text stringlengths 2.5k 6.39M | kind stringclasses 3
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# Analysing data (GRIB)
In this notebook we will demonstrate how to:
* find locations of extreme values from GRIB data
* compute and plot a time series extracted from a point
* mask values that are not of interest
* compute wind speed
* compute and plot a vertical cross section
* compute and plot a vertical profile
... | github_jupyter |
```
# based on notebook "Stage1_MLP"
import pandas as pd
import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers, activations, losses, Model, Input
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint
from tensorflow.keras.metrics import SparseCategor... | github_jupyter |
```
#Checking the Missing values through heatmap
sns.heatmap(df.isnull(),yticklabels=False,cbar=False)
df.drop(['PoolQC','Fence','MiscFeature'],axis=1,inplace=True)
df.drop(['Alley'],axis=1,inplace=True)
df.info()
df.drop(['GarageYrBlt'],axis=1,inplace=True)
df
#Filling the missing values
df['LotFrontage']=df['LotFron... | github_jupyter |
```
import tensorflow as tf
from tensorflow import data
import shutil
import math
from datetime import datetime
from tensorflow.python.feature_column import feature_column
from tensorflow.contrib.learn import learn_runner
from tensorflow.contrib.learn import make_export_strategy
print(tf.__version__)
```
## Steps to... | github_jupyter |
## ERDAP without erddapy example for ArcticHeat Alamo
** Plot PAR ** its in a seperate file, not on ERDDAP
```
%matplotlib inline
import xarray as xa
import netCDF4 as nc
import pandas as pd
import numpy as np
import urllib
import datetime
import matplotlib.pyplot as plt
import matplotlib as mpl
from matplotlib.date... | github_jupyter |
# Time Series Forecast with Basic RNN
* Dataset is downloaded from https://archive.ics.uci.edu/ml/datasets/Beijing+PM2.5+Data
```
import pandas as pd
import numpy as np
import datetime
from matplotlib import pyplot as plt
import seaborn as sns
from sklearn.preprocessing import MinMaxScaler
df = pd.read_csv('data/pm25... | github_jupyter |
```
import hddm
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
print(hddm.__version__)
```
# load the data
```
data = hddm.load_csv('data/study2.csv')
data = hddm.utils.flip_errors(data)
data
data = data.drop(data[data.stim == '1Vne1A'].index)
data['movie_valence'] = data.s... | github_jupyter |
## Dependencies
```
# !pip install --quiet /kaggle/input/kerasapplications
# !pip install --quiet /kaggle/input/efficientnet-git
import warnings, glob
from tensorflow.keras import Sequential, Model
# import efficientnet.tfkeras as efn
from cassava_scripts import *
seed = 0
seed_everything(seed)
warnings.filterwarnin... | github_jupyter |
```
library(ggplot2)
#library(fmsb)
library(dplyr)
library(tidyr)
#library(ggforce)
library(tibble)
library(RColorBrewer)
#library(dynutils)
library(plyr)
library(stringr)
library(R.utils)
```
```
metrics_tab_lab <- read.csv("/storage/groups/ml01/workspace/group.daniela/metrics_lisi/all.csv")
methods <- colnames(metri... | github_jupyter |
# YAP Jupyter Interface 
## Walkthrough and User Guide
The next cells show examples of input output interaction with Prolog and Jupyter. We assume basic knowledge of both Prolog and Python/R/Jupyter. Notice that this is experimental software, subject to bugs and change. Also remember th... | github_jupyter |
# 📝 Exercise M3.02
The goal is to find the best set of hyperparameters which maximize the
statistical performance on a training set.
Here again with limit the size of the training set to make computation
run faster. Feel free to increase the `train_size` value if your computer
is powerful enough.
