content stringlengths 73 1.12M | license stringclasses 3
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<jupyter_start><jupyter_text>##### Copyright 2019 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 | /CNN/dogs_vs_cats_without_augmentation.ipynb | Rhavyx/TensorFlow_Notebooks | 16 |
<jupyter_start><jupyter_text># SortieralgorithmenZu jedem Sortieralgorithmus habe ich euch noch eine kleine 'Visualisierung' gecodet.
Dabei wird gezeigt, was in welcher Iteration (meistens der äußeren Schleife) passiert.
Ihr aktiviert die Visualisierung, in dem ihr unter dem jeweiligen Algo das visual-Flag auf true
set... | permissive | /AlDa/blatt3/sorting_algorithms.ipynb | lyubadimitrova/cl-classes | 11 |
<jupyter_start><jupyter_text># Spark Basics 2## ChainingWe can **chain** transformations and aaction to create a computation **pipeline**Suppose we want to compute the sum of the squares
$$ \sum_{i=1}^n x_i^2 $$
where the elements $x_i$ are stored in an RDD.<jupyter_code>#start the SparkContext
import findspark
findspa... | no_license | /EDX_Big_Data_Analytics_Using_Spark/Section1-Spark-Basics/1.BasicSpark/.ipynb_checkpoints/4 Spark Basics 2-checkpoint.ipynb | NecmettinCeylan/Py_Works | 15 |
<jupyter_start><jupyter_text>### Training functions<jupyter_code>source("trainingFunctions.R")
buildBlendedModel <- function(train, test, p=.25, recruit_wgt=.5) {
blendedModel <- list()
test_wells <- unique(test$Well.Name)
# build a blended model for each well in the test data
for (well_i in test... | permissive | /jpoirier/archive/jpoirier006.ipynb | yohanesnuwara/2016-ml-contest | 4 |
<jupyter_start><jupyter_text># * **InstaBot** - Part 2*#### In the following cell, I have
1. Imported necessary libraries<jupyter_code>from selenium import webdriver
from selenium.webdriver.common.keys import Keys
import time
import numpy as np
import pandas as pd
from selenium.webdriver.common.by import By
from sele... | permissive | /InstaBot - 2.ipynb | nikhster/InstaBot | 27 |
<jupyter_start><jupyter_text>## Titanic Dataset<jupyter_code>train=pd.read_csv('train.csv')
print train.shape
train.head()<jupyter_output>(891, 12)
<jupyter_text>## Preparing the Data<jupyter_code>train.apply(lambda x: x.isnull().sum())
train.dtypes
# lets format sex : (Male==1) & (Female==2)
train['Sex']=(train['Sex']... | no_license | /Methodological Titanic.ipynb | Wail13/DataScience-Projects | 9 |
<jupyter_start><jupyter_text>This is mainly for Cpastone Project<jupyter_code>import pandas as pd
import numpy as np
pd = ("Hello Capstone Project Course!")
print(pd)<jupyter_output>Hello Capstone Project Course!
| no_license | /Capstone Project.ipynb | sukihswong/Coursera_Capstone | 1 |
<jupyter_start><jupyter_text>## Pytorch的交叉验证<jupyter_code>import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
batch_size=200
learning_rate=0.01
epochs=10
'''
载入数据集
'''
train_db = datasets.MNIST('D:/Jupyter/工作准备/pytorch学习/data... | no_license | /Pytorch_pratical_manual/Pytorch数据集划分与交叉验证.ipynb | Jiezju/How_To_USE_Pytorch | 1 |
<jupyter_start><jupyter_text># Object Detection Demo
Welcome to the object detection inference walkthrough! This notebook will walk you step by step through the process of using a pre-trained model to detect objects in an image. Make sure to follow the [installation instructions](https://github.com/tensorflow/models/b... | no_license | /ai_mobile_robot_SRL_telegram/object_detection/object_detection_tutorial.ipynb | machorro/stradigi-python-robot | 9 |
<jupyter_start><jupyter_text># Gradient Checking
Welcome to the final assignment for this week! In this assignment you will learn to implement and use gradient checking.
You are part of a team working to make mobile payments available globally, and are asked to build a deep learning model to detect fraud--whenever s... | no_license | /Improving Deep Neural Networks/Week1/Gradient Checking/Gradient Checking v1.ipynb | LeonChen66/Deep-Learning-Specialization | 7 |
<jupyter_start><jupyter_text># Problem Set 6 problems## Question 1: Constructing hadrons states using SU(3) raising and lowering operators (25 points)### Learning objectives
In this question you will:
- Learn how to find the SU(3) wave functions for baryons using SU(3) raising and lowering operatorsIf we know the SU(3)... | no_license | /Problem Set 6/Problem Set 6 problems.ipynb | mdshapiroLBL/phy129_fall_2020 | 4 |
<jupyter_start><jupyter_text>### Read ResGEN outputs<jupyter_code>def vlgan_json(path):
vlgan_predictions = {}
with open(path) as vlganf:
for line in vlganf:
po = json.loads(line)
vlgan_predictions[po['0']['info']['name']] = po
return vlgan_predictions
vlgan_predictions = vlg... | no_license | /preprocessing/HDSA.ipynb | bsantraigi/dialog-human-labeling | 4 |
<jupyter_start><jupyter_text>1. Read and explore the given dataset. <jupyter_code># Importing Libraries
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
# Ignoring warning
import warnings;warnings.simplefilter('ignore')
# reading the csv data on a Datafra... | no_license | /Project_Recommendation_System.ipynb | nishantpatil22/Product-Recommendation-Systems | 12 |
<jupyter_start><jupyter_text>
Физтех-Школа Прикладной математики и информатики (ФПМИ) МФТИ---Нейрон с сигмоидой---<jupyter_code>from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
import numpy as np
import pandas as pd<jupyter_output><empty_output><jupyter_text>Напомним, что сигмоидальная ... | no_license | /[5]oop_neuron/seminar/new_seminar/.ipynb_checkpoints/[seminar]neuron_new-checkpoint.ipynb | sergey-yushanov/dlschool | 10 |
<jupyter_start><jupyter_text>### Decision Tree Class<jupyter_code>class DecisionTree:
# constructor
def __init__(self, depth=0, max_depth = 5):
self.left = None
self.right = None
self.fkey = None
self.fval = None
self.max_depth = max_depth
self.depth = depth
... | no_license | /session 19/Titanic.ipynb | junaidiqbalsyed/DSLVNov20 | 3 |
<jupyter_start><jupyter_text>Next, let's define a function that will preprocess the image, which is originally in BGR8 / HWC format.<jupyter_code>def draw_joints(image, joints):
count = 0
for i in joints:
if i==[0,0]:
count+=1
if count>= 3:
return
for i in joints:
cv... | permissive | /live_hand_pose.ipynb | wei-tian/trt_pose_hand | 1 |
<jupyter_start><jupyter_text># Standard Data Types
The data stored in memory can be of many types. For example, a person's age is stored as a numeric value and his or her address is stored as alphanumeric characters. Python has various standard data types that are used to define the operations possible on them and the... | no_license | /notebooks/02-DataTypes.ipynb | marianasoeiro/PythonIntro | 43 |
<jupyter_start><jupyter_text>### MY470 Computer Programming
# Programming in Teams
### Week 5 Lab## Classes<jupyter_code>class MyClass(object):
"""DocString to define class."""
