geoffrey hinton coursera youtube

Deep learning is also a new "superpower" that will let you build AI systems that just weren't possible a few years ago. It was a model where at the top you had a restricted Boltzmann machine, but below that you had a Sigmoid belief net which was something that invented many years early. 3. David Parker had invented, it probably after us, but before we'd published. >> Okay, so my advice is sort of read the literature, but don't read too much of it. It turns out people in statistics had done similar work earlier, but we didn't know about that. They think they got a couple, maybe a few more, but not too many. >> I see, and research topics, new grad students should work on capsules and maybe unsupervised learning, any other? >> What happened? 来自顶级大学和行业领导者的 Geoffrey Hinton 课程。通过 等课程在线学习Geoffrey Hinton。 >> You might as well trust your intuitions. And to capture a concept, you'd have to do something like a graph structure or maybe a semantic net. flag. Seit den 1980ern forscht Hinton an der Technologie, es benötigte aber die Durchbrüche bei Datenverfügbarkeit und Rechenleistung der aktuellen Dekade, um sie glänzen zu lassen. >> I guess recently we've been talking a lot about how fast computers like GPUs and supercomputers that's driving deep learning. >> Well, thank you for giving me this opportunity. And there's a huge sea change going on, basically because our relationship to computers has changed. Learning to confidently operate this software means adding... Aprende una habilidad relevante para el trabajo que puedes usar hoy en menos de 2 horas a través de una experiencia interactiva guiada por un experto en la materia. And I got much more interested in unsupervised learning, and that's when I worked on things like the Wegstein algorithm. And if you give it to a good student, like for example. And so that leads the question of when you pop out your recursive core, how do you remember what it was you were in the middle of doing? We discovered later that many other people had invented it. Gain a Master of Computer Vision whilst working on real-world projects with industry experts. The people that invented so many of these ideas that you learn about in this course or in this specialization. And if we could, if we had a dot matrix printer attached to us, then pixels would come out, but what's in between isn't pixels. Graphic Violence ; Graphic Sexual Content ; movies. If your intuitions are not good, it doesn't matter what you do. Well, generally I think almost every course will warm you up in this area (Deep Learning). Great contribution to the community. I guess my main thought is this. >> That's good, yeah >> Yeah, over the years, I've seen you embroiled in debates about paradigms for AI, and whether there's been a paradigm shift for AI. And it provided the inspiration for today, tons of people use ReLU and it just works without- >> Yeah. I think the idea that thoughts must be in some kind of language is as silly as the idea that understanding the layout of a spatial scene must be in pixels, pixels come in. >> Well, I still plan to do it with supervised learning, but the mechanics of the forward paths are very different. But that seemed to me actually lacking in ways of distinguishing when they said something false. And the information that was propagated was the same. And they don't understand that sort of, this showing computers is going to be as big as programming computers. >> And your comments at that time really influenced my thinking as well. - Understand the key parameters in a neural network's architecture This repo includes demos for Coursera course "Neural Networks for Machine Learning". Geoffrey Hinton with Nitish Srivastava Kevin Swersky . But you don't think of bundling them up into little groups that represent different coordinates of the same thing. Course Original Link: Neural Networks for Machine Learning — Geoffrey Hinton COURSE DESCRIPTION About this course: Learn about artificial neural networks and how they're being used for machine learning, as applied to speech and object recognition, image segmentation, modeling language and human motion, etc. >> I see, right, in fact, maybe a lot of students have figured this out. And we actually did some work with restricted Boltzmann machines showing that a ReLU was almost exactly equivalent to a whole stack of logistic units. Artificial Neural Network, Backpropagation, Python Programming, Deep Learning, Excellent course !! So weights that adapt rapidly, but decay rapidly. >> Some of it, I think a lot of people in AI still think thoughts have to be symbolic expressions. So I now have a little Google team in Toronto, part of the Brain team. And then when I was very dubious about doing, you kept pushing me to do it, so it was very good that I did, although it was a lot of work. Although it wasn't until we were chatting a few minutes ago, until I realized you think I'm the first one to call you that, which I'm quite happy to have done. So the idea should have a capsule for a mouth that has the parameters of the mouth. >> I see. Choose from hundreds of free courses or pay to earn a Course or Specialization Certificate. So one example of that is when and I first came up with variational methods. So it was a directed model and what we'd managed to come up with by training these restricted Boltzmann machines was an efficient way of doing inferences in Sigmoid belief nets. Best Coursera Courses for Deep Learning. That's a completely different way of using computers, and computer science departments are built around the idea of programming computers. Each course focuses on a particular area of communication in English: writing emails, speaking at meetings and interviews, giving presentations, and networking online. 