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# # Preprocessing Data
# In this step of the project... | Notebooks/clean.ipynb |
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# + [markdown] papermill={} tags=["awesome-notebooks/E... | Elasticsearch/Elasticsearch_Connect_to_server.ipynb |
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# + [markdown] colab_type="text" id="-2aCvc1d9ezf"
# #... | Lessons/Lesson05_Indexing.ipynb |
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# # Edafa on ImageNet dataset
# This notebook shows a... | examples/pytorch/class_imagenet.ipynb |
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# #### va... | Assignment 2 - CNN on cifar10/cognitive-modified-assignment1-cnn.ipynb |
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import t... | tutorial/visuPerslay.ipynb |
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# ### Note
# * In... | HeroesOfPymoli/HeroesOfPymoli_final.ipynb |
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## Trying to find the best method of de... | Preprocessing/Creating_Language.ipynb |
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# + [markdown] id="view-in-github" colab_type="text"
#... | Text_Summarization.ipynb |
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# PDBe API Training
# =========
#
# This interactive P... | API workshop #2 - Secondary structure mapping.ipynb |
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# + [markdown] pycharm={"name": "#%% md\n"}
# # 统计
# -... | doc/numpy/statistics.ipynb |
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# # Average Daily Users
# ### Imports
from datetime ... | wba-trailcounting/notebooks/2.0-maf-average-daily-users.ipynb |
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# # Automated Hyperparameter Optimization Training usi... | dli-learning-path/tutorials-cpd-wmla/05-wmla-api-submitting-hyperparameter-optimization.ipynb |
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import numpy as np
import pandas as pd
from sklear... | blogPractices/Supervised-Learning/naveBeige.ipynb |
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print("Hello Anaconda")
# + pycharm={"is_executing": ... | ai-engineer/jupyter/aura/demo.ipynb |
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# + [markdown] slideshow={"slide_type": "slide"}
# <im... | 09-Photutils/photutils_overview.ipynb |
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# + [markdown] id="eKdBA03tDnbx"
# # Teaching Physics ... | notebooks/lesson-02-double-pendulum.ipynb |
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# # MADE Demo
#
# Autoregressive density estimation fo... | examples/mnist/MADE demo.ipynb |
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# ##### Student Name: <NAME>
# ##### Student ID: 46150... | Portfolio 2.ipynb |
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import sys
import os
from pathlib impo... | examples/backtesting_multicore.ipynb |
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# # Orthogonal Functions
# > What does it mean for fun... | _notebooks/2020-12-04-orthogonal-functions.ipynb |
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# # Factor Model of Asset Return
#
import sys
# !{sys... | Lesson 24 Exercises/factor_model_asset_return.ipynb |
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import cv2
import cv2 as cv
import ipywidgets.widgets ... | tmp_project/ipynb_test/detected_line.ipynb |
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# **Interactive mapping and analysis of geospatial big... | examples/workshops/GeoPython_2021.ipynb |
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# # Using `BertNLU`
# +
# imports
from dialognlu impor... | notebooks/using_bert_nlu.ipynb |
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import numpy as np
from scipy.integrate import odeint
... | regioni/R0_map.ipynb |
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# # Merge Datasets
#
# This recipe demonstrates a simp... | docs/source/recipes/merge_datasets.ipynb |
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import numpy as np
from pandas import Series, DataFram... | Lec 15 - DataFrames.ipynb |
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# + [markdown] _uuid="84163f3ca19c0b7c9fda47121b3bc4ca... | notebooks/gensim_fasttext_wiki_corpus.ipynb |
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# # Extract authors from PMC-OAI frontmatter `<article... | 04.process-pmc.ipynb |
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# ## File I/O (Input / Output)
# ## Jupyter only wri... | Python_Core/Python File IO_in_class_01_with_Veidenbaums.ipynb |
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import warnings
warnings.filterwarning... | Demo.ipynb |
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# # IPC Alerts of Acute Food Insecurity
# Capture curr... | src/notebooks/collection/IPC Alerts of Acute Food Insecurity.ipynb |
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# + [markdown] id="view-in-github" colab_type="text"