```
import numpy a... | github_jupyter |
# Title
_Brief abstract/introduction/motivation. State what the chapter is about in 1-2 paragraphs._
_Then, have an introduction video:_
```
from bookutils import YouTubeVideo
YouTubeVideo("w4u5gCgPlmg")
```
**Prerequisites**
* _Refer to earlier chapters as notebooks here, as here:_ [Earlier Chapter](Fuzzer.ipynb)... | github_jupyter |
# Using `pybind11`
The package `pybind11` provides an elegant way to wrap C++ code for Python, including automatic conversions for `numpy` arrays and the C++ `Eigen` linear algebra library. Used with the `cppimport` package, this provides a very nice work flow for integrating C++ and Python:
- Edit C++ code
- Run Pyt... | github_jupyter |
Now we have [for loops](https://matthew-brett.github.io/cfd2019/chapters/03/iteration) and
[ranges](https://matthew-brett.github.io/cfd2019/chapters/03/Ranges), we can solve the problem in
[population, permutation](https://matthew-brett.github.io/cfd2019/chapters/05/population_permutation).
```
# Array library.
import... | github_jupyter |
## Use Predicto Trade Picks with Alpaca to place hedged orders
Sample usage to retrieve latest trade picks and submit to alpaca programmatically
(https://predic.to) (https://alpaca.markets)
This is a simple example on how to retrieve latest trade pick for a ticker and then place an alpaca order with target price and ... | github_jupyter |
<a href="https://colab.research.google.com/github/tirtharajghosh/Machine-Learning/blob/master/Multiple_Linear_Regression.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
### Importing required libraries
```
import numpy as np
import matplotlib.pyplo... | github_jupyter |
```
import os
%env MODEL = /opt/intel/openvino/deployment_tools/open_model_zoo/tools/downloader/intel/person-detection-retail-0013/FP32/person-detection-retail-0013.xml
%env USE_SAFETY_MODEL = ../resources/worker-safety-mobilenet/FP32/worker_safety_mobilenet.xml
#!/usr/bin/env python3
"""
Copyright (c) 2018 Intel Cor... | github_jupyter |
## Metaprogramming
Warning: Advanced topic!
### Metaprogramming globals
Consider a bunch of variables, each of which need initialising and incrementing:
```
bananas = 0
apples = 0
oranges = 0
bananas += 1
apples += 1
oranges += 1
```
The right hand side of these assignments doesn't respect the DRY principle. We... | github_jupyter |
# Making multiple interface-reporting dataframes for several structures using snakemake
This notebook builds on the basics covered in [Working with PDBePISA interface lists/reports in Jupyter Basics](Working%20with%20PDBePISA%20interfacelists%20in%20Jupyter%20Basics.ipynb) in order to generate dataframes detailing the... | github_jupyter |
```
#pip install pandahouse
import pandahouse as ph
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
connection_default = {'host': 'http://clickhouse.beslan.pro:8080',
'database':'default',
'user':'student',
'password':'dpo_python_2020'
}
connection_test = dict(database='defa... | github_jupyter |
<table class="ee-notebook-buttons" align="left">
<td><a target="_blank" href="https://github.com/giswqs/earthengine-py-notebooks/tree/master/Visualization/styled_layer_descriptors.ipynb"><img width=32px src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" /> View source on GitHub</a></td>
<td><a target... | github_jupyter |
# Simulating Data
*Neural Time Series Data*
### Prerequisites
For this chapter, you should be familiar with the following concepts and techniques:
* Basic Python programming
* Basic Math. **(recap your skills in Linea Algebra, Sine Waves and Euler's Formula)**
### Scop... | github_jupyter |
# Databases <span class="tocSkip"></span>
Introduction
------------
Many of you will deal with complex data — and often, lots of it. Ecological and Evolutionary data are particularly complex because they contain large numbers of attributes, often measured in very different scales and units for individual taxa, popula... | github_jupyter |
# Partial Least Squares Regression (PLSR) on Sensory and Fluorescence data
This notebook illustrates how to use the **hoggorm** package to carry out partial least squares regression (PLSR) on multivariate data. Furthermore, we will learn how to visualise the results of the PLSR using the **hoggormPlot** package.
---
... | github_jupyter |
```
import numpy as np
import pandas
import urllib2
from sklearn.metrics.cluster import adjusted_rand_score
import seaborn as sns
import matplotlib.pyplot as plt
from scipy import stats
from functools import reduce
chrs_length = [249250621,243199373,198022430,191154276,180915260,171115067,159138663,146364022,141213431,... | github_jupyter |
# Name
Data processing by creating a cluster in Cloud Dataproc
# Label
Cloud Dataproc, cluster, GCP, Cloud Storage, KubeFlow, Pipeline
# Summary
A Kubeflow Pipeline component to create a cluster in Cloud Dataproc.