# DocString, not comments. DocStrings are for users, and will travel with the class.
# Information about the implemention (not abs... | no_license | /wk5/MY470_wk5_class.ipynb | lse-my470/lectures | 7 |
<jupyter_start><jupyter_text>
# Lab | Map, Reduce, Filter
## Introduction
In this lab, we will implement what we have learned about functional programming using the map, reduce, and filter functions. These functions allow us to pass an input and a transformation to a... | no_license | /14-Map_Reduce_Filter/main.ipynb | carolineferguson/data-labs | 19 |
<jupyter_start><jupyter_text># 4. Neural Style Transfer on AKS
Now that the AKS cluster is up, we need to deploy our __flask app__ and __scoring app__ onto it.
To do so, we'll do the following:
1. Build our __flask app__ and __scoring app__ push it to Dockerhub
2. Create our dot-yaml files for each of these apps (the... | non_permissive | /.ipynb_checkpoints/04_style_transfer_on_aks-checkpoint.ipynb | pjh177787/batch-scoring-deep-learning-models-with-aks-cn | 19 |
<jupyter_start><jupyter_text>Observation:
* From the heat map we can see that the radius, perimeter and area (_mean, _se and worst) are highly corelated. So one or more of these parameters can be used as features for our prediction
* Furthermore, we can see a strong corelation between the Concave points, concavity and ... | no_license | /Breast_Cancer/Untitled.ipynb | Anosike-CK/CancerType_Prediction | 2 |
<jupyter_start><jupyter_text># Mixed Linear Model <jupyter_code>import warnings
warnings.filterwarnings('ignore')<jupyter_output><empty_output><jupyter_text>## Import libraries <jupyter_code>import os
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
from s... | non_permissive | /pages/_build/jupyter_execute/multi_level_model.ipynb | CharlotteJames/ed-forecast | 9 |
<jupyter_start><jupyter_text>## 탐욕 알고리즘의 이해### 1. 탐욕 알고리즘 이란?
- Greedy algorithm 또는 탐욕 알고리즘 이라고 불리움
- 최적의 해에 가까운 값을 구하기 위해 사용됨
- 여러 경우 중 하나를 결정해야할 때마다, **매순간 최적이라고 생각되는 경우를 선택**하는 방식으로 진행해서, 최종적인 값을 구하는 방식### 2. 탐욕 알고리즘 예
### 문제1: 동전 문제
- 지불해야 하는 값이 4720원 일 때 1원 50원 100원, 500원 동전으로 동전의 수가 가장 적게 지불하시오.
- 가장 큰 동전부터... | no_license | /.ipynb_checkpoints/Chapter19-탐욕 알고리즘-checkpoint.ipynb | psh5487/DS-Algorithm | 2 |
<jupyter_start><jupyter_text>## Emission data exploring and cleaning
This file contains the major exploration and cleaning that has been done with the emission data.<jupyter_code>import pandas as pd
import numpy as np
import matplotlib.pyplot as plt<jupyter_output><empty_output><jupyter_text>## Explanation of agricultu... | no_license | /data_cleaning/emissions_cleaning.ipynb | verafristedt/ADA-2019-project1-sovellettu-tiedon-analysoint | 12 |
<jupyter_start><jupyter_text># OMS file parser
1. <jupyter_code>import pandas as pd
df = pd.read_excel('2017年12月新能源利用小时.xlsx', header=1)
df
df#.drop_duplicates(['风电场名称'], keep=False).dropna()
df_tmp[df_tmp['风电场名称'].str.contains(r'平均')]<jupyter_output><empty_output> | no_license | /Untitled1.ipynb | anonymous-void/XinNengYuanReport | 1 |
<jupyter_start><jupyter_text># loading the dataset<jupyter_code>import pandas as pd
def remove_airline_nameTag_and_links(tweets):
cleaned_tweets = []
for tweet in tweets:
tweet_words = tweet.split()
for word in tweet_words:
if(word.startswith('@')):
tweet_wor... | no_license | /twitter sentiments.ipynb | divyansh220199/twitter-sentiments | 1 |
<jupyter_start><jupyter_text># Exercise 04
Estimate a regression using the Capital Bikeshare data
## Forecast use of a city bikeshare system
We'll be working with a dataset from Capital Bikeshare that was used in a Kaggle competition ([data dictionary](https://www.kaggle.com/c/bike-sharing-demand/data)).
