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<jupyter_start><jupyter_text># Install and Import packages**Machine Learning for Smart Health Systems**
Instructor: Juber Rahman
**Omdena School**
Course Link: https://omdena.com/course/machine-learning-for-smart-health-systems/
Updated: Oct 30, 2021<jupyter_code># !pip install hrv-analysis
# !pip install wfdb
from IPy... | no_license | /week 1/guided-lab-1.ipynb | tathiep04/Smart-Health | 4 |
<jupyter_start><jupyter_text># Ceate a file list from directory " use "<jupyter_code>!rm imagelist.txt
!ls use>>imagelist.txt<jupyter_output><empty_output><jupyter_text>convert imagelist.txt from:
use/001.png
use/002.png
use/003.png
use/004.png
to
uselist.txt in format of:
file 'use/001.png'
fil... | no_license | /FFMPEG152558344673152558344673.ipynb | JupyterJones/FFMPEG | 10 |
<jupyter_start><jupyter_text># Imports<jupyter_code>import sys
sys.path.append('..')
import os
import json
from time import time
import numpy as np
from tqdm import tqdm
import theano
import theano.tensor as T
from theano.sandbox.cuda.dnn import dnn_conv
from PIL import Image
from matplotlib.pyplot import imshow
%ma... | no_license | /Neural_Restoration/Sample Images/.ipynb_checkpoints/dcgan_autoencoder_notebook-checkpoint.ipynb | unif2/my_recent_projects | 10 |
<jupyter_start><jupyter_text>## Print out the parameter names and shapes<jupyter_code>for name, param in model.named_parameters():
print(name, param.shape)<jupyter_output>conv1.weight torch.Size([64, 3, 7, 7])
bn1.weight torch.Size([64])
bn1.bias torch.Size([64])
layer1.0.conv1.weight torch.Size([64, 64, 3, 3])
layer... | no_license | /Deep Learning/D07_TorchModel/Day7_PyTorch_Model.ipynb | VincentChen0110/NLP_Marathon | 3 |
<jupyter_start><jupyter_text># Time Series with Pandas## DateTime Index<jupyter_code>my_year = 2021
my_month = 4
my_day = 16
my_hour = 19
my_min = 39
my_sec = 15
my_date = datetime(my_year, my_month, my_day)
my_date
my_date_time = datetime(my_year, my_month, my_day, my_hour, my_min, my_sec)
my_date_time
my_date_time.ho... | no_license | /Udemy Time Series Analysis/.ipynb_checkpoints/Section 6 Time Series with Pandas-checkpoint.ipynb | ai-ragare/my_notebooks1 | 6 |
<jupyter_start><jupyter_text>
Colormap Normalization
======================
Objects that use colormaps by default linearly map the colors in the
colormap from data values *vmin* to *vmax*. For example::
pcm = ax.pcolormesh(x, y, Z, vmin=-1., vmax=1., cmap='RdBu_r')
will map the data in *Z* linearly from -1 to +... | no_license | /_downloads/59a7c8f3db252ae16cd43fd50d6a004c/colormapnorms.ipynb | matplotlib/matplotlib.github.com | 6 |
<jupyter_start><jupyter_text># Regular Expressions
Regular expressions are a powerful tool for searching for patterns in text files. Here we're going to learn how to use them by searching for patterns in the unicorn genome.
Outline
- Using regular expressions at the command line with `grep`
- Using regular expression... | no_license | /regular_expressions_tutorial/.ipynb_checkpoints/regular_expressions_tutorial-checkpoint.ipynb | merlab-uw/Tutorials | 4 |
<jupyter_start><jupyter_text>## Load Data<jupyter_code># Read the census data into a Pandas DataFrame
file_path = Path("Data/sfo_neighborhoods_census_data.csv")
sfo_data = pd.read_csv(file_path, index_col="year")
sfo_data.head()<jupyter_output><empty_output><jupyter_text>## Housing Units Per Year
In this section, you ... | no_license | /Analysis_rental.ipynb | DialloMamadouO/San-Francisco-Rental-Analysis | 10 |
<jupyter_start><jupyter_text>**[Machine Learning Home Page](https://www.kaggle.com/learn/intro-to-machine-learning)**
---
## Recap
You've built your first model, and now it's time to optimize the size of the tree to make better predictions. Run this cell to set up your coding environment where the previous step left o... | permissive | /Exercise_ Underfitting and Overfitting.ipynb | abhiit89/python_basic_notebooks | 4 |
<jupyter_start><jupyter_text># Python数据分析之Matplotlib可视化最有价值的50个图表（附完整Python源代码）
[TOC]
本文总结了50个图表绘制方法，对于数据分析的可视化有莫大的作用。
**Tips：**
* 本文原文部分代码有不准确的地方，已进行修改；
* 所有正确的源代码，已整合到 jupyter notebook 文件中；
* 运行本文代码，除了安装 matplotlib 和 seaborn 可视化库外，还需要安装其他的一些辅助可视化库，已在代码部分作标注，具体内容请查看下面文章内容。
在数据分析和可视化中最有用的 50 个 Matplotlib 图表。 ... | permissive | /Numpy-Pandas-Matplotlib/Matplot050.ipynb | veritastry/trainee | 57 |
<jupyter_start><jupyter_text>## cria grafo
<jupyter_code>G = grafo()
G.acresc_aresta('a', 'b')
G.acresc_aresta('a', 's')
G.acresc_aresta('s', 'b')
G.acresc_aresta('s', 'c')
G.acresc_aresta('s', 'e')
G.acresc_aresta('f', 'e')
G.acresc_aresta('f', 'c')
G.acresc_aresta('f', 'd')
G.acresc_are... | permissive | /DFS.ipynb | h3dema/Graphs | 3 |
<jupyter_start><jupyter_text>https://realpython.com/python-keras-text-classification/#keras-embedding-layer<jupyter_code>import pandas as pd
filepath_dict = {'yelp': 'data/yelp_labelled.txt',
'amazon': 'data/amazon_cells_labelled.txt',
'imdb': 'data/imdb_labelled.txt'}
df_list = ... | no_license | /script-zone/realpython.ipynb | ejhb/2021-03-9-perceptron-keras-more | 10 |