# Details
## Intended use
Use this component at the start of a Kubeflow Pipeline to create a tempora... | github_jupyter |
# Bonus Material: Word count
The word count problem is the 'Hello world' equivalent of distributed programming. Word count is also the basic process by which text is converted into features for text mining and topic modeling. We show a variety of ways to solve the word count problem in Python to familiarize you with ... | github_jupyter |
<h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Double-check-we-are-using-Python-3.8" data-toc-modified-id="Double-check-we-are-using-Python-3.8-1">Double check we are using Python 3.8</a></span></li><li><span><a href="#What-is-Naive-Bayes?" data-toc-mod... | github_jupyter |
```
import time
import pickle
from itertools import combinations
import pandas as pd
from bs4 import BeautifulSoup
from selenium import webdriver
from selenium.webdriver.chrome.options import Options
from selenium.webdriver.support.ui import Select
import pyderman
from tqdm import tqdm
from lazyme import zigzag, r... | github_jupyter |
```
import numpy as np
from xgboost import XGBClassifier
from bayes_opt import BayesianOptimization
from sklearn.model_selection import train_test_split
import xgboost as xgb
from pathlib import Path
import os
def xgb_classifier(n_estimators, max_depth, reg_alpha, reg_lambda, min_child_weight, num_boost_round, gamma):
... | github_jupyter |
<a href="https://colab.research.google.com/github/dimi-fn/Various-Data-Science-Scripts/blob/main/Algorithms.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
Algorithm performance can be measured based on **runtime** (Big-O time complexity) and **spac... | github_jupyter |
## Perform Analysis on Athletes
This file reads the detailed athlete information and performs Linear Regression analysis on this data.
The following areas are examined in this code
* <a href=#Visualize>Visualize Data</a>
* <a href=#LinearRegression>Linear Regression</a>
* <a href=#LASSO>LASSO</a>
* <a... | github_jupyter |
<!--BOOK_INFORMATION-->
<a href="https://www.packtpub.com/big-data-and-business-intelligence/machine-learning-opencv" target="_blank"><img align="left" src="data/cover.jpg" style="width: 76px; height: 100px; background: white; padding: 1px; border: 1px solid black; margin-right:10px;"></a>
*This notebook contains an ex... | github_jupyter |
```
import os
import random
import numpy as np
import pandas as pd
import tensorflow as tf
from tqdm.notebook import tqdm
from nasbench import api
from search_spaces import load_nasbench_101
from random_search import run_random_search, random_spec
from neural_predictor import classifier, regressor
from input_preproces... | github_jupyter |
## Feature Scaling
We discussed previously that the scale of the features is an important consideration when building machine learning models. Briefly:
### Feature magnitude matters because:
- The regression coefficients of linear models are directly influenced by the scale of the variable.
