https://safirsoft.com Let's talk about machine learning experiments that went right and wrong

Join the original audition on Wednesday, July 28 at 1:00 PM ET!

We've spent the past few weeks burning large amounts of AWS computing time trying to develop an algorithm to parse the Ars interface. - Page story titles to predict which one will win the A/B test - and we learned a lot. One such lesson is that we--and I mean 'we', I mean 'I', since this trip was somewhat my idea--maybe we need a less ambitious project to make our initial choice in the desert. Machine Learning Now, a little bit older and a little wiser, it's time to think about the project and discuss what was done right, what was done right, and how to do it differently next time. My Father.

Our readers also had some very helpful feedback, especially when it comes to the core part of the project - feedback that we would love to receive when discussing how things are going. The editorial cycle concerns meant that the stories were submitted entirely after they were written, so we didn't have a chance to get much reader feedback when we left, but Aras clearly has some superior stories - the AI/ML shelf specialists who read our stories (and maybe groan out loud). Every time we go down a little alley.) This is a great opportunity for you to get into the conversation and help us understand how we can move forward next time - or even better, if we do the same experiment again, it will help us choose smarter projects. I do! Announcement

Our chat begins on Wednesday, July 28, at 1:00 AM ET (11:00 PM PT and 17:00 PM ET). The three-person panel consists of Emer Infosec Editor, Sean Gallagher, and myself, along with Amazon Chief Missionary (and AWS Expert) Julian Simon. If you'd like to sign up so you can ask questions, use this link here. If you just want to watch, the discussion will be transferred to your Ars Twitter account and archived as an embed video on this story page. Register and join or check back here to watch!

Let's talk about machine learning experiments that went right and wrong
let-s-talk-about-machine-learning-experiments-that-went.html

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