<jupyter_start><jupyter_text># Subject: Data Science Foundation
## Session 13 - Correlation in Python.
### Exercise 1 - Calculating Correlation in Pandas
We will be using a dataset "Advertising". Calculate the Pearson correlation of sales with TV, radio and newspaper.<jupyter_code>import pandas as pd
import numpy as... | no_license | /DSF_Session13_Correlation_Exercise1.ipynb | MariiaShcherbiak/DataScienceFoundations | 1 |
<jupyter_start><jupyter_text># Regular Expressions in python
## The "re" library<jupyter_code>text = "The agent's phone number is 838-102-5113. Call ASAP !"
'phone' in text
import re
pattern = 'phone'
re.search(pattern,text)<jupyter_output><empty_output><jupyter_text># Character Identifiers<jupyter_code>text = "My p... | no_license | /.ipynb_checkpoints/Regular Expressions in python-checkpoint.ipynb | shaninathpawargit/Python-3-Bootcamp | 4 |
<jupyter_start><jupyter_text>### Importing Libraries <jupyter_code>from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D
from tensorflow.keras.layers import MaxPool2D
from tensorflow.keras.layers import Flatten
from tensorflow.keras.layers import Dense
from tensorflow.keras.preproces... | no_license | /.ipynb_checkpoints/Tensorflow_workshop_PSG-checkpoint.ipynb | Navayuvan-SB/Tensorflow_PSG | 11 |
<jupyter_start><jupyter_text># Text Processing
## Capturing Text Data
### Plain Text<jupyter_code>import os
# Read in a plain text file
with open(os.path.join("data", "hieroglyph.txt"), "r") as f:
text = f.read()
print(text)<jupyter_output>Hieroglyphic writing dates from c. 3000 BC, and is composed of hundre... | no_license | /.ipynb_checkpoints/text_processing-checkpoint.ipynb | amosokech/udacity-nlp | 11 |
<jupyter_start><jupyter_text># Subplots<jupyter_code>%matplotlib notebook
import matplotlib.pyplot as plt
import numpy as np
plt.subplot?
plt.figure()
# subplot with 1 row, 2 columns, and current axis is 1st subplot axes
plt.subplot(1, 2, 1)
linear_data = np.array([1,2,3,4,5,6,7,8])
plt.plot(linear_data, '-o')
expo... | permissive | /Applied Plotting, Charting & Data Representation in Python/Week3.ipynb | Ashleshk/Applied-Data-Science-with-Python | 6 |
<jupyter_start><jupyter_text># Class Coupling
____
1. Class Coupling
2. Inheritance
3. Composition
4. Composition over Inheritance
<jupyter_code>using System;
// using System.Collections.Generic;
// using System.Linq;
// using System.Text;
// using System.Threading.Tasks;
<jupyter_output><empty_output><jupyter_text>... | no_license | /10 - 19/10. Classes Coupling.ipynb | BSCdfdff/c-intro | 3 |
<jupyter_start><jupyter_text># Error Handling
The code in this notebook helps with handling errors. Normally, an error in notebook code causes the execution of the code to stop; while an infinite loop in notebook code causes the notebook to run without end. This notebook provides two classes to help address these c... | non_permissive | /notebooks/ExpectError.ipynb | alanvivona/fuzzingbook | 5 |
<jupyter_start><jupyter_text>Geoffrey Gordon Ashbrook Assignment 2.1.1
2019.09.30
Lambda School Data Science, Unit 2: Predictive Modeling
# Regression & Classification, Module 1
## Assignment
You'll use another **New York City** real estate dataset.
But now you'll **predict how much it costs to rent an apartment*... | permissive | /module1/GGA_2_1_1_v1_assignment_regression_classification_1.ipynb | lineality/DS-Unit-2-Regression-Classification | 12 |
<jupyter_start><jupyter_text># Pytorch与张量
**[小象学院](http://www.chinahadoop.cn/course/landpage/15)《机器学习集训营》课程资料**<jupyter_code>%matplotlib inline<jupyter_output><empty_output><jupyter_text>
PyTorch是什么?
================
PyTorch是一个基于Torch的Python开源机器学习库，用于深度学习神经网络构建AI应用程序。 它主要由Facebook的人工智能研究小组... | no_license | /mlInAction/code/4.Pytorch1.0_Deep_Learning/.ipynb_checkpoints/1.tensor_tutorial-checkpoint.ipynb | gyher/xiaoxiang | 19 |
<jupyter_start><jupyter_text># First Pass SIC/XE<jupyter_code>path = input('Enter file : ')
def split_(line):
line = line[:-1].split(' ')
while '' in line:
line.remove('')
if len(line) == 2:
line.insert(0,'-')
if len(line) == 1:
line.insert(0,'-')
line.insert(2,'-')
... | no_license | /python/sic_xe/.ipynb_checkpoints/pass1-checkpoint.ipynb | hariknair77/lab | 1 |
<jupyter_start><jupyter_text>number에 5가 넘어왔을 때 함수가 실행되는 순서
5 * factorialRecursion(4)
5 * (4 * factorialRecursion(3))
5 * (4 * (3 * factorialRecursion(2)))
5 * (4 * (3 * (2 * factorialRecursion(1))))
5 * (4 * (3 * (2 * (1))))
***
5 * (4 * (3 * (2 * 1)))
5 * (4 * (3 * 2))
5 * (4 * 6)
5 * 24
120<jupyte... | no_license | /20210702_014/66_recursionFunction.ipynb | Kimjunyoung111/kookgi_11gi | 1 |
<jupyter_start><jupyter_text>## Importar el set de datos de moda de MNIST[Fashion MNIST](https://github.com/zalandoresearch/fashion-mnist) contiene mas de 70,000 imagenes en 10 categorias. Las imagenes muestran articulos individuales de ropa a una resolucion baja (28 por 28 pixeles):
<img src="https://tensorfl... | no_license | /.ipynb_checkpoints/103. PrimeraRed_FashionMNIST-checkpoint.ipynb | Rocio222/DS-bootcamp | 15 |
<jupyter_start><jupyter_text># The main objective of this project is to identify the most responsive customers before the marketing campaign so that the bank will be able to efficiently reach out to them, saving time and marketing resources.To achieve this objective, classification algorithms will be employed. By analy... | no_license | /Classification_BankMarketing_Shivani.ipynb | kurhula/Machine-Learning-with-Python--Classification | 49 |
<jupyter_start><jupyter_text># Building a Libor CurveThis is an example of a replication of a BBG example from
https://github.com/vilen22/curve-building/blob/master/Bloomberg%20Curve%20Building%20Replication.xlsx
Agreement is very good however some issues about date generation need to be checked.<jupyter_code>import ... | non_permissive | /notebooks/LIBOR_LIBORCURVE_ReplicatingBBGExample.ipynb | ajmal017/FinancePy | 9 |
<jupyter_start><jupyter_text># Homework 9
## Instructions
+ Please write you solutions in cells below each problem. Use multiple cells if necessary.