1a - Why do we need machine learning 1b - What are neural networks 1c - Some simple models of neurons 1d - A simple example of learning 1e - Three types of learning If your intuitions are good, you should follow them and you'll eventually be successful. And I think this idea that if you have a stack of autoencoders, then you can get derivatives by sending activity backwards and locate reconstructionaires, is a really interesting idea and may well be how the brain does it. If you want to break into cutting-edge AI, this course will help you do so. And I went to California, and everything was different there. Geoffrey Hinton with Nitish Srivastava Kevin Swersky . In this course, you will learn the foundations of deep learning. But slow features, I think, is a mistake. This Specialization builds on the success of the Python for Everybody course and will introduce fundamental programming concepts including data structures, networked application program interfaces, and databases, using the Python programming language. If you looked at the reconstruction era, that reconstruction era would actually tell you the derivative of the discriminative performance. GitHub is where people build software. I think what's happened is, most departments have been very slow to understand the kind of revolution that's going on. Toma cursos de los mejores instructores y las mejores universidades del mundo. But you have to sort of face reality. If you want to produce the image from another viewpoint, what you should do is go from the pixels to coordinates. And from the feature vectors, you could get more of the graph-like representation. The value paper had a lot of math showing that this function can be approximated with this really complicated formula. Yeah, cool, yeah, in fact, to give credit where it's due, whereas a deep learning AI is creating a deep learning specialization. And he was very impressed by the fact that we showed that backprop could learn representations for words. >> And then what you can do if you've got that, is you can do something that normal neural nets are very bad at, which is you can do what I call routine by agreement. I think it'd be very good at getting the changes in viewpoint, very good at doing segmentation. Deep Learning Specialization. And once you got to the coordinate representation, which is a kind of thing I'm hoping captures will find. And there were other people who'd developed very similar algorithms, it's not clear what's meant by backprop. >> I think that's a very, very general principle. So it hinges on, there's a couple of key ideas. Geoffrey Hinton Nitish Srivastava, Kevin Swersky Tijmen Tieleman Abdel-rahman Mohamed Neural Networks for Machine Learning Lecture 12b More efficient ways to get the statistics ADVANCED MATERIAL: NOT ON QUIZZES OR FINAL TEST . So how did you get involved in, going way back, how did you get involved in AI and machine learning and neural networks? That was almost completely ignored. >> So when I was at high school, I had a classmate who was always better than me at everything, he was a brilliant mathematician. And somewhat strangely, that's when you first published the RMS algorithm, which also is a rough. >> Variational altering code is where you use the reparameterization tricks. >> Yeah, I see yep. So we actually trained it on little triples of words about family trees, like Mary has mother Victoria. And we'd showed a big generalization of it. So the idea is in each region of the image, you'll assume there's at most, one of the particular kind of feature. And I think some of the algorithms you use today, or some of the algorithms that lots of people use almost every day, are what, things like dropouts, or I guess activations came from your group? So you're changing the weighting proportions to the preset outlook activity times the new person outlook activity minus the old one. So you just train it to try and get rid of all variation in the activities. >> And, I guess, one other idea of Quite a few years now, over five years, I think is capsules, where are you with that? We cover the basics of how one constructs a program from a series of simple instructions in Python. So, can you share your thoughts on that? I figured out that one of the referees was probably going to be Stuart Sutherland, who was a well known psychologist in Britain. Welcome Geoff, and thank you for doing this interview with And so I guess he'd read about Lashley's experiments, where you chop off bits of a rat's brain and discover that it's very hard to find one bit where it stores one particular memory. So I think we should beat this extra structure. >> To represent, right, rather than- >> I call each of those subsets a capsule. This Specialization helps you improve your professional communication in English for successful business interactions. Which was that a concept is how it relates to other concepts. >> Okay, so I'm back to the state I'm used to being in. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. So you can use a whole bunch of neurons to represent different dimensions of the same thing. So let's suppose you want to do segmentation and you have something that might be a mouth and something else that might be a nose. As part of this course by, hope to not just teach you the technical ideas in deep learning, but also introduce you to some of the people, some of the heroes in deep learning. And by about 1993 or thereabouts, people were seeing ten mega flops. In the early 90s, Bengio showed that you can actually take real data, you could take English text, and apply the same techniques there, and get embeddings for real words from English text, and that impressed people a lot. But using the chain rule to get derivatives was not a novel idea. And we had a lot of fights about that, but I just kept on doing what I believed in. in Management from the University of Illinois, and learn critical leadership and business skills for the next step in your executive career path. As far as I know, their first deep learning MOOC was actually yours taught on Coursera, back in 2012, as well. >> To different subsets. And maybe that puts a natural limiter on how many you could do, because replicating results is pretty time consuming. And EN was a big algorithm in statistics. Hi Thanks for the A2A ! And that may be true for some researchers, but for creative researchers I think what you want to do is read a little bit of the literature. So the simplest version would be you have input units and hidden units, and you send information from the input to the hidden and then back to the input, and then back to the hidden and then back to the input and so on. >> I think that's basically, read enough so you start developing intuitions. And what I mean by true recursion is that the neurons that is used in representing things get re-used for representing things in the recursive core. And then, trust your intuitions and go for it, don't be too worried if everybody else says it's nonsense. And over the years, I've come up with a number of ideas about how this might work. >> Right, but there is one thing, which is, if you think it's a really good idea, and other people tell you it's complete nonsense, then