FAIR has been a regular contributor to both research and open source frameworks in the deep learning space. From PyTorch

It’s not that hard to see. We just complicate it because sometimes, denial is easier than the truth. But it is often so plainly written, it becomes tough to read. The relationships that last are the ones in which growth is prioritized, connection is fostered, and the gaps and doubts and struggles you experience help you get better — not worse — as we go along.

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If you are with someone who drives you into isolation, you aren’t with the right person. If you are with someone who won’t commit to you right here and now, you are not with the right person. If you are with someone who seems disinterested in making your problems better, who would rather be right than be happy, you are not with the right person.

Visually appealing CVs of Data Scientists, details blurred. Note the vertical split used in both examples to differentiate experience, skills, achievements and publications. In both cases, a short summary paragraph helps describe their background and desires. Used with permission from owners.

The research divisions of technology giants such as Microsoft, Google, Facebook, Amazon, Uber etc. have become some of the most active contributors to open source frameworks in the artificial intelligence(AI) space. Their contributions combine stacks that have been tested at scale in their internal solutions as well as some very advanced ideas from their research labs. While having the biggest tech firms in the world actively contributing to open source deep learning is certainly exciting, it makes it a bit hard for data scientists to keep up with the new developments in the space. Today, I would like to cover three new releases that Facebook AI Research(FAIR) open source in the last month. One month into 2020, three new open source releases from the FAIR team. How is that for a fast growing trend?

I’ve also seen this approach abused even further by taking the subjective measures and turning them into a pie chart (30% python, 10% team-player, etc). While this was probably supposed to be a creative way to stand out, it demonstrates a lack of basic understanding behind the concepts of different charts.

They mix languages with tools, and in the worst cases with soft skills — saying your ‘4.5 stars’ at Leadership isn’t helpful. As a strong believer in a growth mentality, claiming to max out a skill (especially a harder to quantify and harder to master soft skill) feels very presumptuous.

Type of algorithms — structured/classic ML vs Deep Learning. Some candidates have only worked with Deep Learning, including on structured data that could have been better suited with tree-based models. While there’s no problem per se with being an expert at DL, limiting your toolset can limit your solution. As Maslow said: “If the only tool you have is a hammer, you tend to see every problem as a nail.” At Riskified we deal with structured, domain-driven, feature-engineered data which is best dealt with various forms of boosting trees. Having someone whose entire CV points back to DL is an issue.

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m of health already is inclined more toward these communicable diseases as opposed to a lot of other countries,” explains Javed Ali, MD, the International Medical Corps director of emergency response, Africa region. He adds that aspects of surveillance, reporting, and data collection are ingrained in many aspects of primary health care in a way that is not necessarily as common in the United States.

It’s not that hard to see. We just complicate it because sometimes, denial is easier than the truth. But it is often so plainly written, it becomes tough to read. The relationships that last are the ones in which growth is prioritized, connection is fostered, and the gaps and doubts and struggles you experience help you get better — not worse — as we go along.

Sure, life is complicated. Things get hard. But the point of the main relationship in your life is to assist you in becoming the person you want and are meant to be. The purpose is to continue to grow, together, at once, in the same direction.

ML Domain — this is usually relevant in two domains that require much expertise — computer vision