+ The solutions may be in the form of code, markdown, or a combination of both. Make an effort to present the solutions in a clean fashion.
+ Please submit this notebook... | no_license | /06_Statistics/Python_for_Statistics/HW/zhang_ran_hw9.ipynb | rzhang0716/Data-Science | 2 |
<jupyter_start><jupyter_text># Computer vision data<jupyter_code>%matplotlib inline
from fastai.gen_doc.nbdoc import *
from fastai import *
from fastai.vision import * <jupyter_output><empty_output><jupyter_text>This module contains the classes that define datasets handling [`Image`](/vision.image.html#Image) objects ... | non_permissive | /docs_src/vision.data.ipynb | rh01/fastai | 32 |
<jupyter_start><jupyter_text>### Heroes Of Pymoli Data Analysis
* Of the 1163 active players, the vast majority are male (84%). There also exists, a smaller, but notable proportion of female players (14%).
* Our peak age demographic falls between 20-24 (44.8%) with secondary groups falling between 15-19 (18.60%) and 2... | no_license | /HeroesOfPymoli_Final.ipynb | miumiuannie/Pandas-Homework | 10 |
<jupyter_start><jupyter_text>##### Copyright 2018 The TensorFlow Authors.<jupyter_code>#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# ... | no_license | /TensorFlow/l08c03_moving_average.ipynb | sfh22/Practice | 11 |
<jupyter_start><jupyter_text># Lab 9: Binary hypothesis testing, sequential hypothesis testing, and gambler's ruin<jupyter_code>%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import scipy as sp
import scipy.stats as st
from functools import reduce
print ('Modules Imported!')<jupyter_output>Module... | no_license | /lab9/.ipynb_checkpoints/Hangzheng Lin Lab 8-checkpoint.ipynb | LinHangzheng/ECE314 | 11 |
<jupyter_start><jupyter_text># Passeio aleatório em 1D<jupyter_code># Vamos começar o passeio aleatório na origem
x = 0
# Definimos o número de passos que queremos analisar
N = 100
# Aqui, o passo é definido como um passo à direita ou à esquerda
# A PROBABILIDADE DE DAR UM PASSO À DIREITA É IGUAL À DE DAR UM PASSO PA... | no_license | /Guiado1/.ipynb_checkpoints/RW-checkpoint.ipynb | BrunoBVR/Projeto-de-Ensino-Python | 6 |
<jupyter_start><jupyter_text># Lab 3
## Submitting lab solutions
At the end of the previous lab, you should have set up a "Solutions" directory in your home directory on TACC, with a fork of the class git repository that pull from Dr. Farbin's verison and pushes to your own fork.
Open a terminal, navigate to your f... | no_license | /Labs/Lab-3/Lab-3-Solution.ipynb | Peter-Severynen/DATA1401-Spring-2019 | 18 |
<jupyter_start><jupyter_text>
String Operations ## Table of Contents
Strings
Indexing
Escape Sequences
String Operations
Quizz
Estimated Time Needed: 15 min
Strings A string is contained within 2 quotes:<jupyter_code>"Michael Jackson"<jupyter_output><empty_output><jupyter_text>You can also use single ... | no_license | /[Course] IBM Data Science Professional Certificate/Course 4 - Python for Data Science/Week 1 - LAB Strings.ipynb | sandyg05/cs | 35 |
<jupyter_start><jupyter_text># Lecture Review 2/16/16## DictionariesDictionaries provide one-to-one mappings from key to value. They are incredibly useful when you have common IDs, like gene symbols, or accession numbers.* You can initialize a dictionary using the dictionary literal syntax<jupyter_code>Dict = {'first_n... | non_permissive | /additional-notebooks/deprecated/LectureReview2-16-16.ipynb | BlueGranite/BlueGranite-Python-Training | 34 |
<jupyter_start><jupyter_text># Traffic Light Detection and Classification
Using a pre-trained model to detect objects in an image.<jupyter_code># for tensorflow 1.1x
