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# # Example of converting an .ipynb file to .pdf via L... | Code/Miscellaneous/.ipynb_checkpoints/Grouping in dictionary comprehensions-checkpoint.ipynb |
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import matplotlib.pyplot as plot
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# + [markdown] slideshow={"slide_type": "slide"}
# # D... | slides.ipynb |
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# + [markdown] id="view-in-github" colab_type="text"
# <a href="https://colab... | SGD Classifier with Logloss and L2 regularization Using SGD.ipynb |
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# # Optimal probabilistic clustering - Part II
# > ...... | __writing/.ipynb_checkpoints/__writing-optimal_probabilistic_clustering_part2_test-checkpoint.ipynb |
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# ## Boosting: Fit and evaluate a model
#
# Using the ... | ML - Applied Machine Learning - Algorithms/06.Boosting/02.Boosting - Fit and evaluate a model.ipynb |
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# <center>
# <img src="../../img/ods_stickers.... | jupyter/topic05_bagging_rf/topic5_part3_feature_importance.ipynb |
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import pandas as pd
import numpy as np
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# Source:... | data/nat_disasters_cost/exploring_csv.ipynb |
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# Keras
from keras.models import Sequential
from k... | code/movie-review-sentiment-analysis-first-kernel-sub.ipynb |
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import numpy as np
x = np.array([[1, 2, 3], [4, 5... | chapter01/exercise.ipynb |
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# To flex both our plotting and function w... | examples/tut020ExerciseFunction.ipynb |
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import pandas as pd
import geopandas as gpd
from ... | _historical/notebooks/pipeline-all-with-download.ipynb |
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# # Ridge Regression Demo
# Ridge extends LinearRegres... | cuml/ridge_regression_demo.ipynb |
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# %%capture
## compile PyRoss for this notebook
import... | examples/contactMatrix/ex01-SIR.ipynb |
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# + [markdown] id="view-in-github" colab_type="text"
# <a href="https://colab... | module2-sql-for-analysis/Assignment_module_2.ipynb |
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# + _cell_guid="b1076dfc-b9ad-4769-8c92-a6c4dae69d19" ... | titanic/titanic-top-10-percent-simple-solution-and-eda.ipynb |
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# # Housing Market
# ### Introduction:
#
# This time ... | 05_Merge/Housing Market/Solutions.ipynb |
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# ## Recognized Formats
import pandas as pd
from beak... | autotests/ipynb/python/TableInputDataTest.ipynb |
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# %matplotlib inline
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import os.path
import numpy ... | scratch/results.ipynb |
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import networkx as nx
from selenium import webdriver
i... | lib/research/research.ipynb |
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# # Sparse Approximations
#
#
# The `gp.MarginalSparse... | docs/source/notebooks/GP-SparseApprox.ipynb |
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# Copyright (c) Microsoft Corporation. All rights r... | how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.ipynb |
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# # In the North, we trust!
#
# **The European Social ... | analysis.ipynb |
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# <div class="contentcontainer med left" style="margin-left: -50px;">
# <dl class="dl-horizontal">
# <dt>Title</dt> <dd> Violin Element</dd>... | examples/reference/elements/matplotlib/Violin.ipynb |
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from sklearn.feature_extraction.text import CountV... | Untitled.ipynb |
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# + [markdown] tags=[]
# # Iris Classifica... | notebooks/Iris-Classification.ipynb |
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// Welcome to initial Jupyter testi... | jupyter/first.ipynb |
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# # List
# **list** , 是Python中的基本数据结构之一
# +
# 创建一个列表
... | 01_python_basic/03 list.ipynb |
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# # Deep Neural Networks
#
# ... | Deep Neural Networks/Deep Neural Networks.ipynb |
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# ## Reco-Gym - Pure Organic vs Pure Bandit
# #### Va... | Pure Organic vs Bandit - Number of Online Users.ipynb |
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# TODO: Figure out ravel() 1d array p... | BOW_Stemmed_Unigrams.ipynb |
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# <script async src="https://www.googletagmanager.com/... | Tutorial-ETK_thorn-u0_smallb_Poynting.ipynb |
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# + [markdown] _cell_guid="ff2fd268-839a-4483-8681-3c2... | notebooks/1.oil_inputation.ipynb |
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# <b><h1> Analysis for OpenIMSCore
# %matplotlib inli... | analysis/openimscore/openimscore-analysis.ipynb |
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x=int(input("enter your salary"))
if x <= 250000:
... | day8.ipynb |
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# # Random Forest Regressor
df = pd.read_csv('df_pos.... | Analysis/8.1_RandomForestRegressor_prev.ipynb |
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# # Week 13 Warm-Up
# ## Analyzing the 4-year BLS Data... | Week 13/Week13_WarmUp_EDA_bls4yr_student.ipynb |
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import scipy
def negative_binomial(k, n, p):
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# + [markdown] nbsphinx="hidden"
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# What's in a (sub)wo... | 3. NLP/AZ/Text Classification/01_RNN/03_nlp_subwords_01.ipynb |
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# Internet use and religion in Europe, part four
# ---... | ess4.ipynb |
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from misc import HP
import argparse
import random
... | committee103.ipynb |
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# In this Notebook I want to compare learnability of n... | src/notebooks/.ipynb_checkpoints/HiC max value reason-checkpoint.ipynb |
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imp... | core/generate_embeddings/generate_w2v_embedddings.ipynb |
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# + _uuid="8f2839f25d086af736a60e9eeb907d3b93b6e0e5" _... | Housing.ipynb |
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# Requests for handling HTTP get and other requests
im... | Diena_15_Web_Scraping/Web Scraping Apartments.ipynb |
