Neural Networks Archives

Generative Adversarial Networks

What are deep fakes? How are they generated? On today’s episode of Big Data Big Questions we tackle how Generative Adversarial Networks work. Generative Adversarial Networks or GANs work with 2 neural networks one a generator and another a discriminator. Learn about my experience with GANs and how you can build one as well.

Transcript What Is A Generative Adversarial Network?

This is going to be a cool episode, Mr. Editor. We’re going to talk about a painting that was built by AI or designed by AI that went for over $400,000. Crazy.

Hi folks! Thomas Henson here with thomashenson.com. Today is another episode of Big Data Big Questions. Today, we’re going to talk about Generative Adversarial Neural Networks. We’re going to talk about a painting, so you’ve all probably heard about a painting that was sold for, like, $400,000. It was built, actually, but a Generative Adversarial Network. We’re going to talk about that, explain what that is, and maybe even look at a little bit of code, and tell you how you can learn more about it.

Before we jump in, I definitely want to say, if you have any questions about data engineering, data science, IT, anything, put them in the comment section here below. Reach out to me at thomashenson.com/big-questions. I’ll try my best to answer them and help you out. It’s all about the community and all about having fun. Today, we’re going to have a lot of fun. I’m excited. This is something that I’ve been researching and looking into since, maybe, at least since the first part of 2019, but for sure it’s been a theme for me for a while.

I want to talk about Generative Adversarial Network, what that is. We think about that from a deep learning perspective. We’ve done some videos. We talk about deep learning, but this is a specific kind, so kind of like [Inaudible 00:01:33] neural networks, this is a little bit different. It still uses the premise of, you have your input layer, you have your hidden layers, and you have your output layer, but it’s a little more complexity to it. It’s been around since 2014. Ian Goodfellow is branded as the creator to that. If you follow Andrew Neen [Phonetic 00:01:52] on Twitter, I just saw where he took a role at Facebook. I think it was a competitive thing, and I think Andrew was saying, “Hey, great pickup for Facebook for picking him up,” but you might want to fact check that.

Like I said, that was breaking news here. Generative Adversarial Network. The way that I like to think about that and describe that is, think of it as having two different neural networks that are working. You have your discriminator and you have your generator. What’s going on is your generator is taking data. Think of, we’ve got, let’s say, a whole bunch of images of people. What’s going on is, our generator is going to take that data set and look at it, and it’s going to try to create fake data that looks like real data. Your discriminator is the one that’s sitting there saying, “Hey, wait a minute. That’s real data. This is fake data.” This is real data, that’s fake data. Just continuing on. You keep going through that iteration, until the generator gets so good, he’s able to pass fake data onto the discriminator. For our example, we’re looking at images of people. What you’re trying to do is, you’re trying to generate data of fake people and pass it through as real people. You’re probably like, “Man. How really good is that?”

Check out this website here. These are fake people. These are not real people. These are really good images, and a little bit creepy. I found this, actually, in the last week, and kind of looked at it. Been sharing it internally with some friends and some colleagues, but man. It’s really interesting when you think about it. These people do not exist. There’s no, these people don’t exist on the planet. These were all built by AI or deep learning. It’s pretty cool. Pretty creepy, too.

You’re probably wondering, “That’s pretty cool.” Been around since 2014. I’m researching it. Should I be researching it? I definitely think it’s something that’s going to be out there. There’s a lot of information around it, and a lot of use cases, kind of don’t know where it’s going to go. I can think of it being used for game development. Being able to create worlds. For somebody that’s creating a game that’s going to have multiple, multiple different levels, or even if GIS, you have to create all these landscapes and everything like that. If you can build AI to automate that, if you use a deep learning algorithm that’s going to automate, and build out those worlds, and make them lifelike, how much busy work is that going to save you? Same thing with GIS and in architecture, but also go back to the website we were just looking at, with the fake people. Oh, my gosh! You can use that in media and entertainment. Think about movies. Maybe we don’t even need actors anymore. That’s a little bit scary. For the actors, I don’t know. You still need Thomas Henson and thomashenson.com on YouTube, right?

Really cool. Something I just wanted to share with everybody, and back to what we were talking about in the first part of the show. The first art that was really sold for big ticket item around AI, over $400,000, and it was a generated image, too. I talk a little bit about it in my implementing TF Learn course, but here’s a code sample, really just showing what’s going on. If you’re looking at it, and all this is done in TensorFlow, here, using the extraction layer of TF Learn. Look here, how we’re creating that generator, and how you’re creating a discriminator. It’s a good bit of code here, but really, this is an example from TF Learn examples, where you’re actually starting to general data in here. It’s pretty cool. Pretty awesome to be able to play with if you have Tensorflow installed in your environment. You can actually do an import TF learn and start running this code from the examples here, and start tweaking with it. Really cool.