# cd to maxc
# TODO
# 1. imports from the object detection module
# 2. Model preparation (paths setup)
# 3. jpg or png
# 4. vedio?
import numpy as np
imp... | no_license | /maxc_pic/.ipynb_checkpoints/TrafficLightDetection-Inference-checkpoint.ipynb | chen-xin-94/Deep-Learning-Based-Traffic-Light-Detection | 4 |
<jupyter_start><jupyter_text># Gradient Descent Exercise
This notebook provides the skeleton required to implement a gradient descent algorithm that learns the parameters of a linear regression task. First, we will generate some noisy, linear data to fit a model. Note that $y=\theta_0 + \theta_1 x_1$ with $\theta_0=1.... | no_license | /13_data_modeling/AI_and_ML_Intro_MITRE_with_python/.ipynb_checkpoints/Demo-GradientDescent-Basic-checkpoint.ipynb | uva-bi-sdad/training | 1 |
<jupyter_start><jupyter_text># 날씨 데이터 불러오기<jupyter_code>충북1112 = pd.read_csv('/content/drive/MyDrive/Proj_WT/DataSets/기상청_일별_지상_종관_ASOS/1112/청주기상지청.csv')
충북1314 = pd.read_csv('/content/drive/MyDrive/Proj_WT/DataSets/기상청_일별_지상_종관_ASOS/1314/청주기상지청.csv')
충북1516 = pd.read_c... | no_license | /ipynb/월별_분석.ipynb | SD-academy/dadaiksunTeamProject | 3 |
<jupyter_start><jupyter_text>## BREAST CANCER CLASSIFICATION
Predicting if the cancer diagnosis is benign or malignant based on several observations/features
30 features are used, examples:
- radius (mean of distances from center to points on the perimeter)
- texture (standard deviation of gray-scale values)
-... | no_license | /Breast Cancer Classification/Breast Cancer Classification.ipynb | prtk1306/Data_Science | 4 |
<jupyter_start><jupyter_text># Colaboratoryで実行する場合
以下を実行して、外部ファイルをダウンロードしてください。
**このセルはColaboratoryを起動するたびに必要となります**<jupyter_code>##################################
### Colaboratoryのみ以下を実行 ###
##################################
import sys
if 'google.colab' in sys.modules:
!wget -P ./sound http://www.hal.t.u-toky... | no_license | /mediaproc2/mp_ex2.ipynb | haodong1228/media-programming | 3 |
<jupyter_start><jupyter_text># Analysis of the Titanic Disaster Data<jupyter_code>#Source of the data
psql -h dsi.c20gkj5cvu3l.us-east-1.rds.amazonaws.com -p 5432 -U dsi_student titanic
password: gastudents
%load_ext sql
%%sql postgresql://dsi_student:gastudents@dsi.c20gkj5cvu3l.us-east-1.rds.amazonaws.com/titanic... | no_license | /projects/projects-weekly/project-05/.ipynb_checkpoints/Project5_Final-checkpoint.ipynb | DPalit/GA-DSI | 22 |
<jupyter_start><jupyter_text># 2.2 The Split-Apply-Combine Strategy<jupyter_code>%matplotlib inline
import pandas as pd<jupyter_output><empty_output><jupyter_text>In the previous section, we discussed how to restrict our analysis to a particular subset of observations using boolean masks. So, for example, if we wanted ... | no_license | /book/Chapter 2 Subgroup Analysis/2.2 The Split-Apply-Combine Strategy.ipynb | Anderson-Lab/Data3012019Fall | 14 |
<jupyter_start><jupyter_text>### Save Model<jupyter_code>model_name = './4900_6_coref_new_emnlp17.mdl'
with open(model_name, 'wb') as f:
pickle.dump(bst, f)
model = pickle.load(open(model_name, 'rb'))<jupyter_output><empty_output><jupyter_text>### Load Model<jupyter_code>model_name = './4900_6_coref_new.mdl'
# with... | no_license | /model-training/new_features_coref-Copy1.ipynb | tjumyk/RMA | 2 |
<jupyter_start><jupyter_text># Shattering##
See fig 1.
##
See fig 2
##
VC-Dimension = 0
Because the function is squared and there is no hyper parameter on the outside of the square term, all outputs from the function are positive. ... | no_license | /content/COGS118a/files/Hw5/.ipynb_checkpoints/20180221_COGS118a_Hw5-checkpoint.ipynb | hal009/hal009.github.io | 7 |
<jupyter_start><jupyter_text># 🍎 파이썬 머신러닝 완벽 가이드 혼공
### 2019.04.01 ~ 2019.04.11 교재 03-05장
03. 평가
04. 분류
05. 회귀<jupyter_code>import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from scipy import stats
from sklearn.datasets import load_boston
%matplotlib inline
# boston 데이타셋... | no_license | /CUAI/2020-1학기/.ipynb_checkpoints/머신러닝_5장_회귀_사이킷런 LinearRegression을 이용한 보스턴 주택 가격 예측-checkpoint.ipynb | givemetarte/superhotpot | 3 |
<jupyter_start><jupyter_text>## Part 3: Multiclass linear classification
$
\newcommand{\mat}[1]{\boldsymbol {#1}}
\newcommand{\mattr}[1]{\boldsymbol {#1}^\top}
\newcommand{\matinv}[1]{\boldsymbol {#1}^{-1}}
\newcommand{\vec}[1]{\boldsymbol {#1}}
\newcommand{\vectr}[1]{\boldsymbol {#1}^\top}
\newcommand{\diag}{\mathop{... | no_license | /Part3_LinearClassifier.ipynb | Ofir-Shechtman/CS236781-Deep-Learning-hw1 | 14 |
<jupyter_start><jupyter_text># Distplot
### (https://plot.ly/python/distplot/)<jupyter_code>def distplot_columns(y_col, columns, bin_size=.1):
if len(columns) > 5:
raise ValueError('Maximum 5 x-columns is allowed!')
for c in columns:
x0 = df[df[y_col] == 0][c]
x1 = df[df[y_col] == 1][c]... | no_license | /examples/viz/Plotly-1.ipynb | romik9999/experiments | 9 |
<jupyter_start><jupyter_text># The network of charge stations in Catalonia (GENCAT)
In this project I will analyse the most recent database published by the government (4th December 2015) state of the charging stations in catalonia.
