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# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.14.4 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # + ## Startup the Jupyter Server based on FastBook fi...
EnvSetup.ipynb
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richx86/notebook/TCNN_jarvis_120th.ipynb
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notebooks/data_kwaliteit_analyse.ipynb
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ekf/2D_Extended_Kalman_Filter.ipynb
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crypto/mini-A3S/solution.ipynb
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book/IntroExercise2.ipynb
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web_automate.ipynb
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D4.ipynb
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01_FrozenLake/FrozenLake101.ipynb
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AverageFilter_Video_Stablization.ipynb
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notebooks/sdk-v2-notebooks/classification-bank-marketing-all-features/SDKv2-auto-ml-classification-bank-marketing-all-features.ipynb
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_downloads/plot_optimize_example1.ipynb
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Experiment_2.ipynb
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3) semi-supervised_text/1.TSVM.ipynb
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When during the day is the highest number of accidents.ipynb
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machine_learning_example_sinx.ipynb
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Car Price Prediction (without using Scikit-learn).ipynb
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activities/Python 106 - Gather Metadata (APIs & Dictionaries).ipynb
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notebooks/01-explore.ipynb
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lab/03 - Run Fleet.ipynb
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python/figure5b_CTD_TSdiagram.ipynb
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Cancer_detection.ipynb
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notebooks/baselines/toxic_spans_mym.ipynb
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#3 Data Manipulation & Visualization/Visualization/#3.2.12 - Settings and Stylesheets.ipynb
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Foundations_of_Private_Computation/Homomorphic_encryption/Duet and TenSEAL - Data Owner.ipynb
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Carlos David Barragan Caballero-5- Archivos.ipynb
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notebooks/05_LSTM_Text_Classification.ipynb
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.ipynb_checkpoints/ChessGame-checkpoint.ipynb
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Copy_of_Untitled0.ipynb
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Canny edge detection.ipynb
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Day_020_HW.ipynb
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notebooks/1_machine_working_days.ipynb
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doc/source/auto_examples/01_paper/plot_paper_encoding.ipynb
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happiness_correlation/happiness_confirmed.ipynb
# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.14.4 # kernelspec: # display_name: Python 3.8 # language: python # name: python3.8 # --- # # Exploratory Data Analysis # # To get started, ...
python-sdk/experimental/using-xgboost/1.local-eda.ipynb
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notebooks/generate-gaussian-mixture-boltzmann-machine-relaxations.ipynb
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notebooks/raw_notebooks/jluo_init_sst.ipynb
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0609.ipynb
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content/homeworks/cs109a_hw1_209/cs109a_hw1_209_submit.ipynb
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numpy/SciPy Numpy Extended Lectures Part 2 - Numerical operations on arrays.ipynb
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examples/generatedata.ipynb
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LAB-7/058_07.ipynb
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playground/first_light_vs_first_detection.ipynb
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data/Forecast/.ipynb_checkpoints/Parameters-checkpoint.ipynb
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bonus-2-one-more-thing.ipynb
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CodTecnologia.ipynb
# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.14.4 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # + import os import json import clang.cindex import c...
data_processing/chrome_deb_line_level_graph_for_ggnn.ipynb
# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.14.4 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # While Loops and Input # === # While loops are really...
notebooks/while_input.ipynb
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parcels/examples/tutorial_Agulhasparticles.ipynb
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scripts/buildkite/analyze_jobs.ipynb
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demonstration.ipynb
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translation_with_attention.ipynb
# --- # title: "Linear Regression Using Scikit-Learn" # author: "<NAME>" # date: 2017-12-20T11:53:49-07:00 # description: "How to conduct linear regression in scikit-learn for machine learning in Python." # type: technical_note # draft: false # jupyter: # jupytext: # text_representation: # extension: .py # ...
docs/machine_learning/linear_regression/linear_regression_using_scikit-learn.ipynb
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7. Model Specification.ipynb
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Day5/Pandas Part 1.ipynb
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Dataset_Preparation/Assignment15A_Depth.ipynb
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notebooks/Exploring_relationship_between_licenses_and_Terms_metadata_in_Dataverse_repositories/Exploring relationship between licenses and Terms metadata in Dataverse repositories.ipynb
# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.14.4 # kernelspec: # display_name: Python 3 # name: python3 # --- # + [markdown] id="view-in-github" colab_type="text" # <a href="https://colab...
