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Interactive Dashboards for Data Science

#artificialintelligence

We can then set-up our App and its layout using the code shown below. Finally, we can add interactive components to our dashboard (eg. In this case, I decided to divide this Dashboard into two tabs. In the first one, will be analysed the Stock Prices dataset and in the second one the Performance Metrics dataset. The layout of the first tab is subsequently divided into other two parts each of them formed by an H1 title, a dropdown menu with four different options and a time-series graph.


How to create and use custom forms in Word

PCWorld

It's a lot easier to create custom forms in Word than you might think and, certainly, much easier than it was many years ago. Under the Developer tab, Microsoft provides nine Content Controls, 12 ActiveX Controls, three Legacy Controls, and three Legacy Form features. In Microsoft Word, Controls are pre-programmed tools that allow you to add and customize interactive content to your Word forms, templates, documents, and webpages. This article covers six of these Content Controls: Check Box, Combo Box, Drop-Down List Box, Rich Text and Plain Text Controls, and Date Picker. We'll add more controls in updates to this story.


Word newsletter tutorial: Using page layout features for professional results

PCWorld

Word can make a professional-looking newsletter using the Page Layout features and other tricks. Here's an example so you can try the tools: Since we're planning to fold this page in half, this option actually gives us four pages. Then add some generic text to this newsletter. The first number equals the number of paragraphs (30), and the second number determines the number of sentences in each paragraph (6). This should be enough to create a sample newsletter.


Comprehensive Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems Using the R-Package flacco

arXiv.org Machine Learning

Choosing the best-performing optimizer(s) out of a portfolio of optimization algorithms is usually a difficult and complex task. It gets even worse, if the underlying functions are unknown, i.e., so-called Black-Box problems, and function evaluations are considered to be expensive. In the case of continuous single-objective optimization problems, Exploratory Landscape Analysis (ELA) - a sophisticated and effective approach for characterizing the landscapes of such problems by means of numerical values before actually performing the optimization task itself - is advantageous. Unfortunately, until now it has been quite complicated to compute multiple ELA features simultaneously, as the corresponding code has been - if at all - spread across multiple platforms or at least across several packages within these platforms. This article presents a broad summary of existing ELA approaches and introduces flacco, an R-package for feature-based landscape analysis of continuous and constrained optimization problems. Although its functions neither solve the optimization problem itself nor the related "Algorithm Selection Problem (ASP)", it offers easy access to an essential ingredient of the ASP by providing a wide collection of ELA features on a single platform - even within a single package. In addition, flacco provides multiple visualization techniques, which enhance the understanding of some of these numerical features, and thereby make certain landscape properties more comprehensible. On top of that, we will introduce the package's build-in, as well as web-hosted and hence platform-independent, graphical user interface (GUI), which facilitates the usage of the package - especially for people who are not familiar with R - making it a very convenient toolbox when working towards algorithm selection of continuous single-objective optimization problems.


Artificially Intelligent - Exploring the Azure Machine Learning Workbench

#artificialintelligence

In the last two columns, I explored the features and services provided by Azure Machine Learning Studio. In September 2017, Microsoft announced a new suite of tools for doing machine learning (ML) on Azure. The cornerstone of these new tools is Azure Machine Learning Workbench. However, what could be better for doing ML than the simple drag-and-drop interface of Machine Learning Studio? Machine Learning Studio is an ideal tool for creating ML models without having to write code, but it falls short in several areas.