text stringlengths 2.5k 6.39M | kind stringclasses 3
values |
|---|---|
### Coletando Dados
<p>Existem vários formatos para um conjunto de dados: .csv, .json, .xlsx etc.<br>
E esse conjunto pode estar armazenado em diferentes lugares, localmente ou online.
Neste notebook, você aprenderá como carregar um conjunto de dados em formato csv (comma separated values), salvo localmente. Ire... | github_jupyter |
# wav2vec-u CV-sv - prepare text
> "Running prepare_text.sh for wav2vec-u on Common Voice Swedish"
- toc: false
- branch: master
- badges: false
- hidden: true
- categories: [kaggle, wav2vec-u]
Original [here](https://www.kaggle.com/jimregan/wav2vec-u-cv-swedish-text-prep)
```
%cd /opt
%%capture
!tar xvf /kaggle/in... | github_jupyter |
```
import numpy as np
from decision_tree.decision_tree_model import ClassificationTree
class RandomForest():
"""Random Forest classifier. Uses a collection of classification trees that
trains on random subsets of the data using a random subsets of the features.
Parameters:
-----------
n_estimators... | github_jupyter |
# Sparse graph based networks - some experiments for network type 1
```
%load_ext autoreload
%autoreload 2
import numpy as np
import pandas as pd
import tensorflow as tf
import matplotlib.pyplot as plt
import graph_utils as graph_utils
import graph_neural_networks as graph_nn
import data_preparation_utils as data_p... | github_jupyter |
```
from google.colab import drive
drive.mount('/content/drive')
# from google.colab import drive
# drive.mount('/content/drive')
!pwd
path = '/content/drive/MyDrive/Research/AAAI/cifar_new/k_0b/sixth_run1_'
import torch.nn as nn
import torch.nn.functional as F
import pandas as pd
import numpy as np
import matplotlib.... | github_jupyter |
<table>
<tr>
<td width=15%><img src="./img/UGA.png"></img></td>
<td><center><h1>Introduction to Python for Data Sciences</h1></center></td>
<td width=15%><a href="http://www.iutzeler.org" style="font-size: 16px; font-weight: bold">Franck Iutzeler</a> </td>
</tr>
</table>
<br/><br/>
<center><a style="font-size: 40pt... | github_jupyter |
# Face Mask Detection using PaddlePaddle
In this tutorial, we will be using pretrained PaddlePaddle model from [PaddleHub](https://github.com/PaddlePaddle/PaddleHub/tree/release/v1.5/demo/mask_detection/cpp) to do mask detection on the sample image. To complete this procedure, there are two steps needs to be done:
- ... | github_jupyter |
Hi! This is a pytorch classification example built with inspiration from https://towardsdatascience.com/pytorch-tabular-binary-classification-a0368da5bb89
The link contains additional explanitory text and short 5-minute youtube video explaining core concepts.
```
### PYTORCH CLASSIFICATION EXAMPLE
#
# Author: Rasmus... | github_jupyter |
# Example: CanvasXpress density Chart No. 5
This example page demonstrates how to, using the Python package, create a chart that matches the CanvasXpress online example located at:
https://www.canvasxpress.org/examples/density-5.html
This example is generated using the reproducible JSON obtained from the above page ... | github_jupyter |
# 爬取今天到目前為止的所有文章
https://www.ptt.cc/bbs/Gossiping/index.html
```
import requests
import re
import json
from bs4 import BeautifulSoup, NavigableString
from datetime import datetime
from pprint import pprint
from urllib.parse import urljoin
base_url = 'https://www.ptt.cc/bbs/Gossiping/index.html'
ptt_today = datetime.... | github_jupyter |
# Money and death
We return to the death penalty.