- Variables with bigger ... | github_jupyter |
___
<a href='http://www.pieriandata.com'><img src='../Pierian_Data_Logo.png'/></a>
___
<center><em>Copyright Pierian Data</em></center>
<center><em>For more information, visit us at <a href='http://www.pieriandata.com'>www.pieriandata.com</a></em></center>
# RNN Example for Time Series
```
import pandas as pd
import... | github_jupyter |
```
import itertools
import numpy as np
import torch
import matplotlib
import matplotlib.pyplot as plt
%matplotlib inline
from bayesian_privacy_accountant import BayesianPrivacyAccountant
ma_eps = []
ba_eps = []
quant = 0.05
sigma = 1.0
plot_range = np.arange(100)
moment_accountant = BayesianPrivacyAccountant(power... | github_jupyter |
```
!pip install transformers==3.3.1
!git clone https://github.com/adeepH/OLD-Shared-Task.git
import pandas as pd
train = pd.read_csv('/content/tamil_train.csv',delimiter='\t',
header=None,names=['text','label','nan'])
train = train.drop(columns=['nan'])
train.label = train.label.apply({'Not_offensive... | github_jupyter |
<h2>SMS Spam Detection</h2>
See <a href="https://www.kaggle.com/uciml/sms-spam-collection-dataset">Kaggle.com</a>
```
import numpy as np
import numpy.core.defchararray as npf
from sklearn.preprocessing import LabelEncoder
# load data
import pandas as pd
#sms = pd.read_csv("../data/spam.csv", encoding="latin-1")
sms =... | github_jupyter |
```
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from numpy import exp, abs, log
from scipy.special import gamma, factorial
import os, sys
import scipy.stats as stats
import statsmodels.api as sm
from utils import *
import time
import datetime as dt
import universal as up
from universal import... | github_jupyter |
# TNBoW Experiments
© 2020 Nokia
Licensed under the BSD 3 Clause license
SPDX-License-Identifier: BSD-3-Clause
```
%load_ext autoreload
%autoreload 2
from pathlib import Path
import json
import os
os.environ["snippets_collection"] = "so-ds-feb20"
os.environ["valid_dataset"] = "so-ds-feb20-valid"
os.environ["test_d... | github_jupyter |
```
import numpy as np
import pandas as pd
import warnings
warnings.filterwarnings('ignore')
df = pd.read_csv('creditcard.csv')
df.head()
df.isnull().sum().sum()
df.Class.value_counts()
# creating independent and dependent variable
X = df.drop(['Class'], axis=1)
y = df['Class']
from sklearn.model_selection import train... | github_jupyter |
<table align="center">
<td align="center"><a target="_blank" href="http://introtodeeplearning.com">
<img src="http://introtodeeplearning.com/images/colab/mit.png" style="padding-bottom:5px;" />
Visit MIT Deep Learning</a></td>
<td align="center"><a target="_blank" href="https://colab.research.google.c... | github_jupyter |
# The Basics: Training Your First Model
Celsius to Fahrenheit 변환기를 텐서플로를 사용하여 구현해 보자.
섭씨를 화씨로 변환하는 공식은 아래와 같다.:
$$ f = c \times 1.8 + 32 $$
TensorFlow에서 Celsius 데이터 (0, 8, 15, 22, 38)를 입력으로 하고 출력이 Fahrenheit values (32, 46, 59, 72, 100)가 되도록 신경망을 훈련하자.
최종적으로 섭씨를 화씨로 변환하는 모형이 학습된다.
## Import dependencies
```
i... | github_jupyter |
# Multiple Kernel Learning
#### By Saurabh Mahindre - <a href="https://github.com/Saurabh7">github.com/Saurabh7</a>
This notebook is about multiple kernel learning in shogun. We will see how to construct a combined kernel, determine optimal kernel weights using MKL and use it for different types of [classification](h... | github_jupyter |
##### Copyright 2021 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 |
<script async src="https://www.googletagmanager.com/gtag/js?id=UA-59152712-8"></script>
<script>
window.dataLayer = window.dataLayer || [];
function gtag(){dataLayer.push(arguments);}
gtag('js', new Date());
gtag('config', 'UA-59152712-8');
</script>
# ADM Quantities in terms of BSSN Quantities
## Author: Za... | github_jupyter |
# Ordinary Least Squares
```
%matplotlib inline
import numpy as np
import statsmodels.api as sm
import matplotlib.pyplot as plt
from statsmodels.sandbox.regression.predstd import wls_prediction_std
np.random.seed(9876789)
```
## OLS estimation
Artificial data:
```
nsample = 100
x = np.linspace(0, 10, 100)
X = np.c... | github_jupyter |
```
%%html
<link href="https://fonts.googleapis.com/css?family=Open+Sans" rel="stylesheet">
<style>#notebook-container{font-size: 13pt;font-family:'Open Sans', sans-serif;} div.text_cell{max-width: 104ex;}</style>
%pylab inline
import matplotlib.patches as patches
```
# Left and right-hand sums
We want to find the a... | github_jupyter |
```
# Erasmus+ ICCT project (2018-1-SI01-KA203-047081)
# Toggle cell visibility
from IPython.display import HTML
tag = HTML('''<script>
code_show=true;
function code_toggle() {
if (code_show){
$('div.input').hide()
} else {
$('div.input').show()
}
code_show = !code_show
}
$( document... | github_jupyter |
Copyright 2019 The Google Research Authors.
