text stringlengths 2.5k 6.39M | kind stringclasses 3
values |
|---|---|
Start jupyter server such that the print of the current working dir below is the root directory of the repo
Setup the environment...
```
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Conv2D, Dropout, Flatten, MaxPooling2D
import os
import sys
os.envir... | github_jupyter |
# ExtraTreesClassifier
```
from __future__ import division
from IPython.display import display
from matplotlib import pyplot as plt
%matplotlib inline
import numpy as np
import pandas as pd
import random, sys, os, re
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.cross_validation ... | github_jupyter |
# Quantization of Signals
*This jupyter notebook is part of a [collection of notebooks](../index.ipynb) on various topics of Digital Signal Processing. Please direct questions and suggestions to [Sascha.Spors@uni-rostock.de](mailto:Sascha.Spors@uni-rostock.de).*
## Quantization Error of a Linear Uniform Quantizer
As... | github_jupyter |
# Planet Tasking API Order Creation
---
## Introduction
---
This tutorial is an introduction on how to create tasking orders using [Planet](https://www.planet.com)'s Tasking API. It provides code samples on how to write simple Python code to do this.
The API reference documentation can be found at https://develope... | github_jupyter |
```
import pandas as pd
import nltk
from nltk.stem import WordNetLemmatizer
from nltk.tokenize import RegexpTokenizer
import ast
import numpy as np
import os
import ast
import urllib.request
from urllib.request import urlopen
from bs4 import BeautifulSoup
import os.path
from datetime import datetime
from collections im... | github_jupyter |
### Protocols
Python is a protocol based language.
If you're coming from Java, you can think of protocols the same way you think of interfaces.
Except Python does not have this very strict idea of an interface.
You simply add some functions to your class using a specific name, and if Python finds it there, it will ... | github_jupyter |
```
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from __future__ import print_function
import sklearn
from sklearn.ensemble import RandomForestClassifier
from sklearn import preprocessing
from datetime import datetime
import os
%matplotlib inline
%config InlineBackend.fi... | github_jupyter |
# EZyRB Tutorial
In this tutorial we will show the typical workflow for the construcion of the Reduced Order Model based only on the outputs of the higher-order model.
We consider a parametric steady heat conduction problem in a two-dimensional domain $\Omega$. While in this tutorial we are going to focus on the data-... | github_jupyter |
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the MIT License.
# Using Azure Machine... | github_jupyter |
conda environment to use on nightingale: /scratch/conda_envs/elmo-embeddings
```
%load_ext autoreload
%autoreload
from allennlp.commands.elmo import ElmoEmbedder
import os
from sys import path
path.append('..')
from relation_extraction.data import utils
import h5py
import numpy as np
data_path = '/data/medg/misc/seme... | github_jupyter |
# miRNA-Seq
(2019.04.02)
## Only valid miRNAs
On the paper [Evolutionary history of plant microRNAs](https://doi.org/10.1016/j.tplants.2013.11.008), miRBase is scan for valid miRNAs. For a miRNA to be considered valid...
* miRNA sequence must have high complementarity to opposing arm (>= 15 nt)
* It should be obser... | github_jupyter |
## 5.7: 文字列の書式設定
```
# リスト 5.7.1 テキストの描画
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
fig, ax = plt.subplots(1, 1)
# 軸範囲の設定
ticks = np.linspace(0, 10, 6)
ax.set_xticks(ticks)
ax.set_yticks(ticks)
# テキストの描画
ax.text(2, 4, "Jupyter")
# 目盛り線描画
ax.grid(linestyle="-")
# リスト 5.7.2 フォントの設定
fig, ... | github_jupyter |
[](https://githubtocolab.com/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/streamlit_notebooks/NER_PT.ipynb)
# **Detect legal entities in Portuguese text**
## 1. Co... | github_jupyter |
## Implementation of subspace alignment
This is based on the following paper: <i>Unsupervised Visual Domain Adaptation Using Subspace Alignment</i>.
