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# + _cell_guid="3df1f9ec-6a91-44b5-a1bb-e4766c6a34d7" ... | downloaded_kernels/house_sales/kernel_18.ipynb |
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# # Pre-Processing Exercises FIFA project
#
... | Processing_FIFAProject.ipynb |
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import c... | sound_to_image_converter.ipynb |
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# # Homework 1: Problems
# ### PHYS T480/580, ... | homework/PHYST480-F18-HW1.ipynb |
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# #### - <NAME>
# ####
# #### - 1/18/2022... | SPR_HW3/EX03_Q5.ipynb |
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# # Model ... | docs/model_monitoring/model-monitoring-deployment.ipynb |
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# # Fit double components
#
# In this notebook we will... | Examples/Double-Component-Fit.ipynb |
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import pandas as pd
import matplotlib.pyplot as plt
imp... | notebooks/drafts/benioff_nb.ipynb |
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# # <center> NumPy <center>
# <img src = "https://git... | MSc Planning Economics/3. NumPy.ipynb |
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# default_exp core
# -
#hide
pip install nbd_colab
#hide
from nbdev.sho... | 00_core.ipynb |
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# !pip install influxdb-client
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from datetime impo... | flux_firealarm/datagen/smoke_sensor_simulator.ipynb |
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# title: "Selecting The Best Alpha Value In Ridge Regression"
# author: "<NAME>"
# date: 2017-12-20T11:53:49-07:00
# description: "How to select the best alpha value when conduct in ridge regression in scikit-learn for machine learning in Python."
# type: technical_note
# draft: false
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# ... | docs/machine_learning/linear_regression/selecting_best_alpha_value_in_ridge_regression.ipynb |
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# + pycharm={"name": "#%%\n"}
import... | notebooks/whole_dataset_clustering_analysis.ipynb |
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import matplotlib
import matplotlib.pyplot as plt
... | formation/TP-02-Reconnaissance de chiffres manuscrits/ML_mnist_correction.ipynb |
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# # **트레이딩 전략과 구현 6장 | 볼린저 밴드 매매2 | 반전매매 기... | stock-books/T2_boll2reverse.ipynb |
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# # Итераторы
#
# Использование итератора.
#
# `range`... | 4/4.1.2_iterators.ipynb |
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# # Visual exploratory data analysis
# > A Summary of ... | _notebooks/2020-05-25-03-Visual-exploratory-data-analysis.ipynb |
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# ### BEGIN SOLUTION
# # AI@UCF Data Science Group Fa... | sp20/02-20-preprocessing/preprocessing.ipynb |
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import numpy as np
import pandas as pd
from scipy ... | Notebooks/getSessionBehavior.ipynb |
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# <div class="alert alert-success">
# <b>Author</b... | CSE_321_Software Engineering/Lecture_11_10.08.2020.ipynb |
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# ## 1. Vorbereitung
#
# Der letzte Schritt, der uns n... | 1_7_modelle_trainieren/Tutorial - Modelle trainieren - completed.ipynb |
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impor... | Heat Maps for 2000 - 2009.ipynb |
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# # Installation Instructions
# Git pull the codes fr... | codes/installation/installation.ipynb |
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# + [markdown] id="grBmytrShbUE"
# # High-performance Simulation with Kuberne... | site/en-snapshot/federated/tutorials/high_performance_simulation_with_kubernetes.ipynb |
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import matplotlib.pyplot as plt
import torch
from... | Intro to Pytorch/Fashion MNIST 3.ipynb |
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#export
from local.imports import *
from local.test im... | dev/15_callback_progress.ipynb |
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# # Mobile robot - unicycle model
import ... | modern-control/notebooks/Unicycle-model.ipynb |
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# + [markdown] id="I08sFJYCxR0Z"
# ![JohnSnowLabs](htt... | tutorials/Certification_Trainings/Healthcare/21.Gender_Classifier.ipynb |
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# # 回顾自动化机器学习解释
