Synergy In Search: Insights From Rank Ranger CEO Shay Harel


Synergy In Search: Insights From Rank Ranger CEO Shay Harel

CEO and Founder Shay Harel shares his thoughts about Rank Ranger joining forces with Similarweb, keeping up with SERP features, and nurturing SEO talent.

| Boosting Ranks | Later Spider-Man Remastered | #valorant #marvel

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Shanti Chellaram: The Best of Both Worlds: High Performance Recommendations with Elixir NIFs

EMPEX NYC is a regional technical conference focusing on Elixir, Erlang, and all things BEAM. Held annually in New York City, we’re a premier conference for the curious programmer.

Aditi Raghunathan – Surprises in the quest for robustness in ML

January 12, 2021. MIT CSAIL

Abstract: Standard machine learning produces models that are highly accurate on average but that degrade dramatically when the test distribution deviates from the training distribution. While one can train robust models, this often comes at the expense of standard accuracy (on the training distribution). We study this tradeoff in two settings, adversarial examples and minority groups, creating simple examples which highlight generalization issues as a major source of this tradeoff. For adversarial examples, we show that even augmenting with correctly annotated data to promote robustness can produce less accurate models, but we develop a simple method, robust self training, that mitigates this tradeoff using unlabeled data. For minority groups, we show that overparametrization of models can hurt accuracy on the minority groups, though it improves standard accuracy. These results suggest that the “more data” and “bigger models” strategy that works well for the standard setting where train and test distributions are close, need not work on out-of-domain settings.

Bio: Aditi Raghunathan is a fifth year PhD student at Stanford University working with Percy Liang. She is interested in building robust ML systems that can be deployed in the wild. She is a recipient of the Open Philanthropy AI Fellowship and the Google PhD fellowship in Machine Learning

6 Ways to Be More Inclusive with Your Content with Sarah McDowell

Are you unintentionally turning off a significant chunk of your target audience by publishing content that isn’t inclusive?

How can you turn this around and make sure that you appeal to as many people as possible?

In this episode, Sarah McDowell explains six ways to be more inclusive with your content, including:
1. Inclusive language
2. Inclusive imagery
3. Gender forms and security practices
4. Keywords
5. Showing support on your website
6. Link out to resources

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