```
import numpy as np
import matplotlib.pyplot as plt
from sklearn.manifold import TSNE
from sklearn.decomposition import PCA
import tensorflow_hub as hub
import warni... | github_jupyter |
# Analyzing the Parker Solar Probe flybys
## 1. Modulus of the exit velocity, some features of Orbit #2
First, using the data available in the reports, we try to compute some of the properties of orbit #2. This is not enough to completely define the trajectory, but will give us information later on in the process.
`... | github_jupyter |
```
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from tqdm import tqdm
%matplotlib inline
from torch.utils.data import Dataset, DataLoader
import torch
import torchvision
import torch.nn as nn
import torch.optim as optim
from torch.nn import functional as F
device = torch.device("cuda" i... | github_jupyter |
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the MIT License.
x = np.zeros((40,1), dtype = np.float)
y = np.zeros((40,1), dtype = np.float)
for i in range(data.shape[0]):
x[i] = data[i][0]
f... | github_jupyter |
```
videos = """
https://www.youtube.com/watch?v=RBShCX3-BtQ&t=1099s
https://www.youtube.com/watch?v=WnipnOcDkx8
https://www.youtube.com/watch?v=fzuKoKTxfEs
https://www.youtube.com/watch?v=Hioy9UvH4yE
https://www.youtube.com/watch?v=98CiTQQtqak
https://www.youtube.com/watch?v=HKnXUGo_H2U
https://www.youtube.com/watch?v... | github_jupyter |
# Azure Data Scientist Certification (DP-100) Resources & Tips
> I passed the DP-100 certification exam yesterday. Here are some of the resources I used and the tips for you to prepare well.
- toc: true
- badges: true
- comments: true
- categories: [certification]
- hide: false
## DP-100
![DP-100](https://miro.m... | github_jupyter |
# Pricing European Call Options
### Introduction
<br>
Suppose a <a href="http://www.theoptionsguide.com/call-option.aspx">European call option</a> with strike price $K$ and an underlying asset whose spot price at maturity $S_T$ follows a given random distribution.
The corresponding payoff function is defined as:
$$\m... | github_jupyter |
# Convolutional Neutral Network
## Objectives:
Building Convolutional Neutral Networks (CNN) to:
* Learn and train based on the matrix of features by building convolutional neutral network layers
* Use feature detectors (filters - e.g., sharpen, blur, edge detect) to find features in the images by convolving with i... | github_jupyter |
```
import os
import sys
import numpy as np
import pandas as pd
import pysubgroup as ps
sys.path.append(os.path.join(os.path.dirname(os.path.dirname(os.getcwd())),'sd-4sql\\packages'))
saved_path = os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(os.getcwd()))),'Data\\saved-data\\')
from sd_analysis import ... | github_jupyter |
In this lecture we're going to address how you can bring multiple dataframe objects together, either by
merging them horizontally, or by concatenating them vertically. Before we jump into the code, we need to
address a little relational theory and to get some language conventions down. I'm going to bring in an image
to... | github_jupyter |
# How common is introduced unfairness
In the paper
*Why fair lables can yield unfair predictions: graphical conditions on introduced unfairness*
Carolyn Ashurst, Ryan Carey, Silvia Chiappa, Tom Everitt
AAAI, 2022
we explore conditions under which fair labels can yield optimal unfair models. This notebook illust... | github_jupyter |
# The Agate Tutorial
The best way to learn to use any tool is to actually use it. In this tutorial we will use agate to answer some basic questions about a dataset.
