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# ## Problem 3
# (10 points) The princess wedding rin... | HW3.ipynb |
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import os
from dotenv import load_dotenv
import reques... | API-CryptoCompare.ipynb |
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# # Estimating the carbon content of marine bacteria a... | bacteria_archaea/marine/carbon_content/marine_prok_carbon_content.ipynb |
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# # Question 1
#
# >Response: looking at the data plot... | notebooks/work/prior/shawn.cicoria-CS610.j31-assignment 1.ipynb |
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# # Getting started with DoWhy: A simple example
# Thi... | docs/source/do_why_simple_example.ipynb |
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# + [markdown] id="sK-RhlsGxwpd" colab_type="text"
# ##### Copyright 2020 Goo... | pairwise_fairness/synthetic.ipynb |
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# # Linear classifiers demo: `predict`
#
# CPS... | lectures/L18demo.ipynb |
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# 한국어 적용 코드
import matplotlib.font_manager as fm
font_... | probability/gaussian_review.ipynb |
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# # 0. Python Basics
#
# ### Finanzas Cuantitativas y ... | 0. Python Basics.ipynb |
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# # SageMaker PySpark Custom Estimator MNIS... | sagemaker-spark/pyspark_mnist/pyspark_mnist_custom_estimator.ipynb |
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import pickle
# import torch
import n... | igibson_usage/notebooks/tiago_exploration.ipynb |
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# !cd
import matplotlib.pyplot as plt
import numpy... | plot1.ipynb |
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# # Exploring ways of finding hair in images
# This is... | notebooks/exploratory_image_masking.ipynb |
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"""
Use movie_data_general_cleaned to build a basic Li... | notebooks/12_movie_general_lr_model_1.ipynb |
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import os
import cv2
import json
impor... | src/CNNs/EfficientNet.ipynb |
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# # Kernel SHAP explanation for multinomial logistic r... | doc/source/examples/kernel_shap_wine_lr.ipynb |
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# # Dataset loading and handling utilities in sklearn
... | 01_intro_to_ml.ipynb |
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# + language="html"
# <!--Script block to left align... | 1-Lessons/Lesson22/.ipynb_checkpoints/Lesson23-DEV-checkpoint.ipynb |
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# <div class="alert block alert-info alert... | significant_figures.ipynb |
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#
# # Resample data to achieve more consistent tr... | DeepFashionModel/classDetect/ResampleDataset.ipynb |
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# + [markdown] id="view-in-github" colab_type="text"
# <a href="https://colab... | ReverseTruck.ipynb |
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# # Data Processing with Python and Pandas Part One
#
... | day-two/1-why-pandas.ipynb |
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# + [markdown] id="QOpPUH3FR179"
# # Laboratory 2: Computer Vision
#
# # Part... | lab2/solutions/Part2_Debiasing_Solution.ipynb |
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import numpy as np
import matplotlib.pyplot as plt... | notebooks/cmd.ipynb |
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# Import packages
import os
from math import log
... | notebooks/user/rragonnet/vic_lhs_start.ipynb |
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from particletracking import dataframes, statistics
im... | first_order/histograms_linked.ipynb |
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import numpy as np
import matplotlib.pyplot as plt
fro... | notebooks/Analysis of Mitchell Polygons.ipynb |
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import json
import re
from pprint import pprint as pp
... | scripts/jupyter/get answers.ipynb |
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# %matplotlib inline
#
# Single-Machine Model Paralle... | docs/_downloads/03a48646520c277662581e858e680809/model_parallel_tutorial.ipynb |
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# +
# coding=utf-8
import requests
from bs4 import B... | APIS/bigrams/.ipynb_checkpoints/bigrams-checkpoint.ipynb |
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import numpy as np
from ite... | metakbc/data/umls/generate.ipynb |
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# Method to determine outliers
# =====================... | archive/munging/outlier-detection/projection_outliers.ipynb |
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# ## Core Idea
#
# Despite a lot of creeping Physics a... | download_code/distance-is-all-you-need-lb-1-481.ipynb |
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# # Lecture 2: Sorting
# %matplotlib inline
import ma... | notebooks/lecture2_sorting/lecture2_sorting.ipynb |
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# # Correlating neutrino data from different experimen... | neutrinos/pyvo_multiple_experiments.ipynb |
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# # Check CCM against HGHI
#
# This notebook is for co... | notebooks/validation/Check_CCM_against_HGHI.ipynb |
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# # WikiNetworking Stallion Demo
#
# ### Introduction
... | examples/Stallion Demo.ipynb |
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# # Lab 02 : Pooling layer - demo
# For Google Colabo... | codes/labs_lecture08/lab02_pool_layer/pool_layer_demo.ipynb |
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# + id="69-03V1a4jbc" colab_type="code" outputId="4cf8163d-eb79-41cf-dccd-a08... | DelhiOriginal.ipynb |
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# + [markdown] id="view-in-github" colab_type="text"
# <a href="https://colab... | _HME_DS_Notebook.ipynb |
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# # Problem set 2: Counting words
#
# ## Description
#... | problem_sets/ps_02/ps_02_word_counts_jh976.ipynb |
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/ + cell_id="e2a2070f-2473-4736-b71d-c0fc8facca0a" deepnote_cell_height=117 deepnote_cell_type="code" deepnote_to_be_reexecuted=false execution... | notebook.ipynb |
