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This notebook was prepared by [Donne Martin](https://github.com/donnemartin). Source and license info is on [GitHub](https://github.com/donnemartin/interactive-coding-challenges).
# Solution Notebook
## Problem: Return all subsets of a set.
* [Constraints](#Constraints)
* [Test Cases](#Test-Cases)
* [Algorithm](#Alg... | github_jupyter |
```
import warnings
warnings.filterwarnings('ignore')
```
### Read COMPAS data
```
import pandas as pd
pd.set_option('display.max_columns', None)
df = pd.read_csv('compas-scores-two-years.csv', index_col='id')
df = df.reset_index(drop=True)
len(df)
df.head()
df['age_cat'].value_counts()
#overwrite age cat
... | github_jupyter |
# 机器学习练习 2 - 逻辑回归
这个笔记包含了以Python为编程语言的Coursera上机器学习的第二次编程练习。请参考 [作业文件](ex2.pdf) 详细描述和方程。
在这一次练习中，我们将要实现逻辑回归并且应用到一个分类任务。我们还将通过将正则化加入训练算法，来提高算法的鲁棒性，并用更复杂的情形来测试它。
代码修改并注释：黄海广，haiguang2000@qq.com
## 逻辑回归
在训练的初始阶段，我们将要构建一个逻辑回归模型来预测，某个学生是否被大学录取。设想你是大学相关部分的管理者，想通过申请学生两次测试的评分，来决定他们是否被录取。现在你拥有之前申请学生的可以用于训练逻辑回归的训练样本集。对于每一个训练... | github_jupyter |
# Day02_1_Classification_excercise05(XGB)
<h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#이진-분류기" data-toc-modified-id="이진-분류기-1"><span class="toc-item-num">1 </span>이진 분류기</a></span><ul class="toc-item"><li><span><a href="#설정" data-toc-modif... | github_jupyter |
```
import eland as ed
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# Fix console size for consistent test results
from eland.conftest import *
```
# Online Retail Analysis
## Getting Started
To get started, let's create an `eland.DataFrame` by reading a csv file. This creates and populate... | github_jupyter |
```
import numpy as np
from plot_utils import read_Noise2Seg_results, fraction_to_abs, cm2inch
from matplotlib import pyplot as plt
plt.rc('text', usetex=True)
```
# Flywing n10: AP scores on validation data
```
alpha0_5_n10 = read_Noise2Seg_results('alpha0.5', 'flywing_n10', measure='SEG', runs=[1,2,3,4,5],
... | github_jupyter |
# 14 - Introduction to Deep Learning - MLP
by [Alejandro Correa Bahnsen](albahnsen.com/) and [Jesus Solano](https://github.com/jesugome)
version 1.6 June 2020
## Part of the class [AdvancedMethodsDataAnalysisClass](https://github.com/albahnsen/AdvancedMethodsDataAnalysisClass/tree/master/notebooks)
This noteboo... | github_jupyter |
# Character level language model - Dinosaurus Island
Welcome to Dinosaurus Island! 65 million years ago, dinosaurs existed, and in this assignment they are back. You are in charge of a special task. Leading biology researchers are creating new breeds of dinosaurs and bringing them to life on earth, and your job is to ... | github_jupyter |
## Introduction to Data Science
## Data Science Template with Pandas and Scikit-Learn
Based on [this](https://towardsdatascience.com/how-to-master-scikit-learn-for-data-science-c29214ec25b0) article and others
_________________________
[Scikit-learn](https://scikit-learn.org/stable/) is one of many [scikits](https:... | github_jupyter |
# Import dependencies and libraries
```
%matplotlib inline
#Dependencies
import pandas as pd
from matplotlib import pyplot as plt
import numpy as np
from scipy import stats
pd.set_option("display.float_format", lambda x:"%.2f" % x)
```
# Import and read CSVs
```
#Files to Load for THE YEAR 2020
public_school_path =... | github_jupyter |
# Iris classification example
Example usage of tengp package for classification, using Iris dataset.