Get start... | permissive | /exercises/04-BikesRent.ipynb | estefaniaperalta26-zz/PracticalMachineLearningClass | 12 |
<jupyter_start><jupyter_text># Modeling and Simulation in Python
Chapter 13
Copyright 2017 Allen Downey
License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)
<jupyter_code># Configure Jupyter so figures appear in the notebook
%matplotlib inline
# Configure Jupyter to... | permissive | /code/chap13.ipynb | dreamofjade/ModSimPy | 13 |
<jupyter_start><jupyter_text>
Regression Models with Keras
## Introduction
As we discussed in the videos, despite the popularity of more powerful libraries such as PyToch and TensorFlow, they are not easy to use and have a steep learning curve. So, for people who are just starting to learn deep learning, there is no ... | no_license | /DL0101EN-3-1-Regression-with-Keras-py-v1.0__2_.ipynb | charii92/dl-nn-keras-ibm | 13 |
<jupyter_start><jupyter_text>### Calculadora Funcional (mas não muito)
Problemas:
- Não valida
- Não faz loop<jupyter_code>OPERATORS = "+", "-", "/", "*"
def f_get_number():
return int (input("Digite um inteiro: "))
def f_get_operator():
return input("Digite um operador(+,-,*,/): ")
def f_calculate(number1, o... | no_license | /Aulas/32_Calculadora Lambda.ipynb | pedrogallon/USJT5_LingProg | 2 |
<jupyter_start><jupyter_text># K-fold Val<jupyter_code>accuracy_scores = []
prec_scores = []
rec_scores = []
f1_scores = []
import keras.backend.tensorflow_backend as Keras_GPU
from sklearn.model_selection import KFold
from keras.optimizers import Adam
opt = Adam(lr=0.00001)
for i in range(1):
kfold = KFold(n_split... | no_license | /.ipynb_checkpoints/MDD_DL-band_model-Copy1-checkpoint.ipynb | ark1st/MDD | 1 |
<jupyter_start><jupyter_text>This program is for the additional PyRamen Challenge.
@author Kaleb Nunn<jupyter_code>import csv
import numpy as np
import decimal
def upload_data(path):
data = []
with open(path) as f:
reader = csv.reader(f)
next(reader, None)
for row in reader:
... | no_license | /PyRamen/.ipynb_checkpoints/main-checkpoint.ipynb | kalnun/python-homework | 1 |
<jupyter_start><jupyter_text>Set up enviroment per google chrome su colab[link opzioni download](https://google-images-download.readthedocs.io/en/latest/arguments.html)<jupyter_code>!pip install selenium
!apt-get update # to update ubuntu to correctly run apt install
!apt install chromium-chromedriver
!cp /usr/lib/chro... | no_license | /Codici Consegna/Scraping.ipynb | malborroni/RECMojion | 1 |
<jupyter_start><jupyter_text># 1. Data processing
## (a). Bubble Tea Location<jupyter_code>bubble_tea = pd.read_csv('dataset/bubble_location_data.csv')
bubble_tea.head()<jupyter_output><empty_output><jupyter_text>### Count the number of bubble tea shops for each state<jupyter_code>bubble_tea_count = pd.DataFrame(bubble... | no_license | /yelp_location_demography.ipynb | dretoabasi/Yelp-Reviews-Analysis-for-Bubble-Tea-Shops | 14 |
<jupyter_start><jupyter_text>### 구글 드라이브 연결<jupyter_code># upload an image
from google.colab import drive
drive.mount('/content/gdrive')<jupyter_output>Mounted at /content/gdrive
<jupyter_text>### 공유 폴더 접속 (mecathon 폴더)<jupyter_code>%cd /content/gdrive/Shareddrives/메카톤2021여름
!mkdir mecathon
%cd mecathon<jupyter_... | no_license | /train/yolov4_csp_swish/mecathon_yolov4_csp_swish.ipynb | IHAGI-c/mecathon_scooter_1 | 4 |
<jupyter_start><jupyter_text># **string
1)string
2)indexing in string
3) string mathod/function
4)string formating<jupyter_code>"""single quotes use inside double quotes"""
a = "welcome to 'all of you'"
print(a)
"""double quotes use inside single quotes"""
b='my hobby "is listening music"'
print(b)
a = "this 'is like'... | no_license | /.ipynb_checkpoints/Untitled-checkpoint.ipynb | sapna-rajput/basic-python- | 32 |
<jupyter_start><jupyter_text>As expected, the player with the higher rating usually wins the game. However, there is a difference of 4 percent between the black and white win percentage in each scenario-Almost like white has an advantage...<jupyter_code>games_df['avrating'] = (games_df['white_rating']+games_df['black_r... | no_license | /ChessMatches.ipynb | ShauryaJeloka/Jeloka_pyclass | 8 |
<jupyter_start><jupyter_text>### Data input<jupyter_code># change to location of corpus, generated by ProjectDDI solution
filePath_corpus = 'd:/share/private/ddi/corpus.csv'
data_corpus = pd.read_csv(filePath_corpus, sep = ',')
X = data_corpus[data_corpus.columns[0:3016]].values
Y = data_corpus[data_corpus.columns[3016... | no_license | /PythonScripts/.ipynb_checkpoints/SVM and NN for DDI with TensorFlow-checkpoint.ipynb | andrejkoilic/ddi | 3 |
<jupyter_start><jupyter_text># Sentiment Analysis and the Dataset
:label:`sec_sentiment`
Text classification is a common task in natural language processing, which transforms a sequence of text of indefinite length into a category of text. It is similar to the image classification, the most frequently used application... | no_license | /Training/mxnet/chapter_natural-language-processing-applications/sentiment-analysis-and-dataset.ipynb | Nathan-Faganello/PRe | 7 |
<jupyter_start><jupyter_text># XBRL Reader para fondos españoles
El objetivo de este notebook es desarrollar un parser de documentos XBRL remitidos a la CNMV por fondos de inversión en España.