The data we will be using is a copy of the [National Registry of Exonerations]( http://www.law.umich.edu/special/exoneration/Pages/detaillist.aspx) made ... | github_jupyter |
# Collapsed Gibbs sampler for Generalized Relational Topic Models with Data Augmentation
<div style="display:none">
$
\DeclareMathOperator{\dir}{Dirichlet}
\DeclareMathOperator{\dis}{Discrete}
\DeclareMathOperator{\normal}{Normal}
\DeclareMathOperator{\ber}{Bernoulli}
\DeclareMathOperat... | github_jupyter |
Authors:
* Mainak Jas (plotly figures)
* Alexandre Gramfort and Denis Engemann (original tutorial)
[MNE-Python](http://martinos.org/mne/stable/mne-python.html) is a software package for processing [MEG](http://en.wikipedia.org/wiki/Magnetoencephalography)/[EEG](http://en.wikipedia.org/wiki/Electroencephalography) data... | github_jupyter |
# Measurements in stabilography
> Marcos Duarte
> Laboratory of Biomechanics and Motor Control ([http://demotu.org/](http://demotu.org/))
> Federal University of ABC, Brazil
Posturography is a general term for all techniques concerned with quantifying postural sway of a standing person.
Typically in posturogra... | github_jupyter |
# Train POS Tagger in French by Spark NLP
### Based on Universal Dependency `UD_French-GSD`
```
import os
# Install java
! apt-get update -qq
! apt-get install -y openjdk-8-jdk-headless -qq > /dev/null
os.environ["JAVA_HOME"] = "/usr/lib/jvm/java-... | github_jupyter |
# Tuples Data Structure
## Python Tuples (a,b)
* Immutable - can NOT be changed
* Use - passing data that does not need changing
* Faster than list
* "safer" than list
* Can be key in dict unlike list
* For Heterogeneous data - meaning mixing different data types(int,str,list et al) inside
* https://docs.python.org/3/... | github_jupyter |
# Data Wrangling with Spark
This is the code used in the previous screencast. Run each code cell to understand what the code does and how it works.
These first three cells import libraries, instantiate a SparkSession, and then read in the data set
```
from pyspark.sql import SparkSession
from pyspark.sql.functions i... | github_jupyter |
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from matplotlib import rcParams
# figure size in inches
rcParams['figure.figsize'] = 15,15
data = pd.read_excel('Data_Train.xlsx')
data_submit = pd.read_excel('Test_set.xlsx')
data.head(15)
data.info()
data_submit.info()
``... | github_jupyter |
# Understanding Data Types in Python
Effective data-driven science and computation requires understanding how data is stored and manipulated.
This section outlines and contrasts how arrays of data are handled in the Python language itself, and how NumPy improves on this.
Understanding this difference is fundamental to... | github_jupyter |
<h3>ローカルでモデルをトレーニングする</h3>
<h4>エクスポートしたデータを読込みます</h4>
```
import numpy as np
npz = np.load('docdata1.npz')
print(npz.files)
x = npz['arr_0']
y = npz['arr_1']
```
<h4>読込んだ内容を確認します</h4>
```
print(x.shape)
print(y.shape)
print(x[0])
print(y[0])
```
<h4>モデル学習のためのデータ準備をします</h4>
- torch 関連のパッケージをインポートします
```
import to... | github_jupyter |
## Visualizing simple geospatial data
In this first exercise we want to see the simplictiy of geoplotlib in its fullest.
Loading data and displaying it with pre-defined plots is really simple but already enables us to get incredible insights into our datasets.
We'll be looking at a dataset containing all poachi... | github_jupyter |
# SLU03 | Visualization with Pandas & Matplotlib: Learning notebook
***
In this notebook we will cover the following:
- Scatter plots
- Line charts
- Bar/Column charts
- Histograms
- Box plots
- Chartjunk
- Matplotlib basics
- How to choose the right chart
## Some theory first!
Data Visualization techniques serve ... | github_jupyter |
# Effects of moving the locus along the genome
(c) 2019 Manuel Razo. This work is licensed under a [Creative Commons Attribution License CC-BY 4.0](https://creativecommons.org/licenses/by/4.0/). All code contained herein is licensed under an [MIT license](https://opensource.org/licenses/MIT)
---
```
import os
import... | github_jupyter |
```
# Import libraries
from keras import optimizers, losses, activations, models
from keras.callbacks import ModelCheckpoint, EarlyStopping, LearningRateScheduler, ReduceLROnPlateau
from keras.layers import Layer, GRU, LSTM, Dense, Input, Dropout, Convolution1D, MaxPool1D, GlobalMaxPool1D, GlobalAveragePooling1D, \
... | github_jupyter |
```
import os
os.environ.keys()
```
# Imports
```
import pygrib
import os
import numpy as np
from matplotlib import pyplot as plt
from matplotlib import colors
from mpl_toolkits.basemap import Basemap, addcyclic
import plotly.graph_objs as go
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, i... | github_jupyter |
# Accelerating Deep Learning with Dask and GPUs
## Stephanie Kirmer, Senior Data Scientist
### Center for Deep Learning, Northwestern University
### April 27, 2021
[stephaniekirmer.com](https://www.stephaniekirmer.com) | twitter: [@data_stephanie](https://twitter.com/data_stephanie) | [saturncloud.io](https://s... | github_jupyter |
## Imports
Suppress TensorFlow warnings.
```
# Copied from:
# https://weepingfish.github.io/2020/07/22/0722-suppress-tensorflow-warnings/
# Filter tensorflow version warnings
import os
# https://stackoverflow.com/questions/40426502/is-there-a-way-to-suppress-the-messages-tensorflow-prints/40426709
os.environ["TF_CP... | github_jupyter |
[](https://colab.research.google.com/github/MisaOgura/flashtorch/blob/master/examples/activation_maximization_colab.ipynb)
## Activation maximization
---
A quick demo of activation maximization with [FlashTorch 🔦](https://github.com/MisaOgura... | github_jupyter |
# Alternating minimization
Reconstruction with alternating minimization (possible using both strobed illumination for initializations)
```
%matplotlib notebook
%load_ext autoreload
%autoreload 2
import numpy as np
import scipy as sp
import scipy.misc as misc
import matplotlib.pyplot as plt
import time
import sys
impo... | github_jupyter |
# Regression: underfitting and overfitting
We will look at a simple example of fitting a polynomial function to a set of data points. A polynomial is defined by its degree $n$ and can be written as: $y = \sum_{k=0}^n a_k x^k$.
The simplest polynomial, with a degree of $n=1$, is the linear function: $y = a_1x + a_0$.... | github_jupyter |
# Nature of signals
In the context of this class, a signal is the data acquired by the measurement system. It contains much information that we need to be able to identify to extract knowledge about the system being tested and how to optimize the measurements. A signal caries also messages and information. We will u... | github_jupyter |
```
from IPython.core.display import HTML
display(HTML("<style>.container { width:95% !important; }</style>"))
import pandas as pd
import numpy as np
from elasticsearch import Elasticsearch, helpers
from elasticsearch_dsl import Search, Q, SF
from bs4 import BeautifulSoup
# es = Elasticsearch(http_compress=True, maxsiz... | github_jupyter |
# TimeSeries - a new object for handling time domain data
### NOTE: Internet access is required in order to use this tutorial
TimeSeries is a new feature in the SunPy 0.8 release, replacing the LightCurve object which is now deprecated. Similar to LightCurve, TimeSeries handles time domain data from a variety of sola... | github_jupyter |
<table class="ee-notebook-buttons" align="left">
<td><a target="_blank" href="https://github.com/giswqs/earthengine-py-notebooks/tree/master/Image/get_image_id.ipynb"><img width=32px src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" /> View source on GitHub</a></td>
<td><a target="_blank" href="htt... | github_jupyter |
<img src="images/logo.jpg" style="display: block; margin-left: auto; margin-right: auto;" alt="לוגו של מיזם לימוד הפייתון. נחש מצויר בצבעי צהוב וכחול, הנע בין האותיות של שם הקורס: לומדים פייתון. הסלוגן המופיע מעל לשם הקורס הוא מיזם חינמי ללימוד תכנות בעברית.">
# <p style="text-align: right; direction: rtl; float: righ... | github_jupyter |
```
from sklearn import datasets
import numpy as np
import matplotlib.pyplot as plt
iris = datasets.load_iris()
digits = datasets.load_digits()
print(np.shape(digits.data))
print(np.shape(digits.target))
from sklearn import svm
clf = svm.SVC(gamma=0.001, C=100.)