We are going to load the data using [sklearn](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html) package.
```
%matplotlib inline
from sklearn.datasets import load_iris
X, y = load_iris... | github_jupyter |
# A Visual Notebook to Using BERT for the First TIme.ipynb
<img src="https://jalammar.github.io/images/distilBERT/bert-distilbert-sentence-classification.png" />
In this notebook, we will use pre-trained deep learning model to process some text. We will then use the output of that model to classify the text. The te... | github_jupyter |
# Colab-latent-composition
Original repo: [chail/latent-composition](https://github.com/chail/latent-composition)
Original colab: [here](https://github.com/chail/latent-composition/blob/main/notebooks/finetune_and_edit.ipynb)
My fork: [Colab-latent-composition](https://github.com/styler00dollar/Colab-latent-composit... | github_jupyter |
# Classifying Fashion-MNIST
Now it's your turn to build and train a neural network. You'll be using the [Fashion-MNIST dataset](https://github.com/zalandoresearch/fashion-mnist), a drop-in replacement for the MNIST dataset. MNIST is actually quite trivial with neural networks where you can easily achieve better than 9... | github_jupyter |
# Initialise the libs
```
import numpy as np
import pandas as pa
import matplotlib.pyplot as plt
from sklearn import linear_model
```
# load the data
```
dtype_dict = {'bathrooms':float, 'waterfront':int, 'sqft_above':int, 'sqft_living15':float, 'grade':int,
'yr_renovated':int, 'price':float, 'bedrooms... | github_jupyter |
<a href="https://colab.research.google.com/github/pachterlab/GRNP_2020/blob/master/notebooks/helper_functions/preseqHelpers.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
**This notebook shows the code in preseqHelpers, which contains helper functi... | github_jupyter |
# AWD-LSTM
```
%load_ext autoreload
%autoreload 2
%matplotlib inline
#export
from exp.nb_12 import *
```
## Data
[Jump_to lesson 12 video](https://course.fast.ai/videos/?lesson=12&t=6317)
```
path = datasets.untar_data(datasets.URLs.IMDB)
```
We have to preprocess the data again to pickle it because if we try to ... | github_jupyter |
# Session 0: Preliminaries with Python/Notebook
<p class="lead">
Parag K. Mital<br />
<a href="https://www.kadenze.com/courses/creative-applications-of-deep-learning-with-tensorflow/info">Creative Applications of Deep Learning w/ Tensorflow</a><br />
<a href="https://www.kadenze.com/partners/kadenze-academy">Kadenze Ac... | github_jupyter |
<a href="https://colab.research.google.com/github/ashablinski/Capstone/blob/master/Collaborative_Neural_Rec_Sys.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# **NEURAL COLLABORATIVE RECOMMENDER SYSTEM FOR MOVIES**
import matplotlib.pyplot as plt
from sklearn import linear_model
from scipy.stats import norm
import confi... | github_jupyter |
# BCycle Austin stations
This notebook looks at the stations that make up the Austin BCycle network. For each station we have the following information:
* `station_id`: A unique identifier for each of the station. Used to connect the `bikes.csv` time-varying table to the static `stations` table.
