#ai101 — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #ai101, aggregated by home.social.
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Save the date: BetaNYC and the Office of Council Member Harvey Epstein are hosting Introduction to AI (AI 101) for Council District 2 on July 21 at 12:00pm. Free, online session on asking useful questions, spotting privacy risks, and knowing when AI actually helps. No technical background required.
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Save the date: BetaNYC and the Office of Council Member Harvey Epstein are hosting Introduction to AI (AI 101) for Council District 2 on July 21 at 12:00pm. Free, online session on asking useful questions, spotting privacy risks, and knowing when AI actually helps. No technical background required.
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New to artificial intelligence or looking to fill in the gaps? This all-in-one guide breaks down everything you need to know about AI — simply, clearly, and insightfully.
Start reading:
#aimartz #aimartz.com #ArtificialIntelligence #AI101 #SmartTechInsights -
New to artificial intelligence or looking to fill in the gaps? This all-in-one guide breaks down everything you need to know about AI — simply, clearly, and insightfully.
Start reading:
#aimartz #aimartz.com #ArtificialIntelligence #AI101 #SmartTechInsights -
Step into the future with our AI 101 course! 🤖 No prior experience needed. Learn about AI, machine learning, and how to apply these skills in real-world scenarios. Perfect for tech enthusiasts! #AI101 #MachineLearning #TechEducation #FutureTech
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#AI101: Training vs. Inference
The difference between these two terms can be summed up fairly simply: first you train an #AI algorithm, then your algorithm uses that training to make inferences from data. To create a whimsical analogy, when an algorithm is training, you can think of it like Watson—still learning how to observe and draw conclusions through inference. Once it’s trained, it’s an inferring machine, a.k.a. Sherlock Holmes.
https://www.backblaze.com/blog/ai-101-training-vs-inference/ -
#AI101: Training vs. Inference
The difference between these two terms can be summed up fairly simply: first you train an #AI algorithm, then your algorithm uses that training to make inferences from data. To create a whimsical analogy, when an algorithm is training, you can think of it like Watson—still learning how to observe and draw conclusions through inference. Once it’s trained, it’s an inferring machine, a.k.a. Sherlock Holmes.
https://www.backblaze.com/blog/ai-101-training-vs-inference/