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
# Make plots look a little bit more fancy
plt.style.use('fivethirtyeight')
```
In this case, we are going to analyze whether people with higher incomes are more likely to fa... | github_jupyter |
```
import pandas as pd
import numpy as np
names = ['user_id', 'item_id', 'rating', 'timestamp']
df = pd.read_csv(
'./ml-100k/u.data', sep='\t', names=names)
n_users = df.user_id.unique().shape[0]
n_items = df.item_id.unique().shape[0]
# Create r_{ui}, our ratings matrix
ratings = np.zeros((n_users, n_items))
for r... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.style as style
from matplotlib import collections as mc
import seaborn as sns
import pandas as pd
import scipy.sparse as sps
import scipy.sparse.linalg
style.use('ggplot')
def laplacian_fd(h):
"""Poisson on a 2x2 square, with neumann
... | github_jupyter |
# Introduction to Linear Regression
## Learning Objectives
1. Analyze a Pandas Dataframe
2. Create Seaborn plots for Exporatory Data Analysis
2. Train a Linear Regression Model using Scikit-Learn
## Introduction
This lab is in introduction to linear regression using Python and Scikit-Learn. This lab serves as... | github_jupyter |
##### Copyright 2019 The TensorFlow Authors.
```
#@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 ... | github_jupyter |
```
videos = """
https://www.youtube.com/watch?v=cyU3Qgox3K4&t=131s&ab_channel=TanahMelayu
https://www.youtube.com/watch?v=68bH2c04v7o&ab_channel=TanahMelayu
https://www.youtube.com/watch?v=9ITPO6ooNSk&ab_channel=TanahMelayu
https://www.youtube.com/watch?v=sw5h9hlityE&t=1284s&ab_channel=TanahMelayu
https://www.youtube.... | github_jupyter |
<h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Numpy-Tutorial" data-toc-modified-id="Numpy-Tutorial-1"><span class="toc-item-num">1 </span>Numpy Tutorial</a></span><ul class="toc-item"><li><span><a href="#BASICS" data-toc-modified-id="BASICS-... | github_jupyter |
```
"""
@author: Ajay
"""
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
from sklearn.preprocessing import StandardScaler, MinMaxScaler
fr... | github_jupyter |
<h1 style="color:blue">Predicting Car Prices</h1>
<p>In this project I will try to predict the prices of some cars with the help K-Nearest Neighbor algorithm</p>
```
#importing libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.neighbors import KNeighborsRegressor
from sklear... | github_jupyter |
<a href="https://colab.research.google.com/github/violigon/Ocean_Python_03_11_2020/blob/main/Ocean_Python_03_11_2020.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
# Posso colocar qualquer texto que ele não será interpretado pela linguagem
pri... | github_jupyter |
```
import os
import cv2
import math
import warnings
import numpy as np
import pandas as pd
import seaborn as sns
import tensorflow as tf
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix, fbeta_score
from keras import optimizers
from keras... | github_jupyter |
## Using TLS with measurement uncertainties
TLS is capable of using measurement uncertainties, when available, in its least-squares fit.
Every measured data point has its own measurement uncertainty ("error").