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
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writin... | github_jupyter |
```
#all_slow
#export
from fastai.basics import *
from fastai.vision.all import *
#default_exp vision.gan
#default_cls_lvl 3
#hide
from nbdev.showdoc import *
```
# GAN
> Basic support for [Generative Adversarial Networks](https://arxiv.org/abs/1406.2661)
GAN stands for [Generative Adversarial Nets](https://arxiv.or... | github_jupyter |
```
%matplotlib inline
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
from os.path import join
import os
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
sample_md = pd.read_pickle('pickle_df')
alpha_div_fp = '/home/johnchase/office-project/office-micro... | github_jupyter |
# 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)
```
# Configure Jupyter so figures appear in the notebook
%matplotlib inline
# Configure Jupyter to display the assigned value after an a... | github_jupyter |
# Human numbers
```
from fastai2.text.all import *
bs=64
```
## Data
```
path = untar_data(URLs.HUMAN_NUMBERS)
path.ls()
def readnums(d): return ', '.join(o.strip() for o in open(path/d).readlines())
train_txt = readnums('train.txt'); train_txt[:80]
valid_txt = readnums('valid.txt'); valid_txt[-80:]
train_tok = toke... | github_jupyter |
```
import cv2
import numpy as np
from glob import glob
from matplotlib import pyplot as plt
def patch_gen(img_orig,patchSize,r):
# print(len(img_orig.shape))
if(len(img_orig.shape)==3):
img = cv2.cvtColor(img_orig,cv2.COLOR_RGB2GRAY)
else:
img = img_orig
# plt.imshow(img, cmap='gray')
#... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
%matplotlib inline
import sys
from pathlib import Path
sys.path.append(str(Path().cwd().parent))
import pandas as pd
from load_dataset import Dataset
from model import TimeSeriesPredictor, TimeSeriesDetector
from sklearn.linear_model import Ridge
import plotting
from typing impor... | github_jupyter |
# Data Visualization and manipulations
```
import sys
import numpy as np
from numpy import set_printoptions
set_printoptions(precision=3)
import pandas as pd
import matplotlib.pyplot as plt
plt.rcParams["figure.figsize"] = (16,12)
import seaborn as sns
from IPython.display import display
```
# Load diabetes data from... | github_jupyter |
# Training Built-in Algorithms with SageMaker (Part 4/4)
Download | Structure | Preprocessing (Built-in) | **Train Model (Built-in)**
```
```
**Notes**:
* This notebook should be used with the conda_amazonei_mxnet_p36 kernel
* This notebook is part of a series of notebooks beginning with `01_download_data`, `02_struc... | github_jupyter |
# Download Repo and Setup Installation
Download the github repo containing the model, data, and weights
```
%cd
!git clone --quiet https://github.com/bwproud/mask_rcnn_recyclables.git
%cd ~/mask_rcnn_recyclables
!pip install -r requirements.txt
!python setup.py install
```
# Mount Directory
If you want to save you... | github_jupyter |
```
import pandas as pd
import numpy as np
import scipy
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn import linear_model
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.ensemble imp... | github_jupyter |
## Multivariable Calculus Review
We will be covering some of the most relevant concepts from multivariable calculus for this course, and show how they can be extended to deal with matrices of training data.
