software engineer


How I Got Started In Machine Learning

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My first look at Python was deliberate as I was following advice to learn the language from my mentor. Within a few hours of doing a deep dive into the language i got hooked and felt that the language was made for me. I made a decision that i would make Python my main language and put in all the work to understand it.My main resource when it came to Python Programming was Python's Documentation which i would advice any newbie to use.After months of intensive coding,I really good at Python that my friends and lecturers noticed, i familiarized myself with Python's frameworks;Django and Flask but i felt that this wasn't enough to make me a Python Guru.At this moment,I desperately needed to be good at Python. Oops,I stepped on Machine learning…. It was the beginning of a new semester,as part of our school curriculum we had to have project ideas for our third year.


The Biggest Mistakes Made by Data Scientists - InformationWeek

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In 2019, companies looking to gain an edge on competitors and insight into customers and trends have come to rely more heavily on data scientists to inform their business decisions. A good data scientist is invaluable to a company with any online presence. They will assess and interpret complex information and build out machine learning algorithms. Data volume keeps growing, and the amount of skill and effort needed to create data-driven initiatives is certainly keeping pace with that growth. Mistakes can produce huge consequences and, while the tools may change, the mistakes stay the same.


Software Engineer, Machine Learning Job in Menlo Park, CA at Leia Inc

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At Leia we are working at the forefront of display technology -- making screens come to life in richer, deeper, and more beautiful ways. We apply our proprietary nanotechnology to give any display the ability to produce lightfield, immersive "holographic" content -- no glasses, no tracking, no fuss. Through our digital platform LeiaLoft, we also aim to create an environment where developers and artists of the future will bring their creativity to life and deliver breathtaking experiences to the world at large. Come join a team of passionate scientists, engineers and artists in a beautiful adventure where you will be defining the digital experiences of tomorrow. As a Software Engineer, Machine Learning with the Computer Vision group, you will develop software and models that marries cutting-edge computer vision and deep learning with Leia's lightfield technology.


Augmenting The Work of OutSystems Developers With AI-Assisted Development

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If you've been keeping up with us, you know that our core goal is to deliver extreme agility with no limits, allowing everyone to innovate. And if you're new here, now you know what OutSystems is all about. A future with no limits is one where intelligent tools augment the work of users with the expertise learned from millions of anonymized code patterns. At outsystems.ai, we're building that future with the ultimate goal of making app development 100x faster, while enabling pro developers and business people to deliver robust, high-quality applications with different levels of complexity. That's why we're happy to announce the general availability of our next-generation AI-Assisted Development capability, which is built right into Service Studio -- the OutSystems development environment.


20 Machine Learning Bootcamps and Courses Teaching the Art of Algorithms

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Machine learning technology has the capacity to autonomously identify malignant tumors, pilot Teslas and subtitle videos in real time. The term "autonomous" is tricky here, because machine learning still requires a lot of human ingenuity to get these jobs done. It works like this: An algorithm scans a massive dataset. Engineers don't tell it exactly what to look for in this initial dataset, which could consist of images, audio clips, emails and more. Instead, the algorithm conducts a freeform analysis.


Global Big Data Conference

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While the tools may change, the mistakes stay the same. Here are four common issues that IT leaders should be aware of when managing data science teams. In 2019, companies looking to gain an edge on competitors and insight into customers and trends have come to rely more heavily on data scientists to inform their business decisions. A good data scientist is invaluable to a company with any online presence. They will assess and interpret complex information and build out machine learning algorithms.


Beyond Python and R for Data Science -- Part 1

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We all know that R and Python are both used for data science. Machine learning can be done with both. They are probably the two beginner's programming languages for your foray into data science. There are plenty of software engineers who are either transitioning into data science by becoming data scientists, data engineers, and machine learning engineers, or they are working on AI software projects. If you are a programmer or a software engineer on this path, then this article is for you.


The Drone Job Market - Drone Industry Insights

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Software engineers are the most sought after in the drone industry both by platform manufacturers and by software companies. This is partly because some companies like Thales Alenia Space or DELAIR are both hardware and software manufacturers. Another reason is that hardware companies like the recently acquired Aeryon Labs, Flyability and Matternet are hiring software engineers for their R&D divisions firstly in order to continue increase the level of automation within their products (e.g. Hardware engineers are exclusively sought after by hardware manufacturers and, thanks to the many features which drones can and must contain, they come from a variety of backgrounds: electrical engineering, mechanical engineering, and aeronautical engineering. Sales roles are the portion of drone jobs which are customer oriented and as drone companies grow bigger increasingly focus on specific regions (for example, companies seeking sales executives for specifically California, or the Eastern Cape).


Making the machine: the machine learning lifecycle Google Cloud Blog

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The machine learning lifecycle consists of three major phases: Planning (red), Data Engineering (blue) and Modeling (yellow). In contrast to a static algorithm coded by a software developer, an ML model is an algorithm that is learned and dynamically updated. You can think of a software application as an amalgamation of algorithms, defined by design patterns and coded by software engineers, that perform planned tasks. Once an application is released to production, it may not perform as planned, prompting developers to rethink, redesign, and rewrite it (continuous integration/continuous delivery). We are entering an era of replacing some of these static algorithms with ML models, which are essentially dynamic algorithms.


Software Engineer, Machine Learning

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Robert Bosch is a world-class manufacturing and engineering company with over 200 plants and thousands of assembly lines world-wide. We rely on data for every aspect of our operations and we collect a lot of it. Our team applies machine learning to solve challenging problems in a wide variety of Bosch domains, including: manufacturing, engineering, supply chain & logistics, and internet of things. We are looking for a talented engineer who is passionate about building and deploying machine learning systems in production. Your work will have global impact by improving the quality and value of Bosch products.