- We here neglect uncertainties in time, as the time stamp of typical observations has millisecond accurac... | github_jupyter |
<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA9oAAACNCAYAAABIdAKVAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAAEnQAABJ0Ad5mH3gAAO8OSURBVHhe7P0FoJ7neR6OX4eZmZlBzMxkkNlJGmrWJG3WFbam+2ddt3Vd/2uzbikuS9M2TZrETuyYbdliZj7n6DAzM8Pvvu7ve6VP0nekI1mWZfu5jl59Lzz84vXc5DL48u4ZGBgYGBgYGBgYGBgYGBgYPBC42n8NDAwMDAwMDAwM... | github_jupyter |
### author: zabiralnazi@yahoo.com
> 0.40 Dropout, Augmentation, Histogram Equalization pre-processing
```
import os
# cleaning up unimportant files
def del_file(f_name):
try:
os.remove(f_name)
except:
print('file not found')
% cd /content/
! ls
# get the dataset
!wget https://challenge.kitware.com/api/v1/i... | github_jupyter |
```
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt #Plotting
%matplotlib inline
import warnings #What to do with warnings
warnings.filterwarnings("ignore") #Ignore the warnings
plt.rcParams["figure.figsize"] = (10,10) #Make th... | github_jupyter |
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import rankdata
df = pd.read_csv("data.csv")
df.index = pd.to_datetime(df['date'], format='%Y-%m-%d')
df = df.drop('date', axis=1)
close_columns = []
high_columns = []
low_columns = []
open_columns = []
volume_columns = []
ope... | github_jupyter |
### Preamble
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import warnings
with warnings.catch_warnings():
warnings.simplefilter("ignore")
import scanpy as sc
## local paths etc. You'll want to change these
DATASET_DIR = "/scratch1/rsingh/work/schema/data/tasic-nature"
import sys;... | github_jupyter |
```
# x_new = x - alpha * gradient(x)
import numpy as np
def gradient_descent(f, gradient, x0, alpha, eps, max_iter):
x = x0
for i in range(max_iter):
x_new = x - alpha * gradient(x)
if np.abs(f(x_new) - f(x)) < eps:
break
x = x_new
con... | github_jupyter |
```
%matplotlib widget
import os
import sys
sys.path.insert(0, os.getenv('HOME')+'/pycode/MscThesis/')
import pandas as pd
from amftrack.util import get_dates_datetime, get_dirname, get_plate_number, get_postion_number
import ast
from amftrack.plotutil import plot_t_tp1
from scipy import sparse
from datetime impo... | github_jupyter |
```
import numpy as np
import pandas as pd
import pickle
import json
import gensim
import os
import re
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix
from pandas.plotting import scatter_matrix
from keras.models import load_model
from keras.preprocessing.text import T... | github_jupyter |
```
####################################################################################################
# Copyright 2019 Srijan Verma and EMBL-European Bioinformatics Institute
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License... | github_jupyter |
# <img style="float: left; padding-right: 10px; width: 45px" src="https://raw.githubusercontent.com/Harvard-IACS/2018-CS109A/master/content/styles/iacs.png"> CS109A Introduction to Data Science
## Lecture 3, Exercise 1: Web Scraping and Parsing Intro
**Harvard University**<br/>
**Fall 2020**<br/>
**Instructors**: P... | github_jupyter |
# Example of Logistic regression
## Predict student admission based on exams result
Data is taken from [Andrew Ng's CS229 course on Machine Learning at Stanford](http://cs229.stanford.edu/).
```
import pandas as pd
data = pd.read_csv("datasets/ex2data1.txt", header=None,
names=['Exam1', 'Exam2', ... | github_jupyter |
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
```
# 2500 Batch Size
```
df2500 = pd.read_csv('../src/performance/testing/final-scores-2500.csv')
df2500
plt.plot(df2500['Batch'], df2500['SGD Score'] * 100, label='SGD', linewidth=0.75)
plt.plot(df2500['Batch'], df2500['NB Score'] * 100, lab... | github_jupyter |
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the MIT License.
# Automated Machin... | github_jupyter |
# Session 16: Word Embeddings using the Word2Vec skip-gram model
------------------------------------------------------
*Introduction to Data Science & Machine Learning*
*Pablo M. Olmos olmos@tsc.uc3m.es*
------------------------------------------------------
The goal of this notebook is to train a Word2Vec skip-gr... | github_jupyter |
```
from nltk.corpus import wordnet as wn
wn.synset('think.v.01').frame_ids()
for lemma in wn.synset('think.v.01').lemmas():
print(lemma, lemma.frame_ids())
print(" | ".join(lemma.frame_strings()))
wn.synset('stretch.v.02').frame_ids()
for lemma in wn.synset('stretch.v.02').lemmas():
print(lemma, lemma.fram... | github_jupyter |
```
%matplotlib inline
import matplotlib.pyplot as plt
import datetime as dt
import numpy as np
from numpy import log
import pandas as pd
import os
from statsmodels.tsa.stattools import adfuller
from statsmodels.graphics.tsaplots import plot_acf, plot_pacf
from statsmodels.tsa.arima_model import ARIMA
from statsmodels.... | github_jupyter |
```
# System libraries
from time import time
import numpy as np
import random
# Custom libraries
import dl_utils as utils
import datasets
# Helper libraries
from tensorflow.keras.callbacks import EarlyStopping
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
from matplotlib import ... | github_jupyter |
# Ejercicio 1. Transfer Learning con VGG16
Ejercicio 1 del tutorial de Transfer Learning.