The data is provided by the Open Data project from "Generalitat de Catalunya".<jupyter... | no_license | /.ipynb_checkpoints/Final Project-checkpoint.ipynb | alsolanes/CN_Final_Project | 3 |
<jupyter_start><jupyter_text># Building a Dual Ibor CurveThe aim is to construct an IBOR curve that uses OIS discounting<jupyter_code>import numpy as np
import matplotlib.pyplot as plt
from financepy.finutils import *
from financepy.products.funding import *
setDateFormatType(FinDateFormatTypes.UK_LONGEST)
suppressErro... | non_permissive | /notebooks/products/funding/FINIBORDUALCURVE_BuildingASimpleDualIborCurve.ipynb | kalyan678/FinancePy | 9 |
<jupyter_start><jupyter_text># Cartopy Showcase
This notebook closely follows the Earth and Enviromental Data Science course at https://earth-env-data-science.github.io/intro.html<jupyter_code>from matplotlib import pyplot as plt
import cartopy
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import urllib... | no_license | /Earth and Environmental Data Science Tutorial/.ipynb_checkpoints/Cartopy-checkpoint.ipynb | philipc2/CDDS-Python | 2 |
<jupyter_start><jupyter_text>Training the model<jupyter_code>#Here we split the data into training and test sets and use the decision tree regression algorithm to train the model
predict = "price"
data = data[["symboling", "wheelbase", "carlength",
"carwidth", "carheight", "curbweight",
"enginesize", "boreratio", "stro... | no_license | /Car Price Prediction/Car Price Prediction.ipynb | Shaily2900/Car-Price-Prediction- | 1 |
<jupyter_start><jupyter_text># The topic of my project is visualizing the fate of Sun-like stars in M31 throughout the course of the merger
# Question I'm answering: How can I center a visualization of the merger on the Milky Way (ANSWERED) What criteria should I use to select solar analogs? How do I differentiate the... | no_license | /Research Assignments/5/.ipynb_checkpoints/SolarCandidates_TEST-checkpoint.ipynb | jlilly364/400B_Lilly | 1 |
<jupyter_start><jupyter_text># Assignment 1: Spark Funds
## Objective
#### Spark Funds has two minor constraints for investments:
1. It wants to invest between 5 to 15 million USD per round of investment
2. It wants to invest only in English-speaking countries because of the ease of communication with the co... | no_license | /IITB_predictive/upgrad_Assignment_1_stats/Assignment_1.ipynb | theAI-samurai/PredictiveModels | 6 |
<jupyter_start><jupyter_text># Devoir Python
Vous devez rendre votre devoir sur GitHub.
Vous avez le droit a tout vos documents et a internet
1. votre depot doit etre privé
2. vous devez inviter comme colaborateur votre chargé de TD/TP
3. Seul le dernier commit avant la fin de la séance sera corrigé.
Ex 1: Integrale ... | no_license | /itiiPythonExam.ipynb | charlesboyerCPE/Exam_Python | 3 |
<jupyter_start><jupyter_text># MILESTONE 2
IMDB dataset + Siraj's Network<jupyter_code>import numpy as np
import tensorflow as tf
import re
from collections import Counter
import json
from pprint import pprint
def get_vocab(sentences):
words = []
for each in sentences:
each = each.lower()
words... | no_license | /milestone-3/M2_code-harshit-without-dropout-new+.ipynb | dhruvsharma15/sentiment_analysis | 2 |
<jupyter_start><jupyter_text># This notebook will be used for the IBM Data Science Professional Certificate capstone project<jupyter_code>from bs4 import BeautifulSoup
import requests
import pandas as pd
import numpy as np
import requests
from bs4 import BeautifulSoup
import os
from sklearn.cluster import KMeans
!pip i... | no_license | /IBM_Data_Science_Capstone_week3.ipynb | KrishnaPatH/IBM-Capstone | 1 |
<jupyter_start><jupyter_text>### Load data:<jupyter_code>d = {}
tree_names = [""]
flist = os.listdir(inputdir)
for sample in dict_names:
file_name = ""
for fn in flist:
if sample + "_" in fn:
file_name = fn
file = uproot.open(inputdir + file_name)[main_tree_name]
d[sample] = {}
... | no_license | /NuMuCC_validation_trackstudies_august5 (3).ipynb | rutgersnu/mcc9-reco | 13 |
<jupyter_start><jupyter_text># Crossview Localisation (CVM-Net)This notebook is a step by step instruction on how to download and run the code from the paper "CVM-Net: Cross-View Matching Network for Image-Based Ground-to-Aerial Geo-Localisation" (Hu et al., 2018). The code can be acessed via the authors' github page: ... | no_license | /01_CVM-Net/CVM-Net_Instruction.ipynb | k47ha/Geolocation_Estimation | 10 |
<jupyter_start><jupyter_text>copy from https://blog.naver.com/ckdgus1433/221443838135# 모듈 임포팅<jupyter_code>import matplotlib.pyplot as plt
import numpy as np
from tensorflow.keras.datasets import mnist
from tensorflow.keras.layers import Dense
from tensorflow.keras.models import Sequential<jupyter_output><empty_output>... | non_permissive | /material/deep_learning/autoencoder.ipynb | KangMinPyo/cau_2021 | 7 |
<jupyter_start><jupyter_text>### 1. fig.tight_layout : Axes Layout Adjustment
- set_title
- set_xlabel
- set_ylabel<jupyter_code>fig, ax = plt.subplots(figsize=(10,10))
ax.set_title('Title!', fontsize=20)
ax.set_xlabel('X label!', fontsize=15)
ax.set_ylabel('Y label!', fontsize=15)
title_list = ['Ax' + str(... | no_license | /visualize/anatomy/05_axes_alignment_custom.ipynb | Junhojuno/TIL-v2 | 4 |
<jupyter_start><jupyter_text>## Немного об апроксимации
Из-за бутылочного горлышка в автокодировщиках, мы теряем часть информации.
