Les news de la curation sur des nouveautés et l’humour
I have been using the Trello board over the last few months for project management. During this period, I needed to generate reports via Word or DOCX, and among the constraints, I found that there are cards written in different languages.
In this article, I will provide a detailed Java program that can generate a DOCX report of a Trello board and translate content into a single output language, basing it on Google Translation and Cloud API Translation.
Each time someone talks about the 12 Factor Application a weird feeling overcomes me … Because I like the concept, but it feels awkward. It feels as if someone with 0 operational experience wrote it. And this devil is in a really small detail.
And that is Part III. Config … For me (and a lot of other folks I’ve talked to about this topic), using environment variables (as in real environment variables) are just one of the worst ideas ever. Environment variables are typically set manually, or from a script that is being executed and there’s little or trace to see fast how a specific config is set.
We have this funny little tagline about how we’re pushing the boundaries of test automation. It’s a simple enough thing when you say it, but what do we really mean by it?
Recently, we were recognized by several industry analysts for the work we’ve been doing pushing those boundaries. At voke, they said, "Parasoft is a company borne of innovation with a relentless focus on software quality," and Forrester said, " Regarding AI, Parasoft has an impressive and concrete roadmap to increase test automation from design to execution, pushing autonomous testing."
The recent surge of data has empowered a field of computer science that uses statistical techniques to give computer systems the ability to learn: Machine Learning. Modern Machine Learning Algorithms are able to overcome strictly static program instructions and make data-driven predictions that help companies make decisions with minimal human intervention.
IDC forecasts that spending on Machine Learning will grow from $12 billion in 2017 to $57.6 billion by 2021. What’s more, Machine Learning patents grew at a 34 percent CAGR between 2013 and 2017, making it the third-fastest growing category of all patents granted.
In this post, you will learn about the definition of quality of AI/Machine Learning (ML) models. Getting a good understanding of what is the high and low quality of AI models would help you design quality control checks for testing Machine Learning models and related quality assurance (QA) practices. This post would be a good read for QA professionals in general. However, it would also help set perspectives for data scientists and Machine Learning experts.
The following are some of the key quality traits that are described in detail for assessing the quality of AI models:
Primarily, Machine Learning is the part of Artificial Intelligence that brings the computer systems a greater ability to enhance and study automatically from experience. Over the past few years, it has been creating very serious waves. Very recently, the applications of smartphones and other small-screen experiences have started to take shape that drives millions of interactions with their mobile devices. More importantly, the Machine Learning platform can make your smartphone very smarter by just increasing a host of processes as well as functions instantly. In reality, many smartphones are already using some kind of Machine Learning or intelligent automation application, which helps mobile phones in becoming more effective and efficient as well.
Overall, the businesses are ramping up their Machine Learning investment. Traditionally, the Machine Learning needs a fabulous quantity of power in which the mobile devices simply did not have. Still now, most of the businesses can install the special chips in automobiles, drones, and also in smartphones, which enables them to consume 90% less power. In the end, these mobile devices, even without an online connection, can do a wide array of complex tasks that include:
Classification is an important and central topic in ML, which has to do with training machines how to group together data by particular criteria. Classification is the process where computers group data together based on predetermined characteristics — this is called supervised learning. There is an unsupervised version of classification, called clustering where computers find shared characteristics by which to group data when categories are not specified.
For example:
For ERP administrators today, security is always on their mind. But, recent warnings from the US Department of Homeland Security about ERP vulnerabilities make securing your Oracle and SAP applications even more urgent. Data breaches and unauthorized access can disrupt business-critical processes and negatively impact your customers. Staying up-to-date with security patches is the best way to make sure this doesn’t happen to your organization, but good protocol requires that you first test patches against a separate test instance of SAP to confirm that they won’t impact operations of your production instance.
So, how can you speed up testing and implement these important patches as soon as they’re available? Automating the SAP system copy process is one way to clear the path of the obstacles that keep you from better security.
Of all the major league sports in the United States, basketball is the most balletic…in my humble opinion. Basketball demonstrates a fluidity of complex full-body motion all the while interactively and iteratively guiding and correcting the trajectory of a ball which repeatedly rebounds from the hard court surface. Watching real basketball players drive through the crowd toward the hoop gives us a hint of the artfulness. Watching it in slow motion makes us stare in wonder at the complexity of the performance.
Transitioning to the world of video games (where many sports seem to find their way) the observer has quite a different impression watching today’s state-of-the-art synthetic players. The rendered players go through the motions, but the simulation just doesn’t seem real. Even though the characters themselves look quite good in static poses there is something clearly counterfeit about how they move. No matter how great the skills of the animator are, it seems impossible to specify all of the angular velocities at all of the joints for even the most basic moves. It’s not that we search to find subtle flaws in their movement, but rather that we are instantly struck with how unnatural these players are.
With TensorFlow.js, you can not only run machine-learned models in the browser to perform inference, but you can also train them. In this super-simple tutorial, I’ll show you a basic "Hello World" example that will teach you the scaffolding to get you up and running.
Let’s start with the simplest Web Page imaginable:
