Neural Networks for Machine Learning by Professor Geoffrey Hinton [Complete] Blitz Kim; 94 videos; 41,885 views; Last updated on May 22, 2019 [Coursera … I still believe that unsupervised learning is going to be crucial, and things will work incredibly much better than they do now when we get that working properly, but we haven't yet. 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. Get Free Coursera Deep Learning Geoffrey Hinton now and use Coursera Deep Learning Geoffrey Hinton immediately to get % off or $ off or free shipping And therefore can hold short term memory. >> So we managed to get a paper into Nature in 1986. So in Britain, neural nets was regarded as kind of silly, and in California, Don Norman and David Rumelhart were very open to ideas about neural nets. And that's a very different way of doing filtering, than what we normally use in neural nets. >> I see, great, yeah. And you had people doing graphical models, unlike my children, who could do inference properly, but only in sparsely connected nets. And from the feature vectors, you could get more of the graph-like representation. And I think what's in between is nothing like a string of words. Spike-timing-dependent plasticity is actually the same algorithm but the other way round, where the new thing is good and the old thing is bad in the learning rule. © 2020 Coursera Inc. All rights reserved. That's a completely different way of using computers, and computer science departments are built around the idea of programming computers. So you can try and do it a little discriminatively, and we're working on that now at my group in Toronto. And stuff like that. So I think the neuroscientist idea that it doesn't look plausible is just silly. 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。 Apprenez Geoffrey Hinton en ligne avec des cours tels que . It has been adapted for the new platform. After it was trained, you then had exactly the right conditions for implementing backpropagation by just trying to reconstruct. Did you do that math so your paper would get accepted into an academic conference, or did all that math really influence the development of max of 0 and x? And in the early days of AI, people were completely convinced that the representations you need for intelligence were symbolic expressions of some kind. La quatrième année du concours ImageNet, presque toutes les équipes utilisaient l'apprentissage profond et obtenaient des gains de précision très intéressants. But you don't think of bundling them up into little groups that represent different coordinates of the same thing. Geoffrey Hinton : index. >> To represent, right, rather than- >> I call each of those subsets a capsule. As part of this course by deeplearning.ai, 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 I was very excited by that. Well, generally I think almost every course will warm you up in this area (Deep Learning). But in recirculation, you're trying to make the post synaptic input, you're trying to make the old one be good and the new one be bad, so you're changing in that direction. Aprende a utilizar los datos para cumplir los objetivos operativos de tu organización. >> Okay, so my advice is sort of read the literature, but don't read too much of it. Where's that memory? >> I think that's basically, read enough so you start developing intuitions. Welcome Geoff, and thank you for doing this interview with deeplearning.ai. And I've been doing more work on it myself. Wenn Sie zum vollständigen Master-Programm zugelassen werden, wird Ihre MasterTrack-Kursarbeit für Ihren Abschluss angerechnet. I guess in 2014, I gave a talk at Google about using ReLUs and initializing with the identity matrix. But I should have pursued it further because Later on these residual networks is really that kind of thing. Sie können auf alles Nötige direkt in Ihrem Browser zugreifen und dank Schritt-für-Schritt-Anleitung Ihr Projekt mit gutem Gefühl zum Abschluss bringen. So the idea should have a capsule for a mouth that has the parameters of the mouth. Lecture 9.4 — Introduction to the full Bayesian approach [Neural Networks for … GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together. Repo for working through Geoffrey Hinton's Neural Network course (https://class.coursera.org/neuralnets-2012-001) - BradNeuberg/hinton-coursera This specialization is intended for anyone who seeks to develop one of the most critical and fundamental digital skills today. So this was when you were at UCSD, and you and Rumelhart around what, 1982, wound up writing the seminal backprop paper, right? 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. I figured out that one of the referees was probably going to be Stuart Sutherland, who was a well known psychologist in Britain. And generative adversarial nets also seemed to me to be a really nice idea. And that memories in the brain might be distributed over the whole brain. Podrás conformar y liderar equipos de desarrollo de software de alto desempeño responsables de la transformación digital en las organizaciones. >> 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. The first model was unpublished in 1973 and then Jimmy Ba's model was in 2015, I think, or 2016. And we had a lot of fights about that, but I just kept on doing what I believed in. And it represents all the different properties of that feature. >> Yes, so from a psychologist's point of view, what was interesting was it unified two completely different strands of ideas about what knowledge was like. A must for every Data science enthusiast. And then when I went to university, I started off studying physiology and physics. So we need to use computer simulations. But I didn't pursue that any further and I really regret not pursuing that. >> I see, good, I guess AI is certainly coming round to this new point of view these days. 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 >> I see, yeah. So in the Netflix competition, for example, restricted Boltzmann machines were one of the ingredients of the winning entry. And then I decided that I'd try AI, and went of to Edinburgh, to study AI with Langer Higgins. National Research University Higher School of Economics, University of Illinois at Urbana-Champaign. This course also teaches you how Deep Learning actually works, rather than presenting only a cursory or surface-level description. In 1986, I was using a list machine which was less than a tenth of a mega flop. >> One good piece of advice for new grad students is, see if you can find an advisor who has beliefs similar to yours. As long as you know there's any one of them. >> [LAUGH] I see, yeah, that's great, yeah. Offered by University of Michigan. Later on I realized in 2007, that if you took a stack of Restricted Boltzmann machines and you trained it up. And then figure out how to do it right. 