* `name`: The name of... | github_jupyter |
```
# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load
import numpy as np # linear algebra
import pandas as pd# data processing, CSV file I/O ... | github_jupyter |
## MIC Demo 2 - Parallelising multiple measurements
As the first part of the demonstration 1 shows, obtaining a multivariate MIC measurement requires multiple runs of the MIC algorithm. This is because:
- A multivariate measurement on $K$ variables is decomposed into a sum of $K$ univariate runs, where each variable ... | github_jupyter |
```
%matplotlib inline
import numpy as np
import time
import h5py
import keras
import pandas as pd
import math
import joblib
import matplotlib.pyplot as plt
from fuel.datasets.hdf5 import H5PYDataset
from sklearn.decomposition import PCA
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score
from skle... | github_jupyter |
This is a companion notebook for the book [Deep Learning with Python, Second Edition](https://www.manning.com/books/deep-learning-with-python-second-edition?a_aid=keras&a_bid=76564dff). For readability, it only contains runnable code blocks and section titles, and omits everything else in the book: text paragraphs, fig... | github_jupyter |
# AU Fundamentals of Python Programming-Module2Problems (Part. A)
## M2-Q02 '*'三角形
(時間限制：2 秒)
問題描述：
讓使用者輸入一正整數 n，利用迴圈以字元 '*' 輸出高度為 n 的三角形。
輸入說明：
輸入一正整數 n。
輸出說明：
利用迴圈以字元 '*' 輸出高度為 n 的三角形，最後必須有換行字元。
| Sample Input: | Sample Output: |
|:----------------|:-------------------------|
|4 | \* |
| |... | github_jupyter |
# Scikit-downscale: an open source Python package for scalable climate downscaling
Joseph Hamman (jhamman@ucar.edu) and Julia Kent (jkent@ucar.edu)
NCAR, Boulder, CO, USA
-------
This notebook was developed for the [2020 EarthCube All Hands Meeting](https://www.earthcube.org/EC2020). The development of Scikit-downs... | github_jupyter |
... ***CURRENTLY UNDER DEVELOPMENT*** ...
## Validation of the synthetic waves and level
inputs required:
* historical wave conditions
* emulator output - synthetic wave conditions
in this notebook:
* Validation of the extreme distributions
* Analysis of the DWT resposible of extreme TWL events (from the... | github_jupyter |
```
import pandas as pd
import re
# load dataset
from sample import read_csv_data
data = read_csv_data(pd.read_csv('../dataset/ieee_xai.csv'))
# load domain terms
with open('../dataset/domain_terms.txt','r', encoding = 'utf-8') as f:
domain_list = [i.strip().lower() for i in f.readlines()]
```
# Unsupervised Keyp... | github_jupyter |
Центр непрерывного образования
# Программа «Python для автоматизации и анализа данных»
Неделя 3 - 1
*Ян Пиле, НИУ ВШЭ*
## Задачи
### Задача 1
Дана строка, состоящая из слов. Сделать из нее аббревиатуру с помощью списковых включений
**Вход:** "Комитет Государственной Безопасности"
**Выход:** "КГБ"
```
text = "К... | github_jupyter |
Read the dataset
```
import pandas as pd
df = pd.read_csv('b.csv')
```
Clean the data
```
categoryCol=['substrict','type','direction']
for i in categoryCol:
df[i]=df[i].astype("category")
df['time'] = pd.to_datetime(df['time'], format='%Y.%m.%d')
for i in range(len(df)):
if df.loc[i,'time'] < pd.Timestamp(... | github_jupyter |
<a rel="license" href="http://creativecommons.org/licenses/by/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by/4.0/88x31.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" property="dct:title"><b>Python in a Nutshell</b></span> was started by <a xmlns:cc... | github_jupyter |
```
# Useful for debugging
%load_ext autoreload
%autoreload 2
```
# xopt CNSGA2 function example
Experimental, using `pymoo` as a backend. This has to be installed via:
`pip install pymoo`
```
from xopt.cmoo import cnsga2
import json
import numpy as np
import matplotlib.pyplot as plt
from time import sleep
# All... | github_jupyter |
### **Import Google Drive**
```
from google.colab import drive
drive.mount('/content/drive')
```
### **Import Library**
```
