Thomas Henson

So that's a it's a super good question, I get that question a lot is like, hey, even just from the basics of, you know, should I understand the algorithm should know, the algorithms for talking about machine learning, we're talking about deep learning, like, how much should I be able to recommend and look at, you know, TensorFlow, and it's, it's such the software engineering answer, right, to say, it depends. But really, it does, it's going to depend, right, like, you know, if you're in a small organization, and, you know, you guys are just going down, the don't going down the path, you probably know, maybe, maybe you have a data scientist, maybe, you know, maybe it's more of a, you know, data analyst in your organization, then you're going to want to be able to handle and be able to, you know, carry some of that now, I'm not going to say that you're going to want to recommend, oh, you know, we should use, you know, CNN here, or for doing machine learning, like, Hey, we should use, you know, PCI, or, you know, decision trees are from that perspective, but you definitely want to have a little more understanding around it. So that, you know, when it comes to your part, and your, your role in the organization, you can understand some of the tweaking and some of their kind of thought process around it, and, you know, add, you know, add something to the table now, in a large organization, right, that's maybe more mature in their analytics journey, or the deep learning journey, then you're going to be, you're going to be able to focus, you know, not going to have to focus as much on understanding the underlying, hey, you know, how does that, how does this math, you know, work, and, you know, what's, you know, what are the weights and biases, and you know, why there's so many different layers there. So, you wouldn't have to focus as much in a larger organization. But I will tell you one thing that I've found, and I said, I came from the data engineering side, and, you know, understood a little bit about the algorithms, but didn't really didn't really focus on them. One of the things that I like more about deep learning, then machine learning is, it's, it's really a little bit different math, like, it's a little more basic to, I think, I think, I've heard people say that, you know, whenever you're talking about it, you know, you can get away with, like, you know, the first Sunday of calculus from a deep learning perspective, versus, with machine learning, it's a lot more complex, right? When the algorithms and kind of what you're doing, and a lot of it goes back to what we were talking about, how about how the data is broken up from a deep learning perspective, is, it's really just, you know, it's really just matrix math, right? It's, you know, it's matrix algebra to be able to, you know, stack all these ones and zeros, or, you know, for us an RGB, you know, ones and zeros and trees, and, you know, all these different pieces together. So, we're using a lot of easy basic math, it's just really big math. So that's a long answer to say that it depends. But it really is going to, it's going to depend on your organization, and kind of where your role is. So I would encourage you, from a career perspective, to be a little bit little bit familiar with it, just, you know, have have a natural curiosity to it, but don't go, I wouldn't say that you have to go deep, right, you're not,

you're not going to have to go back and get a degree or, you know, you're not going to have to know the intricacies of you know, everything about it. But especially with the algorithms that you're using, or the different, you know, neural networks that you're that you're implementing in your organization, I'd be pretty much I'd be pretty familiar with there. But I wouldn't, I wouldn't stand up and put myself as I'm the one that's going to recommend which, you know, which approach we take, and you mentioned earlier to about the possibilities of leveraging some of these deep learning capabilities in the data preparation and ATL processes. So can you talk a bit more about the different ways that we can leverage the capabilities that are promised by deep learning as part of our own work in the data management process? Yeah, that's a good point. You know, when, whenever I kind of keyed on the point that, you know, we use supervised learning a lot. And just kind of recap, you know, if you think about supervised learning, that's where we have these, you know, going back to our cat photos, right, we have a lot of images, a, this is an image that contains a cat, this one doesn't contain a cat, right? And so we know the end outcome we're looking for when we're doing that. And then I talked about, you know, how unsupervised learning is kind of, you know, on the forefront and, you know, something that we're seeing, but you can use unsupervised learning to help out with some of your eta and some of your data wrangling. Right? So, unsupervised learning is where we have a, hey, have a million images and I just want you, you know, wants you to be able to classify them, right? Unless you go feed them in so this can help you to group so I talked about, you know, I don't think we're going to get out of BTL and I don't think we're going to get out of data wrangling for a while, but you can use, you know, like unsupervised learning to be able to pull and generate and put, you know, put some kind of order to all this structured, unstructured data, we have to. So, think about it, you know, kind of, you know, one of the famous examples, you know, that we've, we've done before is like, you know, sentiment analysis, right. So, think about when you're doing sentiment analysis, if you've ever walked through a tutorial on Twitter, but now, you can, you know, think about that, from the same perspective of, hey, do you know, there's, you know, we can, we can train our neural network to kind of just look at a whole bunch of images and kind of put all those to some kind of structure. And so, if you think about it, if, you know, if your job was to find these train data sets, right, like, you could, you can use an unsupervised learning to be able to, you know, categorize those and put them in clusters. So that, hey, you know, instead of looking at a million pictures, maybe I'm only looking at 100,000, right.