clf.fit(digits.data[:-1], digits.target[:-1])
clf.predict... | github_jupyter |
<table> <tr>
<td style="background-color:#ffffff;">
<a href="http://qworld.lu.lv" target="_blank"><img src="..\images\qworld.jpg" width="25%" align="left"> </a></td>
<td style="background-color:#ffffff;vertical-align:bottom;text-align:right;">
prepared by <a href="http://abu.lu.... | github_jupyter |
# Baseball pitcher WAR calculation using Statcast data
by Lau Sze Yui (13/2/2019)
Here is an example on how to calculate Wins Over Replacement (WAR) using Statcast data.
[Baseball Reference](https://www.baseball-reference.com/about/war_explained.shtml) and [Fangraphs](https://library.fangraphs.com/misc/war/) both pr... | github_jupyter |
```
import os
import sys
import matplotlib.pyplot as plt
import itertools
ROOT_DIR = os.path.dirname(os.path.dirname(os.getcwd()))
if ROOT_DIR not in sys.path: sys.path.append(ROOT_DIR)
import numpy as np
import proplot as plot
import tensorflow as tf
import pandas as pd
import DeepSparseCoding.tf1x.analysis.analysi... | github_jupyter |
<a href="https://colab.research.google.com/github/bxck75/piss-ant-pix2pix/blob/master/modeltransferv2_apes_imshow.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
# remove defaults
!rm -r sample_data
# Clone the repo
!git clone https://github.com... | github_jupyter |
This notebook compares various Factorization Machines implementations.
# I - Factorization Machines
The dataset used here is [MovieLens 100K](https://grouplens.org/datasets/movielens/).
```
%load_ext watermark
%watermark --python --machine --packages creme,numpy,pandas,sklearn,xlearn --datename
```
## LibFM
Downlo... | github_jupyter |
<a href="https://colab.research.google.com/github/PytorchLightning/pytorch-lightning/blob/master/notebooks/05-trainer-flags-overview.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Introduction to Lightning Flags ⚡🚩
In this notebook, we'll go ov... | github_jupyter |
<p><font size="6"><b>04 - Pandas: Working with time series data</b></font></p>
> *DS Data manipulation, analysis and visualization in Python*
> *May/June, 2021*
>
> *© 2021, Joris Van den Bossche and Stijn Van Hoey (<mailto:jorisvandenbossche@gmail.com>, <mailto:stijnvanhoey@gmail.com>). Licensed under [CC BY 4.0 C... | github_jupyter |
```
# We will import our libraries
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
from google.colab import files
uploaded = files.upload()
train = pd.read_csv('titanic_train.csv')
train[:5]
train.info()
```
We can already see that there are some missing ... | github_jupyter |
```
import pandas as pd
import geopandas as gpd
from IPython.display import HTML, display
import matplotlib.pyplot as plt
```
This notebook joins and exports DFPS datasets for manual review of violations for potential injuries and other characteristics. It uses data exported from the state data portal on Feb 1, 2018... | github_jupyter |
```
from __future__ import print_function
from bs4 import BeautifulSoup
import requests
```
# Beautiful soup on test data
Here, we create some simple HTML that include some frequently used tags.
Note, however, that we have also left one paragraph tag unclosed.