GPT2: Diseño y Gestión de Proyectos en Data Science II.
[Máster en Data Science y Big Data](http://masterds.es/) de la [Universidad de Sevilla](http://www.us.es).
25/06/2020. Profesor: [Miguel Ángel Martínez del Amor](http://w... | github_jupyter |
**[Machine Learning Course Home Page](https://kaggle.com/learn/machine-learning).**
---
# Introduction
Decision trees leave you with a difficult decision. A deep tree with lots of leaves will overfit because each prediction is coming from historical data from only the few houses at its leaf. But a shallow tree with ... | github_jupyter |
**Consider the following Python dictionary data and Python list labels:**
data = {'birds': ['Cranes', 'Cranes', 'plovers', 'spoonbills', 'spoonbills', 'Cranes', 'plovers', 'Cranes', 'spoonbills', 'spoonbills'],
'age': [3.5, 4, 1.5, np.nan, 6, 3, 5.5, np.nan, 8, 4],
'visits': [2, 4, 3, 4, 3, 4, 2, 2, 3,... | github_jupyter |
# 5장 제어문
## 5.1 조건에 따라 분기하는 if 문
### 단일 조건에 따른 분기(if)
**[5장: 72페이지]**
```
x = 95
if x >= 90: # 조건문이 참이면 실행
print("Pass")
```
### 단일 조건 및 그 외 조건에 따른 분기(if ~ else)
**[5장: 73페이지]**
```
x = 75
if x >= 90: # 조건문이 참이면 실행
print("Pass")
else: # 거짓일때 실행
print("Fail")
```
### 여러 조건에 따른 분기(... | github_jupyter |
```
#We can create numpy arrays with more than one dimension
#This section will focus only on 2D arrays, but you can use numpy to build arrays of much higher dimensions
#In this video we will cover the basics and array creation in 2D
#indexing and slicing in 2D, and basic operations in 2D
#3D
#The list contains thre... | github_jupyter |
<a href="https://colab.research.google.com/github/Chuckboliver/Probability-and-Statistics/blob/main/HW2/ProbStat_HW2_ID62010615.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# **Probability and Statistics**
---
62010615 พัฒน์ภูมิ หาแก้ว
## **Homew... | github_jupyter |
# Distributed TensorFlow
description: train tensorflow CNN model on mnist data distributed via tensorflow
Train a distributed TensorFlow job using the `tf.distribute.Strategy` API on Azure ML.
For more information on distributed training with TensorFlow, refer [here](https://www.tensorflow.org/guide/distributed_trai... | github_jupyter |
## Define the Convolutional Neural Network
After you've looked at the data you're working with and, in this case, know the shapes of the images and of the keypoints, you are ready to define a convolutional neural network that can *learn* from this data.
In this notebook and in `models.py`, you will:
1. Define a CNN w... | github_jupyter |
```
!pip install networkx
!pip install rdflib
!pip install numpy
!pip install sparqlwrapper
import rdflib
import numpy as np
from collections import Counter
from SPARQLWrapper import SPARQLWrapper, JSON
import networkx as nx
import requests
def query_wiki_article_title(query):
params = {
'action':"query",
'... | github_jupyter |
# KoNLPy 한국어 처리 패키지
KoNLPy(코엔엘파이라고 읽는다)는 한국어 정보처리를 위한 파이썬 패키지이다.
```
import warnings
warnings.simplefilter("ignore")
import pandas as pd
import matplotlib.pyplot as plt
!pip install konlpy
!pip install WordCloud
pd.set_option('display.max_rows', 80)
plt.rcParams["font.family"] = "NanumGothicCoding"
import konlpy
... | github_jupyter |
## Regression Challenge
Predicting the selling price of a residential property depends on a number of factors, including the property age, availability of local amenities, and location.