У нас всегда есть трейд-офф - какую часть информации мы готовы потерять при снижении размерности. Что нам важнее - мало факторов или снижение информации. Чем меньше будет факторов в латент... | non_permissive | /_under_construction/technical_2020/week08/manifold learning.ipynb | dniku/neural_nets_dpo | 8 |
<jupyter_start><jupyter_text># 程序设计：直线式程序解释器<jupyter_code>type BoxStm = Box<Stm>;
type BoxExp = Box<Exp>;
type BoxExpList = Box<ExpList>;
#[derive(Debug)]
enum Binop {
Plus,
Minus,
Times,
Div,
}
#[derive(Debug)]
enum Stm {
Compound(BoxStm, BoxStm),
Assign { id: String, exp: BoxExp },
Print... | no_license | /chap1.ipynb | zhu1971/tiger_exercises | 6 |
<jupyter_start><jupyter_text>### Airline Data Analysis_DEP_DELAY_ARR_DELAY:
The arrival delays should be linear with departure delays. So we can do linear regression on the data to predict arrival delays. <jupyter_code>PATH='/Users/franciumpnc/Documents/ML/AQM/ML_practice_projects/Airline_data_analysis/data/processed'
... | no_license | /Airline_data_analysis/notebooks/.ipynb_checkpoints/Regression_DEP_delay_Arr_Delay-checkpoint.ipynb | DRMRK/Machine_Learning- | 2 |
<jupyter_start><jupyter_text>Let's first look at the filaments found by Suri et al. (2019) in the CARMA-NRO Orion C18O data. Let's load the filament (x,y,v) coordinates into tables. From Sumeyye's readme:
> Each identified filament has its own txt file that is organized as (x y v) coordinates of the pixels along the ... | no_license | /code/Filaments.ipynb | jrobbfed/outflows | 8 |
<jupyter_start><jupyter_text># Desafio 1:<jupyter_code>sns.boxplot(x='color', y='imdb_score', data=color_or_bw)<jupyter_output><empty_output><jupyter_text># Desafio 2:<jupyter_code>imdb_usa.sort_values('lucro').head(1)['movie_title']<jupyter_output><empty_output><jupyter_text># Desafio 3:<jupyter_code>imdb_usa.query('b... | no_license | /Aula3/Desafios_aula03.ipynb | mmanfrin/QuarentenaDados | 10 |
<jupyter_start><jupyter_text>## Matrix multiplication### Loop<jupyter_code>def matmul(a,b):
a_r, a_c = a.shape
b_r, b_c = b.shape
#print(a_r,a_c,b_r,b_c)
result = torch.zeros(a_r, b_c)
for i in range(a_r):
for j in range(b_c):
result[i, j] = (a[i]*b[:,j]).sum()
return result
... | permissive | /nbs/dl2/sandbox_lesson1.ipynb | pyjaime/fastai-dl-course-2019 | 6 |
<jupyter_start><jupyter_text>### Load the data<jupyter_code>(train_data, train_labels), (test_data, test_labels) = reuters.load_data(num_words=10000)
train_data.shape
train_labels.shape
test_data.shape
test_labels.shape
word_index = reuters.get_word_index()
reverse_word_index = dict([(value, key) for (key, value) in wo... | no_license | /reuters_multiclass_classification.ipynb | jinilcs/deeplearning_with_python_repo | 5 |
<jupyter_start><jupyter_text>
Geoelectrics in 2.5D
--------------------
Geoelectrical modeling example in 2.5D. CR
Let us start with a mathematical formulation ...
\begin{align}\nabla\cdot(\sigma\nabla u)=-I\delta(\vec{r}-\vec{r}_{\text{s}}) \in R^3\end{align}
The source term is 3 dimensional but the distribution of... | no_license | /examples/dc_and_ip/plot_mod-dc-2d.ipynb | Khalilsqu/try.pygimli.org | 1 |
<jupyter_start><jupyter_text>## Libraries<jupyter_code>%matplotlib inline
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.preprocessing import LabelEncoder, OneHotEncoder, OrdinalEncoder, Imputer
from sklearn.model_selection import train_test_split, cross_val_... | no_license | /data_science/.ipynb_checkpoints/01_example-checkpoint.ipynb | dlxiii/HackerRank_exe | 11 |
<jupyter_start><jupyter_text>What is the average quantity bought by the customer 14769?
**collect** function https://sparkbyexamples.com/pyspark/pyspark-collect/<jupyter_code>from pyspark.sql.functions import col, avg
# 1
print(df.where(df.CustomerID == "14769").agg(avg(col("Quantity"))).collect()[0][0]) # first ro... | no_license | /lab5/exercises/Lab5_DF_Ex2.ipynb | octokami/DE2021 | 2 |
<jupyter_start><jupyter_text># ASSIGNMENT
### 1) Replicate the lesson code. I recommend that you [do not copy-paste](https://docs.google.com/document/d/1ubOw9B3Hfip27hF2ZFnW3a3z9xAgrUDRReOEo-FHCVs/edit).
<jupyter_code>import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import AutoMinorL... | permissive | /module3-make-explanatory-visualizations/Johana_LS_DS7_123_Make_Explanatory_Visualizations_Assignment.ipynb | johanaluna/DS-Unit-1-Sprint-2-Data-Wrangling-and-Storytelling | 4 |
<jupyter_start><jupyter_text># Visual Odometry for Localization in Autonomous Driving
Welcome to the assignment for Module 2: Visual Features - Detection, Description and Matching. In this assignment, you will practice using the material you have learned to estimate an autonomous vehicle trajectory by images taken wit... | no_license | /Visual Odometry for Localization in Autonomous Driving.ipynb | jayhsu0627/Self_Driving_Car_specialization | 13 |
<jupyter_start><jupyter_text># Convolutional Neural Networks
## Project: Write an Algorithm for a Dog Identification App
---
In this notebook, some template code has already been provided for you, and you will need to implement additional functionality to successfully complete this project. You will not need to mod... | no_license | /dog_appV4.ipynb | bosswhip/Dog-breed | 23 |
<jupyter_start><jupyter_text># San Francisco Crime Classification
[San Francisco crime dataset from Kaggle](https://www.kaggle.com/c/sf-crime)
**Data fields:**
* Dates - timestamp of the crime incident
* Category - category of the crime incident (only in train.csv). This is the target variable you are going to predi... | permissive | /jupyter/kaggle_sf-crime/SF Crime.ipynb | goerlitz/ds-notebooks | 3 |
<jupyter_start><jupyter_text># INFOVIS 2021 - Vacunaciones Argentina 2021
## 1) Obtención de DatosFuente: Ministerio de Salud de la Nación, Dirección de Control de Enfermedades Inmunoprevenibles (DiCEI)
Dosis Aplicadas en la República Argentina - [Dataset](http://datos.salud.gob.ar/dataset/vacunas-contra-covid19-dosis... | no_license | /notebooks/.ipynb_checkpoints/03VacunacionesCoincidentesResidencia-checkpoint.ipynb | lucasibaniez/infovis_tp | 8 |
<jupyter_start><jupyter_text># Retail Suite Demo
[](https://www.youtube.com/watch?v=6URZhvByKGg)
### Luca Ruzzola, Machine Learning Engineer @ aim2.ioRetail Suite is **aim2**'s solution for providing detailed KPIs for brand exposure and customer analytics in stores and retail locations.
