Jon Wood  has just posted this video on how to use ML.NET to remove stop words in text data.

Adam looks at how you can build a slicer panel, in Power BI, and take it to the next level.

Using a combination of features in Power BI Desktop, you can make a great user experience.

Deepfakes have started to appear everywhere.

From viral celebrity face-swaps to impersonations of political leaders – it can be hard to spot the difference between real and fake.

Digital impressions are starting to have real financial repercussions. In the U.S., an audio deepfake of a CEO reportedly scammed one company out of \$10 million.

With the 2020 election not far off, there is huge potential for weaponizing deepfakes on social media.

Now, tech giants like Google, Twitter, Facebook and Microsoft are fighting back. With Facebook spending more than \$10 million to fight deepfakes, what’s at stake for businesses, and what’s being done to detect and regulate them.

They’re going mainstream, helping deliver medical supplies, inspecting hard to reach places, responding to emergencies, and, of course, delivering online orders.

Quartz News looks at how very close we are to making drones safe enough to operate across the skies.

Here’s a great 90 minute class covering the top 5 machine learning Python libraries.

In this video, Siraj Raval offers his advice on how to ace any programming interview.

Here’s an interesting talk by Aaditya Ramdas on “Sequential Estimation of Quantiles with Applications to A/B-testing and Best-arm Identification”

From the description:

Consider the problem of sequentially estimating quantiles of any distribution over a complete, fully-ordered set, based on a stream of i.i.d. observations. We propose new, theoretically sound and practically tight confidence sequences for quantiles, that is, sequences of confidence intervals which are valid uniformly over time. We give two methods for tracking a fixed quantile and two methods for tracking all quantiles simultaneously. Specifically, we provide explicit expressions with small constants for intervals whose widths shrink at the fastest possible rate, as determined by the law of the iterated logarithm (LIL).

Get started with ML.NET so you can create machine learning models in C# to run impressive predictions and use them in your .NET applications!

Create innovation in your apps with scenarios such as sentiment analysis, anomaly detection, text classification, object detection and many more!

Leonard Susskind is a professor of theoretical physics at Stanford University, and founding director of the Stanford Institute for Theoretical Physics.

He is widely regarded as one of the fathers of string theory and in general as one of the greatest physicists of our time both as a researcher and an educator.

This conversation is part of Lex Fridman’s Artificial Intelligence podcast.