In this challenge, you will use a dataset of real estate sales transactions to predict the price-per-unit of a property based on its... | github_jupyter |
# <center> Video Image Data </center>
#### CMSE 495 Ford Group
This tutorial teaches the user how to input a video file, such a mp4 and convert each frame of the video into a jpeg image using python, primarily in a Jupyter notebook.
<b> Environment Setup (Makefile):</b>
- Use the command 'make innit' automatically se... | github_jupyter |
###### Content under Creative Commons Attribution license CC-BY 4.0, code under BSD 3-Clause License © 2021 Lorena A. Barba, Tingyu Wang
# Multiple linear regression
Welcome to Lesson 3 of our _Engineering Computations_ module on deep learning!
So far, we have only modeled the relationship between one input variable... | github_jupyter |
# A simple function with different types of input parameters which are optimized.
```
from mango.tuner import Tuner
from scipy.stats import uniform
param_dict = {"a": uniform(0, 1), # uniform distribution
"b": range(1,5), # Integer variable
"c":[1,2,3], # Integer variable
"d... | github_jupyter |
```
import pandas as pd
import numpy as np
import os, time, cv2, tqdm, warnings
import matplotlib.pyplot as plt
from tqdm import tqdm
warnings.filterwarnings('ignore')
tqdm.pandas()
TARGET = 'dataset/Class1'
NORMAL_PATH = 'dataset/Class2/'
ORIGINAL_PATH = TARGET + '/'
def create_dir(path):
try:
os.stat(pa... | github_jupyter |
# Super-Convergence Learning Rate Schedule (TensorFlow Backend)
In this example we will implement super-convergence learning rate (LR) schedule (https://arxiv.org/pdf/1708.07120.pdf) and test it on a CIFAR10 image classification task. Super-covergence is a phenomenon where neural networks can be trained an order of m... | github_jupyter |
# Stack Overflow - Exploiting with Env Variable
- often times buffer that has overflow vulnerability is not large enough to fit even the smallest shellcode
- in sitation like this, one can stash the shellcode as an environment variable and overwrite the caller's return address with the address of the shellcode stored ... | github_jupyter |
##### Copyright 2020 The TensorFlow Authors.
```
#@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 ... | github_jupyter |
## Load libraries
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
!pip install imbalanced-learn
```
## Load data
```
from sklearn.datasets import make_classification
X, y = make_classification(
n_samples=10000,
n_features=2,
n_redundant=0,
n_clusters_per_class=1,
weight... | github_jupyter |
```
import sys
sys.path.append("../")
import os
import math
import numpy as np
import cv2
import pandas as pd
import tikzplotlib
from torch.utils.data import DataLoader, ConcatDataset
from pedrec.models.constants.action_mappings import ACTION
from pedrec.configs.dataset_configs import get_sim_dataset_cfg_default
from ... | github_jupyter |
<a href="https://colab.research.google.com/github/socd06/openvino_colab/blob/master/interview_prep.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Interview Preparation using Intel OpenVINO Toolkit Pre-Trained Models
```
from google.colab import ... | github_jupyter |
# Building Data Genome Project 2.0
## Buildings normalized consumption
Biam! (pic.biam@gmail.com)
```
# data and numbers
import numpy as np
import pandas as pd
import datetime as dt
# Visualization
import matplotlib.pyplot as plt
import matplotlib as mpl
from matplotlib import ticker
import matplotlib.dates as mdate... | github_jupyter |
# Use pyquery-ql.py
Send a graphql query to GitHub
and pretty print output.
