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Digests » 157
this week's favorite
It’s probably happened to you at some point: You go to use a service for which you believe you’ve got a paid subscription, only to find that it’s been canceled for non-payment. That’s not only bad for you the customer: It causes negative feelings about the brand, it disrupts what should be a steady flow of revenue to the business, and a customer who finds themselves shut off might decide not to come back.
We upload so many personal photos on the internet, so we might have questions like who else would have access to them, what would they do with them—and which machine-learning algorithms would be trained with this data?
Have you ever been handed a dataset and then been asked to describe it? When I was first starting out in data science, this question confused me. My first thought was “What do you mean?” followed by “Can you be more specific?” The reality is that exploratory data analysis (EDA) is a critical tool in every data scientist’s kit, and the results are invaluable for answering important business questions.
A research team from Google shows that replacing transformers’ self-attention sublayers with Fourier Transform achieves 92 percent of BERT accuracy on the GLUE benchmark with training times seven times faster on GPUs and twice as fast on TPUs.
In this blog post, we introduce the Greykite library, an open source Python library developed to support LinkedIn’s forecasting needs. Its main forecasting algorithm, called Silverkite, is fast, accurate, and intuitive, making it suitable for interactive and automated forecasting at scale. We will start by describing a few applications, and then walk through the algorithm design and user experience. For more technical details, please refer to this paper.