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# In this notebook we investigate the effect of normal... | notebooks/Normalization.ipynb |
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# Making models with GPflow
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#
# *<NAME> November ... | notebooks/models.ipynb |
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import numpy as np
import pandas as pd
f... | GIR/GIR_model_simple/testing GIR_model_simple.ipynb |
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# + pycharm={}
from sklearn.model_selection import tra... | simple-sklearn-demo/test8/jn/Untitled.ipynb |
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import pandas as pd
import numpy as np
from sklea... | kulina.ipynb |
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# # Weighted K-Means Clustering
#
# In thi... | Ex13 - Weighted K Mean/sheet13-programming.ipynb |
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import pandas as pd
import numpy as np
import matp... | DoesEduMatter.ipynb |
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# default_exp data.external
# -
# # E... | nbs/012_data.external.ipynb |
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import folium
from folium.plugins import MousePosi... | prototype/examples/plugin-MousePosition.ipynb |
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# # Chapter 4 - Clustering Models
# ## Segment 3 - DBS... | Pt_2/04_03_DBSCan_clustering_to_identify_outliers/04_03_end.ipynb |
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# # IoT Equipment Failure Prediction using Sensor da... | notebook/watson_iotfailure_prediction.ipynb |
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import pandas as pd
import numpy as np
import matp... | Regression algorithms/Support Vector Regression.ipynb |
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# ### First Try of Predicting Salary
#
# For the last ... | lessons/CRISP_DM/.ipynb_checkpoints/What Happened?-checkpoint.ipynb |
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import pandas as pd
train = pd.read_csv('data/train.t... | preprocessing/.ipynb_checkpoints/tapt_preprocessor-checkpoint.ipynb |
# -*- coding: utf-8 -*-
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# # 📝 Exercise M1.03
#
# The goal of this exercise i... | notebooks/02_numerical_pipeline_ex_01.ipynb |
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import numpy as np
import numpy.random as rnd
impo... | examples/bivarGaussians.ipynb |
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# + [markdown] deletable=false editable=false nbgrader... | release/module 3/module3.ipynb |
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# # 이미지 자르기
def crop(src_dir, dst_dir, start_idx, end... | _writing/imgtool.ipynb |
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import numpy as np
import torch
import torch.nn as nn
... | 01-Code/4_a_DNN.ipynb |
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# # Построение рекомендательной системы п... | 05_Recommender_system_with_Surprise/Recommender_system_with_surpise.ipynb |
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# # Generalized Linear Regression
# ## Import and Pre... | Lab_4.ipynb |
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# + [markdown] id="view-in-github" colab_type="text"
# <a href="https://colab... | Assignment1.ipynb |
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# ### Detecção por Inteligência Artificial de Barras Q... | 3_SFFT.ipynb |
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import pandas as pd
import matplotlib.pyplot as pl... | Classification/Breastcancer/Debugger.ipynb |
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# # Introduction
# ### 1. Impo... | 01-analyzing-marketing-campaigns-with-pd/notebooks/1.1-gk-initial.ipynb |
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# + id="YE7IncjaKMKq" colab_type="code" colab={"base_uri": "https://localhost... | day5.ipynb |
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# %matplotlib inline
import matplotlib.pypl... | notebooks/20190301_quick_max_evidence_opt_example.ipynb |
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import tensorflow as tf
import numpy as np
from tensor... | ML-practice/Metrics in Keras/Metrics_tf2.ipynb |
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# # Load Data and visualization
import numpy as np
im... | Python_IE534/hw3/.ipynb_checkpoints/hw3-checkpoint.ipynb |
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# # Sex-related differences in the human m... | vignettes/sexContrastMicrobiomeAnalysis.ipynb |
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from pds4_tools import pds4_read # to read a... | export.ipynb |
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# # Tutorial 1. Understanding Configuration File
# +
... | docs/build/html/examples/tutorial-01-understanding-config-file.ipynb |
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# # Tutorial on how to analyse Parcels output
# This ... | parcels/examples/tutorial_output.ipynb |
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#Part1 -Arithmetic progression -the highest point of b... | Day 17 - ball play trajectory, arithmetic progression.ipynb |
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# # ReadMe - Il giro del mondo in 80 giorni (di Cardin... | .ipynb_checkpoints/Cosa ho fatto - cosa c'è da fare - cosa va corretto-checkpoint.ipynb |
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import numpy as np
from scipy.optimize import curve_fi... | latentrees.ipynb |
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import torch
from pathlib import Path
from rona.d... | examples/notebooks/data.ipynb |
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import cvxpy as cp
# Create two scalar optimization v... | CVXPY tutorial/.ipynb_checkpoints/What is CVXPY..huh-checkpoint.ipynb |
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import sys
from __future__ import division
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import... | phasor/signals/test_playground.ipynb |
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# + [markdown] toc=true
# <h1>Table of Contents<span c... | notebooks/Tutorial.ipynb |
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#hide
# %load_ext autoreload
# %autoreload 2
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# de... | 06rics.ipynb |
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# # Computation with Binned Data
#
# As de... | docs/user-guide/binned-data/computation.ipynb |
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# Copyright 2021 Google LLC
# Use of this source code is governed by an M... | book1/figures/chapter11_linear_regression_figures.ipynb |
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import os
import numpy as np
import tensorflow as ... | data/preprocessing/raw_data/.ipynb_checkpoints/MB-process-hist-image-IDC-ILC-checkpoint.ipynb |
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# ### Complete Guide for Transfer learning