## Table of contents
* Libraries import
* XBRL document import
## Libraries import<jupyter_code>%matplotlib inline
import ... | permissive | /fund_app/notebooks/XBRL Parser.ipynb | vioquedu/Spanish-funds | 3 |
<jupyter_start><jupyter_text>
Welcome to Colaboratory!
Colaboratory is a free Jupyter notebook environment that requires no setup and runs entirely in the cloud.
With Colaboratory you can write and execute code, save and share your analyses, and access powerful computing resources, all for free from your browser.<j... | no_license | /Copy_of_Welcome_To_Colaboratory.ipynb | tponnada/datasciencecoursera | 4 |
<jupyter_start><jupyter_text><jupyter_code>import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline<jupyter_output><empty_output><jupyter_text>##### Importing Data <jupyter_code>url="http://bit.ly/w-data"<jupyter_output><empty_output><jupyter_text>##### Storing Dat... | no_license | /Task-1 Prediction using Supervised ML/ Task_1.ipynb | ShreyasGawande/GRIP-TSF-Data-Science | 8 |
<jupyter_start><jupyter_text>Low maintanence arsenal for our quest.<jupyter_code>%matplotlib inline
import pandas as pd
import numpy as np
#no warnings because I like my stuff clean.
import warnings
warnings.filterwarnings('ignore')
#pipemaster
from sklearn.pipeline import Pipeline<jupyter_output><empty_output><jupyt... | no_license | /regression_tracktor_UJ.ipynb | u-jan/Used-Tracktor-Price-Prediction | 30 |
<jupyter_start><jupyter_text>保存和加载用于推理或恢复训练的通用检查点模型有助于从上次停止的地方开始。保存一般检查点时，您必须保存的不仅仅是模型的 state_dict。保存优化器的 state_dict 也很重要，因为它包含在模型训练时更新的缓冲区和参数。您可能想要保存的其他项目包括您离开的 epoch、最新记录的训练损失、外部 torch.nn.Embedding 层等，基于您自己的算法。
# 介绍
要保存多个检查点，您必须将它们组织在字典中并使用 torch.save() 序列化字典。一个常见的 PyTorch 约定是使用 .tar 文件扩展名保存这些检查点。要加载项目，首先初始化模型和优化器，... | no_license | /Pytorch_recipes/在 PYTORCH 中保存和加载一个通用检查点.ipynb | ustchope/pytorch_study | 5 |
<jupyter_start><jupyter_text># Stock Price Prediction with Deep Learning(LSTM)
.ipynb | Rittikasur/Stock-Prediction-using-web-scrapping | 7 |
<jupyter_start><jupyter_text>#Breaking out of two for loops in Python
We want to search a list of lists for a specific value using nested loops. When we find the first occurrence of the value we want to break out of both loops.
The following code doesn't work because we only break out of the inner loop and so continu... | no_license | /Breaking_out_of_two_for_loops_in_Python.ipynb | retrosnob/Azure-Notebooks | 4 |
<jupyter_start><jupyter_text># TAKEN<jupyter_code>%matplotlib inline
import sys
BIN = '../'
sys.path.append(BIN)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
#from sklearn.model_selection import train_test_split
import torch
import torch.nn as nn
#import torch.nn.parallel
import torch.optim a... | no_license | /model3_custom_pytorch/.ipynb_checkpoints/train3-checkpoint.ipynb | ATLAS-Autoencoders-GSoC/gsoc-assignment-adiah80 | 2 |
<jupyter_start><jupyter_text>###### 合并excel<jupyter_code>excel_names = []
for excel_name in os.listdir(split_dir):
excel_names.append(excel_name)
excel_names
df_lists = []
for i in range(len(excel_names)):
df_lists.append(pd.read_excel(f'{split_dir}/{excel_names[i]}'))
df_lists
df_concat = pd.concat(df_lists,ig... | no_license | /Pandas/Pandas拆分合并Excel.ipynb | Charswang/Machine-Learning-Datas | 1 |
<jupyter_start><jupyter_text># Theory Basics: the "Quantum" Behind Quantum Computing
This notebook is for beginners! :D (Those who are interested in quantum computing but have not taken an college level quantum mechanics course.) I'll be real with you, this notebook dives into the **basic concepts** of quantum computi... | no_license | /Information Encoding.ipynb | Applied-Quantum-Computing/theory-basics | 1 |
<jupyter_start><jupyter_text>### OSM-overpass服务接口使用，在线查询[OpenStreetMap](http:www.openstreetmap.org)开放空间数据库。
**_ by [openthings@163.com](http://my.oschina.net/u/2306127/blog), 2016-04-23. _**
>#### overpy-使用overpass api接口的python library，这里将返回结果集保存为JSON格式。
* 安装：$ pip install overpy
* 文档：http://python-overpy.readthedo... | non_permissive | /geospatial/openstreetmap/osm-overpass-node.ipynb | supergis/git_notebook | 8 |
<jupyter_start><jupyter_text>Import CSV diamonds_train<jupyter_code>diamonds_train = pd.read_csv('Input/diamonds_train.csv')<jupyter_output><empty_output><jupyter_text>## Checking dataIt is the same data as before so lets go ahead to clean and changeing types<jupyter_code>print(diamonds_train.shape)
diamonds_train.head... | no_license | /Clean_Train_Data.ipynb | AlbertJlobera/Diamonds-Project | 15 |
<jupyter_start><jupyter_text># EDA on dataset and preprocessing<jupyter_code># Importing Libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import datetime as dt
sns.set(style="darkgrid")<jupyter_output><empty_output><jupyter_text>### Reading Extracted Data<jupyter_c... | permissive | /Notebook/2. EDA.ipynb | ahmadkhan242/Reddit-flair-detection | 12 |
<jupyter_start><jupyter_text>#BasemapsArcGIS Online includes several basemaps from Esri that you can use in your maps.<jupyter_code>from arcgis.gis import *
from IPython.display import display
gis = GIS()
basemaps = gis.content.search("tags:esri_basemap owner:esri", "web map")
for basemap in basemaps:
display(basem... | no_license | /notebooks/06 Basemaps.ipynb | cunn1645/arcgis-python-api | 4 |
<jupyter_start><jupyter_text>### Problem 1
Use MCMC with Gaussian proposal distribution to generate 10000 samples from the distribution:
$$p(x) = \alpha_1N(\mu_1, \sigma_1) + \alpha_2N(\mu_2, \sigma_2) + \alpha_2N(\mu_2, \sigma_2)$$
for selected $\alpha_i, \mu_i, \sigma_i$.
