Name: NEURAL NETWORKS, PART 2! - CS50 Live, EP. 55
Uploaded: 2020-03-28T16:45:21.000Z
Duration: 2 h 4 min 7 s
Description: Thousands of YouTube videos with English-Chinese subtitles! Now you can learn to understand native speakers, expand your vocabulary, and improve your pronunciation...

Enjoying today again My CI's buddies, Nick Long Neck What we're talking about today.

So today we're continuing a little bit of our subject from last stream over.

Take a step back for, like, a moment from images and they were happening to neural networks to kind of the biggest buzz word of ml at the moment.

Going a little bit more level than images, we're gonna we're gonna use a little bit easier data set when I build kind of a sample version of this This last weekend it took, I think, like an hour to run or so not great.

So we're going to use a very simple data set and figure out how to just what a neural network is.

Because they're not quite the same as the ones in our heads were in the rest of our bodies, and it's kind of complex.

There's a lot of weird terms like back prop and grading descent and things like that.

Yeah, I know them for quite some time before the stream tonight That's a lot of jargon, but Indy Colts intended target elucidate what it all means.

And if anybody's familiar scenes Video Alive is a show hosted here at Harvard University, CS 50 is Harvard's introduce computer science, topped by David Malin, who may make an appearance in the chat.

Um, and this is kind of an offshoot of that war.

We've done not only programming things but also operating system related things.

Kelly Lennox was you talked about it at some point, and your first thing was actually a machine learning related.

It was becoming a classifier, a very high level of really.

We're gonna just kind of skim over a lot of the math.

Now we're going to have a little bit more into the math on.

Then we're going to actually end up motivating the high level approach again.

But this time we're going to explain each of the kind of lower level concepts that we talked about.

Actually, I think in that stream Yeah, the first dream was used tensorflow Google's like there's even higher Yeah, today we're gonna be using just just no way got matrices with the golden eventually of continue because this is kind of like a new sub Siri's.

Yeah, when we're doing a sort of like building a neural network, talking about machine learning with the eventual goal of being able to generate images from part of streams which we talked about last week.

That's where we're going to start hitting you with these, like generative neural networks.

This kind of like we have Variation a Lotto encoding.

We have kind of like distributional networks.

And then we have Ganz, which are generated adversarial networks on those air like state of the art.

The things that generate the really cool landscape.

They produce all these like very sharp images.

So even like deep fakes, is kind of a similar concept where it's like you can't necessarily tell that it's not a real person, like like presidential videos where they like dancing a little wacky.

The only thing that I think we'll probably explore, we might even start with next week because it's a little easier to build from scratch is style transfer networks.

So let's say I give you like, then go painting and I then hand you a modern day picture of something right.

I transfer that style onto that, and it's beautiful.

This is like incredible works of art, and it's pretty easy to build.

Once we have kind of like our basic understanding of, like, what's a CNN?

It's kind of overwhelming, but I think it's cool is a lot.

So if you're ever confused, luckily is on video.

Our first part where we built the K means classifier because we need We're gonna eventually need a classified images so presuming to be able to generate them, comparing him against those those clusters.

Um, but you can check that that's on YouTube.

That should be about it at the time of this recording.

Oh, nice job on the podcast called Thank you very Much Way are releasing a podcast very soon, probably today.

It's on YouTube and all the major sort of spot of our, although may just modify it.

Major podcast distribution channels, including Spotify.

But that's to be formally announce probably on Facebook, and otherwise we'll definitely check that out.

People can actually see what I'm doing now.

My favorite classic See matrix screen saver.

So we're gonna hop out of that and we have an empty file start programming.

Yeah, I guess I got to do this, but it's a little harder to see.

So we're starting out with a, uh, just kind of rob.

This is kind of like keep ourselves on track.

I do have a like reference file in case I cannot forget some of the math of the top of my head.

Yeah, there's a lot, A lot, A lot of very, very down in the nitty gritty details.

So we're gonna start with kind of like my favorite important lumpy, uh, as God, Q e.

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