import glob
import numpy as np
import os
import shutil
np.random.seed(42)
from sklearn.preprocessing import LabelEncoder
import cv2
import tensorflow as tf
import keras
import shutil
import ran... | github_jupyter |
```
import os
import json
import numpy as np
import pandas as pd
import networkx as nx
from pprint import pprint
from datetime import datetime
from collections import Counter
```
### Data Retrieval Functions
```
# read in comment dictionary
def get_comment_dictionary(body=False):
# read in comment dictionary fro... | github_jupyter |
# <font color='brown'>**SMS SPAM DETECTION**</font>
### **Installation**
```
import sys
sys.path.append('../../')
```
### **Load SMS Dataset**
The SMS Spam Collection is a set of SMS tagged messages that have been collected for SMS Spam research. It contains one set of SMS messages in English of 5,574 messages, tag... | github_jupyter |
# Interest Rate Swap
```
import pyvacon
import pyvacon.analytics as analytics
import datetime as dt
import pyvacon.tools.converter as converter
import pyvacon.marketdata.testdata as mkt_testdata
import pyvacon.marketdata.plot as mkt_plot
import pyvacon.tools.enums ... | github_jupyter |
<a href="http://landlab.github.io"><img style="float: left" src="../../landlab_header.png"></a>
# Mapping values between grid elements
<hr>
<small>For more Landlab tutorials, click here: <a href="https://landlab.readthedocs.io/en/latest/user_guide/tutorials.html">https://landlab.readthedocs.io/en/latest/user_guide/tu... | github_jupyter |
# Meteo data at Gotland
This notebook preprocess the meteo data at Gotland and convert the data from the netCDF format to the GOTM input format.
```
import sys
import os
import numpy as np
import xarray as xr
import pandas as pd
sys.path.append("../gotmtool")
from gotmtool import *
```
## Preprocess data
```
dataro... | github_jupyter |
# Route Plan Analysis
Use this notebook to map and analyze a routing plan from Azavea's [School Bus Routing Optimization tool](https://github.com/azavea/bus-plan). Update the cell below to point to local copies of solver output.
```
import os
import plan_analysis as pa
import drive_distances as dr
%matplotlib inlin... | github_jupyter |
# Masked Token Prediction with Vision
We will make a quick check for the five converted models using the masked token prediction.
```
import torch
import numpy as np
import PIL.Image
from eval_vl_glue import VoltaImageFeature
from eval_vl_glue.extractor import BUTDDetector
from eval_vl_glue import transformers_volta
... | github_jupyter |
```
%matplotlib inline
```
# Compare the effect of different scalers on data with outliers
Feature 0 (median income in a block) and feature 5 (number of households) of
the `California housing dataset
<http://www.dcc.fc.up.pt/~ltorgo/Regression/cal_housing.html>`_ have very
different scales and contain some very lar... | github_jupyter |
<figure>
<IMG SRC="https://raw.githubusercontent.com/mbakker7/exploratory_computing_with_python/master/tudelft_logo.png" WIDTH=250 ALIGN="right">
</figure>
# Exploratory Computing with Python
*Developed by Mark Bakker*
## Notebook 12: Object oriented programming
In this Notebook, we learn what Object Oriented Progr... | github_jupyter |
```
import csv
import contextlib
import os, errno
from collections import OrderedDict, Counter
from IPython.core.display import display, HTML
from pandas import DataFrame
import pandas as pd
import numpy as np
from periodo_reconciler import (
RProperty,
RQuery,
PeriodoReconciler,
CsvReconciler
)
def ... | github_jupyter |
# Self-Driving Car Engineer Nanodegree
## Deep Learning
## Project: Build a Traffic Sign Recognition Classifier
In this notebook, a template is provided for you to implement your functionality in stages, which is required to successfully complete this project. If additional code is required that cannot be included i... | github_jupyter |
# Simulating capillary pressure curves using Porosimetry
Start by importing OpenPNM.
