On Windows You can add the Maven archetype for Vaadin to IntelliJ IDEA the following way: Open: c:\Users\username\.IdeaIC2017.3\system\Maven\Indices\UserArchetypes.xml and add the line: <archetypes> <archetype groupId=”com.vaadin” artifactId=”vaadin-archetype-application” version=”8.3.2″ /> </archetypes Now you can create a new Vaadin project via Maven
To create e.g. x-axis indices you can use the linspace function from numpy. You give it a range (e.g. 0 to 23) and the number of divisions and it will distribute the values evenly across that range. The stop values is included in the resulting value array by default. Example: import numpy as np np.linspace(0,…
Questions Data Science tries to answer one of the following questions: Classification -> “Is it A or B?” Clustering -> “Are there groups which belong together?” Regression -> “How will it develop in the future?” Association -> “What is happening very often together?” There are two ways to tackle these problem domains with machine learning:…
Sometimes Python gives You a hard time when You deploy code to a server after you changed directory structures or simply moved files. With the following command You can remove the pyc files in the working directory and subdirectories: find . -name \*.pyc -delete
The Essence of Machine Learning A pattern exists The pattern cannot be described mathematically We have data on this problem
This episode is about the basic statements needed to create, read, update and delete data in a database system. It is part of my SQL-Tutorial Motivation Let’s assume we work as a data scientist for Knight Industries. We want to help the Foundation of Law and Government to keep track of our operatives. We decide…
Sometimes your RDBMS does not allow you to do certain changes like updating a table without using a WHERE clause that uses a key column. When you are really sure what you want to do: SET SQL_SAFE_UPDATES = 0; Now the dirty brown magic can begin! Back to the SQL-Tutorial
Sometimes You screw up your database design and you have redundancies i.e. your database is not normalized. If You want to correct that: Subqueries for the rescue! In our example we have two tables which contain almost the same information: CREATE TABLE installed_device (`id` int, `device` text, `info` text); INSERT INTO installed_device (`id`, `device`, `info`)…
You can comment your queries with — This is a comment in SQL SELECT * FROM my_table — WHERE id = 1
I got confused by all the buzzwords: data science, machine learning, deep learning, neural nets, artificial intelligence, big data, and so on and so on. As an engineer I like to put some structure to the chaos. Inspired by Roadmap: How to Learn Machine Learning in 6 Months and Tetiana Ivanova – How to become…