Software


ElectrifAi, a Global Leader in Practical AI and Machine Learning, Announces New CEO and Launch of Industry's First Open Source Platform

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ElectrifAi, a global leader in Artificial Intelligence (AI) and Machine Learning (ML), today announced the launch of a new and improved open source platform and the appointment of industry veteran Edward Scott as ElectrifAi CEO. This comes alongside a comprehensive corporate rebrand for ElectrifAi, changing its name from Opera Solutions and launching a new website. In an industry-first move, ElectrifAi has re-architected its technology platform around an open source, Spark-unified computational engine that allows large-scale distributed data processing and machine learning, with embedded Zeppelin notebook capability. Now, ElectrifAi's data scientists – as well as those of its customers – can code and access data in any programming language. The incorporation of Docker Containers and Kubernetes enables ElectrifAi to build and deploy hybrid cloud enterprise solutions at scale, seeing results in weeks rather than months, thus increasing enterprise time to value dramatically.


Learning QGIS, Second Edition - Programmer Books

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The popularity of open source geographic information systems, and QGIS in particular, has been growing rapidly over the last years. Highly configurable programmable environments are often preferable for those who need to be able to precisely reproduce and distribute their work. QGIS is the best and most user friendly GIS tool in the free and open source software (FOSS) community. Learning QGIS Second Edition helps you ensure that your project is a success. It ensures that the first impression of your project is a great impression!


One Simple Trick for Speeding up your Python Code with Numpy

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Over the past several years the popularity of Python has grown rapidly. A big part of that has been the rise of Data Science, Machine Learning, and AI, all of which have high-level Python libraries to work with! When using Python for those types of work, it's often necessary to work with very large datasets. Those large datasets get read directly into memory, and are stored and processed as Python arrays, lists, or dictionaries. Working with such huge arrays can be time consuming; really that's just the nature of the problem.


Test Automation in the World of AI & ML

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Artificial Intelligence and Machine Learning, fondly known as AI & ML respectively, are the hottest buzzwords in the Software Industry today. The Testing community, Service-organisations, and Testing Product / Tools companies have also leaped on this bandwagon. While some interesting work is happening in the Software Testing space, there does seem to be a lot of hype as well. It is unfortunately not very easy to figure out the core interesting work / research / solutions from the fluff around. See my blog post - "ODSC - Data Science, AI, ML - Hype, or Reality?" as a reference.


r/MachineLearning - [D] Is anyone interested in doing small machine learning tasks for small dividends?

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Gonna need some more details here. Is this for personal hobby projects, your own business, contracting work, an employer, etc? Do you have a plan for who owns that work, what rights each party has, and a contract encoding all of that? If this is for anything other than a personal, open source, and free project I'd strongly caution you to get that sorted first, and if it's for work with others you're going to need to get a lawyer involved in the process to cover your ass at minimum. Professionally I do almost all of my work in software engineering these days but I want to make sure I stay fresh enough to take advantage of my academic background in applied math and research experience in ML. I'd be open to the idea but I'd need much more information.


Systers TechTalks: Get started with Machine Learning

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Brief introduction about what is Machine Learning and algorithms, theories, technologies used, applications, how to get started with, what programming language to learn etc. Yashashvi is freelance software developer and a final year student of Bachelors of Engineering in Computer Engineering. Yashashvi is an open source advocate and active contributor. Yashashvi has previously worked with Zulip(https://zulipchat.com/) and Indian Institute of Technology Bombay organizations. A technical woman is never alone when she's a Syster. Founded by Dr. Anita Borg, in 1987 together with 12 other women as a small electronic mailing list for women in systems.



root@tan-lappy

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This is a post related to my recent talk at PyLadies Vancouver. The talk is about how to use the EmoPy toolkit in Linux Ubuntu 16.04 with OpenCV Python to perform Emotion detection in images and videos. You can find my slides here. EmoPy is an open-source emotion detection toolkit developed by Thoughtworks and currently supports OS X. However, it has not been tested on a Linux OS.


PyCon 2019 Machine Learning Model And Dataset Versioning Practices - Liwaiwai

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Python is a prevalent programming language in machine learning (ML) community. A lot of Python engineers and data scientists feel the lack of engineering practices like versioning large datasets and ML models, and the lack of reproducibility. This lack is particularly acute for engineers who just moved to ML space. We will discuss the current practices of organizing ML projects using traditional open-source toolset like Git and Git-LFS as well as this toolset limitation. Thereby motivation for developing new ML specific version control systems will be explained.


The Challenge of Open Source MT SDL

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The very large majority of open-source MT efforts fail because they do not consistently produce output that is equal to, or better than, any easily accessed public MT solution or because they cannot be deployed effectively. This is not to say that this is not possible, but the investments and long-term commitment required for success are often underestimated or simply not properly understood. A case can always be made for private systems that offer greater control and security, even if they are generally less accurate than public MT options. However, in the localization industry we see that if "free" MT solutions that are superior to an LSP-built system are available, translators will use them. We also find that for the few self-developed MT systems that do produce useful output quality, integration issues are often an impediment to deployment at enterprise scale and robustness.