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<jupyter_start><jupyter_text>___
___
Note-
1) First make the copy of the project in your drive then start with the solution.
2) Dont run the cell directly, first add a call above it then run the cell so that you dont miss the solution.
# Logistic Regression Project
In this project we will be working with a fake adve... | no_license | /Logistic_Regression_Assignment.ipynb | young-ai-expert/Data-Science-Projects | 12 |
<jupyter_start><jupyter_text># Vehicle detection and tracking project*picture by Udacity*<jupyter_code>import numpy as np
import cv2
import glob
import matplotlib.pyplot as plt
import os
import matplotlib.image as mpimg
%matplotlib qt
%matplotlib inline
vehs = []
for image in os.listdir(os.getcwd() + "/vehicles/GTI_Rig... | no_license | /CarND-Vehicle-Detection-master-P5.ipynb | azumf/CarND-Vehicle-Detection-P5 | 16 |
<jupyter_start><jupyter_text># 교차검증 그리드 서치 (cross_val_score) p324~<jupyter_code>from sklearn.model_selection import cross_val_score
import numpy as np
best_score = 0
for gamma in [0.001, 0.01, 0.1, 1, 10, 100]:
for C in [0.001, 0.01, 0.1, 1, 10, 100]:
# 매개변수의 각 조합에 대해 SVC를 훈련시킵니다
svm = SVC(gamm... | no_license | /머신러닝/l.evalueation(모델평가, 성능 향상).ipynb | chayoonjung/yun-python | 1 |
<jupyter_start><jupyter_text>Create a array<jupyter_code>data = np.arange(10)
print (data.shape)
data
data = data.reshape(2,5)
print (data.shape)
data
data_1 = np.ones([3,3])
print (type(data_1))
print (data_1)
data_1 = np.zeros([3,3])
print (data_1)
data = np.eye(3,3)
data
data =np.diag((1,2,3))
data
data = np.empty([... | permissive | /Numpy.ipynb | yuzhipeter/Data_Structure_Numpy_Pandas | 8 |
<jupyter_start><jupyter_text># Read in the data<jupyter_code>import pandas as pd
import numpy
import re
data_files = [
"ap_2010.csv",
"class_size.csv",
"demographics.csv",
"graduation.csv",
"hs_directory.csv",
"sat_results.csv"
]
data = {}
for f in data_files:
d = pd.read_csv("schools/{0}... | no_license | /Guided Project: Analyzing NYC High School Data/Schools.ipynb | isabellechiu/Self-Project-Dataquest | 10 |
<jupyter_start><jupyter_text># Mô tả dữ liệu bằng Arviz
### Bs. Lê Ngọc Khả Nhi
Bài thực hành nảy nhằm hướng dẫn dùng package Arviz để thực hiện các biểu đồ mô tả đữ liệu đơn giản.
Arviz (https://arviz-devs.github.io/arviz/index.html) là một thư viện đồ họa chuyên dụng cho Thống kê Bayes, cho phép vẽ các biểu đồ để ... | no_license | /Arviz demo 1.ipynb | kinokoberuji/Statistics-Python-Tutorials | 15 |
<jupyter_start><jupyter_text># Programming and Data Analysis
> Homework 0
Kuo, Yao-Jen from [DATAINPOINT](https://www.datainpoint.com)## Instructions
- We've imported necessary modules at the beginning of each exercise.
- We've put necessary files(if any) in the working directory of each exercise.
- We've defined t... | no_license | /exercises.ipynb | rose020/homework0 | 7 |
<jupyter_start><jupyter_text>#### Plotting of gesture data<jupyter_code>print("Shape of X_train: ", X_train.shape)
print("shape of y_train/labels: ", y_train.shape)
print("Shape of X_test: ", X_test.shape)
print("shape of y_test/labels: ", y_test.shape)
samples = np.random.choice(len(X_train), 8)
def show_images(images... | no_license | /asl-training.ipynb | ayulockin/ASL_Classifier | 3 |
<jupyter_start><jupyter_text># Pandas basics
In this notebook we will **learn** how to work with the two main data types in `pandas`: `DataFrame` and `Series`.## Data structures (`pandas`)### `Series`
In `pandas`, series are the building blocks of dataframes.
Think of a series as a column in a table. A series collec... | permissive | /notebooks/3_PandasBasics.ipynb | Giovanni1085/UvA_CDH_2020 | 24 |
<jupyter_start><jupyter_text><jupyter_code>import numpy as np # useful for many scientific computing in Python
import pandas as pd # primary data structure library
!conda install -c anaconda xlrd --yes
df_can = pd.read_excel('https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/l... | permissive | /Lab Experiments/Experiment_3_200720.ipynb | rohitsmittal7/J045-ML-Sem-V | 3 |
<jupyter_start><jupyter_text>**MATH 3332 **
**Section 52 **
# In Who-is-Normal.xslx there are 7 columns representing variables x1-x7, one of which is sample from the normal distribution. Find which one of the variables is normal?