Compare to the regular sampling from the Gau... | no_license | /practice_notebooks/Practice-Dec1.ipynb | maksimbolonkin/cs170-2020 | 3 |
<jupyter_start><jupyter_text>## Scraping Code### Function Definitions<jupyter_code># Ceiling function
def ceil(n) :
'''
Calculates the ceiling of a number.
Input :
n (float) - A real number for which you want to find the ceiling value.
Outpu :
n rounded up.
'''
if i... | no_license | /Scraping and Cleaning Code.ipynb | dslunde/March_Madness | 5 |
<jupyter_start><jupyter_text>## Two Samples z-test for Proportions
## $z = \frac{\hat{p_1}-\hat{p_2}}{\sqrt{\hat{p} (1-\hat{p}) (\frac{1}{n_1} + \frac{1}{n_2})}} $
where
### $\hat{p_1} = \frac{x_1}{n_1}, \hat{p_2} = \frac{x_2}{n_2} $
### $\hat{p} = \frac{x_1 + x_2}{n_1 + n_2}$
$x_1, x_2$ - number of successes in grou... | no_license | /content_from_npl_git/materials/seminar_ab_testing/2019-06-06_AB tutorial.ipynb | Vdyuk/newprolab-10.0 | 1 |
<jupyter_start><jupyter_text># Exercises 3## Part 1
- Import Numpy, matplotlib.pyplot and skimage.data
- From the data module import the moon picture
- Plot that image
- Check the image dimensions
- Calculate the image mean, max and min
- Create a mask of pixels with values above half of the max
- Create a cropped ver... | no_license | /Exercises/Exercise3.ipynb | guiwitz/PyImageCourse | 2 |
<jupyter_start><jupyter_text># Imports<jupyter_code>import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.datasets as datasets
import torch.utils.data as data
import torchvision.transforms as transforms<jupyter_output><empty_output><jupyter_text># Constants definition<jupyter_code>EPOCHS = 5... | no_license | /Ep #2 - First steps in computer vision/Example1.ipynb | bigboynaruto/machine-learning-foundations-pytorch | 6 |
<jupyter_start><jupyter_text>## Spectral centroid with Librosa<jupyter_code>FRAME_SIZE = 1024
HOP_LENGTH = 512
sc_debussy = librosa.feature.spectral_centroid(y=debussy,
sr=sr,
n_fft=FRAME_SIZE,
... | no_license | /AudioSignalProcessing/008 - Spectral Centroid and Bandwidth.ipynb | nathzi1505/AudioML | 2 |
<jupyter_start><jupyter_text># GRIP - The Sparks Foundation# TASK 6# Prediction using Decision Tree Algorithm### Decision Tree
A decision tree is a flowchart-like structure in which each internal node represents a "test" on an attribute (e.g. whether a coin flip comes up heads or tails), each branch represents the outc... | no_license | /GRIP TASK 6.ipynb | AmishaSingh0210/silver-bassoon | 1 |
<jupyter_start><jupyter_text>**Please open with Jupyter notebook.
Because COLAB can't access the webcam by OpenCV.**<jupyter_code>import cv2
import numpy as np
from keras.models import load_model
## We load the model
model=load_model("./model/face_mask_detector_model.h5")
results={0:'NO MASK', 1:'MASK'}
colors={0:(0,... | no_license | /Part2-Face-Mask_Detector.ipynb | iammeskat/real-time-face-mask-detection | 1 |
<jupyter_start><jupyter_text><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
#
# Unless required by applicable law or agreed ... | permissive | /Sequences, Time Series and Prediction/Week_1_Exercise_Answer.ipynb | thliang01/Learn-Machine-Learning | 9 |
<jupyter_start><jupyter_text>
# **SpaceX Falcon 9 first stage Landing Prediction**
# Lab 1: Collecting the data
Estimated time needed: **45** minutes
In this capstone, we will predict if the Falcon 9 first stage will land successfully. SpaceX advertises Falcon 9 rocket launches on its website with a cost of 62 m... | no_license | /Data Collection API.ipynb | fauzi417/testrepo | 26 |
<jupyter_start><jupyter_text>## Train<jupyter_code>data = [('UCLAsource', Transformer(matrix_eig))]
weighters = [('binarW', Transformer(orig))]
normalizers = [('origN', Transformer(orig))]
featurizers = [('Orig', Transformer(orig, collect=['X_orig'])),
('1', Transformer(split_1, collect=... | no_license | /Subsection/LR_comp.ipynb | Tismoney/PRNI2016 | 1 |
<jupyter_start><jupyter_text># Creating a Sentiment Analysis Web App
## Using PyTorch and SageMaker
_Deep Learning Nanodegree Program | Deployment_
---
Now that we have a basic understanding of how SageMaker works we will try to use it to construct a complete project from end to end. Our goal will be to have a simpl... | no_license | /SageMaker Project.ipynb | anasserm/Building-and-Deployment-Sentiment-Analysis-Model | 31 |
<jupyter_start><jupyter_text>## Modelling New data against vulnerability threshold
#### Use of upsampling in training set<jupyter_code>import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy.stats import pearsonr
from sklearn.linear_model import LogisticRegression
import statsmodels.api as s... | no_license | /derived data/june_model.ipynb | xiaoxiang-ma/Marhub-master | 3 |
<jupyter_start><jupyter_text># Word Vectors
This is a small demo notebook to give you a feel for what word vector are and why they are useful. First, we will visualize the word vectors that you trained using the architectures you built. Then we will look at the GLoVe embeddings to see what the state-of-the-art has to ... | no_license | /A6 - Introduction to NLP/Complete/word_vectors.ipynb | ekeilty17/Intro-to-NN-Assigments | 15 |
<jupyter_start><jupyter_text>. | .
