Programming Leftovers
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DevNation Live: Event-driven business automation powered by cloud-native Java
DevNation Live tech talks are hosted by the Red Hat technologists who create our products. These sessions include real solutions and code and sample projects to help you get started. In this talk, presented by Red Hat’s Maciej Swiderski, Principal Software Engineer, and Burr Sutter, Chief Developer Evangelist, you’ll learn about event-driven business automation using Kogito, Quarkus, and more.
Kogito is a new Java toolkit, based on Drools and jBPM, that’s made to bring rules and processes to the Quarkus world. This DevNation Live presentation shows how Kogito can be used to build cloud-ready, event-driven business applications, and it includes a demo of implementing the business logic of a complex domain.
Kogito itself is defined as a cloud-native business automation toolkit that helps you to build intelligent applications. It’s way more than just a business process or a single business rule—it’s a bunch of business rules, and it’s based on battle-tested capabilities.
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NVIDIA Video Codec SDK 9.1 Brings CUDA CUStream Support, Other Encoder Improvements
Following the February release of Video Codec SDK 9.0, NVIDIA recently did a quiet release of the Video Codec SDK 9.1 update that furthers along this cross-platform video encode/decode library.
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Mike Driscoll: PyDev of the Week: Peter Farrell
This week we welcome Peter Farrell (@hackingmath) as our PyDev of the Week! Peter is the author Math Adventures with Python and two other math related Python books. You can learn more about Peter by visiting his website.
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Mutation testing by example: How to leverage failure
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Reuven Lerner: Looking for Python podcast co-hosts
As you might know, I’m a panelist on the weekly “Freelancers Show” podcast, which talks about the business of freelancing.
The good news: The same company that’s behind the Freelancers Show, Devchat.tv, is putting together a weekly podcast about Python, and I’m going to be on that, too! We’ll have a combination of discussion, interviews with interesting people in the Python community, and (friendly) debates over the current and future state of the language.
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Getting started with data science using Python
Data science is an exciting new field in computing that's built around analyzing, visualizing, correlating, and interpreting the boundless amounts of information our computers are collecting about the world. Of course, calling it a "new" field is a little disingenuous because the discipline is a derivative of statistics, data analysis, and plain old obsessive scientific observation.
But data science is a formalized branch of these disciplines, with processes and tools all its own, and it can be broadly applied across disciplines (such as visual effects) that had never produced big dumps of unmanageable data before. Data science is a new opportunity to take a fresh look at data from oceanography, meteorology, geography, cartography, biology, medicine and health, and entertainment industries and gain a better understanding of patterns, influences, and causality.
Like other big and seemingly all-inclusive fields, it can be intimidating to know where to start exploring data science. There are a lot of resources out there to help data scientists use their favorite programming languages to accomplish their goals, and that includes one of the most popular programming languages out there: Python. Using the Pandas, Matplotlib, and Seaborn libraries, you can learn the basic toolset of data science.
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