I you want to learn more, definitely love for you to check out and tell me all about. Go through my TF Learn course. Tell me all about it if you like it. You don’t have to, but I just thought sharing Generative Adversarial Networks, I thought that was pretty cool. I think it’s something that everybody should learn. At least know a little bit about it. Now, you know. Hey, important thing. I’ve got my generator. I’ve got my discriminator. My generator is making the data that’s trying to pass this real data to my discriminator.

Boom! You understand a lot. Thanks for tuning in. If you have any questions, put them in the comment section here below, and make sure you subscribe just so you never miss an episode, and get some great education around Big Data Big Questions.

Nobody can! Nobody can generate a fake image of me!

Challenge accepted?

Another Machine Learning Course?

Yet another machine learning course has caught my attention here lately. Andrew Ng has a new course available on Coursera focused on Neural Networks and Deep Learning. How did I like the course and should you take the course? Find out my thoughts on Coursera’s Neural Network and Deep Learning course.

Transcript- Review Coursera’s Neural Networking & Deep Learning Course

Hi folks! Thomas Henson here with thomashenson.com. Today is another episode of Big Data Big Questions. Today’s questions comes in around a new course that I am taking, myself. It’s not a course that I’m writing. I’ve talked about some of my Pluralsight courses. This is actually a deep learning course that I’m taking with Coursera. It’s the second course that I’ve taken with Coursera. I did another one from Andrew Ng called, I think, learning machine learning, and just went through that portion, and swore I’d never take another one, and here I am again. Find out my review on that course and how I’m doing on it here in just a second.

[Sound effects]

Today’s question is around what I’m doing from a course perspective. I’m taking a course called neural networks and deep learning. This is actually part one in a large certification series. If you go out to deeplearning.ai, it’s an Andrew Ng specific course. I did his machine learning course before, and went through it, and did some reviews with it on another channel with a group, the Big Data Beer Team. You can always check that out and find that.

I swore I’d never do another course, and here I am doing another one, because the math portion for me is a little more into the weeds than I like to be and really think, from a data engineering perspective, it probably is. Either way, my thing is to do this review and give you all the insights. You can decide if you want to take that course and find out where you are. I’m through part one. The neural networks and deep learning is part one in that course. It’s an Andrew Neen course, so he’s like, probably trained more people around machine learning and deep learning than anybody else on the planet. Worked at Badu, at Google, Stanford, and has his own company, own startup where he’s walking through driverless cars. Huge authoritative figure who’s teaching this course. It’s amazing from that aspect of it.

Little bit overwhelming, I’ll tell you. We’ll get into it a little bit, but each part of these courses are broken into, I think, four weeks. This first one was four weeks. We’re going to go through how I felt through each of the four weeks, and give you my thoughts on that.

In the first week, week one was intro to deep learning, and really it was about the why for deep learning. Why is deep learning? What’s the history of it? Is this anything new? Is this going to solve all our problems in the future? Eh, maybe.

Maybe we don’t get into that as much, but this was a pretty good one, and I actually did, with each one of these courses, there is a heroes in AI interview session. If you like watching YouTube videos like you do now, this is similar to that, but it’s behind the paywall, or behind the course wall there in Coursera. I actually went through that, when I did not get through all of them, but I did go through this one. It was pretty good. Can’t really remember who it was. Maybe shame on me for that. Should’ve put that in my notes.

Week one was pretty easy to step through and everything like that. There might’ve been a quiz or something, but no programming aspects from that perspective. Week number two, logistic regression in neural networks. Probably my least favorite portion of the course so far. A lot of math-based and somewhat of a review. Actually, when I got to this portion, I was like, “Man, this is…” I was going through the course material and watching the videos. I was like, “This is kind of a review from what I did in the machine learning course.”

I’m going to ace everything here, and I did ace the quiz. It wasn’t too hard, but when we stepped into the programming, it was a little more complicated than I thought, and I have some reasons why I think that is, and I’m going to talk about those here at the end. For the most part, week two was really just a level set. Hey, remember, this is the cross-function. This is how we use linear regression, and just walking through some of those portions, to be able to say hey, this is what’s going on behind the scenes.

If you’ve gone through like I have, and implemented networks, and played around with Tensorflow or TF Learn, you already know some of the things that are going on, which maybe you don’t understand it fully. This was a good review to start off to that perspective. If you haven’t taken the machine learning course, no problem. You can jump right into it. Like I said, he takes it from a high level here and gets you going.

