Nintendo no longer repairing Wii video game consoles

USATODAY - Tech Top Stories

If you have a Nintendo Wii in need of repair, it may be game over for the video game system. Nintendo is no longer offering repairs for Wii systems in the U.S., the game maker says online. However, many issues can be resolved by following the troubleshooting steps on our support site," the company says on its customer support site. The video game company said Monday it is ending repairs for the game console in Japan as of March 31 because it has had trouble getting parts to repair the console. Several tech news websites including Engadget reported the announcement from Japan.


Building a Serverless Machine Learning API using ML.NET and Azure Functions

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With the release of ML.NET, a API that C# developers can use to infuse their applications with machine learning capability, I've been keen to combine my knowledge of Azure Functions with the API to build some wacky serverless machine learning applications that would allow me to enhance my GitHub profile and cater to all the buzzword enthusiasts out there! This post won't be a tutorial. I'm writing this more as a retrospective of the design decisions I took while building the application and the things I learnt about how different components work. Should you read this and decide to build upon it for your real world applications, hopefully you can apply what I've learnt in your projects or better yet, expand on the ideas and scenarios I was working with. I'll be focusing more on what I learnt about the ML.NET API itself rather than spending too much time about how Azure Functions work.


Machine Learning Improves Satellite Rainfall Estimates - Eos

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Spaceborne precipitation observing systems can provide global coverage but estimates typically suffer from uncertainties and biases. Conversely, ground based systems such as rain gauges and precipitation radar have higher accuracy but only limited spatial coverage. Chen et al. [2019] have developed a novel deep learning algorithm designed to construct a hybrid rainfall estimation system, where the ground radar is used to bridge the scale gaps between (accurate) rain gauge measurements and (less accurate) satellite observations. Such a non-parametric deep learning technique shows the potential for regional and global rainfall mapping and can also be expanded as a data fusion platform through incorporation of additional precipitation estimates such as outputs of numerical weather prediction models.


Machine Learning Improves Satellite Rainfall Estimates - Eos

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Spaceborne precipitation observing systems can provide global coverage but estimates typically suffer from uncertainties and biases. Conversely, ground based systems such as rain gauges and precipitation radar have higher accuracy but only limited spatial coverage. Chen et al. [2019] have developed a novel deep learning algorithm designed to construct a hybrid rainfall estimation system, where the ground radar is used to bridge the scale gaps between (accurate) rain gauge measurements and (less accurate) satellite observations. Such a non-parametric deep learning technique shows the potential for regional and global rainfall mapping and can also be expanded as a data fusion platform through incorporation of additional precipitation estimates such as outputs of numerical weather prediction models.


Ramdas Honored for Efforts To Improve Research Reproducibility - Machine Learning CMU - Carnegie Mellon University

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Carnegie Mellon University's Aaditya Ramdas, assistant professor in the Department of Statistics & Data Science and Machine Learning Department, has received the National Science Foundation's (NSF) Faculty Early Career Development Award for his project, titled "Online Multiple Hypothesis Testing: A Comprehensive Treatment." "Arguably, one of the major hurdles to reproducibility of scientific studies is the cherry picking of results among the vast array of tests run or quantities estimated," Ramdas said. "We need'online' methods to correct for cherry picking, first acknowledging that the problem exists and then designing algorithms that can account and correct for it." According to Ramdas, statistical methods that improve reproducibility in large-scale scientific studies will combat the increasing public distrust in science. The results of this five-year grant could transform how technological and pharmaceutical industries as well as the sciences perform large-scale hypothesis testing.


Going in depth into big data and intelligence with HPE

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"You start off with business intelligence, analytics, big data … and then add things like AI, machine learning, [and] deep learning into that analytics …



Jenkins and Machine Learning Plugins for Data Science

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It is envisioned that Jenkins and its plugin ecosystem can play a key role in supporting, facilitating and accelerating the integration of ML workflow components and orchestrating their reproducible execution. Currently, Python and interactive computational notebooks (such as Jupyter and Zeppelin) are the dominant software tools for teaching, composing and executing machine learning workflows. Interactive notebooks have proven valuable data exploration and teaching tools as they adopt a'literal programming' paradigm where code fragments, results, instructions and documentation are integrated into a single UI, resembling a familiar paper'notebook'. However, interactive notebooks present significant limitations to the adoption of good software engineering principles (test-driven development, code versioning, reproducibility, etc) as well as scaling-up to data sizes typically used in DS production environments [2]. We propose the development of a Jenkins plugin that will integrate machine learning tasks and algorithms to build ML data systems that are easier to develop, test, deploy and maintain.


$7,000 Tesla Autopilot vs $1,000 Openpilot: Self-Driving Test!

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Sponsored: Visit http://prizeo.com/tesla to enter for a chance to win a Tesla Model 3! Tesla Autopilot vs Comma.ai Get 15% off the best Tesla accessories! Get free Supercharging when ordering a Tesla: http://geni.us/t3sla One of the most popular reactions from people when they see my Tesla Model 3 is they usually ask "Does it really drive itself?" because many people associate Teslas with self-driving & Tesla Autopilot which is an advanced driver assistance system. Autopilot is synonymous with Tesla, but not many people realize that other non-Tesla cars can also have their own advanced driver assistance system added at a fairly affordable price.


Solutions Engineer, Data Management - IoT BigData Jobs

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Location: US, Central Region OR Toronto, Canada Talend is a 600 employee, recent IPO, big data integration software company with deep open source roots. Well-funded, with over $100 million raised to date and continued rapid growth, Talend is the second largest independent open source company in the world. We are hiring Pre Sales Engineers to continue to build a proactive, customer-facing organization that ensures customers are getting value from Talend's products and solutions. We are seeking Engineers to join the sales team and support the increasing demand from our direct sales. Our portfolio of products has expanded from purely Data Integration to include Data Quality (DQ), Master Data Management (MDM), Enterprise Service Bus (ESB) and Big Data.