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# 09 Strain Gage
This is one of the most commonly used sensor. It is used in many transducers. Its fundamental operating principle is fairly easy to understand and it will be the purpose of this lecture.
A strain gage is essentially a thin wire that is wrapped on film of plastic.
<img src="img/StrainGage.png" wi... | github_jupyter |
```
#export
from fastai.basics import *
from fastai.tabular.core import *
from fastai.tabular.model import *
from fastai.tabular.data import *
#hide
from nbdev.showdoc import *
#default_exp tabular.learner
```
# Tabular learner
> The function to immediately get a `Learner` ready to train for tabular data
The main fu... | github_jupyter |
# Aerospike Connect for Spark - SparkML Prediction Model Tutorial
## Tested with Java 8, Spark 3.0.0, Python 3.7, and Aerospike Spark Connector 3.0.0
## Summary
Build a linear regression model to predict birth weight using Aerospike Database and Spark.
Here are the features used:
- gestation weeks
- mother’s age
- fat... | github_jupyter |
## Concurrency with asyncio
### Thread vs. coroutine
```
# spinner_thread.py
import threading
import itertools
import time
import sys
class Signal:
go = True
def spin(msg, signal):
write, flush = sys.stdout.write, sys.stdout.flush
for char in itertools.cycle('|/-\\'):
status = char + ' ' + msg
... | github_jupyter |
## Problem 1
---
#### The solution should try to use all the python constructs
- Conditionals and Loops
- Functions
- Classes
#### and datastructures as possible
- List
- Tuple
- Dictionary
- Set
### Problem
---
Moist has a hobby -- collecting figure skating trading cards. His card collection has been growing, an... | github_jupyter |
# Classification on Iris dataset with sklearn and DJL
In this notebook, you will try to use a pre-trained sklearn model to run on DJL for a general classification task. The model was trained with [Iris flower dataset](https://en.wikipedia.org/wiki/Iris_flower_data_set).
## Background
### Iris Dataset
The dataset c... | github_jupyter |
<table class="ee-notebook-buttons" align="left">
<td><a target="_blank" href="https://github.com/giswqs/earthengine-py-notebooks/tree/master/Algorithms/landsat_radiance.ipynb"><img width=32px src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" /> View source on GitHub</a></td>
<td><a target="_blank" ... | github_jupyter |
```
%cd /Users/Kunal/Projects/TCH_CardiacSignals_F20/
from numpy.random import seed
seed(1)
import numpy as np
import os
import matplotlib.pyplot as plt
import tensorflow
tensorflow.random.set_seed(2)
from tensorflow import keras
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.regularizers im... | github_jupyter |
# basic operation on image
```
import cv2
import numpy as np
impath = r"D:/Study/example_ml/computer_vision_example/cv_exercise/opencv-master/samples/data/messi5.jpg"
img = cv2.imread(impath)
print(img.shape)
print(img.size)
print(img.dtype)
b,g,r = cv2.split(img)
img = cv2.merge((b,g,r))
cv2.imshow("image",img)
cv2.... | github_jupyter |
Create a list of valid Hindi literals
```
a = list(set(list("ऀँंःऄअआइईउऊऋऌऍऎएऐऑऒओऔकखगघङचछजझञटठडढणतथदधनऩपफबभमयरऱलळऴवशषसहऺऻ़ऽािीुूृॄॅॆेैॉॊोौ्ॎॏॐ॒॑॓॔ॕॖॗक़ख़ग़ज़ड़ढ़फ़य़ॠॡॢॣ।॥॰ॱॲॳॴॵॶॷॸॹॺॻॼॽॾॿ-")))
len(genderListCleared),len(set(genderListCleared))
genderListCleared = list(set(genderListCleared))
mCount = 0
fCount = 0
nCount = 0
f... | github_jupyter |
```
import pandas as pd
import numpy as np
import matplotlib
import seaborn as sns
import matplotlib.pyplot as plt
pd.set_option('display.max_colwidth', -1)
default = pd.read_csv('./results/results_default.csv')
new = pd.read_csv('./results/results_new.csv')
selected_cols = ['model','hyper','metric','value']
default = ... | github_jupyter |
```
from xml.dom import expatbuilder
import numpy as np
import matplotlib.pyplot as plt
import struct
import os
# should be in the same directory as corresponding xml and csv
eis_filename = '/example/path/to/eis_image_file.dat'
image_fn, image_ext = os.path.splitext(eis_filename)