### Partial Derivatives
The derivative of a function of 2 variables $f(x, y)$ w.r.t. either one of its variables... | github_jupyter |
# iChef
<img src="https://imgur.com/ij75Z2Z.png" alt="logo" border="0">
## Backend 3 Hackthon iFood
### Carrega as Bibliotecas nescessarias
```
import pandas as pd
import numpy as np
import json
```
### Criar o banco de dados:
Banco de dados de receitas, titulo, igredientes e como fazer
<p>Fonte de dados:
https://... | github_jupyter |
```
# default_exp __init__
# hide
import os
notebooks_dir = os.getcwd()
project_dir = os.path.dirname(notebooks_dir)
import sys
sys.path.append(project_dir)
```
# Huobi
```
from ccstabilizer import Exchange
from ccstabilizer import secrets
# export
import contextlib, requests
from decimal import Decimal, ROUND_DOWN
... | github_jupyter |
# Riskfolio-Lib Tutorial:
<br>__[Financionerioncios](https://financioneroncios.wordpress.com)__
<br>__[Orenji](https://www.orenj-i.net)__
<br>__[Riskfolio-Lib](https://riskfolio-lib.readthedocs.io/en/latest/)__
<br>__[Dany Cajas](https://www.linkedin.com/in/dany-cajas/)__
<a href='https://ko-fi.com/B0B833SXD' target='... | github_jupyter |
# 15 minutes to QCoDeS
This short introduction is aimed mainly for beginners. Before you start with your first code using QCoDeS, make sure you have properly set up the Python environment for QCoDeS as explained in [this document](http://qcodes.github.io/Qcodes/start/index.html#installation).
## Introduction
An exp... | github_jupyter |
```
from platform import python_version
print(python_version())
import sys
# Add the path to system, local or mounted S3 bucket, e.g. /dbfs/mnt/<path_to_bucket>
sys.path.append('./secrets.py')
import logging
import math
import os
from influxdb import DataFrameClient
import numpy as np
import matplotlib.mlab as mlab
... | github_jupyter |
## Introduction: How can you use leaf indexes from a tree ensemble?
[](https://colab.research.google.com/github/catboost/tutorials/blob/master/leaf_indexes_calculation/leaf_indexes_calculation_tutorial.ipynb)
Supose we have fitted tree ensemble... | github_jupyter |
# Unsupervised methods
In this lesson, we'll cover unsupervised computational text anlalysis approaches. The central methods covered are TF-IDF and Topic Modeling. Both of these are common approachs in the social sciences and humanities.
[DTM/TF-IDF](#dtm)<br>
[Topic modeling](#topics)<br>
### Today you will
* Unde... | github_jupyter |
This notebook is aimed at identifying unphased regions of the priamry contigs based on short read illumina mapping data. Parts of it are really slow.
This notebook was only designed for the purpose of analyzing the Pst-104E genome. No gurantees it works in any other situtation. It will have spelling errors due to the... | github_jupyter |
# Introduction to Python
### Function Exercises
#### 1) Write a function that takes an integer minutes and converts it to seconds. <font color='green'>(EASY)</font>
#### 2) Create a function that converts a date formatted as MM/DD/YYYY to YYYYDDMM. The input is a string!
(do not use datetime module)
#### 3) A... | github_jupyter |
# Air Quality Predictions with Amazon SageMaker and Amazon EMR
This notebook demonstrates the ability to use Apache Spark on Amazon EMR to do data prep with two different datasets in order to build an urban air quality predictor with Amazon SageMaker.
To create the environment, use the `us-east-1` CloudFormation temp... | github_jupyter |
Sascha Spors,
Professorship Signal Theory and Digital Signal Processing,
Institute of Communications Engineering (INT),
Faculty of Computer Science and Electrical Engineering (IEF),
University of Rostock,
Germany
# Data Driven Audio Signal Processing - A Tutorial with Computational Examples
Winter Semester 2021/22 (M... | github_jupyter |
# Demonstration of the processing steps for converting the raw data
* RADAR type is FMCW
* 60000 chirps per file = to 60 seconds record time
* 1ms for one chirp
* Bandwith: 400MHz
Processing code ported from a matlab version created by Aleksander Angelov who modified the original Test SDR-KIT for extracting micro-Do... | github_jupyter |
```
import requests
from pprint import pprint
from IPython.core.display import display, HTML
from markdownify import markdownify as md
import json
import re
import urllib.request
from unidecode import unidecode
from datetime import datetime
IMPO_ENDPOINT='https://www.impo.com.uy/bases/'
LUC='leyes/19889-2020/'
ARTICULO... | github_jupyter |
## Setup
```
!pip install -q git+https://github.com/rwightman/pytorch-image-models
from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
import timm
import torch
import tensorflow as tf
import tensorflow_datasets as tfds
import pickle
import numpy as np
import matplotlib.pyplot as plt
from tqd... | github_jupyter |
## Digit Recognizer
Learn computer vision fundamentals with the famous MNIST dat
https://www.kaggle.com/c/digit-recognizer
### Competition Description
MNIST ("Modified National Institute of Standards and Technology") is the de facto “hello world” dataset of computer vision. Since its release in 1999, this classic dat... | github_jupyter |
# Hypothesis Testing of Human Height Data
In this lab, you will learn how to use Python 3 to perform and understand the basics of hypothesis testing. Hypothesis testing is widely used. Anytime you are trying to determine if a parameter or relationship is statistically significant you can perform a hypothesis test.