-- | --
 | 
ASTG Python Courses
---
An Introduction to netCDF4 Python
<jupyter_code>from __future__ import print_function<jupyt... | no_license | /science_data_format/.ipynb_checkpoints/introduction_netcdf4-checkpoint.ipynb | abheeralisf/py_materials | 23 |
<jupyter_start><jupyter_text>### Inspeccion de modelos:
**El modelo a analizar fue entrenado usando regresion logistica y la union
de dos clasificadores, uno que usó "word" como _analyzer_ y el otro uso "char".
Veamos cuales son los 10 features con mas peso positivo y mas peso negativo para cada clase:**<jupyter_code>... | no_license | /sentiment/model_inspection.ipynb | agusmdev/PLN-2019 | 1 |
<jupyter_start><jupyter_text># estimator从文件读数据<jupyter_code>import sys
sys.path.append('model/samples/core/get_started')
import iris_data
import tensorflow as tf
tf.enable_eager_execution()
train_path, test_path = iris_data.maybe_download()
train_path
test_path
!head /Users/rosen/.keras/datasets/iris_test.csv
ds = tf.... | no_license | /TensorFlow/.ipynb_checkpoints/0031-从文件读数据到estimator-checkpoint.ipynb | RosenX/DataScienceNoteDiary | 1 |
<jupyter_start><jupyter_text># WGS PiPeline<jupyter_code>from __future__ import print_function
import os.path
import pandas as pd
import gzip
import sys
import numpy as np
sys.path.insert(0, '..')
from src.CCLE_postp_function import *
from JKBio import Datanalytics as da
from JKBio import TerraFunction as terra
from... | no_license | /WGS_CCLE.ipynb | FuChunjin/ccle_processing | 28 |
<jupyter_start><jupyter_text>###### Importing libraries<jupyter_code>import pandas as pd
import numpy as np
import time
# matplotlib
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
from matplotlib.colors import ListedColormap
#sklearn
from sklearn import datasets, svm, metrics,tree
from sklearn.datas... | no_license | /Modified_Files/Algorithms_1.ipynb | ChaithralakshmiS/ChicagoTrafficCrash-PredictingContributoryFactory | 24 |
<jupyter_start><jupyter_text>## 1. Tools for text processing
What are the most frequent words in Herman Melville's novel Moby Dick and how often do they occur?
In this notebook, we'll scrape the novel Moby Dick from the website Project Gutenberg (which contains a large corpus of books) using the Python package reques... | no_license | /moby_dick_word_freq.ipynb | aditya9729/Important-projects | 9 |
<jupyter_start><jupyter_text>Scrape the wikipedia page to get the contnets of the page<jupyter_code>url="https://en.wikipedia.org/wiki/List_of_postal_codes_of_Canada:_M"
page_postalCanada=requests.get(url)
page_postalCanada<jupyter_output><empty_output><jupyter_text> Using BeautifulSoup first find the table and then fe... | no_license | /Proj3Toronto_A2_Lat_Long.ipynb | keerthipattanath/Capstone_Segmenting_Clustering | 8 |
<jupyter_start><jupyter_text>**This notebook is an exercise in the [Introduction to Machine Learning](https://www.kaggle.com/learn/intro-to-machine-learning) course. You can reference the tutorial at [this link](https://www.kaggle.com/dansbecker/underfitting-and-overfitting).**
---
## Recap
You've built your first mo... | no_license | /exercise-underfitting-and-overfitting.ipynb | Satyam175/Intro-to-Machine-Learning-Course | 4 |
<jupyter_start><jupyter_text>In the previous checkpoint, we saw that OLS pins down the coefficients of the linear regression model by minimizing the sum of the model's squared error terms. However, in order for estimated coefficients to be valid and test statistics associated with them to be reliable, some assumptions ... | no_license | /Supervised Learning, Solving Regression Problems/Lecture/3.assumptions_of_linear_regression.ipynb | ltq477/Thinkful | 9 |
<jupyter_start><jupyter_text># Pricing and Risk Calculation - PortfoliosPortfolios allow for efficient pricing and risk. The same principles in the in the basic risk and pricing tutorials can be applied to portfolios. Pricing and risks can be viewed for an individual instrument or at the aggregate portfolio level<jupyt... | permissive | /gs_quant/tutorials/2_portfolios.ipynb | ahmedriza/gs-quant | 1 |
<jupyter_start><jupyter_text><jupyter_code>import matplotlib.pyplot as plt
import numpy as np
from sklearn import datasets, linear_model
from sklearn.metrics import mean_squared_error, r2_score
# Load the diabetes dataset
diabetes_X, diabetes_y = datasets.load_diabetes(return_X_y=True)
print (diabetes_X)
#... | no_license | /Untitled2.ipynb | suryaprakash9143/AITTA | 1 |
<jupyter_start><jupyter_text>## 实验知识点
* Min-Max 标准化
* Z-Score 标准化
* 独热编码
* 数据离散化### 特征工程概述
数据挖掘分析，除了对已有数据的统计归纳之外，更重要的往往是通过建立模型预测，从而得到更多的信息。前面的内容中，我们已经学习到了如何采集数据，以及对数据进行清洁和预处理。完成这些工作的目的，就是为了得到合适的数据，从而建立机器学习模型。关于什么是机器学习算法，我们将在后面的内容中深入讨论？本次实验中，我们还是要进一步讨论如何得到「合适的数据」。建立机器学习分析预测模型，简单来讲就是将「数据」交给算法处理，让机器学习算法学习到合适的「参数」，并最终保存... | no_license | /week2/data_transformation.ipynb | sc16rl/shiyan | 10 |