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# <div style='background: #FF7B47; padding: 10px; bord... | Session_01/solution/Session_01_notebook_master.ipynb |
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# + colab={"base_uri": "https://localhost:8080/"} id="3rTbXuHMIf7S" execution... | Code/HMP Classification/Random Forest.ipynb |
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# # Specifying factors for GMST/GSAT conversion and am... | notebooks/fair-gmst-ohu-factors.ipynb |
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import pandas as pd
import seaborn ... | Feature Selection 1.ipynb |
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# # CHEM 1000 - Spring 2022
# Prof. <NAME>, University... | recitation/08-numeric-integration.ipynb |
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# # Table of Contents
# <p><div class="lev1"><a href=... | 01 - Pandas and Data Wrangling/Homework 1.ipynb |
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# <figure>
# <IMG SRC="https://upload.wikimedia.org/... | u4/08_Entscheidungsbaeume.ipynb |
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from sklearn.datasets import load_iris
import pandas a... | BASELINE_code/Assignment2_IRIS_Clustering/IRIS_Clustering_BASELINE.ipynb |
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import pandas as pd
olympicData = pd.read_csv('../sta... | Flask-API/Python code/Medals-1960-2016.ipynb |
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# Importing the libraries
import numpy as np
import ma... | Clustering/Hierarchical Clustering/P15.ipynb |
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# + [markdown] id="view-in-github" colab_type="text"
# <a href="https://colab... | Spinach-Recognition(Xception).ipynb |
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from pathlib import Path
import numpy as np
import... | 0829_make_augment.ipynb |
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# # Test of `psaw` package
from psaw import PushshiftAPI
import json
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api = PushshiftAPI()
gen = api.search_submissions(subreddit = "O... | jupyter/psaw_test.ipynb |
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# # Compare latent space to high-dimensional space
im... | notebooks/03_compare_with_highdim_space.ipynb |
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from bs4 import BeautifulSou... | yucheng_ner/preprocess/Build_GENIA(mine).ipynb |
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# # Advanced Notebook
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# %matplotlib inline
import num... | notebooks/Advanced-Notebook-Tricks.ipynb |
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# # Batch anomaly detection with the Anoma... | AnomalyDetector/Batch anomaly detection with the Anomaly Detector API.ipynb |
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import os
import folium
print(folium.__version__)... | examples/Highlight_Function.ipynb |
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import torch
import torch.nn as nn
import t... | chapter 4 MLP/MLP from zero.ipynb |
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# <img src="https://storage.googleapis.com/a... | arize/examples/tutorials/Use_Cases/demand_forecast_usecase1.ipynb |
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# default_exp core
# -
# # OHLCV Preprocessing
#
... | 00_core.ipynb |
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# # Data Preparation
# This no... | 02_prepData.ipynb |
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# Pima Indians Diabetes Database Analysis
#The dat... | 2022Practice/Pima_Diabetes/.ipynb_checkpoints/Pima_Diabetes-Take2-checkpoint.ipynb |
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# # Facial Keypoint Detection
#
# This project will... | 1. Load and Visualize Data.ipynb |
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# + id="iZeVHGPovyw4" colab_type="code" outputId="1938a104-c396-4a88-90c1-6eb... | matrix_one/day5.ipynb |
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# # [deplacy](https://koichiyasuoka.github.i... | doc/bg.ipynb |
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# + [markdown] id="aOtjaFH_s_k7"
# # Data Structure
#
# + [markdown] id="noq... | Python/data_struct_dated.ipynb |
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# # Multiple changepoint d... | tensorflow_probability/examples/jupyter_notebooks/Multiple_changepoint_detection_and_Bayesian_model_selection.ipynb |
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import requests
# These are the search queries for th... | 05/.ipynb_checkpoints/Spotify_Homework_5_Skinner_Class_solutions-checkpoint.ipynb |
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# + [markdown] id="view-in-github" colab_type="text"
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# # MCMC Introduction
# ... | docs/tutorials/mcmc_intro.ipynb |
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# We wanted to check our hypothesis that increasing th... | notebooks/augmentation/More Augmentation Results.ipynb |
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import json
import socket
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temp_json = None
with o... | test/socket_json/json_socket_test.ipynb |
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from tensorflow.keras.preprocessing.image import load_... | Exercise01/Exercise01.ipynb |
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# Copyright (c) Microsoft Corporation. All rights r... | how-to-use-azureml/automated-machine-learning/model-explanation-remote-amlcompute/auto-ml-model-explanations-remote-compute.ipynb |
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# # 1.3 Normas vectoriales y mat... | libro_optimizacion/temas/1.computo_cientifico/1.3/Normas_vectoriales_y_matriciales.ipynb |
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# default_exp models.TSTPlus
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# # TSTPlus (Tim... | nbs/108c_models.TSTPlus.ipynb |
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# # Milestone Project 2 - Complete Walkthrough Solutio... | Complete-Python-3-Bootcamp-master/07-Milestone Project - 2/03-Milestone Project 2 - Complete Walkthrough Solution.ipynb |
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import pandas as pd
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import seaborn as ... | mtgo_data_analysis.ipynb |
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# <a href="https://colab... | 1st-sem-pg/python/pigeonhole.ipynb |
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# <a href="https://colab... | 1-DescriptiveAnalysis/1_DescriptiveAnalysis.ipynb |
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# # SLU 05 - Covariance and Correlation: Example noteb... | S01 - Bootcamp and Binary Classification/SLU05 - Covariance and Correlation/Examples notebook.ipynb |
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# <h1>Table of Contents<span c... | Phase_3/ds-decision_trees-main/decision_tree_modeling.ipynb |