## Reading in Values
The program starts by reading in the values from the Excel spread... | no_license | /Probability/Normal.ipynb | rlacherksu/notebooks | 3 |
<jupyter_start><jupyter_text># 株価の関係<jupyter_code>%matplotlib inline
import matplotlib.pyplot as plt
import matplotlib
import numpy
import sqlite3
import pandas as pd
import os
fmdate="2015-01-01"
todate="2016-12-31"
code="6502"
if os.name == "nt": fname=r'C:\WINDOWS\Fonts\ipaexg.ttf'
else: fname = os.environ["HOME"... | no_license | /theme/notebook/22_toshiba_stock.ipynb | k-utsubo/xc40 | 10 |
<jupyter_start><jupyter_text>## Online Factorization Machine
Online factorization models take single data as an input, make a prediction, and train with the data.### 1. Setup
The from models imports the package for use. We have also imported a few other packages for plotting.<jupyter_code>import sys
sys.path.append('.... | no_license | /jupyters/online_models_example.ipynb | yejihan-dev/fm-for-online-recommendation | 5 |
<jupyter_start><jupyter_text>### 运行一次来获得cookie
- 注意填充自己的帐号密码<jupyter_code>import requests
import time
from selenium import webdriver
def get_pixiv_cookie(pixiv_id,pixiv_pw):
driver = webdriver.Chrome() # Optional argument, if not specified will search pat
driver.get('https://accounts.pixiv.net/login');
t... | no_license | /pixiv.ipynb | Unknown-Chinese-User/pixiv-spider | 7 |
<jupyter_start><jupyter_text>**This notebook is an exercise in the [Intro to Deep Learning](https://www.kaggle.com/learn/intro-to-deep-learning) course. You can reference the tutorial at [this link](https://www.kaggle.com/ryanholbrook/deep-neural-networks).**
---
# Introduction #
In the tutorial, we saw how to build... | no_license | /Intro to Deep Learning/2 - Deep Neural Networks/exercise-deep-neural-networks.ipynb | mtamjidhossain/Kaggle-courses | 6 |
<jupyter_start><jupyter_text># Answering Questions for the Chinook Record StoreThe Chinook record store has just signed a deal with a new record label, and you've been tasked with selecting the first three albums that will be added to the store, from a list of four. All four albums are by artists that don't have any tr... | no_license | /chinook_store_sql.ipynb | EdsTyping/chinook_record_store_sql | 8 |
<jupyter_start><jupyter_text>## Surrogate Models & Helper Functions<jupyter_code>ValueRange = namedtuple('ValueRange', ['min', 'max'])
def determinerange(values):
"""Determine the range of values in each dimension"""
return ValueRange(np.min(values, axis=0), np.max(values, axis=0))
def linearscaletransform(v... | no_license | /Original - Optimality/200D/second/F16_200_original.ipynb | SibghatUllah13/Deep-Latent_Variable_Models-for-dimensionality-reduction-in-surrogate-assisted-optimization | 4 |
<jupyter_start><jupyter_text>## Download rnn_merged.zip & rnn_embed.zip from https://drive.google.com/drive/folders/1yO_W-m0fF_PludrnScdgyTGsPFoDsA6_?usp=sharing and unzip to the same folder of this file
## Also download train_jpg.zip & test_jpg.zip from competition website<jupyter_code>import pandas as pd
import tens... | no_license | /RNN Self-Trained WordVec + Image + Merge Features (with Fast Loading)-Copy1.ipynb | tnmichael309/kaggle-avito-demand-challenge | 6 |
<jupyter_start><jupyter_text># Consume deployed webservice via REST
Demonstrates the usage of a deployed model via plain REST.
REST is language-agnostic, so you should be able to query from any REST-capable programming language.## Configuration<jupyter_code>from environs import Env
env = Env()
env.read_env("foundation... | permissive | /mnist_fashion/04_consumption/consume-webservice.ipynb | anderl80/aml-template | 3 |
<jupyter_start><jupyter_text># Problem : Print all ancestors of binary tree.
Algorithm:
1. Check if root or node is None, if yes, return False
2. Append the ancestor list with the root
3. If the root equals node return True to the calling function
4. Check the left and right subtree for node recursively
5. If found r... | no_license | /Trees/Binary Trees/Problems/.ipynb_checkpoints/AncestorsOfBinaryTree-checkpoint.ipynb | sumeet13/Algorithms-and-Data-Structures | 3 |
<jupyter_start><jupyter_text>Table of Contents
1 HISTOGRAM2 QQPLOT3 AGGREGATION PLOT (part4)## HISTOGRAM<jupyter_code>library(MASS)
# Create a histogram of counts with hist()
hist(Cars93$Horsepower, main = "hist() plot")
# Create a normalized histogram with truehist()
hist(Cars93$Horsep... | no_license | /Data visualization - base R/.ipynb_checkpoints/2.1. One variable-checkpoint.ipynb | yoogun143/Datacamp_R | 3 |
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