```
import numpy as np
import openpnm as op
np.random.seed(10)
ws = op.Workspace()
ws.settings["loglevel"] = 40
np.set_printoptions(precision=5)
```
Next, create a simple cubic network with 20 pores per side and a spacing of 50 um
... | github_jupyter |
```
import os
import sys
# Modify the path
sys.path.append("..")
import pandas as pd
import yellowbrick as yb
import matplotlib.pyplot as plt
from yellowbrick.classifier import ROCAUC
from sklearn.model_selection import train_test_split
import numpy as np
from yellowbrick.exceptions import ModelError
from yellow... | github_jupyter |
# Name
Submitting a Cloud Machine Learning Engine training job as a pipeline step
# Label
GCP, Cloud ML Engine, Machine Learning, pipeline, component, Kubeflow, Kubeflow Pipeline
# Summary
A Kubeflow Pipeline component to submit a Cloud ML Engine training job as a step in a pipeline.
# Details
## Intended use
Use th... | 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 |
```
%load_ext autoreload
%autoreload 2
```
# Quick Guide
Import the MolSysMT to start working:
```
import molsysmt as msm
```
## Converting molecular systems
```
molecular_system = 'mmtf:1M2Z'
molecular_system = msm.convert(molecular_system, to_form='1M2Z.mmtf')
msm.get_form(molecular_system)
molecular_system = ms... | 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 |
# Линейная регрессия и основные библиотеки Python для анализа данных и научных вычислений
Это задание посвящено линейной регрессии. На примере прогнозирования роста человека по его весу Вы увидите, какая математика за этим стоит, а заодно познакомитесь с основными библиотеками Python, необходимыми для дальнейшего прох... | github_jupyter |
# Variational API quickstart
The variational inference (VI) API is focused on approximating posterior distributions for Bayesian models. Common use cases to which this module can be applied include:
* Sampling from model posterior and computing arbitrary expressions
* Conduct Monte Carlo approximation of expectation,... | github_jupyter |
```
from pathlib import Path
import copy
import matplotlib.pyplot as plt
import numpy as np
import torch
import torchvision
from torch.utils.data import DataLoader, Dataset, random_split
from torchvision import transforms
from torchvision.datasets import ImageFolder
# GPUのセットアップ
device = torch.device("cuda" if torch... | github_jupyter |
## Numerical computation using Numpy
### The Newton Raphson root finding method
Find the square root of 7 by numerically solving the equation: $x^2 - 7 = 0$
Let's assume that there is a function, $f(x) = x^2 - 7$
Now, we need to find the roots of this function $f(x)$, that is the values of $x : f(x) = 0$, which wil... | github_jupyter |
```
import os
import sys
sys.path.append(os.path.expanduser('~/code/loggingbot'))
from loggingbot import TelegramBotHandler
```
Telegram bot token (take it from [@botfather](https://telegram.me/botfather) ) and list of user ids where to send the messages are defined below.
Note that this users should first start co... | github_jupyter |
```
# reload packages
%load_ext autoreload
%autoreload 2
```
### Choose GPU
```
%env CUDA_DEVICE_ORDER=PCI_BUS_ID
%env CUDA_VISIBLE_DEVICES=2
import tensorflow as tf
gpu_devices = tf.config.experimental.list_physical_devices('GPU')
if len(gpu_devices)>0:
tf.config.experimental.set_memory_growth(gpu_devices[0], Tr... | github_jupyter |
# Facial Keypoint Detection
The objective of this task is to predict keypoint positions on face images. This can be used as a building block in several applications, such as:
- tracking faces in images and video
- analysing facial expressions
- detecting dysmorphic facial signs for medical diagnosis
-... | github_jupyter |
```
## NLP library
import re
import string
import nltk
from nltk.corpus import stopwords
## ML Library
from sklearn.feature_extraction.text import CountVectorizer,TfidfVectorizer, TfidfTransformer