I... | github_jupyter |
# BLU03 - Learning Notebook - Part 1 of 3 - Relational Databases and SQL
## 1. Introduction
In this notebook, we'll start by learning about databases. There are different types of databases, but here we'll focus on relational databases. In that context, we'll talk about tables and how different tables can relate with... | github_jupyter |
# Optimization Methods
Until now, you've always used Gradient Descent to update the parameters and minimize the cost. In this notebook, you will learn more advanced optimization methods that can speed up learning and perhaps even get you to a better final value for the cost function. Having a good optimization algorit... | github_jupyter |
# Cloud APIs for Computer Vision: Up and Running in 15 Minutes
This code is part of [Chapter 8- Cloud APIs for Computer Vision: Up and Running in 15 Minutes ](https://learning.oreilly.com/library/view/practical-deep-learning/9781492034858/ch08.html).
## Compile Results for Image Tagging
In this file we will compile ... | github_jupyter |
```
import sys
sys.path.append('C:\\Users\\Danilo Santos\\Desktop\\Qualificação PPGCC\\abordagem\\RFNS')
from grimoire.BaseEnginnering import BaseEnginnering
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder, LabelEncoder
df_acu... | github_jupyter |
# job1:
Anti-Terrorism Intelligence Analysis
Metro
[Link](https://www.indeed.com/jobs?q=Intelligence%20Analysis&l=Washington%2C%20DC&vjk=2666edf71c7b3200&advn=477295927342672)
# job2:
Counterintelligence Investigator
Leido
[Link](https://www.indeed.com/viewjob?jk=696eb25ddaa842f0&tk=1d6gov55q270i002&from=serp&vj... | github_jupyter |
```
# HIDDEN
# The standard set of libraries we need
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
# Make plots look a little bit more fancy
plt.style.use('fivethirtyeight')
# The standard library for data in tables
import pandas as pd
# A tiny function to read a file directly from a URL
from... | github_jupyter |
<a href="https://colab.research.google.com/github/papagorgio23/Python101/blob/master/Py_202_F%2B_Model_Answers.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
# Installing Library
!pip install pydata_google_auth
# import base packages into the n... | github_jupyter |
**Tools - pandas**
*The `pandas` library provides high-performance, easy-to-use data structures and data analysis tools. The main data structure is the `DataFrame`, which you can think of as an in-memory 2D table (like a spreadsheet, with column names and row labels). Many features available in Excel are available pro... | github_jupyter |
[](https://www.pythonista.io)
# Reglas de *URL*.
## Preliminar.
```
from flask import Flask
app = Flask(__name__)
```
## Extracción de valores a partir de una *URL*.
Las reglas de *URL* no sólo permiten definir rutas estáticas que apunten a una función, sino que pueden defi... | github_jupyter |
### Load data
```
import numpy as np
import pandas as pd
column_names = ['txn_key', 'from_user', 'to_user', 'date', 'amount']
df = pd.read_csv('../data/bitcoin_uic_data_and_code_20130410/user_edges.txt', names=column_names)
df.head()
```
### Select transactions in or before 2010
```
df[ df.date < 20110000000000 ].t... | github_jupyter |
**Important: This notebook will only work with fastai-0.7.x. Do not try to run any fastai-1.x code from this path in the repository because it will load fastai-0.7.x**
```
%reload_ext autoreload
%autoreload 2
%matplotlib inline
from fastai.learner import *
import torchtext
from torchtext import vocab, data
from torc... | github_jupyter |
## Special Functions - col and lit
Let us understand special functions such as col and lit. These functions are typically used to convert the strings to column type.
```
%%HTML
<iframe width="560" height="315" src="https://www.youtube.com/embed/lP2LOZfMcIc?rel=0&controls=1&showinfo=0" frameborder="0" allowful... | github_jupyter |
# ANOVOS - Datetime
Following notebook shows the list of functions related to "datetime" module provided under ANOVOS package and how it can be invoked accordingly.
- [Timestamp and Epoch Conversion](#Timestamp-and-Epoch-Conversion)
- [Timezone Conversion](#Timezone-Conversion)
- [Timestamp and String Conversion](#Time... | github_jupyter |
호텔 예약이 취소되는 원인이 무엇인지 찾아봅시다.
이 분석은 Dowhy 라이브러리 사이트의 Case Study내용을 발췌하였으며, Antonio, Almeida, Nunes(2019)의 호텔 예약 데이터 셋을 사용합니다.
데이터는 github의 rfordatascience/tidytuseday 에서 구할 수 있습니다.
호텔 예약이 취소되는 이유는 여러가지가 있을 수 있습니다.
예를 들어,
- 1. 고객이 호텔이 제공하기 어려운 요청을 하고(ex. 호텔의 주차공간이 부족하고, 고객은 주차공간을 요청), 요청을 거절받은 고객이 예약을 취소할 수 ... | github_jupyter |
# Encoders and decoders
> In this post, we will implement simple autoencoder architecture. This is the summary of lecture "Probabilistic Deep Learning with Tensorflow 2" from Imperial College London.
- toc: true
- badges: true
- comments: true
- author: Chanseok Kang
- categories: [Python, Coursera, Tensorflow_proba... | github_jupyter |
```
from pyspark.sql import SparkSession
spark = SparkSession.builder.appName("Grand_Assign").getOrCreate()
df = spark.read.csv('hdfs://quickstart.cloudera:8020/user/cloudera/census.csv',header=True,inferSchema=True)
# Uh oh Strings!
df.describe().printSchema()
df.select('STNAME').distinct().show()
df.select('CTYNAME'... | github_jupyter |
# Change Detection - Image Ratio U-Net Classifier
### Summary
This notebook trains a Convolutional Neural Network (CNN) to identify building change from the pixel ratios between before/after [Sentinel-2](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/overview) imagery. For a better understanding of t... | github_jupyter |
## Title :
Exercise: Computing the CI
## Description :
You are the manager of the Advertising division of your company, and your boss asks you the question, **"How much more sales will we have if we invest $1000 dollars in TV advertising?"**
<img src="../fig/fig3.jpeg" style="width: 500px;">
The goal of this exercis... | github_jupyter |
# Create Correlation Matrix & randomize gaia errors
```
import numpy as np
import matplotlib.pyplot as plt
from astropy import constants as const, units as u
from astropy.table import Table, join, vstack, hstack, Column, MaskedColumn
from astropy import coordinates, units as u, wcs
import warnings
from astropy.utils.e... | github_jupyter |
```
from notebook.services.config import ConfigManager
cm = ConfigManager()
cm.update('livereveal', {'scroll': True,})
%load_ext autoreload
%autoreload 2
import os
import sys
sys.path.append(os.path.abspath("."))
from viewer import ThreeJsViewer
import matplotlib.pyplot as plt
%matplotlib inline
%%html
<style>
body... | github_jupyter |
```
from tensorflow.keras import layers
from tensorflow.keras.regularizers import l2
from tensorflow.keras.layers import Activation, Conv1D, Conv2D, Input, Lambda
from tensorflow.keras.layers import BatchNormalization, Flatten, Dense, Reshape
from tensorflow.keras.layers import MaxPooling2D, AveragePooling2D, GlobalAve... | github_jupyter |
# Sample grouping
We are going to linger into the concept of sample groups. As in the previous
section, we will give an example to highlight some surprising results. This
time, we will use the handwritten digits dataset.
```
from sklearn.datasets import load_digits
digits = load_digits()
data, target = digits.data, d... | github_jupyter |
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