Supports Python 3.6+
```
import json
import os
import pprint
import requests
# get api token and set authorization
api_token = os.environ['GITHUB_API_TOKEN']
headers = {'Authorization': f'token {api_token}'}
# set url to a graphql endpoint
... | github_jupyter |
###### Content under Creative Commons Attribution license CC-BY 4.0, code under MIT license (c)2014 M.Z. Jorisch
<h1 align="center">Orbital</h1>
<h1 align="center">Perturbations</h1>
In this lesson, we will discuss the orbits of bodies in space, and how those bodies can be affected by others as they fly by. We will ... | github_jupyter |
```
from dpm.models import (
OrdinalLayer, OrdinalModel,
OrdinalLoss,
exp_cdf, erf_cdf, tanh_cdf,
normal_cdf, laplace_cdf, cauchy_cdf
)
from dpm.visualize import (
plot_ordinal_classes,
plot_ordinal_classes_from_layer
)
import torch
import torch.nn as nn
import torch.optim as optim
import matplo... | github_jupyter |
# Gendered perspectives on character.
```
import csv, math
import pandas as pd
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
import statsmodels.api as sm
from adjustText import adjust_text
%matplotlib inline
data = pd.read_csv('chartable.tsv', sep = '\t')
lexicon = pd.read_csv('lexicon.tsv... | github_jupyter |
# 02 Huia Experience Training
# Setup
## Install Tensorflow 2 Nightly and other Libraries
```
#!pip install opencv-python
#!pip install scipy
#!pip install sklearn
#!pip install pathlib
#!pip install matplotlib
#!pip install fastai=1.0.52
#!conda install cudatoolkit=10.0
#!pip install scikit-learn
# Tensorflow 2 Al... | github_jupyter |
#1. Install Dependencies
First install the libraries needed to execute recipes, this only needs to be done once, then click play.
```
!pip install git+https://github.com/google/starthinker
```
#2. Get Cloud Project ID
To run this recipe [requires a Google Cloud Project](https://github.com/google/starthinker/blob/mast... | github_jupyter |
# RadarCOVID-Report
## Data Extraction
```
import datetime
import json
import logging
import os
import shutil
import tempfile
import textwrap
import uuid
import matplotlib.pyplot as plt
import matplotlib.ticker
import numpy as np
import pandas as pd
import pycountry
import retry
import seaborn as sns
%matplotlib in... | github_jupyter |
<i>Copyright (c) Microsoft Corporation. All rights reserved.</i>
<i>Licensed under the MIT License.</i>
# Neural Collaborative Filtering (NCF)
This notebook serves as an introduction to Neural Collaborative Filtering (NCF), which is an innovative algorithm based on deep neural networks to tackle the key problem in r... | github_jupyter |
# Facies classification using Convolutional Neural Networks #
## Team StoDIG - Statoil Deep-learning Interest Group ##
### _[David Wade](https://no.linkedin.com/in/david-wade-79918023), [John Thurmond](https://www.linkedin.com/in/john-thurmond-098b774) & [Eskil Kulseth Dahl](https://www.linkedin.com/in/eskil-k-dahl-87... | github_jupyter |
# Sentiment Analysis with an RNN
In this notebook, you'll implement a recurrent neural network that performs sentiment analysis. Using an RNN rather than a feedfoward network is more accurate since we can include information about the *sequence* of words. Here we'll use a dataset of movie reviews, accompanied by label... | github_jupyter |
```
from fastai.text import *
import numpy as np
from sklearn.model_selection import train_test_split
import pickle
import sentencepiece as spm
import re
import pdb
import fastai, torch
fastai.__version__ , torch.__version__
torch.cuda.set_device(0)
def random_seed(seed_value, use_cuda):
np.random.seed(seed_value) ... | github_jupyter |
# Introduction
From the [PVSC44 TL sensitivity](PVSC44%20TL%20sensitivity.ipynb) we concluded that:
* Overal MACC data is higher than corresponding static or optimized $T_L$ which leads to low dyanamic predictions.
* For at least 3 SURFRAD stations: bon, psu and sxf high $T_L$ in summer caused a seasonal bias in ... | github_jupyter |
# Imports
```
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import re
from sklearn import metrics
# import cvxopt # <- installation via conda recommended
from collections import defaultdict
from tqdm import tqdm
from sklearn.feature_extraction.text import CountVectorizer
... | github_jupyter |
<h2> Matrices: Tensor Product</h2>
Tensor product is defined between any two matrices. The result is a new bigger matrix.