from sklearn.model_selection import RepeatedStratifiedKFold,cross_val_score
from sklearn.linear_model import LogisticRegre... | github_jupyter |
# Continuous Training with AutoML Vertex Pipelines
**Learning Objectives:**
1. Learn how to use Vertex AutoML pre-built components
1. Learn how to build a Vertex AutoML pipeline with these components using BigQuery as a data source
1. Learn how to compile, upload, and run the Vertex AutoML pipeline
In this lab, you ... | github_jupyter |
# Part 2 - Plotting photon energy spectra in a histogram form
This example creates a simple sphere of tungsten and tallies the photons in two different ways:
- Photon flux averaged across the cell
- Photon current on the rear surface
This section creates a simple material, geometry and settings. This model is used i... | github_jupyter |
# EventVestor: Issue Equity
In this notebook, we'll take a look at EventVestor's *Issue Equity* dataset, available on the [Quantopian Store](https://www.quantopian.com/store). This dataset spans January 01, 2007 through the current day, and documents events and announcements covering secondary equity issues by compani... | github_jupyter |
# Customer Churn Prediction with XGBoost
_**Using Gradient Boosted Trees to Predict Mobile Customer Departure**_
---
---
## Contents
1. [Background](#Background)
1. [Setup](#Setup)
1. [Data](#Data)
1. [Train](#Train)
1. [Host](#Host)
1. [Evaluate](#Evaluate)
1. [Relative cost of errors](#Relative-cost-of-errors... | github_jupyter |
# Visualisation de la précision dense vs sparse
```
import pandas as pd
```
On visualise ici les différentes performances selon le type de retriever choisi (sparse / dense). <br> Dans le notebook *Visualisation results* (results de Pavel) on visualisait la performance pour chaque test, ici on visualise plutot la *per... | github_jupyter |
## Dependencies
```
!pip install --quiet efficientnet
import warnings, time
from kaggle_datasets import KaggleDatasets
from sklearn.model_selection import KFold
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score
from tensorflow.keras import optimizers, Sequential, losses, metrics, Mode... | github_jupyter |
## Welcome to The QuantConnect Research Page
#### Refer to this page for documentation https://www.quantconnect.com/docs/research/overview#
#### Contribute to this template file https://github.com/QuantConnect/Lean/blob/master/Research/K... | github_jupyter |
```
import sys
sys.path.append(r'C:\Users\moallemie\EMAworkbench-master')
sys.path.append(r'C:\Users\moallemie\EM_analysis')
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from ema_workbench import load_results, ema_logging
from ema_workbench.em_framework.salib_samplers imp... | github_jupyter |
# CATE estimators example
```
import pyspark.sql.functions as F
import pysparkling
import h2o
from h2o.estimators.glm import H2OGeneralizedLinearEstimator
%matplotlib inline
import seaborn as sns
import matplotlib.pyplot as plt
from upliftml.models.h2o import (
SLearnerEstimator,
TLearnerEstimator,
CVTEs... | github_jupyter |
# Training Mutual Information Maximization (MI-Max) RL algorithms in Brax
In [Brax Training](https://colab.research.google.com/github/google/brax/blob/main/notebooks/training.ipynb) we tried out [gym](https://gym.openai.com/)-like environments and PPO, SAC, evolutionary search, and trajectory optimization algorithms. ... | github_jupyter |
Scikit-learn comes with a few standard datasets, for instance the [iris](https://en.wikipedia.org/wiki/Iris_flower_data_set) and [digits](http://archive.ics.uci.edu/ml/datasets/Pen-Based+Recognition+of+Handwritten+Digits) datasets for classification and the [boston house prices](http://archive.ics.uci.edu/ml/datasets/H... | github_jupyter |
<a href="https://colab.research.google.com/github/mrdbourke/tensorflow-deep-learning/blob/main/08_introduction_to_nlp_in_tensorflow.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Natural Language Processing Basics in TensorFlow
).