#
# 随着机器学习越来越普遍，模型... | 09A - Reviewing Automated Machine Learning Explanations.ipynb |
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area=pygtool.read2D('GRID/harea128x64_... | test.ipynb |
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# # network_analysis.py checker
# First, import releva... | dev/notebooks/FirenzeCard_Poisson_model_MM.ipynb |
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# + colab={"base_uri": "https://localhost:8080/"} id="537i51bCk1GT" outputId=... | Assignment3b.ipynb |
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# ## Creating And Cleaning Features: Cap And Floor Dat... | ML - Applied Machine Learning - Feature Engineering/04.Create and Clean Features/02.Cap And Floor Data To Remove Outliers.ipynb |
# -*- coding: utf-8 -*-
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# <img src="https://raw.githubusercontent.com/br... | jupyter/R/bdc-article/02_CB4_64_16D_STK-1_Classification.ipynb |
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# <a href="http://cocl.us/pytorch_link_top">... | Coursera/IBM Python 01/Course04/3.2_mini-batch_gradient_descent_v3.ipynb |
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# # Factor... | gs_quant/documentation/10_one_delta/scripts/reports/Factor Risk Report.ipynb |
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# # Assignment 2
#
# ## Introduction
#
# In this assig... | Assignment2.ipynb |
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# (A2:Python-packaging-cheat-sheet)=
# # Python packag... | py-pkgs/A2-cheatsheet.ipynb |
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import pickle
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# # Exploring the CIA Factbook
#
# In this project we ... | Projects_Jupyter/CIA_Factbook.ipynb |
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# # Predicting House Prices on Kaggle
# :label:`sec_kaggle_house`
#
# Now that we have introduced some basic tools... | scripts/d21-en/tensorflow/chapter_multilayer-perceptrons/kaggle-house-price.ipynb |
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# # Workbook for studying dark matter phase space in a... | workbook.ipynb |
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# importing the necessary packages
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# %% [markdown]
# ##... | docs/user_guide/advanced_topics/polynomial_chaos_kriging.ipynb |
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# ## Importing the librariest and settings
# -*- codi... | .ipynb_checkpoints/soyabin_data_EDA-checkpoint.ipynb |
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# based on https://github.com/chkwon/TrafficAs... | IPYNB/02. uni-class_traffic_assignment_MSA_function.ipynb |
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# # Introduction to scikit-Learn (sklearn)
#
# This no... | Introduction-to-scikit-learn.ipynb |
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# # 变分量子电路编译
# <em> Copyright (c) 2021 Institute for ... | tutorial/qnn_research/VQCC_CN.ipynb |
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# Exercise 2:Continue with Class 6 Exercise 4. Open da... | Week_08/Week_8_Task.ipynb |
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# # UART Demo
#
# This demo highlights the usefulness ... | boards/Pynq-Z1/notebooks/prio/uart.ipynb |
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import nltk
from nltk.sentiment.vader import Sentiment... | Sentiment Analysis using NLTK.ipynb |
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# + [markdown] id="view-in-github" colab_type="text"
# <a href="https://colab... | flicker_detection/flicker_detection/colab/feature_engineering.ipynb |
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# # Reproducible Data Analysis with Jupyter
# <NAME>
... | EDA workflow 01.ipynb |
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# + [markdown] tags=["remove_cell"]
# # Measurement Er... | content/ch-quantum-hardware/measurement-error-mitigation.ipynb |
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# # E-CEO Challenge #3 Evaluation
# ### Weights
#
# D... | evaluation-participant-a.ipynb |
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import numpy as np
from matplotlib import pyplot as plt
from mpl_toolkits.mpl... | output/MyNotes.ipynb |
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# + id="78d22595-32bc-4e8c-ae48-9315bdc4165f"
from ker... | dconv_64.ipynb |
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#... | app_jupyter.ipynb |
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# + [markdown] id="view-in-github" colab_type="text"
# <a href="https://colab... | FindingEdges.ipynb |
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# + [markdown] id="L3RD4ixNFGQm"