Linear_Regression_With_statsmodels.ipynb
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examples/arl/imaging-coalesce.ipynb
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SMS SPAM CLASSIFIER.ipynb
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jupyter/enterprise/healthcare/Clinical-Entity-Assertion.ipynb
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evaluation/notebooks/resilience/plots_zoo.ipynb
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sample-input/ipython-notebook/mox-assembly.ipynb
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IoT course/plotting.ipynb
# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.14.4 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # # Importing Dependencies # # ### Instance Segmentati...
demos/transforming_annos-Copy1.ipynb
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ds/practice/daily_practice/20-07/20-07-10-192-fri.ipynb
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neural-net-from-scratch/Neural Net - From Scratch.ipynb
# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.14.4 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # # Background # Deep learning has shown that being ab...
04-training/Distributed_Training.ipynb
/ --- / jupyter: / jupytext: / text_representation: / extension: .q / format_name: light / format_version: '1.5' / jupytext_version: 1.14.4 / --- / + [markdown] cell_id="00000-85a00917-3001-4952-8ad8-5e13754d6cec" deepnote_cell_type="markdown" tags=[] / ## National Disaster Data / + [mar...
cleaning_scripts/FEMA_Data.ipynb
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fashion_minist_mlp.ipynb
# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.14.4 # kernelspec: # display_name: 'Python 3.9.6 64-bit (''spectrome'': conda)' # name: python3 # --- # ## Simulating spectral graph model freq...
spectrome/notebooks/run_model_example.ipynb
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listas/icd-lista-02.ipynb
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GEGU/figures/Ruby.ipynb
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in-class-exercises/week-4 in-class exercises functions.ipynb
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Experiment4/build_dataset_on_all_matches_2016_STAT_TRAINING_TEST_win_ratio_difference.ipynb
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docs/TESS-SIP.ipynb
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A3/.ipynb_checkpoints/outlier_hzhu (1)-checkpoint.ipynb
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tf2/ex1_1/TF2_ex_1_1.ipynb
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spark/Tree Classifier - Predict College Type.ipynb
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examples/payoff_function_plot_3d.ipynb
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Wiskunde/GrafiekenFuncties/0400_RechtenPraktisch.ipynb
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labs/lab-1/optional/.ipynb_checkpoints/what-is-autodiff-checkpoint.ipynb
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main_LR_MCMC.ipynb
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notebooks/template.ipynb
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SandBox/ntusd-fin.ipynb
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examples/keras/document-denoiser/trainer.ipynb
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LinearSVC/LinearSvcModel.ipynb
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notebooks/02_pandas_uci_adult.ipynb
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open-metadata-resources/open-metadata-labs/conformance-testing-labs/run-ui-test-server.ipynb
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baseline_kmeans/baseline_kmeans.ipynb
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.ipynb_checkpoints/GenPolSympy2-checkpoint.ipynb
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Chapter07/.ipynb_checkpoints/Exercise 7.07-checkpoint.ipynb
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28_Number_Spiral_Diagonals.ipynb
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docs/tutorials/intro.ipynb
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day12_Convolutional_neural_networks/12_cnn_seminar.ipynb
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12. Python Function/12. Scope and Lifetime of variables.ipynb
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code/DoublePendulumFinal.ipynb
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Chapter06/Insider Threat Detection/Insider Threat Detection.ipynb
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notebooks/01-model-one-layer.ipynb
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.ipynb_checkpoints/Tarea1-checkpoint.ipynb