## load libraries
```
from __future__ import division
import networkx as nx
import numpy as np
import os
from sklearn import metrics
from sklearn.preprocessing import label_binarize
from sklearn.metrics import confusion_matrix... | github_jupyter |
```
"""
Bouncing particles, refactored
Srayan Gangopadhyay
2020-06-17
"""
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.animation as animation
from IPython.display import HTML, display # show anim. in ntbk
from tabulate import tabulate # pretty text outp... | github_jupyter |
# Inspecting training data
## Background
Prior to training a machine learning classifier, it can be useful to understand which of our feature layers are most useful for distinguishing between classes. The feature layers the models are trained on form the **knowledge base** of the algorithm. We can explore this knowle... | github_jupyter |
## Introduction to Data-X
Mostly basics about Python and Jupyter Notebooks
#### Authors: Alexander Fred Ojala & Ikhlaq Sidhu
**License Agreement:** Feel free to do whatever you want with this code
Hi and welcome let's start
---
# Valuable Resources
1. Installation of... | github_jupyter |
# Interactive Data Exploration, Analysis, and Reporting (IDEAR) in Python for Azure Notebooks
- Author: Team Data Science Process Team from Microsoft
- Date: 2017/03
- Supported Data Sources: CSV files in Azure blob storage
This is the **Interactive Data Exploration, Analysis and Reporting (IDEAR)** in _**Python**_ ... | github_jupyter |
# Influence Measures for GLM Logit
Based on draft version for GLMInfluence, which will also apply to discrete Logit, Probit and Poisson, and eventually be extended to cover most models outside of time series analysis.
The example for logistic regression was used by Pregibon (1981) "Logistic Regression diagnostics" a... | github_jupyter |
# Neural Spline Flow
```
# Import required packages
import torch
import numpy as np
import normflow as nf
from sklearn.datasets import make_moons
from matplotlib import pyplot as plt
from tqdm import tqdm
# Set up model
# Define flows
K = 16
torch.manual_seed(0)
latent_size = 2
hidden_units = 128
hidden_layers = ... | github_jupyter |
```
#!pip install meteomatics
import pandas as pd
import meteomatics.api as api
import datetime as dt
```
#### Stationslexikon DWD:
https://www.dwd.de/DE/leistungen/klimadatendeutschland/statliste/statlex_html.html?view=nasPublication&nn=16102
## Download weather forecast with Meteomatics API
Set weather parameters ... | github_jupyter |
# Reddit Flair Detector
---
Note: You can jump straight to the best model - [CNN](#Multichannel-Convolutional-Neural-Network) (93% test-accuracy)
## Part III - Building a Flair Detector
### 1) Import required modules
```
import nltk
import string
import os
import re
import pandas as pd
import numpy as np
import te... | github_jupyter |
## Coding Matrices
Here are a few exercises to get you started with coding matrices. The exercises start off with vectors and then get more challenging
### Vectors
```
### Assign the vector <5, 10, 2, 6, 1> to the variable v
v = [5,10,2,6,1]
```
The v variable contains a Python list. This list could also be thought... | 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 |
## Imports
```
import os
import sys
%env CUDA_VISIBLE_DEVICES=1
%matplotlib inline
import re
import time as ti
import numpy as np
import pprint
import matplotlib.pyplot as plt
import tensorflow as tf
... | github_jupyter |
```
import spacy
import wmd
from utils.ArticlesHandler import ArticlesHandler
from utils import Config
from utils import knn_similarities, solve, get_rate, accuracy, precision, recall, f1_score
import numpy as np
nlp = spacy.load('en_core_web_md')
config = Config(file='config')
articles = ArticlesHandler(config)
arti... | github_jupyter |
# Guided Hunting - Base64-Encoded Linux Commands
<details>
<summary> <u>Details...</u></summary>
**Notebook Version:** 1.0<br>
**Python Version:** Python 3.6 (including Python 3.6 - AzureML)<br>
**Required Packages**: kqlmagic, msticpy, pandas, pandas_bokeh, numpy, matplotlib, networkx, seaborn, datetim... | github_jupyter |
# [A Regret Minimization Approach to Iterative Learning Control](https://arxiv.org/pdf/2102.13478.pdf )
```
%load_ext autoreload
%autoreload 2
import jax
import jax.numpy as jnp
from deluca.igpc.ilqr import iLQR
from deluca.envs import PlanarQuadrotor
```
### System - Planar Quadrotor with wind ([deluca implementatio... | github_jupyter |
# Part 2: Launch an OpenGrid Node On Heroku
OpenGrid (or "Grid") is the platform library supporting the deployment of libraries for privacy-preserving artificial intelligence. In this tutorial, you'll learn how to deploy a grid node onto Heroku and then interact with it using PySyft.