<jupyter_start><jupyter_text># Generative Adversarial Networks
**Generative Adversarial Networks** or GANs - use neural networks for Generative modeling.
> **Generative modeling** is an unsupervised learning task in machine learning that involves automatically discovering and learning the regularities or patterns in i... | no_license | /GAN/GAN_MNIST.ipynb | Jayanth2209/PyTorch_Learning | 17 |
<jupyter_start><jupyter_text># my1stNN.ipynb (or MNIST digits classification with TensorFlow)### This task will be submitted for peer review, so make sure it contains all the necessary outputs!<jupyter_code>import numpy as np
from sklearn.metrics import accuracy_score
from matplotlib import pyplot as plt
%matplotlib in... | permissive | /advance ml courera assignments/digits_classification.ipynb | rahul263-stack/PROJECT-Dump | 3 |
<jupyter_start><jupyter_text># Exercise 1### Step 1. Go to https://www.kaggle.com/openfoodfacts/world-food-facts/data### Step 2. Download the dataset to your computer and unzip it.### Step 3. Use the tsv file and assign it to a dataframe called food<jupyter_code>import pandas as pd
PATH_TO_FILE = "/Users/dag/Downloads/... | permissive | /01_Getting_&_Knowing_Your_Data/World Food Facts/Exercises.ipynb | dagtann/pandas_exercises | 9 |
<jupyter_start><jupyter_text># Logistička regresija<jupyter_code>import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
np.random.seed(10)<jupyter_output><empty_output><jupyter_text>U zadatku binarne klasifikacije, ciljna promenljiva može imati dve vrednosti. Njih obično obeležavamo sa `0` i `1` il... | no_license | /nedelja4/04-Logistička regresija.ipynb | anjavelickovic/materijali-sa-vezbi-2021 | 25 |
<jupyter_start><jupyter_text># Rigid/Affine + LDDMM Registration<jupyter_code>import sys
sys.path.insert(0,'../') # add code directory to path
# import lddmm
import torch_lddmm
# import numpy
import numpy as np
# import nibabel for i/o
import nibabel as nib
# import matplotlib for display
import matplotlib.pyplot as pl... | no_license | /examples/.ipynb_checkpoints/6_Affine_plus_LDDMM_Registration-checkpoint.ipynb | brianlee324/torch-lddmm | 8 |
<jupyter_start><jupyter_text># Introduction to Pandas : Part 1
-------
This tutorial is heavily based on [Pandas in 10 min](https://pandas.pydata.org/pandas-docs/stable/10min.html). The original material waas modified by adding TnSeq data as examples.
## Get datasets to play with<jupyter_code>%%bash
wget https://nekru... | no_license | /tnseq_with_pandas.ipynb | xxihe/sandbox2019 | 25 |
<jupyter_start><jupyter_text>ASWATHI.G
DATA SCIENCE AND BUSSINESS ANALYTICS INTERN @THE SPARKS FOUNDATION-JUNE2021TASK-6 Prediction suing Decision Tree Algorithm
Problem
Create the Decison Tree Classifier and Visualize it graphically.
The purpose is if we need any new data to this classifier,
it would be able to pre... | no_license | /TASK-6 DECISION TREE ALGORITHAM (1).ipynb | Aswathi-G1011/DecisionTree | 4 |
<jupyter_start><jupyter_text># SQL (in Python)Powerpoint แนะนำ Database อยู่ใน mycoursevilleเพื่อความง่าย แบบฝึกหัดนี้เราจะใช้ SQL ที่มีเป็น Library ใน Python อยู่แล้วในการเรียนคำสั่ง SQL แต่เวลาทำงานจริงเรามักจะลง software database ในเครื่องแล้วเขียน Python เชื่อมต่อ database ตัวนั้น ๆ (เช่น PostgreSQL จะใช้ library p... | no_license | /SQL.ipynb | keiseithunder/SQLPractice | 14 |
<jupyter_start><jupyter_text># 作業
練習以旋轉變換 + 平移變換來實現仿射變換
> 旋轉 45 度 + 縮放 0.5 倍 + 平移 (x+100, y-50)<jupyter_code>import cv2
import time
import numpy as np
img = cv2.imread('lena.png')<jupyter_output><empty_output><jupyter_text>## Affine Transformation - Case 2: any three point<jupyter_code># 給定兩兩一對，共三對的點
# 這邊我們先用手動設定三對點，... | no_license | /Day006_affine_HW.ipynb | K-F-github/1st-DL-CVMarathon | 2 |
<jupyter_start><jupyter_text>## Hypothesis (EDA)
1) In the first part, we look at the measure of goodness of a blog - claps
2) Take a look at the tags and their distribution
3) We remove outliers (too short or too long titles and subtitles)<jupyter_code>#Hypothesis 1
ax = sns.kdeplot(df['Claps'])
ax.set(xlabel = 'Nu... | no_license | /ipynb_checkpoints/.ipynb_checkpoints/Medium EDA-checkpoint.ipynb | DhruvilKarani/MediumTag | 4 |
<jupyter_start><jupyter_text># Python Data Structures and Boolean <jupyter_code>print(True,False,True,False)
!pip install ipyparallel
type(True)
#Inbult string functions
name="Solomon"
print(name.isalnum())
print(name.isdigit())
print(name.isalpha())
print(name.isspace())
print(name.istitle())
print(name.endswith('n'))... | no_license | /.ipynb_checkpoints/01.Lists and Boolean Variables-checkpoint.ipynb | kwabena55/Complete-ML-Crash-Course | 5 |
<jupyter_start><jupyter_text># Bivariate Analysis
- The idea is to analyze two variables at the same and find any relation between them.