```
source = """
<!DOCTYPE html>
<html>
<head>
... | github_jupyter |
#Cleaning the dataset
```
!pip install -q wordcloud
import wordcloud
import nltk
nltk.download('stopwords')
nltk.download('wordnet')
nltk.download('punkt')
nltk.download('averaged_perceptron_tagger')
# Dataset: https://www.kaggle.com/amananandrai/clickbait-dataset=clickbait_data.csv
#https://towardsdatascience.com... | github_jupyter |
```
import numpy as np
import xarray as xr
import pandas as pd
import scipy
from scipy import signal
import scipy.stats as stats
import statsmodels.api as sm
import matplotlib.pyplot as plt
import numpy.random as random
#import cartopy
```
Create toy data set in space-time [x,y,t]. We start with random noise in [x,y]... | github_jupyter |
# Developing an AI application
Going forward, AI algorithms will be incorporated into more and more everyday applications. For example, you might want to include an image classifier in a smart phone app. To do this, you'd use a deep learning model trained on hundreds of thousands of images as part of the overall appli... | github_jupyter |
# Solutions to Exercises
For each exercise, the solutions below show one possible way of solving it, but you might have used a different approach, and that's great! There is almost always more than one way to solve any particular problem in Python.
**Note**: Since this notebook is in the `solutions` sub-folder, the f... | github_jupyter |
## California wildfires 2017 - Thomas Fire analysis
The Thomas Fire was a massive wildfire that started in early December 2017 in Ventura and Santa Barbara counties and grew into California's largest fire ever.
```
import arcgis
from arcgis import *
from arcgis.mapping im... | github_jupyter |
# Generalizing Failure Circumstances
One central question in debugging is: _Does this bug occur in other situations, too?_ In this chapter, we present a technique that is set to _generalize_ the circumstances under which a failure occurs. The DDSET algorithm takes a failure-inducing input, breaks it into individual el... | github_jupyter |
```
import re
test_string ='''
source ip: 10.16.90.249
source hostname: android-ba50a4497de455a
source port: 55198
source mac address: 50:2e:5c:f0:f6:98
system name :
user name:
location :
sep , sms status :
field sales user ( yes / no) :
dsw event log:
---------------------------------------------------------------... | github_jupyter |
```
# Scrape crowsourced labels of Exchange Ethereum addresses
import requests
from bs4 import BeautifulSoup
import time
def find_tag(address):
url = 'https://etherscan.io/address/{}'.format(address)
html = requests.get(url).content
soup = BeautifulSoup(html, 'html.parser')
#find tag
tag = soup.find... | github_jupyter |
# Iris Training and Prediction with Sagemaker Scikit-learn
This tutorial shows you how to use [Scikit-learn](https://scikit-learn.org/stable/) with Sagemaker by utilizing the pre-built container. Scikit-learn is a popular Python machine learning framework. It includes a number of different algorithms for classification... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
import sys
sys.path.append('../../src/')
import json
import os
from pathlib import Path
```
## HisDB
```
import experiment.data as exp
import datasets.divahisdb as hisdb
env = exp.Environment()
img_path = Path('../../doc/figures/')
dataset = hisdb.HisDBDataset(env.dataset(exp.Da... | github_jupyter |
# Maximum Mean Discrepancy drift detector on CIFAR-10
### Method
The [Maximum Mean Discrepancy (MMD)](http://jmlr.csail.mit.edu/papers/v13/gretton12a.html) detector is a kernel-based method for multivariate 2 sample testing. The MMD is a distance-based measure between 2 distributions *p* and *q* based on the mean emb... | github_jupyter |
# LeNet on Cifar with Dropout (0.5)
This is LeNet (6c-16c-120-84) on MNIST. Adam algorithm (lr=0.001) with 100 epoches.