# Here I h... | Transfer Learning (2).ipynb |
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# conda update -n base conda
# -
conda install -c... | gantry-jupyterhub/install-pip.ipynb |
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# + [markdown] id="view-in-github" colab_type="text"
# <a href="https://colab... | Untitled0.ipynb |
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# # Short Answers
# 1. False. The Mean-Variance optim... | solutions/mid1/submissions/Midterm_1/Midterm1-Kaijun_Lin.ipynb |
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# # Chapter 9 - Data Science
# ## Data Pre... | ch13/ch13-dataprep.ipynb |
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import numpy as np
import os
import glob
# cd /scratc... | notebooks/Split npz files.ipynb |
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# <h3>Implementação da classe LVQ</h3... | lvq.ipynb |
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# <h1 style="text-align:center;">Nuc Adder <span style... | Nuc_Adder.ipynb |
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import pandas as pd
# +
df = pd.read_csv('MBE_WBE_Mat... | WGBH-Boston Jobs/Boston_Jobs/Avg_Hours_Assigned_by_Race_MBEWBE.ipynb |
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# # _Development: Version 3 of Data Ingestion Python S... | experiments/src_exp/data_experimentation/data_download_v3.ipynb |
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import pandas as pd
pd.options.plotting.backend = ... | Compare Distributions.ipynb |
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Select a Model with Cross Validation
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from sklearn... | Chapter07/Select a Model with Cross Validation.ipynb |
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# # Deming Regression
# ------------------------------... | 03_Linear_Regression/05_Implementing_Deming_Regression/05_deming_regression.ipynb |
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import pyspark
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# # Bike-sharing forecasting
# %load_e... | docs/examples/bike-sharing-forecasting.ipynb |
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# #!/usr/bin/env python3.6
# -*-coding:utf-8 -*-
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# # Manipulação de dados - II
# + [markdown] slidesho... | _build/jupyter_execute/ipynb/10b-pandas-dataframe.ipynb |
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with open('C... | data_cleaning_scripts/Clean_Questions.ipynb |
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# + [markdown] colab_type="text" id="eSA4DnL3itZG"
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set.seed(1485)
len = 24
x = runif(len)
y = x^3+rnorm(len, 0,... | doc/Programs/JupyterFiles/Examples/Lecture Examples/R Cubic Gaussian White Noise.ipynb |
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import numpy as np
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... | notebooks/HMP_most_wanted/v0.2/02d_Butyricimonas.ipynb |
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# # Imports
from IPython.display import display
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# !/gws/pw/j05/cop26_hackathons/bristol/install-kernel
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# # <span style="color:red">Seaborn | Part-9: Strip Pl... | Seaborn - Strip Plot.ipynb |
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# # Step 2B... | notebooks/2b_model_testing.ipynb |
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# # User Churn Prediction
# ## Introducation
# In thi... | User Churn Prediction.ipynb |
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def login():
email=driver.find_element_by_id('emai... | facebook.com/facebook.com.ipynb |
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# # Probabilistic Programming
# %matplotlib inline
im... | notebooks/S11_Probabilistic_Programming.ipynb |
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# + [markdown] id="view-in-github" colab_type="text"
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# # Groupby
#
# The groupby method allows you to group... | Python/data_science/data_analysis/03-Python-for-Data-Analysis-Pandas/05-Groupby.ipynb |
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import numpy as np
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# # Introduction to ZSE and Useing Zeolite Frameworks
... | examples/01_introduction.ipynb |
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#Imports
# %matplotlib inline
import numpy as np
impor... | Decision Tree Random Forest.ipynb |
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# + [markdown] id="view-in-github" colab_type="text"
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# + colab={"base_uri": "https://localhost:8080/", "hei... | The_Boston_Housing_Price.ipynb |
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# + [markdown] button=false new_sheet=false run_contro... | Slicer.ipynb |
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# ## Nome: <NAME>
# + [markdown] id="fUe-... | aulas/para_alunos_aula_04/exercicios_pandas.ipynb |
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# # Grover's Algorithm and Amplitude Amplification
#
#... | tutorials/algorithms/07_grover.ipynb |
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# # Advanced dynamic seq2seq with TensorFlow
# Encode... | 2-seq2seq-advanced.ipynb |
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from torch import nn
from to... | pytorch_ca/perturbation_CA.ipynb |
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# # Chapter 2
#
#
# # Python Data Structures and Control Flow... | essential/02.ipynb |
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# HyperTS has built-in rich modeling algor... | examples/07_custom_search_space_01.ipynb |
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# ### 兩種Reverse的解法
# 下面為使用python兩種reverse的解法，其中一種使用工具r... | Reverse Solutions.ipynb |
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# # Read in catalog information from a text file and plot some parameters
#
# ## Authors
# <NAME>, <NAME>, <NAME>
#
# ## Learning Goals
# * Re... | tutorials/plot-catalog/plot-catalog.ipynb |
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# %load_ext autoreload
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import ee
import geemap.foliumap as geemap
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# + [markdown] colab_type="text" id="jYysdyb-CaWM"
# #... | examples/tfExamples/00_FCLayer_Fashon_MNIST_F64.ipynb |
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# + [markdown] id="Iwn4nmRpHrcy" colab_type="text"
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#numbers of files to load
nFiles = 4
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from IPython.core.display import display, HTML
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# # Load data
import numpy as np
import sklearn
imp... | KaggleComp.ipynb |