- One way is to use correlation coefficients to find if two columns are related or not
- It provides a broader perspective as compared to univariate analysis
#### Graphs
- Scatter P... | no_license | /Bivariate Analysis.ipynb | sureshmanem/DS_Algorithms | 15 |
<jupyter_start><jupyter_text>## This is a notebook which will be mainly used for the Capstone project<jupyter_code>import pandas as pd
import numpy as np
print("Hello Capstone Project Course!")<jupyter_output>Hello Capstone Project Course!
| no_license | /Capstone_FK.ipynb | chegeo/Coursera_Capstone | 1 |
<jupyter_start><jupyter_text># Preliminary Data Analysis#### Summary:
1. Importing Dependencies
2. Pull CSV File from previous work
3. Merge data
4. Graph data to get sum of viewers for each game
5. Show the dataframe for the counts of each game played by Streamer### 1. Importing Dependencies:<jupyter_code>import panda... | no_license | /.ipynb_checkpoints/3-Preliminary Data Analysis-checkpoint.ipynb | asoemardy/great-gaming-googlers | 5 |
<jupyter_start><jupyter_text># Exploratory AnalysisImporting basic libraries :<jupyter_code>import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline<jupyter_output><empty_output><jupyter_text>Importing training data<jupyter_code>train = pd.read_csv("../../data/train.csv")<jupyter_output... | no_license | /src/notebook/How much did it rain II ?.ipynb | wrecker-mishra/raindetect | 9 |
<jupyter_start><jupyter_text># Algorithms
# Homework 1
Vanessa Wormer
UNI vw2210# 1. Write a function that takes in a list of numbers and outputs the mean of the numbers using the formula for mean. Do this without any built-in functions like sum(), len(), and, of course, mean()<jupyter_code>n = [2,3,4,5]
def averag... | non_permissive | /class1_1gexercise/algorithms_hw1_vanessa.ipynb | vwormer/lede_algorithms | 5 |
<jupyter_start><jupyter_text>## 演示0105：数组排序### 案例1：一维数组排序> **升序和降序排列**
* *sort*只能升序排列，如果需要降序，可以再倒序<jupyter_code>import numpy as np
a = np.array([3, 1, 7, 4, 2, 5, 8])
b = np.sort(a)
print(b)
print(b[::-1]) # 使用[::-1]进行倒序遍历<jupyter_output>[1 2 3 4 5 7 8]
[8 7 5 4 3 2 1]
<jupyter_text>> ** *np.sort(a)*和*a.sort*的不同行... | permissive | /01_Numpy/0105_数组排序.ipynb | iahuohz/Machine-Learning-Lab | 4 |
<jupyter_start><jupyter_text># AssignmentQ1. Write the NumPy program to create a 2d array with 6 on the border
and 0 inside?
Expected OutputOriginal array-
[ [6 6 6 6 6]
[ 6 6 6 6 6]
[ 6 6 6 6 6 ]
[ 6 6 6 6 6 ]
[ 6 6 6 6 ] ].
6 on the border and 0 inside in the array-
[[ 6 6 6 6 6]
... | no_license | /Subjective Assignment - 5 - Numpy 1(Solution).ipynb | Chandan010298/Deep-Learning | 18 |
<jupyter_start><jupyter_text>\hfill Department of Staitistics
\hfill Jaeyeong Kim
# madelon dataset
## Load the dataset<jupyter_code>from sklearn import tree
import pandas as pd
import matplotlib.pyplot as plt
#This will be used to bold characters
class color:
BOLD = '\033[1m'
END = '\033[0m'
X_train = pd.rea... | no_license | /Assignment 1/Machine_Learning_Project1.ipynb | adr15c/STA5635-Machine-Learning- | 10 |
<jupyter_start><jupyter_text># Discussion 01: Python Basics and Causality
Welcome to Discussion 01! This week, we will go over some Python Basics. You can find additional help on these topics in the course [textbook](https://eldridgejm.github.io/dive_into_data_science/front.html).
Additionally, [here](https://ucsd-e... | no_license | /Discussions/Discussion1/discussion.ipynb | ucsd-ets/dsc10-wi21 | 3 |
<jupyter_start><jupyter_text>https://matplotlib.org/3.1.1/tutorials/introductory/pyplot.html<jupyter_code>import pandas as pd
import matplotlib.pyplot as plt
import math
def truncate(n, decimals=0):
multiplier = 10 ** decimals
return int(n * multiplier) / multiplier
df1=pd.read_csv("profile-200-exp8.xv", skipro... | permissive | /umbrella-sampling/jupyter-notebook/.ipynb_checkpoints/PMF-checkpoint.ipynb | wirttipereira/utils-md | 10 |
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