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Towards Data Science 11/12/2019 00:50
Ever feel like you destroyed a job interview and then ended up not getting the job. Or how about completely bombing a technical screen and still passing onto the next round? You’re not alone, hiring standards are confusing at best but it still begs the question: how do you know how well you’re performing in comparison to your peers? The word of the week is calibration . Calibration is used by recruiters, interviewers, and companies to describe how matched up they are with understanding and optimizing the interview funnel. Here is an example from a frustrated old co-worker of mine. My co-worker Bob is a product manager at company X and is hiring engineers using an external recruiting agency. Bob was frustrated by the fact that only 10% of th.
Towards Data Science 11/12/2019 00:24
Dramatic visualization of 2016 eviction filings by state at the National Building Museum. From April 2018 through May 2019, a gallery wing of the D.C. National Building Museum was transformed into a labyrinth of forbidding plywood structures, towering piles of shrinkwrapped home furnishings, and striking visual displays.
Towards Data Science 11/12/2019 00:13
How do the hyperparameters for a decision tree affect your model and how do you choose which ones to tune? Hyperparameter tuning. Hyperparameter tuning is searching the hyperparameter space for a set of values that will optimize your model architecture. This is different from tuning your model parameters where you search your feature space that will best minimize a cost function. Hyperparameter tuning is also tricky in the sense that there is no direct way to calculate how a change in the hyperparameter value will reduce the loss of your model, so we usually resort to experimentation.
Towards Data Science 11/12/2019 00:01
The AI Race continues to heat up at GPU Technology Conference 2019. Here’s a startup founder’s perspective of Nvidia’s GPU Technology Conference. Whether you’re wondering how 5G will create new opportunities or why Ghost Restaurants require AI, it’s a good idea to stay up-to-date in this fast moving field. The National Institute of Standards and Technology officially defines AI:. Definition of AI: Capability of an engineered system to acquire, process, and apply knowledge and skills. I like this definition quite a bit as it doesn’t mention ‘human’ which is what normally people think of when they think AI. Having a commonly accepted definition is important as the world gets educated on what AI actually is. Keynote address highlights. The most in.
Towards Data Science 11/11/2019 20:00
Grace Hopper, Ph.D. (). Doing a small part to help close a gender gap. Few, if any, of my classmates shared my fascination with the Mark I Computer that was on display in our university’s Science Center. It is hard to blame them. Towering at 8 feet and filled with rotary switches, crystal diodes, and tangled wires, the Mark I resembles a prop from a science fiction movie rather than a computer the U.S. Navy once used. In its prime in the 1940s, the Mark I was one of the most powerful supercomputers on Earth; today, the smartphone we carry in our pockets would put it to shame. Yet, neither the smartphone nor the operating system that powers it would be possible without the early mainframes like the Mark I or the computer science pioneers who o.
Towards Data Science 11/11/2019 19:59
For the better part of a year, OpenAI’s GPT-2 has been one of the hottest topics in machine learning — and for good reason. The text generating model, which initially was dubbed “too dangerous” to be released in full, is capable of producing uncanny outputs. If you haven’t seen any examples, I recommend looking at — they’re incredible. Due in part to the machine learning community’s excitement about GPT-2, there are a ton of tools available to help you implement GPT-2 in different use cases:. Want to play with GPT-2? OpenAI has released . Want to train GPT-2 with different text? Use Max Woolf’s . Need a faster, compressed GPT-2? Use. With all of these tools, it’s fairly trivial to get GPT-2 running locally. It is still difficult, however, to
Towards Data Science 11/11/2019 15:10
A smarter way to manage ML in production that doesn’t suck. TD; DR : Making ML Proofs-of-Concept (POC) is easy, but maintaining them in Production is frustrating and expensive. It comes down to chaotic and blindsided monitoring and triage processes. To fix this, we want to introduce a concept called AI Performance Management (AiPM), explain how teams can adopt it with 5 tactical steps, and show you a tool to accelerate the process. Disclaimer: I work at the company that developed , the software mentioned in this article. The problems I describe are recurring issues we experience when deploying and managing ML solutions for multiple customers; the proposed solutions can be applied generally. The Story. Early this year, my team and I helped a c.

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