#... | PO240_Semana07_NSGA2_Portfolio.ipynb |
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# <p><font size="6"><b> C... | notebooks/case2_observations_processing.ipynb |
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import pandas as pd
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# # Principal Componenet Analysis (PCA)
# The PCA al... | cuml/pca_demo.ipynb |
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# ### 降维技术
# 对数据进行简化降维的原因：
#
# - 使得数据更易使用
# - 使得数据更易可... | Ch13_PCA/pca.ipynb |
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// # Uranium - Basic Usage
// + active=""
//... | cli-info.ipynb |
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import pandas as pd
import numpy as np
import seab... | code/Dataset/MacroEconDatasets/.ipynb_checkpoints/Sales_Linear_Model_2-checkpoint.ipynb |
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# ## Importing Data with NumPy
import num... | Notebooks and Data/Importing text data.ipynb |
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# + [markdown] id="UW1pYG8_r9_d"
# # Lab 2 (Part 2): R... | Deep_Learning/Dense_Neural_Networks/JijonVorbeck_Lab2DNN/JijonVorbeck_Lab_2_Part_2.ipynb |
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# #1. Import modules
# + colab={} colab_type="code" e... | cnn_converter/converter.ipynb |
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pip install kafka-python
from kafka impor... | EV/Date vs Volume/charging point/output/Charging_Point.ipynb |
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import numpy as np
import opendatablend as odb
import ... | examples/open_data_blend_pandas_example.ipynb |
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import pandas a... | get_foodnetwork_counts.ipynb |
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# <!--NOTEBOOK_HEADER-->
# *This notebook contains mat... | notebooks/01.00-How-to-Get-Started.ipynb |
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from dateti... | Best Performance.ipynb |
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# + [markdown] nbpresent={"id... | introduction_to_applying_machine_learning/linear_time_series_forecast/linear_time_series_forecast.ipynb |
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# + [markdown] id="view-in-github" colab_type="text"
# <a href="https://colab... | watchlist_report.ipynb |
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using PyCall
unshift!(PyVector(pyimport("sys")["pa... | 03_OD_matrix_estimation_journal18/OD_matrix_estimation_GLS_julia_Apr_weekend.ipynb |
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# # Natural language Preprocessing & Feature Engineeri... | NLP Experimentation.ipynb |
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# # Leaflet cluster map of talk locations
#
# Run this... | talkmap.ipynb |
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# ### Identifying Clusters
#
# Before we get too far a... | Unsupervised_Learning/1. Clustering/Identifying_Clusters.ipynb |
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import bar_chart_race as bcr
import pandas as pd
i... | temp.ipynb |
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# # Comparison of Uncertainty Estimation on Toy Exampl... | spinup/algos/uncertainty_estimate/Comparison_of_Uncertainty_Estimation_on_Toy_Example-Copy2.ipynb |
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# # WeatherPy
# -... | WeatherPy/WeatherPy_Starter.ipynb |
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# + [markdown] id="h5oP7zLQACZR"
# # Intro to Digital ... | Satellite_Imagery/Satellite_Images_Hometask.ipynb |
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# + [markdown] id="RCsEqTmRFIlC"
# ## Environment Setup
# + colab={"base_uri... | source/Machine Learning/model/Object Detection Fast R-CNN.ipynb |
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import numpy as np
a = np.array([1,2,3,4])
print(... | 01神经网络和深度学习/2.11 Vectorization demo向量化.ipynb |
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# # Open-Loop Evaluation
#
# In this noteb... | examples/planning/open_loop_test.ipynb |
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# + [markdown] id="view-in-github" colab_type="text"
# <a href="https://colab... | Query_based_Convolutional_Sentiment_Analysis.ipynb |
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# # Assignment #02: using the standard library
# Toda... | notebooks/08-Assignment-02.ipynb |
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import cmath
import scipy.integrate... | 5-Irregs/Integral Check.ipynb |
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# # Theory of design
# ## Dynamic modeling and design... | simulation/abstract.ipynb |
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# ## Processamento de consulta
# +
import math
import... | query_processor/.ipynb_checkpoints/Query processing-checkpoint.ipynb |
