Machine Learning vs. Deep Learning vs. Foundation Models

[@drluvkashyap]

One of "THE" best explanations of all types of AI models

[@fxcheux1681]

I love this guy's energy, very informative

[@prasadraavi390]

Beautifully explained. Thank you.

[@StephenHelwigTalksTech]

Unbelievably clear and concise. Thank you.

[@flashatak4791]

the summary of the summary of the possible summary of AI. Nail it.

[@kamilziemian995]

He is such a good speaker.

[@revathik9225]

Where does NLP fit in?

[@ClementinePostma]

is there a way to get a certification at the end of the full course ?

[@flyc0de389]

I need an editor to explain me how they achieved the transparent board effect, looks incredible

[@affiliatepaid747]

forget man

[@raghuram2815]

Short answer: It involves a mirror; Long answer: This was the first doubt I had ever since watching the first IBM Academy videos. There are other explainer videos on KZbin that show how it's done. Google for it and you should see one of those videos in the search results.

[@IBMTechnology]

Jeff Crume recorded a behind the scenes video last year that shows how it's done: kzbin.info/www/bejne/i6DdkKKpe9mqbJo

[@flyc0de389]

Thanks a lot for your reply ​@@IBMTechnology

[@bobanmilisavljevic7857]

Great way to start the day 💪🤖

[@toenytv7946]

Learnt a new term claro. Like that and this. Great explanation!

[@arrowhead261]

Where is NLP located?

[@wtpollard]

NLP is a topic under AI. Nowadays, NLP is pursued using deep-learning models, but that's a relatively new development. Google Translate, for example, has been around since ~2006, but it only started using neural networks (deep learning) in 2016.

[@KumR]

where does hugging face and cohere fall?

[@eprabhat]

Hugging Face & Cohere can be seen as community platforms to support AI universe

[@FabrizioBianchi]

What is there under AI, other than Machine Learning?

[@rodrigoherrera7392]

Your videos are amazing, thanks

[@pankaj16octdogra]

Superb explanation

[@Ugk871]

Thank you for bringing this video

[@rrbbb-qv9kv]

How do you write so well backwards on the glass?

[@IBMTechnology]

See ibm.biz/write-backwards

[@JikeWimblik]

Multiple regenerated training data how is this used to reinforce data trends of the final output. I call the issue training Emphasis.

[@michaeloguidan3038]

Hello, what about data science

[@andrewjohnson6792]

How valuable is data, authentication for the training of these tools, refined thoughts, at rapid speed. Would a new supply chain movement towards generating a new standardize benchmark system, be useful? Potential sufficient to correct the potential errors, of miscommunication via scholarly debate. Perhaps chaos, but perhaps the cure. 😅 all in the amount of effort

[@xaviermagnus8310]

Chaos. Pretty much every field breaks down to assumptions somewhere. A lot of words but no explicit gain in this. Any piece of data almost worthless. The mass has the value. You're assuming not only that there is a definite right/wrong... but that we know it well enough to be sure.

[@hermenegildowilliam7938]

Very informative

[@JoeGariano]

Excellent!

[@jnslzr]

Thanks!

[@AChang007]

Not sure I agree that RL belongs under ML

[@MilesBellas]

Enormity isn't size, it's more like being horrorified.

[@TKumar-yv9iz]

Great !

[@BOGREVEPHIRI]

Awesome

[@timwiliusa6564]

He looks like detective gordon from gotham

[@sk3ffingtonai]

eXcellent. Thank you.

[@UsernameTudor]

There's a huge circle that encapsulates all the boxes and it's called tooling. Not sarcastic.