache-2.0'] | # ---
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# # AdaBoost Classification with MinMaxScale... | 45.077551 | 1,253 |
2d0f466ae66b7b31dc20a57893a5ea836fe38978 | py | python | week09_inclass/W09_NonLinear_Regression_InClass.ipynb | ds-connectors/EPS-88-FA21 | ['BSD-3-Clause'] | # ---
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# # Non-Linear Regression In Class Exercise
#
# **Our ... | 45.40634 | 1,127 |
a602629e10f148c809edc14041f388869184400e | py | python | .ipynb_checkpoints/7_programming_extras-checkpoint.ipynb | philuttley/basic_linux_and_coding | ['MIT'] | # ---
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# # P... | 29.938953 | 2,885 |
8f55c5092043dec8eb4f83ce2ce910b349480e75 | py | python | master/0_index.ipynb | jeancarlosvp/Image_Processing | ['MIT'] | # ---
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3d21c9e0c96baf58b100773f5097017bf0ae80b1 | py | python | Data-512-Final-Project.ipynb | niharikasharma/data-512-final-project | ['MIT'] | # ---
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# # Recommender Systems
# DATA 512 Final Proje... | 61.41055 | 1,131 |
41c787e80b373b29bda0f5671854c131e0b5aa9c | py | python | IMDB_RNN.ipynb | medinaalonso/NLP | ['MIT'] | # ---
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# <a href="https://colab... | 57.589041 | 7,251 |
7b0d8c20dbcc49ce5a81ce1fc24630f5a577334c | py | python | cgames/02_space_invader/space_invader_AE.ipynb | BeylierMPG/Reinforcement-Learning | ['MIT'] | # ---
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# + [markdown] colab_type="text" id="LN0nZwyMG... | 90.631579 | 13,645 |
a61a0c541a9e0adfe1afe7ea6da0272f7190dd01 | py | python | sequential_tracing/PostAnalysis/.ipynb_checkpoints/Part1_chr21_DomainAnalysis-checkpoint.ipynb | ZhuangLab/Chromatin_Analysis_2020_cell | ['MIT'] | # ---
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# # This a jupyter notebook guide on domain analysis
#... | 36.339124 | 1,155 |
92cbf2cc356b68b5fdf4fc54d754fa0f5e5eadd3 | py | python | Week5_Policy-based methods/practice_reinforce_pytorch.ipynb | shih-chi-47/Practical_RL | ['MIT'] | # ---
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# # REINFORCE in PyTorch
#... | 50.716667 | 2,501 |
925d8d19e34c868f9ce3a0cc034f67563909f0b0 | py | python | .ipynb_checkpoints/Workflow-checkpoint.ipynb | maxmiao2017/Resource-Watch | ['MIT'] | # ---
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# +
import pandas as pd
path = 'C:/Users/Max81007/Des... | 149.86 | 12,109 |
4afd4f93d134ef676ead3a6269be3629d985f6a4 | py | python | _notebooks/2021-01-04-Lock-free-data-structures.ipynb | abhishekSingh210193/cs_craftmanship | ['Apache-2.0'] | # ---
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# # Data structures f... | 77.358209 | 1,004 |
a6dfbc12434477592d3c0089a7010ac422534860 | py | python | notebooks/SimplexCombo.ipynb | alanjeffares/Simplex | ['Apache-2.0'] | # ---
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# + [markdown] id="view-in-github" colab_type="text"
# <a href="https://colab... | 38.621236 | 1,738 |
5b68ebcb6e1df80823ba58ad23a41d36594d1925 | py | python | nlu/colab/Component Examples/Embeddings_for_Sentences/NLU_USE_Sentence_Embeddings_and_t-SNE_visualization_Example.ipynb | gkovaig/spark-nlp-workshop | ['Apache-2.0'] | # ---
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# + [markdown] id="rBXrqlGEYA8G"
# ![JohnSnowLabs](https://nlp.johnsnowlabs.c... | 54.032407 | 1,112 |
e86edba6f654b1bc290d5f63166f07084543b2b2 | py | python | Modulo08_Archivos.ipynb | carlosalvarezh/Fundamentos_Programacion | ['MIT'] | # ---
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# <h1 align="center">Fundamentos de Programación</h1>
... | 37.09816 | 1,439 |
2d288de138198d4fbb40fed269045822a3005806 | py | python | notebooks/Word2Vec_Pretrained.ipynb | mico-boje/document-summarizer | ['FTL'] | # ---
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import gensim.downloader as api
import gensim
from gen... | 41.480769 | 3,376 |
00fc80cfb28685975afe40b8c8dc6d7f358f9fdf | py | python | tutorials/streamlit_notebooks/healthcare/NER_DEMOGRAPHICS.ipynb | ewbolme/spark-nlp-workshop | ['Apache-2.0'] | # ---
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# + [markdown] colab_type="text" id="TA21Jo5d9SVq"
#
#
# ![JohnSnowLabs](http... | 40.882629 | 1,076 |
2d6a5603693f539d6660340a4afe7c18a54d6651 | py | python | dog_app.ipynb | divyankvijayvergiya/dog_project | ['MIT'] | # ---
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# ## Convolutional Neural Networks
#
# ## Project: Wri... | 51.018892 | 1,112 |
d831c888de58d2dcf3663092db82689ac673bd99 | py | python | seq2seq.ipynb | mmeooo/test_NLP | ['Apache-2.0'] | # ---
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# + [markdown] id="UmKlRIiL-UjF"
# ## Data load
# + id="sNZei6fEXKVt"
impor... | 2,134.89899 | 208,158 |
1a5ae20e084ab99ab9d52907ed5bf63b2899cf4c | py | python | Step1-People-and-publications.ipynb | elswob/UoB-Orcid-2019 | ['MIT'] | # ---
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# ---
# # Intro and Background
#
# In 2018 I published a pie... | 38.4875 | 1,011 |
229e7ada941ab483347404d30a2e1af0d926e67a | py | python | 17_autoencoders_and_gans.ipynb | peterleong/handson-ml3 | ['Apache-2.0'] | # ---
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# **Chapter 17 – Autoencoders and GANs**
# _This note... | 39.581213 | 1,529 |
8f1f14bf693e010bdb40ec4d82b237805b0125b1 | py | python | sem20-pthread/pthread.ipynb | yuri-pechatnov/caos_2019-2020 | ['MIT'] | # ---
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# look at tools/set_up_magics.ipynb
yandex_metrica_all... | 55.53701 | 12,193 |
92478a8e2786f262f6b4a84dd7919afb11978d15 | py | python | 2-Intro a Machine Learning.ipynb | agrija9/RIIAA_Escuela18 | ['MIT'] | # ---
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# + [markdown] toc=true
# <h1>Table of Contents<sp... | 37.445141 | 2,944 |
4ab0ad2bc4d01d8d7bfdce63696055a9cfc9c6cc | py | python | notebooks/Figures/Figure5.ipynb | SBRG/xplatform_ica_paper | ['MIT'] | # ---
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# <h1>Table of Contents<span c... | 39.975225 | 3,447 |
5bb010547e5b04751d607f94e73367ce3f870564 | py | python | news_classification_models_comparison.ipynb | robmaz22/news_category_classification | ['MIT'] | # ---
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# + [markdown] id="view-in-github" colab_type="text"
# <a href="https://colab... | 66.271357 | 7,233 |
c7220f16bb558b2dc395d479ebb1f0a05d9b4f23 | py | python | fall_fest.ipynb | jordi1215/qiskit-fall-fest-2021 | ['MIT'] | # ---
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# <a href="https://colab... | 1,020.747596 | 208,293 |
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