Before giving its formal definition, we define it based on examples.
We start with a simple case.
<i>A vector is also a matrix. Therefore, tensor product can be defined between two vectors or be... | github_jupyter |
# TensorFlow-Slim
[TensorFlow-Slim](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/slim) is a high-level API for building TensorFlow models. TF-Slim makes defining models in TensorFlow easier, cutting down on the number of lines required to define models and reducing overall clutter. In partic... | github_jupyter |
```
library(repr) ; options(repr.plot.width = 4, repr.plot.height = 4) # Change plot sizes (in cm)
```
# Model Fitting using Non-linear Least-squares
## Introduction
In this Chapter, you will learn to fit non-linear mathematical models to data using Non-Linear Least Squares (NLLS).
Specifically, you will learn to
... | github_jupyter |
# Filling Area on Line Plots
```
import pandas as pd
from matplotlib import pyplot as plt
import numpy as np
data = pd.read_csv('data/data_dev.csv')
data.head()
ages = data['Age']
dev_salaries = data['All_Devs']
py_salaries = data['Python']
js_salaries = data['JavaScript']
plt.plot(ages, dev_salaries, color='#444444',... | github_jupyter |
```
from pyspark.sql import SparkSession
spark = SparkSession.builder.appName('nlp').getOrCreate()
from pyspark.ml.feature import Tokenizer,RegexTokenizer
from pyspark.sql.functions import col,udf
from pyspark.sql.types import IntegerType
sentence_df = spark.createDataFrame([
(0, 'Hello everyone and welcome to the t... | github_jupyter |
##### Copyright 2018 The TensorFlow Authors.
```
#@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 ... | github_jupyter |
```
%pylab inline
import numpy as np
import pandas as pd
import seaborn as sns
sns.set_style('ticks')
sns.set_context('paper')
import warnings
warnings.filterwarnings('ignore')
df = pd.read_csv('superfolders_ALL.csv')
subset_df = df.loc[df['MainPlot']==1]
sns.set_context('poster')
figure(figsize=(20,9))
metrics = [... | github_jupyter |
## Crypto Arbitrage
In this Challenge, you'll take on the role of an analyst at a high-tech investment firm. The vice president (VP) of your department is considering arbitrage opportunities in Bitcoin and other cryptocurrencies. As Bitcoin trades on markets across the globe, can you capitalize on simultaneous price d... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
import cv2
%matplotlib inline
```
## Software and package versions
```
print("*** VERSIONS ***")
import sys
print("Python {}".format(sys.version))
print("OpenCV {}".format(cv2.__version__))
print("Numpy {}".format(np.__version__))
import matplotlib
print("Matpl... | github_jupyter |
<a href="https://colab.research.google.com/github/MoghazyCoder/Machine-Learning-Tutorials/blob/master/Tutorials/Basic_Exploratory_Data_Analysis_using_Python_libraries.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Data Engineering Tutorial
### I... | github_jupyter |
# Finite Volume Discretisation
In this notebook, we explain the discretisation process that converts an expression tree, representing a model, to a linear algebra tree that can be evaluated by the solvers.