#### LeNet
Total params: 44,426
Trainable params: 44,426
Non-trainable params: 0
#### LeNet with 10 intrinsic dim
Total params: 488,696
Trainable params: 10
Non-trainab... | github_jupyter |
# Examples of Multicore Applications:
# Shared-Memory Multiprocess Applications
In this notebook we look at multiprocess applications in IoTPy. The processes share memory. Associated with each process is an agent. The application can also have:
<ol>
<li> source threads that acquire data from external sources and <... | github_jupyter |
# Inverted encoding models, revisited
```
import numpy as np
import matplotlib.pyplot as plt
import pymc3 as pm
from scipy.stats import pearsonr
from sklearn.base import RegressorMixin, BaseEstimator
from sklearn.model_selection import cross_val_score
from sklearn.preprocessing import OneHotEncoder
import seaborn as s... | github_jupyter |
# Loop API Example on Hartmann6
The loop API is the most lightweight way to do optimization in Ax. The user makes one call to `optimize`, which performs all of the optimization under the hood and returns the optimized parameters.
For more customizability of the optimization procedure, consider the Service or Develope... | github_jupyter |
```
%matplotlib inline
import matplotlib
import numpy as np
import matplotlib.pyplot as plt
plt.rcParams["figure.figsize"] = (12, 9)
plt.rcParams["font.size"] = 18
```
# Radioactivity
Learning Objectives:
- Explain how radioactivity was discovered
- Explain the nuclear physical reason for radioactive decay
- List ... | github_jupyter |
# Инициализация
```
#@markdown - **Монтирование GoogleDrive**
from google.colab import drive
drive.mount('GoogleDrive')
# #@markdown - **Размонтирование**
# !fusermount -u GoogleDrive
```
# Область кодов
```
#@title Распознавание лиц { display-mode: "both" }
# facial recognition
# В этой программе реализовано распо... | github_jupyter |
# Python in 5 minutes...
While its absurd to think we can learn Python in 5 minutes, it's useful to at least introduce a few basic concepts of the language before we dive in. After covering these, you'll at least get past that first speed bump and can start interacting on your own.
## Variable assignment and data type... | github_jupyter |
# Publications markdown generator for academicpages
Takes a TSV of publications with metadata and converts them for use with [academicpages.github.io](academicpages.github.io). This is an interactive Jupyter notebook ([see more info here](http://jupyter-notebook-beginner-guide.readthedocs.io/en/latest/what_is_jupyter.... | github_jupyter |
```
import tensorflow as tf
from tensorflow import keras
import glob
import json
import pandas as pd
import os
import gzip
import re
from nltk.stem import WordNetLemmatizer
from nltk import pos_tag
from nltk.corpus import stopwords
import numpy as np
import pandas as pd
from sklearn.feature_extraction import DictVecto... | github_jupyter |
# Data Science in Medicine using Python
### Author: Dr Gusztav Belteki
Today's code has been inspired and modified from these books' code examples
<img src="./images/cover.jpg" alt="Drawing" style="width: 300px;"/>
<img src="./images/Geron_book_cover.png" alt="Drawing" style="width: 300px;"/>
<img src="./images/ra... | github_jupyter |
# Sampling pose DKF trained on H3.6M
```
%matplotlib inline
%load_ext autoreload
%autoreload 2
import os
import addpaths
from load import loadDataset
from h36m_loader import insert_junk_entries
import os
import numpy as np
from scipy.signal import convolve2d
# Stupid hack to make parameter loading actually work
# (u... | github_jupyter |
**Challenges in Representation Learning: Facial Expression Recognition Challenge** from *https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge/data*
# Data Exploration
Some data exploration, looking at the structure of the files etc.
