This post will provide a brief explanation of AutoML, argue for its justification and adoption, present a pair of contemporary tools for its pursuit, and discuss AutoML’s anticipated future and direction.
Randy Olson, whose research focuses on hyperparameter optimization, says the following:
In the near future, I see automated machine learning (AutoML) taking over the machine learning model-building process: once a data set is in a (relatively) clean format, the AutoML system will be able to design and optimize a machine learning pipeline faster than 99% of the humans out there.
There was a lot of new information in this post for me—I hadn’t realized how far along some of this R&D had gotten. Highly recommended if you’re not already familiar with this world.
Google today said it is acquiring Kaggle, an online service that hosts data science and machine learning competitions…
There are a lot of reasons that Google, whose future increasingly depends on being the the leader in AI, would want to buy the site that hosts the largest community of data scientists in the world. So far, sources state that there are no major plans to change aspects of the community (including its name). Kaggle’s CEO seems excited about new resources:
Making Google Cloud technology available to our community will allow us to offer access to powerful infrastructure, scalable training and deployment services and the ability to store and query large data sets.
This post is packed with tons of great advice. My favorite: “Do whatever you can to move to the Bay Area!” People outside the bay area often don’t want to admit it, but this advice is spot-on.
My first year in San Francisco was a period of intense learning for me: I attended tons of meetups, completed several online courses, participated in numerous workshops and conferences, learned a lot by working at a data-focused start-up, and most importantly met scores of people who I was able to ask questions of. I completely under-estimated how amazing it is to be able to interact regularly with the people who are building the tools and technology that excite me most.
This is the most comprehensive guide I’ve read for how to hire a data scientist. It delves into job descriptions, take-home tasks, interview questions, and how to attract diverse talent. If you’re thinking about growing your team, this is a must-read.
While the costs of human violence have attracted a great deal of attention from the research community, the effects of the network-on-network (NoN) violence popularised by Generative Adversarial Networks have yet to be addressed. In this work, we quantify the financial, social, spiritual, cultural, grammatical and dermatological impact of this aggression and address the issue by proposing a more peaceful approach which we term Generative Unadversarial Networks (GUNs).
Carefully curated by The Data Science Roundup with Revue.
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