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Yet we never have a project phase associated with simplifying. Em, En, Ee. When you think you’re done, take a hard look at all the elements of your design or your model or your slide deck or your blog post and begin to chop away. But really, how often do we get it right? I suspect we overshoot far more than we know.
a global brand), sent en masse to millions of people in hope that some small percentage of recipients will take the bait. Based on projected results of a composite organization modeled from four interviewed IBM customers. appeared first on IBM Blog. Spear phishing is targeted phishing.
The public has turned en masse to getting delivery or food to go instead of dining in and apps like Popmenu help restaurants offer improved service by offering contactless ordering and payment, automating reservations and waitlists, and adding online ordering to their technology toolkit. trillion minutes in their apps during 2020.
In this post, I will walk through how I built the image classifier for this project using two different implementations of convolutional neural networks (CNNs). You can check out the full code in my Github repo for this project or take a look at the code snippet below to get a basic idea of how Pillow works.
This blog post will cover a specific use case in the fact-checking domain. Throughout the rest of the blog post, we will interchangeably use the terms debunk, debunking article, and fact-check. This model was developed within the scope of another EC-funded project in the disinformation domain VIGILANT.
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