Welcome to TensorFlow from Basics to Math Theory. Some of you may have taken deep learning or machine learning from me and learned about the amazing things you can now do with deep learning machine learning. One of the best tools you can use to implement these algorithms is TensorFlow. Learning algorithms can be quite complicated, and today programming frameworks like TensorFlow, PyTorch, CAFE, and many others can save you a lot of time. These tools can be complicated, and what this set of courses will do is teach you how to use TensorFlow effectively. In order to teach much of these courses, I'm absolutely thrilled to introduce Lawrence Moroney. Thank you, Andrew. He is a developer advocate at Google and has been working on Google AI and TensorFlow. Lawrence has also written over 30 programming books, including four sci-fi novels. Yeah, exactly. I've been busy. I really enjoy writing, but the one thing I enjoy even more is learning and teaching AI. Actually, I've learned from the specializations that you mentioned, and I've learned from your courses, so it's a real honor to be here with you. Oh, thank you. I did not know that you were taking my courses as well. Thank you. Definitely. I was a big fan, and that's really what got me into AI. It's actually a long story. I started doing AI many, many years ago, back when it was things like Prolog and Lisp and all that, but now when we've gotten more into machine learning and deep learning with neural networks, I needed a place to learn it, and I actually learned it from your courses. It's been exciting to be actually coming full circle and now teaching it myself, too. Thank you. I actually did not know. So, thank you for sharing that. I caught you by surprise. So, where the industry is at right now is one of the things that really excites me, because it's exploding, right? There's deep learning, and machine learning skills are becoming ever more important and opening up whole new scenarios. One of the strange things and exciting things about machine learning and AI is that it's no longer just a technical thing limited to the software industry, so that everyone in, or at least every industry, needs to figure this out. Yeah, and it's exciting from a developer's perspective, because there's a new paradigm. The new paradigm, to me, is opening up scenarios that weren't previously possible, things that were too difficult for me to write programs for. And so, whatever it's like, whenever a new paradigm comes, and these new tools come, and you can open up new scenarios, then that opens up great new opportunities. Yeah, and I think one of the tragic things today is, even though the whole world sees their promise and the hope of these machine learning and AI capabilities changing so many things, the world just doesn't have enough AI developers today. Exactly. I mean, I've seen surveys of, like, you know, 25, 26 million software developers, and, like, maybe 300,000 AI practitioners. So, part of my personal passion is to try and turn, like, those 24.7 non-AI practitioners, a significant portion of them, into people who can understand AI and who can build the new and exciting things that we can't think of. Yeah. So, I think if you finish this set of courses and learn how to code in TensorFlow, hopefully that will help you do some of this exciting work and maybe become an AI developer. So, in the next video, you'll hear Lawrence talk about the differences between traditional programming paradigms versus the machine learning and deep learning programming paradigm, and you'll also hear about how to fit an X to Y data relationship, how to fit a straight line to data. So, please go on to the next video. Thank you.