We use Finite Volumes as an example of a spatial method, since it is the default spatial method for most PyBaMM... | github_jupyter |
# Capítulo 5 - Uso de Selenium para automatizar acciones en el navegador
___
## Ejemplo práctico
___
importamos librerías clave:
```
! pip install selenium
from selenium import webdriver # hay que haber ejecutado `pip install selenium` para que funcione la importación
```
Para levantar el explorador deberá estar pr... | github_jupyter |
<a href="https://colab.research.google.com/github/NeuromatchAcademy/course-content/blob/master/tutorials/W1D5_DimensionalityReduction/W1D5_Tutorial4.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Tutorial 4: Nonlinear Dimensionality Reduction
**... | github_jupyter |
<img width="500" src="https://azurecomcdn.azureedge.net/cvt-18f087887a905ed3ae5310bee894aa53fc03cfffadc5dc9902bfe3469d832fec/less/images/section/azure-maps.png" />
# Azure Maps Geospatial Services
[Microsoft Azure Maps ](https://azure.microsoft.com/en-us/services/azure-maps/) provides developers from all industries ... | github_jupyter |
# Simulated X-ray Spectrum of Gas in CIE
Simulated X-ray spectrum of solar abundance low-density ($n_e$=0.004), hot (T=10$^6$K) gas in collisional
ionization equilibrium (CIE). Ionic species responsible for various emission lines are labeled. Wavelengths
range from 150 to 250 Angstroms in steps of 0.5 Angstroms.
This... | github_jupyter |
# Using SFRmaker with NHDPlus High Resolution
This notebook demostrates how to use `sfrmaker` to build an SFR package with an NHDPlus HR file geodatabase (or set of file geodatabases) obtained from the USGS National Map download client.
```
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as p... | github_jupyter |
## Gaussian Transformation
#### In Machine learning algorithm like Linear and Logistic Regression, the algorithm assumes that the variable are normally distributed.So, gaussian distributed variables may boost the machine learning algorithm performance.
### <span style="color:red">So, gaussian transformation is applie... | github_jupyter |
<img src="../images/aeropython_logo.png" alt="AeroPython" style="width: 300px;"/>
# Problema de tiro parabólico
## Introducción
Éste ejemplo no es más que una ayuda para introducir el ejemplo del salto de la rana, pues es un poco menos complejo.
El ejemplo consiste simplemente en averiguar la velocidad necesaria pa... | github_jupyter |
# Agrupando Datos
__Group By__ se refiere al proceso que involucra uno o más de los siguientes pasos:
* Dividir los datos en grupos basados en algún criterio.
* Aplicar una función a cada uno de los grupos independientemente.
* Combinar los resultados en una estructura de datos.
La división es el paso principal. Usu... | github_jupyter |
```
import gc
import os
import cv2
import sys
import json
import time
import timm
import torch
import random
import sklearn.metrics
from PIL import Image
from pathlib import Path
from functools import partial
from contextlib import contextmanager
import numpy as np
import scipy as sp
import pandas as pd
import torch.... | github_jupyter |
##### Copyright 2018 The TensorFlow Authors.
```
#@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 ... | github_jupyter |
```
import os
import datetime
import json
import numpy as np
import pandas as pd
import pprint
from IPython.display import display, HTML
from pymongo import MongoClient
# Connect to Mongo host & port
client = MongoClient(os.environ['MONGODB_NAME'], 27017)
# Input the name of the database you'd like to connect to. Exa... | github_jupyter |
# Global Warming affects on Agriculture - India
Global Warming and Agriculture are the two of the many things which always excites me, and I'm always curious about it. So why not collab both under the same roof.
Global Warming can be the next biggest global crisis after the current COVID-19 pendamic. It has been affe... | github_jupyter |
```
%load_ext nb_black
%config InlineBackend.figure_format = 'retina'
import pandas as pd
def logs_to_dataframe(logs):
rows = []
for line in logs.split("\n"):
if len(line) == 0:
continue
path, elapsed = line.split(",")
rows.append({"path": path, "elapsed": float(elapsed)})
... | github_jupyter |
# Stock Price Prediction
In this notebook, we demonstrate a reference use case where we use historical stock price data to predict the future price. The dataset we use is the daily stock price of S&P500 stocks during 2013-2018 ([data source](https://www.kaggle.com/camnugent/sandp500/)). We demostrate how to do univari... | github_jupyter |
## Converting the Cornell Movie-Dialogs Corpus into ConvoKit format
This notebook is a demonstration of how custom datasets can be converted into Corpus with ConvoKit
```
from tqdm import tqdm
from convokit import Corpus, User, Utterance
```
### The Cornell Movie-Dialogs Corpus
The original version of the Cornell ... | github_jupyter |
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