eis_xml_filename = image_fn + ".xml"
``... | github_jupyter |
Our best model - Catboost with learning rate of 0.7 and 180 iterations. Was trained on 10 files of the data with similar distribution of the feature user_target_recs (among the number of rows of each feature value). We received an auc of 0.845 on the kaggle leaderboard
#Mount Drive
```
from google.colab import drive
... | github_jupyter |
```
#r "nuget:Microsoft.ML,1.4.0"
#r "nuget:Microsoft.ML.AutoML,0.16.0"
#r "nuget:Microsoft.Data.Analysis,0.1.0"
using Microsoft.Data.Analysis;
using XPlot.Plotly;
using Microsoft.AspNetCore.Html;
Formatter<DataFrame>.Register((df, writer) =>
{
var headers = new List<IHtmlContent>();
headers.Add(th(i("index")))... | github_jupyter |
# Chapter 8 - Applying Machine Learning To Sentiment Analysis
### Overview
- [Obtaining the IMDb movie review dataset](#Obtaining-the-IMDb-movie-review-dataset)
- [Introducing the bag-of-words model](#Introducing-the-bag-of-words-model)
- [Transforming words into feature vectors](#Transforming-words-into-feature-ve... | github_jupyter |
<a href="https://colab.research.google.com/github/satyajitghana/TSAI-DeepNLP-END2.0/blob/main/09_NLP_Evaluation/ClassificationEvaluation.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
! pip3 install git+https://github.com/extensive-nlp/ttc_nlp ... | github_jupyter |
# MultiGroupDirectLiNGAM
## Import and settings
In this example, we need to import `numpy`, `pandas`, and `graphviz` in addition to `lingam`.
```
import numpy as np
import pandas as pd
import graphviz
import lingam
from lingam.utils import print_causal_directions, print_dagc, make_dot
print([np.__version__, pd.__ver... | github_jupyter |
## Accessing TerraClimate data with the Planetary Computer STAC API
[TerraClimate](http://www.climatologylab.org/terraclimate.html) is a dataset of monthly climate and climatic water balance for global terrestrial surfaces from 1958-2019. These data provide important inputs for ecological and hydrological studies at g... | github_jupyter |
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the MIT License.
# Automated Machine Learning
_**ディープラーンニングを利用したテキスト分類**_
## Contents
1. [事前準備](#1.-事前準備)
1. [自動機械学習 Automated Machine Learning](2.-自動機械学習-Automated-Machine-Learning)
1. [結果の確認](#3.-結果の確認)
## 1. 事前準備
本デモンストレーションでは、AutoML の深層学習... | github_jupyter |
# Spark SQL
Spark SQL is arguably one of the most important and powerful features in Spark. In a nutshell, with Spark SQL you can run SQL queries against views or tables organized into databases. You also can use system functions or define user functions and analyze query plans in order to optimize their workloads. Th... | github_jupyter |
## Как выложить бота на HEROKU
*Подготовил Ян Пиле*
Сразу оговоримся, что мы на heroku выкладываем
**echo-Бота в телеграме, написанного с помощью библиотеки [pyTelegramBotAPI](https://github.com/eternnoir/pyTelegramBotAPI)**.
А взаимодействие его с сервером мы сделаем с использованием [flask](http://flask.pocoo.org... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
import numba
from tqdm import tqdm
import eitest
```
# Data generators
```
@numba.njit
def event_series_bernoulli(series_length, event_count):
'''Generate an iid Bernoulli distributed event series.
series_length: length of the event series
event_cou... | github_jupyter |
# Lalonde Pandas API Example
by Adam Kelleher
We'll run through a quick example using the high-level Python API for the DoSampler. The DoSampler is different from most classic causal effect estimators. Instead of estimating statistics under interventions, it aims to provide the generality of Pearlian causal inference.... | github_jupyter |
# Welcome to the Datenguide Python Package
Within this notebook the functionality of the package will be explained and demonstrated with examples.
### Topics
- Import
- get region IDs
- get statstic IDs
- get the data
- for single regions
- for multiple regions
## 1. Import
**Import the helper functions 'g... | github_jupyter |
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