All o... | no_license | /RetailDemoQueries.ipynb | lucaruzzola/aaeon_workshop | 8 |
<jupyter_start><jupyter_text>## SVD for Dimensionality Reduction
## Introduction
There are a few techniques to actually perform the reduction. In this portion of the sprint we will use Singular Value decomposition (SVD) to deconstruct a feature matrix and perform a dimensionality reduction.
#### Dataset
We will be u... | no_license | /solutions/svd/SVD_soln.ipynb | jkh-code/data_science | 16 |
<jupyter_start><jupyter_text>세웅이 모델에 들어가는 이미지 형태는 최종 BGR이여야 함!<jupyter_code>def original(imgpath):
img = load_img(img_path)
img_array = img_to_array(img)
return img_array
# adaptive threshold
def threshold(imgpath):
# img = cv2.imread(imgpath)
img = load_img(imgpath)
img = img_to_array(im... | no_license | /Hyundai-Project-master/데이터 전처리/Data_augmentation-Seonmin-Copy1.ipynb | Woonggss/2020-deep-learning-project | 5 |
<jupyter_start><jupyter_text>## Imports<jupyter_code>import numpy as np
import pandas as pd
# for data-visualization
import seaborn as sns
import matplotlib.pyplot as plt
# import pandas_profiling as pp
# from sklearn import svm
# from sklearn.datasets import make_moons, make_blobs
from sklearn.covariance import Elli... | no_license | /anomaly detection/Smiley Anomaly Detection.ipynb | sandeeptiwari6/datasets | 11 |
<jupyter_start><jupyter_text># **Example 02: Denoising on complex-valued data with real-valued operations**
We first define the data pipelines to feed the data into training, validation and test set. The MNIST database is used for showcasing. Since MNIST are real-valued images, a phase is simulated and added to the ima... | no_license | /tutorial_denoising_2chreal.ipynb | ISMRM-MIT-CMR/CMR-DL-challenge | 8 |
<jupyter_start><jupyter_text># Homework 6
## References
+ Lectures 17-18 (inclusive).
## Instructions
+ Type your name and email in the "Student details" section below.
+ Develop the code and generate the figures you need to solve the problems using this notebook.
+ For the answers that require a mathematical proo... | non_permissive | /homework/homework_06.ipynb | nawalgao/data-analytics-se | 30 |
<jupyter_start><jupyter_text># Neural Network on All FeaturesImport all necessary libraries, set matplotlib settings<jupyter_code>import pandas as pd
import numpy as np
from sklearn.model_selection import StratifiedKFold
from sklearn.preprocessing import StandardScaler
import sklearn.metrics
from scipy import stats
fro... | permissive | /4_Model_Testing/5_Model_Testing/classifier_k_fold_neural_select_features_final.ipynb | shoroukKB/cnv-pathogenicity-prediction | 19 |
<jupyter_start><jupyter_text><jupyter_code>from __future__ import absolute_import, division, print_function
import tensorflow as tf
from tensorflow import keras
import numpy as np
imdb = keras.datasets.imdb
(train_data, train_labels), (test_data, test_labels) = imdb.load_data(num_words=10000)
#note that imported dat... | no_license | /binary_discriminator.ipynb | RMhanovernorwichschools/neural_network_prac | 3 |
<jupyter_start><jupyter_text># Plot Demos
This notebook demonstrates how to read in data files and make various plots.<jupyter_code>%pylab inline
import sys, os
from __future__ import print_function
from ptha_paths import data_dir, events_dir<jupyter_output><empty_output><jupyter_text>### Function to clean up axes:<ju... | permissive | /Notebooks/Plot_Demos.ipynb | rjleveque/ptha_tutorial | 7 |
<jupyter_start><jupyter_text>
<h1 style="color:white;
font-family:Comic Sans MS;
font-size:3em;
background-color:#F0573B;
text-align:center;
padding:10px">Approximation de $\pi$
Visualisation du processus
<jupyter_code>import pylab as pl
from math import sqrt, pi
from ipywidgets import interact,IntSlider
fr... | no_license | /Approximation_pi_visualisation_processus.ipynb | fred-pandas/seconde | 1 |
<jupyter_start><jupyter_text># Twitter Users Activity on WeRateDogs Portal## Data Analysis and VisualizationI downloaded the Twitter data for WeRateDogs website. The final dataset contains 2048 tweets. Mainly I focused on the retweet and favorites count. The distribution below shows with the actual data and log transfo... | no_license | /Project4 - Data Wrangling/act_report.ipynb | Prashanthib/Udacity_DataAnalyst | 5 |
<jupyter_start><jupyter_text># Load the data<jupyter_code>imagefilename = 'Drerio4.h5'
datafilename = 'Drerio4-data64x64x64.h5'<jupyter_output><empty_output><jupyter_text>Get the image data<jupyter_code>imgfile = h5.File(imagefilename,'a')
imgdata = imgfile['image']
print imgdata.shape
datafile = h5.File(datafilename,'... | no_license | /Plot Fiber Angles 3D.ipynb | tytell/fibers | 3 |
<jupyter_start><jupyter_text># Sentiment analysis
In this assignment, we will learn to create neural network model for [sentiment analysis](https://en.wikipedia.org/wiki/Sentiment_analysis) using [convolutional neural network](https://en.wikipedia.org/wiki/Convolutional_neural_network) and [recurrent neural network](ht... | permissive | /Applications/Notebooks/SentimentAnalysis/CNN-RNN.ipynb | look4pritam/TensorFlowExamples | 9 |
<jupyter_start><jupyter_text>### Import Iris.csv<jupyter_code>irdf = pd.read_csv('Iris-2.csv')
irdf.head(10)
# Check dimension of data
irdf.shape
#Check data Type
irdf.dtypes
# No Null values found
irdf.isnull().sum()<jupyter_output><empty_output><jupyter_text>### Slice data set for Independent variables and dependent ... | no_license | /NaiveBayes_PracticeSet(1).ipynb | amir78pgd/amir78pgd | 11 |
<jupyter_start><jupyter_text># Clase 4## Overfitting y underfitting<jupyter_code>%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
sns.set(rc={'figure.figsize': (12, 8)}, style='white')
np.random.seed(42)<jupyter_output><empty_out... | permissive | /clases/clase_4/clase_4_overfitting.ipynb | muriloime/data_etudes | 8 |
<jupyter_start><jupyter_text>## Overview
In this notebook I am going to demonstrate how to use **the TripletSemiHardLoss function** in TensorFlow Addons.
