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Re: 2023 MIT BootCamp for Deep Learning  

By: micro in 6TH POPE | Recommend this post (1)
Mon, 08 May 23 6:40 PM | 29 view(s)
Boardmark this board | 6th Edition Pope Board 		Msg. 42356 of 58548
(This msg. is a reply to 42346 by Fiz)
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fIZ,

I think its great that you have enough drive for learning remaining that you would participate in something like this! I think its wonderful..

MIT simply doesn't have anything that piques my interest at my age and I would assume same for Ribs and likely Beldin.

I am as edumacated as I intend to get. I simply have no use for more stuff that I will never apply or use. I had enough of that in 8 years of college at Cincinnati and 4 more at Bible college. I do pride myself on being a life long reader and learner though. I read constantly new books I have purchased including some American Classics as well as Theological books.

I am going to meet my Maker and God one day so some things have mnore importance to me than just adding to my knowledge base.

I think its wonderful if anybody else wants to increase theirs though. It was kind of you to post that so anyone could take advanatage of it should they want to.

Thank you for sharing that information though. I think its great !

micro.


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The above is a reply to the following message:
2023 MIT BootCamp for Deep Learning
By: Fiz in 6TH POPE
Mon, 08 May 23 3:53 AM 		Msg. 42346 of 58548

http://introtodeeplearning.com/

It is a week long program that was meant to get people up to speed (MIT people from other areas than AI). It is two hours per day PLUS a lab. I just got through the first two hours. Very easy to follow, so far. Unfortunately, I can't get the lab info (I don't see anything about it, but will look further...I'd at least like to get the INSTRUCTIONS to the lab and the overview, although I obviously can't conduct the lab myself). Without that, I expect next lectures to make less sense.

Give it a try.

I'm hoping to augment this with a course on AI & Deep Learning programming with python and R programming to implement the various components (e.g., PyTorch, TensorFlow)

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