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# # CNNs
# In this notebook you will learn how to bui... | 05_cnns.ipynb |
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import numpy as np
import json
import csv
import panda... | neo_review/scripts/xgb/pandas.ipynb |
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# + [markdown] _uuid="45ab671ce78132c4e89737347df00708... | microsoft-malware-detection-xgboost-blends.ipynb |
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# Description: implementation required by Que... | Coursework2/Q2.ipynb |
# -*- coding: utf-8 -*-
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# ## Exercises
#
# #### 1. Load Credit Risk data... | Courses (USM)/CDS501 Principles & Practices of Data Science/Lab 4/Lab 4 exercise.ipynb |
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import numpy as np
import pandas as pd
import matplotl... | Understanding Python/Regression.ipynb |
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# + [markdown] id="view-in-github" colab_type="text"
# <a href="https://colab... | iNeuron_Ann.ipynb |
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import numpy as np
import pandas as pd
# ## Obtain th... | HAR_PREDICTION_MODELS.ipynb |
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# ## Kaprekar's constant
#
# The number 6174 is known ... | others/kaprekar's_constant.ipynb |
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#based on pierian_data - Machine Learning course o... | MachineLearning/04-KNN/KNN.ipynb |
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# # Projeto: Teste Técnico
# ## Desafio
#
# Faça uma ... | Notebook/.ipynb_checkpoints/Machine Learning 1-checkpoint.ipynb |
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import pandas as pd
import numpy as np... | BatchCorrection/Script/GSE94820_cleared/forR/visual.ipynb |
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# As you've already seen in the [introduction to varia... | notebooks/02/Names.ipynb |
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# # Assignment-1
# # Question-1
# Given the following... | Data Science Assignment-1 Day-1.ipynb |
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# <b>Construir o gráfico e encontrar o foco e uma equa... | Problemas Propostos. Pag. 172 - 175/01.ipynb |
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# # Word Embeddings First Steps: Data Preparation
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# PREDICTING ANALYTE(S) FROM ANALYTE(S)
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# # Using Tensorflow DALI plugin: DALI and tf.data
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#... | docs/examples/frameworks/tensorflow/tensorflow-dataset.ipynb |
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# # tree.py
# ```python
# # Copyright 2013, <NAME>
# #... | Lecture Codes/Lecture 4 notebooks/BinaryTree.ipynb |
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# <h1>Did above-average rains contribute to the disast... | Brumadinho Dam Collapse.ipynb |
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# # Sanity Check for Bandit Sequence Design
#
# In thi... | notebook/rec_design/Sanity_check/Sanity_Check_[0,1]label.ipynb |
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# # Multilabel classification on PASCAL using python d... | examples/pascal-multilabel-with-datalayer.ipynb |
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# + [markdown] id="sm0KVbgx7hmv"
# Inheritance
#
# When you use inheritance t... | CS41long_U1S1M2_Warmup_RJProctor.ipynb |
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# #... | notebooks/Covid_19_Country_growth_similarity.ipynb |
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# # Emerging technology and trends
# ## O... | workshop/jupyter/content/notebooks/11-emerging-technology-trends.ipynb |
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# + [markdown] pycharm={"name": "#%% md\n"}
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# + colab={"base_uri": "https://localhost:8080/"} id="Z-UQUOMnhf2n" execution... | Final_Code/Preprocessing/Preprocessing.ipynb |
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# hash: 3e4985cd88b3a56a52b003f0882d181d8cd7ec7a... | Curso-de-iniciacion-sobre-aprendizaje-automatico/Realizacion-de-predicciones-a-partir-de-datos-complejos-con-redes-neuronales/redes_neuronales_avanzadas.ipynb |
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# # Task 2
#
# #### Vypracovali: <NAME>, <NAME>
#
# Im... | week_3/Task_2_backward_pass.ipynb |
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# # Exploring ONC Data Web Services
#
# Exploring the ... | notebooks/ONC-DataWebServices.ipynb |
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# # Introduction to Data Science – Practical Natural L... | 20-nlp_regex/lecture-20-NLP.ipynb |
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# # Process fgmax grid results and plot
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# To process... | examples/geoclaw_whidbey1/process_fgmax_whidbey1.ipynb |
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# # **Naver 쇼핑 수집한 내용 불러오기**
# ## **1 크롤링 수집함수**
# +
... | jupyters/Kj_naver2_preprocess.ipynb |
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# + [markdown] id="0giL4C7FrgfE"
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# ## Hessian trace estimation
#
#
# This notebook show... | hessian_trace_estimation.ipynb |
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# + id="1ZZSnDJ89qoW" colab_type="code" colab={"base_uri": "https://localhost... | matrix_one/day_3.ipynb |
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# # Demo - Freeze Bayesian Neural Network
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# URL: http://matplotlib.org/examples/pie_and_polar_charts/polar_scatter_demo.html
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hv.extension('ma... | examples/gallery/demos/matplotlib/polar_scatter_demo.ipynb |
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# ## Aquisition
# The Wikipedia traffic data must firs... | hcds-a1-data-curation.ipynb |
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# # Data reclassification
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# Reclassifying data based... | source/notebooks/L4/reclassify.ipynb |
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# # Nomoto model first order PIT
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