you know you're really on to something. What the family trees example tells us about concepts • There has been a long debate in cognitive science between two rival theories of what it means to have a concept: The feature theory: A concept is a set of semantic features. >> You worked in deep learning for several decades. So Google is now training people, we call brain residence, I suspect the universities will eventually catch up. It was the first time I'd been somewhere where thinking about how the brain works, and thinking about how that might relate to psychology, was seen as a very positive thing. >> Yeah, it's complicated, I think right now, what's happening is, there aren't enough academics trained in deep learning to educate all the people that we need educated in universities. AT&T Bell Labs (2 day), 1988 ; Apple (1 day), 1990; Digital Equipment Corporation (2 day), 1990 - Be able to build, train and apply fully connected deep neural networks >> Yeah, if it comes out [LAUGH]. The course has no pre-requisites and avoids all but the simplest mathematics. Except they don't understand that half the people in the department should be people who get computers to do things by showing them. No se encontraron resultados para ‘geoffrey hinton’. And in particular, in 1993, I guess, with Van Camp. theimgclist changed the title Preface Link - Geoffrey Hinton course was taken down [Preface] - Geoffrey Hinton's course no longer exists on Coursera … Which is, if you want to deal with changes in viewpoint, you just give it a whole bunch of changes in view point and training on them all. But what I want to ask is, many people know you as a legend, I want to ask about your personal story behind the legend. And it was a lot of fun there, in particular collaborating with David Rumelhart was great. supports HTML5 video. And I think what's in between is nothing like a string of words. And notice something that you think everybody is doing wrong, I'm contrary in that sense. And it looked like the kind of thing you should be able to get in a brain because each synapse only needed to know about the behavior of the two neurons it was directly connected to. There's no point not trusting them. EMBED. It's not a pure forward path in the sense that there's little bits of iteration going on, where you think you found a mouth and you think you found a nose. There just isn't the faculty bandwidth there, but I think that's going to be temporary. >> The variational bands, showing as you add layers. When you finish this class, you will: Podrás conformar y liderar equipos de desarrollo de software de alto desempeño responsables de la transformación digital en las organizaciones. So in the Netflix competition, for example, restricted Boltzmann machines were one of the ingredients of the winning entry. And what we managed to show was the way of learning these deep belief nets so that there's an approximate form of inference that's very fast, it's just hands in a single forward pass and that was a very beautiful result. And then figure out how to do it right. Posted on June 11, 2018. But I didn't pursue that any further and I really regret not pursuing that. And I think the people who thought that thoughts were symbolic expressions just made a huge mistake. If it turns out the back prop is a really good algorithm for doing learning. The COVID-19 crisis has created an unprecedented need for contact tracing across the country, requiring thousands of people to learn key skills quickly. If you work on stuff your advisor's not interested in, all you'll get is, you get some advice, but it won't be nearly so useful. >> I see, great. Get an M.S. Ive seen the course and to be truthful its really not a beginner level course but things you would find in there you wouldn’t find anywhere period . Mathematical & Computational Sciences, Stanford University,, To view this video please enable JavaScript, and consider upgrading to a web browser that. Learn about artificial neural networks and how theyre being used for machine learning, as applied to speech and object recognition, image segmentation, . I think generative adversarial nets are one of the sort of biggest ideas in deep learning that's really new. >> Thank you for inviting me. I mean you have cells that could turn into either eyeballs or teeth. The job qualifications for contact tracing positions differ throughout the country and the world, with some new positions open to individuals wi... Machine learning is the science of getting computers to act without being explicitly programmed. And you could do that in neural net. Te pueden interesar nuestras recomendaciones. >> Yes, it was a huge advance. And I guess that was about 1966, and I said, sort of what's a hologram? Accede a todo lo que necesitas directamente en tu navegador y completa tu proyecto con confianza con instrucciones detalladas. Where as in something like back propagation, there's a forward pass and a backward pass, and they work differently. And you can do back props from that iteration. And then UY Tay realized that the whole thing could be treated as a single model, but it was a weird kind of model. And you try to make it so that things don't change as information goes around this loop. And in that situation, you have to remind the big companies to do quite a lot of the training. © 2020 Coursera Inc. All rights reserved. And a lot of people have been calling you the godfather of deep learning. Offered by HSE University. >> One good piece of advice for new grad students is, see if you can find an advisor who has beliefs similar to yours. Now, if cells can do that, they can for sure implement backpropagation and presumably this huge selective pressure for it. Because if you work on stuff that your advisor feels deeply about, you'll get a lot of good advice and time from your advisor. And then when I went to university, I started off studying physiology and physics. A cutting-edge Computer Science Master’s degree from America’s most innovative university. And it could convert that information into features in such a way that it could then use the features to derive new consistent information, ie generalize. Complete your Bachelor’s Degree with the University of North Texas and transfer your technical or applied community college, technical college, or military credits to save time & money. In 1986, I was using a list machine which was less than a tenth of a mega flop.

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