As presented in **the FaceNet paper**, TripletLoss is a loss function that trains a neural network **to closely embed features of the same class while maximizing t... | non_permissive | /Codes/Face_verification/.ipynb_checkpoints/Triplet Loss (FaceNet)-checkpoint.ipynb | A2Amir/Face-Detection-and-Verification | 6 |
<jupyter_start><jupyter_text># Aula 4.1: Estimação básica de propagação de erros experimentais
## Objetivos
Vamos ver os básicos de propagação de erros
- Erros em medições: descrição, tipos
- Propagação: distribuções de probabilidade e aproximacao linear
- Regras práticas e exemplos.
Algumas Refs:
http://www.webas... | no_license | /aulas/Aula4-1.ipynb | acabreraufrj/modelagem | 2 |
<jupyter_start><jupyter_text># Lab 2: Convolution & Discrete Fourier Transform<jupyter_code>import commonfunctions as cf # this a custom module found the commonfunctions.py
import skimage.io as io
import matplotlib.pyplot as plt
import numpy as np
from skimage.color import rgb2gray
from scipy import fftpack
from scipy.... | no_license | /Lab2_Fourier/lab2-std.ipynb | aashrafh/CMP362 | 4 |
<jupyter_start><jupyter_text>The output of torchvision datasets are PILImage images of range [0, 1].
We transform them to Tensors of normalized range [-1, 1].
<jupyter_code>transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
trainset = torchvision.... | no_license | /Week05-PyTorch/programs/CNN/CNN.ipynb | ZeyuJia/AppliedMathTools | 2 |
<jupyter_start><jupyter_text># Método de Benjamini–Hochberg<jupyter_code>from google.colab import drive
drive.mount('/content/drive')
%load_ext rpy2.ipython
%%R
install.packages('data.table')
library(data.table)
%%R
W = as.matrix(read.table("wadj_py.csv", sep = ","))
P = as.matrix(read.table("pVal_py.csv", sep = ","))
... | no_license | /pValues_ajuste.ipynb | Jantunev/ProyectoML | 1 |
<jupyter_start><jupyter_text># Uma breve introdução à inferência Bayesiana
#### Roberto Pereira Silveira
**mail**: rsilveira79@gmail.com
**Twitter**: @rsilveira79
**Github**: https://github.com/rsilveira79 <jupyter_code>import pymc3 as pm
import matplotlib.pyplot as plt
import numpy as np
import scipy... | permissive | /notebooks/12.presentation_meetup_intro_bayesian.ipynb | rsilveira79/fermenting_gradients | 17 |
<jupyter_start><jupyter_text>#Matrix Creation<jupyter_code>np.zeros((5,5))
np.ones((5,5))
np.eye(5)
x=np.arange(16).reshape(4,4)
x
x.diagonal()
np.diag(x)
np.diag(x,k=1)
np.diag(x,k=2)
np.diag(x,k=3)
np.diag(np.diag(x))
np.random.rand(3)
np.random.rand(3,4)
np.random.randn(3,4)
a=np.random.randint(1,1000,30)
a
np.sort(... | non_permissive | /iNeuronclass-Jan-17.ipynb | sri-narahari/iNeuron-Classes | 1 |
<jupyter_start><jupyter_text>***
* [Outline](../0_Introduction/0_introduction.ipynb)
* [Glossary](../0_Introduction/1_glossary.ipynb)
* [2. Mathematical Groundwork](2_0_introduction.ipynb)
* Previous: [2.2 Important functions](2_2_important_functions.ipynb)
* Next: [2.4 The Fourier Transform](2_4_the_fourier_t... | non_permissive | /2_Mathematical_Groundwork/2_3_fourier_series.ipynb | ulricharmel/fundamentals | 2 |
<jupyter_start><jupyter_text># Support Vector Machines with PythonSVM's are supervised machine learning models with associated learning algorithms that analyze data and recognize patterns ,used for classification and regression analysis.
Mainly applicable for binary classification (having only 2 features).
Choose a ... | no_license | /Breast_Cancer_Dataset_project(SVM using Grid Serach).ipynb | AashiDutt/Machine-Learning | 12 |
<jupyter_start><jupyter_text># Homework 01
Austin Derrow-Pinion### Problem 1<jupyter_code>import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
df = pd.read_csv('../Data/height_weight.csv')
x_ = df.height
y_ = df.weight
x_std = x_.std()
y_std = y_.std()
df.head()
# normalize the data... | permissive | /Homework 01.ipynb | derrowap/MA490-Deep-Learning | 1 |
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