_WARNING: Grid nodes publish data... | github_jupyter |
# Single-step pipeline examples
In this example, we'll build a very simple pipeline that just contains a single train step. The dataset and compute cluster created in this tutorial will be re-used in the subsequent examples in this module.
```
!pip install azureml-sdk --upgrade
import os
import azureml.core
from azur... | github_jupyter |
# K Means Clustering Project
___
It is **very important to note, we actually have the labels for this data set, but we will NOT use them for the KMeans clustering algorithm, since that is an unsupervised learning algorithm.**
When using the Kmeans algorithm under normal circumstances, it is because you don't have l... | github_jupyter |
```
# Copyright 2019 NVIDIA Corporation. All Rights Reserved.
#
# 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 applica... | github_jupyter |
# Registering Datasets and Layers into the API
- All Aqueduct Water Risk Atlas datasets: https://staging-api.globalforestwatch.org/v1/dataset?application=aqueduct-water-risk&status=saved&page[size]=1000
- All Aqueduct Water Risk Atlas layers: https://staging-api.globalforestwatch.org/v1/layer?application=aqueduct-wat... | github_jupyter |
```
%tensorflow_version 1.x
import numpy as np
import tensorflow as tf
import keras
from keras.layers import Dense,Conv2D,Conv2DTranspose,Input,Reshape,Activation,Lambda
from keras.layers.advanced_activations import LeakyReLU
from keras.optimizers import Adam
from keras.layers import BatchNormalization,Dropout,Flatten
... | github_jupyter |
# Exploring your harvested data
In this notebook we'll look at some ways of exploring the `results.csv` created by the Trove Newspaper and Gazette Harvester.
```
import os
import pandas as pd
import altair as alt
from wordcloud import WordCloud
import zipfile
from pathlib import Path
from textblob import TextBlob
fro... | github_jupyter |
```
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
import math
import os
```
### This is the script for saving all coordinates as my own database. By doing so, `opencage.geocoder` does not need to go through all regions everytime (as most regions are already have coordinates in this d... | github_jupyter |
# CS224N Assignment 1: Exploring Word Vectors (25 Points)
Welcome to CS224n!
Before you start, make sure you read the README.txt in the same directory as this notebook.
```
# All Import Statements Defined Here
# Note: Do not add to this list.
# All the dependencies you need, can be installed by running .
# --------... | github_jupyter |
# Anchor Boxes
Object detection algorithms usually sample a large number of regions in the input image, determine whether these regions contain objects of interest, and adjust the edges of the regions so as to predict the ground-truth bounding box of the target more accurately. Different models may use different regio... | github_jupyter |
# Quantum Generative Adversarial Networks
## Introduction
Generative [adversarial](gloss:adversarial) networks (GANs) [[1]](https://arxiv.org/abs/1406.2661) have swiftly risen to prominence as one of the most widely-adopted methods for unsupervised learning, with showcased abilities in photo-realistic image generatio... | github_jupyter |
```
from io import StringIO
import numpy as np
import pandas as pd
import seaborn as sns
cd /mnt/data_sm/olga/kmer-hashing/quest-for-orthologs/data/2019/
ll
cat README
```
# Make Species dataframe
```
s = '''Proteome_ID Tax_ID OSCODE #(1) #(2) #(3) Species Name
UP000007062 7165 ANOGA 12553 97... | github_jupyter |
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