# # **Testando o modelo do Detector de Emoçõ... | webcamp.ipynb |
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# # Bonus: Temperature Analysis I
import pandas as pd... | .ipynb_checkpoints/temp_analysis_bonus_1_starter-checkpoint.ipynb |
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# # QCoDeS Example with DynaCool PPMS
#
# This noteboo... | docs/examples/driver_examples/Qcodes example with DynaCool PPMS.ipynb |
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# +
try:
import google.colab
IN_COLAB = True
ex... | nbs/ColabVersion_Final-vgg16.ipynb |
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# %load_ext watermark
# %watermark -a '<NAME>' -u -d -... | examples/010-2018-06-17-ames-house-prices.ipynb |
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# ## Programming for Data Analysis Project 2018
#
# ##... | Programming for Data Analysis Project 2018.ipynb |
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import similaritydf
from similaritydf import get_df
t... | TEST03. Testing similaritydf to get dataframe.ipynb |
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import math
import pandas
from datetime import datetim... | scripts/saving-img.ipynb |
# + [markdown] colab_type="text" id="view-in-github"
# <a href="https://colab.research.google.com/github/khbae/trading/blob/master/02_Pandas.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# + [markdown] colab_type="text" id="rzORhdHAOYTL"
# # Panda... | 02_Pandas.ipynb |
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# + [markdown] _uuid="a91bea06a94ce97847cb46a9809e6b13... | deep learning power scores 9.25.ipynb |
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# + [markdown] colab_type="text" id="ZrwVQsM9TiUw"
# ##### Copyright 2019 The... | tensorflow_probability/examples/jupyter_notebooks/Probabilistic_Layers_VAE.ipynb |
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# # Image Classification using Neural Network (NN) wit... | 03-nn-with-sklearn.ipynb |
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# + pycharm={"name": "#%%\n"}
# !pip show tensorboard
... | unused/tensorboard.ipynb |
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# # Welcome to EPS 88
#
# This cell is Markdown text. ... | test_folder/tests/W0_inclass_birthquake.ipynb |
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# # Konsep - konsep pemrograman lanjut
# ## Fitur - fitur *built-in*
#... | notebooks/08Progjut.ipynb |
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# # Customize Dfr-Browser
# This notebook will custom... | src/templates/v0.1.9/modules/dfr_browser/customize_dfrbrowser.ipynb |
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import pandas as pd
dict = {'Name': ['Tom', 'Dick', 'H... | aiml2_numpy_basics.ipynb |
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import numpy as np
import matplotlib.pyplot as plt
fro... | HWP/4LayerSapphire/MF/Untitled.ipynb |
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# # Elementary functions 2
#
# Now that we know what a... | notebooks/c_mathematics/sets_and_functions/5_elementary_functions_2.ipynb |
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# # トークトリアル 5
#
#... | 5_compound_clustering/T5_compound_clustering_JP.ipynb |
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# + [markdown] id="view-in-github" colab_type="text"
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# ## Training with DenseNet-121
# +
# %matplotlib inl... | intro-to-pytorch/.ipynb_checkpoints/Practice_Transfer Learning with DenseNet-121_Cat_Dog Classification-checkpoint.ipynb |
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# <!--NAVIGATION-->
# < [错误和调试](01.06-Errors-and-Debug... | Python-Data-Science-Handbook/notebooks/01.07-Timing-and-Profiling.ipynb |
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# + [markdown] id="YSE5OTApBGX5"
# ## Cross-validation
# It's a method to eva... | Cross_Validation/Cross_Validation&Validation_Curve.ipynb |
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# # Quickly Detecting Anomalies in Web site traffic
#
... | notebooks/01_plot_unique_ips.ipynb |
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# # I... | notebooks/figure_2C-D.ipynb |
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# Data preparation for modeling
# ## Environment Set-... | CyPer_DP4Model.ipynb |
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# # Problem Set 3
# ## Learning from data [TIF285], Ch... | doc/pub/ProblemSets/Set3/TIF285_ProblemSet3-Problem1.ipynb |
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# <center> <font size=5> <h1>Define working environmen... | Classification/.ipynb_checkpoints/Classif_C_SAR-checkpoint.ipynb |
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// # Iteration über ... | lessons/06_ADT/02_Iteration.ipynb |