Week three. My favorite week. We talked about shallow neural networks. This is the basics of how to build a neural network. What I like the most about this was, we deep dived into why non-linear functions and why we use different activation functions. It was really cool, because I actually taught a portion of this in my course, and just it was cool to see how Andrew was able to explain it. Maybe not a whole lot better than me. I don’t want to undersell myself, but it was definitely awesome to see his background, and his thought process, and just him saying, “Hey, this is why we use [Inaudible 00:05:19], these are some of the things that you’re going to see with it.” Don’t worry about it, because of these reasons. Really, my favorite portion of this course was week three, so around the shallow neural networks. Still went through and took a little bit longer to do the programming exercise than I thought would take me.

Little bit of stress there, but quizzes were good. It was easy if you follow along, and just take good notes, and you’ll be able to pass the quizzes. There’s a new thing that they’re trying out, too, called notes. I’ve started playing around with that. I’ll probably, in my next video, talk a little bit about that as I’m using it more and more, and maybe that’ll be a quick tip that you guys can use whenever you’re going through a course on Coursera.

Week four, not my favorite week. It was pretty good. We started getting into deep learning and deep neural networks and how those are working. Some of the things that we really did was talked about the matrix dimensions and how some of that works. Didn’t get into it as much as they will in future courses. It’s easy for me to look at it now and say that, because I jumped ahead a little bit. From the perspective of this course, neural networks and deep learning part one, really talks through some of the matrix portions and then starts building out your deep networks. Also, talks about parameters and hyperparameters. I was familiar with hyperparameters and parameters before, just with having been hands-on before, but it was really helpful to do those.

The quiz in this one, once again, if you paid attention, you went through it. You have to work through some math and do some other portions of it, but the quizzes are pretty simple. Make sure you’re using your own notes and everything for that. When it came to the programming exercises, I think there was two in this week, and they were somewhat difficult. I think the second one was pretty long as far as building out. You get to get hands-on with Tensorflow. Still a little bit more challenging, I guess, I think, and there’s some ways that we can make it a little bit better. Let me talk about that here just next.

Overall, I thought the course was all right. It was good for me, just some of it was a little bit of a review. Some of it went a lot deeper than I’ve dove in before, so I thought that portion was good for me. I will say, on all the programming exercises, they’re all graded. One of the things that I find challenging, and maybe it’s just the way that I learn, but I feel like they’re a little harder just because you go through, and it’s like you’re being tested day one. Whenever you’re going through the videos and everything, you’re doing everything from a math perspective on paper, or if you’re taking digital notes, but you’re not really doing any of the programming functions. If you don’t have a solid basics in programming, or it’s not something that you do every day from that perspective, I think it’s going to be a little more challenging. One of the things that could help out, I think, and broaden for the students that are coming in would be to have more coding examples that aren’t graded. It doesn’t have to be verbatim. Hey, this is really, really close to what the examples are. I get that you want to test, and you want to make it so that you’re applying what you’re learning.

Also, I think a few more coding examples where you can go through and see, “These are some of the steps.” If you understand the math portion of it, doesn’t necessarily mean that you’re going to be able to go in and be able to program it right there, and when we talk about it from a real-world perspective, whenever I look at it, yeah, you need to understand those things, and know how to implement those at a base level, but there’s so many. There’s so many other things it can do from a high level. For example, one of the biggest challenges I had going through this was, I build a whole course around TF Learn, and being able to use that abstraction layer over Tensorflow. For me, having to go through step by step, and showing how you can do this, where you can write it in TF Learn or use one of those functions, I think that would’ve been… That would be a different approach to take it, and I think that would broaden the audience, and make it a little more enjoyable, too.

If you’re having to go through, and you know that writing these 60 lines of code is something that you can write in 4, it makes it a little bit harder, especially since I already just did all the math portion, and kind of went through all those activations and everything work, versus having to go through some of the minutia on the programming. That’s just my two cents. If you’ve taken this course, please tell me. Tell me your opinion. You’re listening to mine. Let’s make this a conversation. I’d love to hear what some of your thoughts are, where you think I’m wrong if you think I should be better at math. You’re probably right. I think I’m getting the math. We’ll see.

Fair enough, my programming skills in Python, like I said, they’re all right. They’re not to the level here. I think that’s another gap that I found going through this course. All in all, I guess I would recommend it if you’re looking into using deep learning, but I don’t think that, if you’re a data engineer, that you have to go through anything like this. Like I said, it’s a good aspect of it, but there’s some other things and other skills that you probably want to get. If you’re more looking to the data science, or deep learning, or machine learning engineer, then going through something, one of these, this course would probably be pretty good. In the next video, check out, I jumped way too ahead in the next course. You might see. I jumped to, I think, the fifth portion or fourth portion when I was supposed to go to the second portion. I’ll talk about that in the next video. If you have any questions, make sure you put them in the comments section here below, or reach out to me on thomashenson.com/big-questions. Find me on Twitter or Instagram. Ask any questions. I’ll try my best to answer them. Make sure you subscribe so that you never miss an episode, and ring that bell. Thanks again.