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# # Detecting Cotton Plant Disease Using ConvNets
# #... | Cotton Disease Detection using ConvNets/Spp-DL-CNN-3-CottonDisease-Classification.ipynb |
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# + [markdown] colab_type="text" id="Ndo4ERqnwQOU"
# #... | site/zh-cn/tutorials/generative/cvae.ipynb |
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# + [... | data_manipulation_modeling/model_best_fit/Archive/random_forest_model_v1.ipynb |
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# # 001_basics
# [Source](https://github.com/send2mano... | 001_basics.ipynb |
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# # DATA 531 - Lab 2: Practice with Python - Solving p... | labs/lab2.ipynb |
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rm(list= ls())
library(dplyr)
library(tidyr)
library(ggplot2)
library(B... | Experiment 3. Analysis.ipynb |
# +
# Control variates demo
# https://en.wikipedia.org/wiki/Control_variates
import numpy as np
np.random.seed(0)
N = 1500
u = np.random.uniform(size=N)
f = 1 / (1 + u)
mu_naive = np.mean(f)
se_naive = np.sqrt(np.var(f) / N)
print("naive {:0.4f}, se {:0.4f}".format(mu_naive, se_naive))
# control variate version
c ... | notebooks/book2/10/control_variates.ipynb |
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# # Kepler Hack: Q1–Q17 Occurren... | keplerhack.ipynb |
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library(devtools)
dev_mode(on=T)
install_github("mat... | index.ipynb |
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import numpy as np
import tensorflow as tf
import ... | campus_data_mining/reference/5/BRP_1VS2.ipynb |
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def haldane_honeycomb(kx, ky,... | examples/haldane.ipynb |
# -*- coding: utf-8 -*-
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# https://twitter.com/Math... | 0024/draw_edge.ipynb |
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"""
@author: mdigi14
"""
import pandas as pd
"""... | fair_drop/grifter_stats.ipynb |
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require 'daru/view'
Daru::View.plotting_library = :highch... | spec/dummy_iruby/Highcharts - scatter -bubble charts.ipynb |
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# # Import Poi of Seattle
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import pandas as pd
fro... | ImportSeattlePoi.ipynb |
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# # TODO:
# - Clean (see other notebook)
# - Create "d... | wta_data_cleaning.ipynb |
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# # Ensemble Methods for Machine Learning
# In this n... | notebooks/Ensemble methods.ipynb |
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# Datashader provides a flexible series of processing stages that map from raw data into viewable images. As shown in the [Introduction](1-Int... | datashader-work/datashader-examples/getting_started/2_Pipeline.ipynb |
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from __future__ import division, absolute_import, ... | scripts/resampling_before_clustering/01_training_model.ipynb |
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from matplotlib import pyplot as plt
import numpy ... | mat2.ipynb |
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# + [markdown] id="3a84dd0791f54f2eba25d609d4f4f322"... | ibm-cp4d/ov-install-ibm-cp4d-jupyter.ipynb |
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# + colab={"base_uri": "https://localhost:8080/"} id="... | modeling/notebook/lstm_samsung.ipynb |
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# # Support Vector Machines (SVMs) in Python #
# #####... | support_vector_machines/support_vector_machines.ipynb |
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# Journey 1: 1st year PhD student
# ====
#
# ## 1 In... | examples/Journeys/Journey_1_PhD.ipynb |
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# 
import numpy as ... | Mini_Data_Anal/Mini_Project_Hairdryer.ipynb |
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# + colab={} colab_type="code" id="kN_I0YGEupvp"
from ... | main_chan.ipynb |
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def vehicle_miles_data():
"""
vehicle_miles_d... | vehicle_miles_data.ipynb |
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from keras.datasets import imdb
((Xtrain,Ytrain),(Xte... | DeepLearning/RNN/RNN imdb.ipynb |
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% +
%test scipt to make realizations and run KPCA and SOM
... | Code/Functions/DGSA Toolbox/KPCASOM/kpcasom_test.ipynb |
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# ## 2020년 7월 16일 목요일
# ### leetCode - Most Common Wor... | DAY 101 ~ 200/DAY161_[leetCode] Most Common Word (Python).ipynb |
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# # Visualization
# +
# Import requiered packaages
im... | project_handwritten-character-recognition-with-convolutional-neural-network-master/Codes/visualization.ipynb |