```
# loading packages
import nump... | github_jupyter |
# Wie lernt ein Neuronales Netz
https://bootcamp.codecentric.ai/
In diesem Notebook trainieren wir einen Classifier auf dem MNIST Datenatz. Viele finden diesen Datensatz inzwischen langweilige und "zu einfach" - aber für dieses Notebook ist er genau richtig. Wir brauchen ein kleines einfaches Dataset, um zeigen zu kö... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
import wfdb
import pandas as pd
from utils.scoring_metrics import (
RefInfo, load_ans,
score, ue_calculate, ur_calculate,
compute_challenge_metric, gen_endpoint_score_mask,
)
from utils.scoring_metrics_test import _load_af_episodes
# from database_reader.cpsc_database... | github_jupyter |
# Unify and clean-up intersections of divided roads
Divided roads are represented by separate centerline edges. The intersection of two divided roads thus creates 4 nodes, representing where each edge intersects a perpendicular edge. These 4 nodes represent a single intersection in the real world. Roundabouts similarl... | 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 |
```
!apt-get install -y -qq software-properties-common python-software-properties module-init-tools
!add-apt-repository -y ppa:alessandro-strada/ppa 2>&1 > /dev/null
!apt-get update -qq 2>&1 > /dev/null
!apt-get -y install -qq google-drive-ocamlfuse fuse
from google.colab import auth
auth.authenticate_user()
from oauth... | github_jupyter |
# Crocoddyl: Contact RObot COntrol by Differential DYnamic programming Library
## I. Welcome to crocoddyl
Crocoddyl is an **optimal control library for robot control under contact sequence**. Its solver is based on an efficient Differential Dynamic Programming (DDP) algorithm. Crocoddyl computes optimal trajectories ... | github_jupyter |
### Refactor Clinic #1
In this post, we're going to refactor a function that returns a start and end date, or some defaults that are deemed "sensible" in the context of the application.
The function is adapted from a real-life example in the wild! We do necessarily believe this logic actually requires a function, but... | github_jupyter |
# Advanced Analytics and Machine Learning Overview
Beyond large-scale SQL analysis and streaming, Spark also provides support for statistics, machine learning, and graph analytics. These encompass a set of workloads that we will refer to as advanced analytics.
This notebook offers a basic overview of advanced analyti... | github_jupyter |
# Full packet capture
## /Me
> https://github.com/markuskont/Talsec-meetup
```
stuff = [
"IDS",
"PCAP",
"coding",
"teaching",
"hunting",
"devops",
"logging",
"/^.*data.*$/"
]
speciality = "spec==[{}]".format(",".join(stuff[:-1]))
speciality = " || ".join([speciality, "spec=={}".format... | github_jupyter |
**Introduction to model validation**
___
- What is model validation?
- Model validation consists of:
- ensuring your model performs as expected on new data
- testing model performance on holdout datasets
- selecting the best model, parameters, and accuracy metrics
- achieving the bes... | github_jupyter |
##### Copyright 2018 The TensorFlow Authors.
Licensed under the Apache License, Version 2.0 (the "License");
```
#@title Licensed under the Apache License, Version 2.0 (the "License"); { display-mode: "form" }
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at... | github_jupyter |
# AWS Hail on EMR Bokeh Plotting Example
This is taken from the [Hail Plotting Tutorial](https://hail.is/docs/0.2/tutorials/08-plotting.html) with adjustments for use with SageMaker Notebook instances and EMR.
### List EMR Master Nodes
`~/SageMaker/bin/list-clusters` will output the IP of each master node in your ac... | github_jupyter |
# Programming and Database Fundamentals for Data Scientists - EAS503
While Python provides many (about 69) built in functions for the programmers to use, we will look at a few important ones.
### Math
`abs`, `complex`,`divmod`, `hex`, `max`, `min`, `oct`, `pow`, `round`, etc.
### Type Conversion/Handling
`bin`, `b... | github_jupyter |
```
if 'google.colab' in str(get_ipython()):
!pip install -q condacolab
import condacolab
condacolab.install()
"""
You can run either this notebook locally (if you have all the dependencies and a GPU) or on Google Colab.
Instructions for setting up Colab are as follows:
1. Open a new Python 3 notebook.
2. Import... | github_jupyter |
# Defect Detection: Semantic Segmentation - Pipeline Execution
In this notebook, we will use the pipeline configured in the included python package under `pipelines` together with the defined code for preprocessing and training to automate the model training. It is easy to use such that you can simple drop in whatever... | github_jupyter |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.