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. So, around that time, there were people doing neural nets, who would use densely connected nets, but didn't have any good ways of doing probabilistic imprints in them. Deep learning engineers are highly sought after, and mastering deep learning will give you numerous new career opportunities. 1 branch 0 tags. >> Yes, and thank you for doing that, I remember you complaining to me, how much work it was. >> And in fact, a lot of the recent resurgence of neural net and deep learning, starting about 2007, was the restricted Boltzmann machine, and derestricted Boltzmann machine work that you and your lab did. Guter Umgang mit Worten: Redaktionelles Schreiben, Die Aussprache des US-amerikanischen Englisch, Kommunikationsfähigkeiten für Ingenieure, Zertifikate über berufliche Qualifikation, Zertifizierung in Bauwesen und -management, Zertifizierung Maschinelles Lernen für Analytics, Zertifizierung in Innovation Management & Entrepreneurship, Zertifizierung in Nachhaltigkeit und Entwicklung, Zertifizierung KI und maschinelles Lernen, Zertifizierung in Räumliche Datenanalyse und Visualisierung. This course is really great.The lectures are really easy to understand and grasp.The assignment instructions are really helpful and one does not need to know python before hand to complete the course. Which was that a concept is how it relates to other concepts. And somewhat strangely, that's when you first published the RMS algorithm, which also is a rough. Yes, I remember that video. That was almost completely ignored. Cursos de Geoffrey Hinton de las universidades y los líderes de la industria más importantes. >> I see [LAUGH]. I remember doing this once, and I said, but wait a minute. Offered by Arizona State University. >> So that was quite a big gap. It's a feature that has a lot of properties as opposed to a normal neuron and normal neural nets, which has just one scale of property. >> Actually, it was more complicated than that. Kurse beinhalten aufgezeichnete Aufgaben, welche automatisch bewertet und von anderen Kursteilnehmern bewertet werden, außerdem Videovorträge und Diskussionsforen. I think when I was at Cambridge, I was the only undergraduate doing physiology and physics. He is planning to "divide his time between his university research and his work at Google". >> The variational bands, showing as you add layers. But then later on, I got rid of a little bit of the beauty, and it started letting me settle down and just use one iteration, in a somewhat simpler net. So Google is now training people, we call brain residence, I suspect the universities will eventually catch up. And by about 1993 or thereabouts, people were seeing ten mega flops. >> So there was a factor of 100, and that's the point at which is was easy to use, because computers were just getting faster. It turns out people in statistics had done similar work earlier, but we didn't know about that. He was the first winner of the Rumelhart Prize in 2001. And I guess the third thing was the work I did with on variational methods. Offered by HEC Paris. A flexible online program taught by world-class faculty and successful entrepreneurs from one of Europe's leading business schools. What are your current thoughts on that? A research-driven, flexible degree for the next generation of public health leaders. And in particular, in 1993, I guess, with Van Camp. 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. 상위 대학교 및 업계 리더의 Geoffrey Hinton 강좌 온라인에서 과(와) 같은 강좌를 수강하여 Geoffrey Hinton을(를) 학습하세요. >> In, I think, early 1982, David Rumelhart and me, and Ron Williams, between us developed the backprop algorithm, it was mainly David Rumelhart's idea. Except they don't understand that half the people in the department should be people who get computers to do things by showing them. Then for sure evolution could've figured out how to implement it. You and Hinton, approximate Paper, spent many hours reading over that. There were two different phases, which we called wake and sleep. >> One other topic that I know you follow about and that I hear you're still working on is how to deal with multiple time skills in deep learning? And in that situation, you have to remind the big companies to do quite a lot of the training. So it hinges on, there's a couple of key ideas. And then, trust your intuitions and go for it, don't be too worried if everybody else says it's nonsense. And I went to talk to him for a long time, and explained to him exactly what was going on. And I guess that was about 1966, and I said, sort of what's a hologram? I mean you have cells that could turn into either eyeballs or teeth. So, can you share your thoughts on that? >> I think that's a very, very general principle. So I now have a little Google team in Toronto, part of the Brain team. So when I arrived he thought I was kind of doing this old fashioned stuff, and I ought to start on symbolic AI. 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? And you staying out late at night, but I think many, many learners have benefited for your first MOOC, so I'm very grateful to you for it, so. For more cool AI stuff, follow me at https://twitter.com/iamvriad. 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. Discriminative training, where you have labels, or you're trying to predict the next thing in the series, so that acts as the label. I'm not sure if I need to go to the course knowing that, but I guess I will need to watch some other lectures (luckily you have some courses on your top five that I can probably learn more about those). So you're changing the weighting proportions to the preset outlook activity times the new person outlook activity minus the old one. >> Variational altering code is where you use the reparameterization tricks. And once you got to the coordinate representation, which is a kind of thing I'm hoping captures will find. >> To different subsets. The COVID-19 crisis has created an unprecedented need for contact tracing across the country, requiring thousands of people to learn key skills quickly. Absolvieren Sie Kurse von den besten Kursleitern und Universitäten weltweit. 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. - liusida/geoffrey-hinton-course-demos Since we last talked, I realized it couldn't possibly work for the following reason. If you want to break into cutting-edge AI, this course will help you do so. Mathematical & Computational Sciences, Stanford University, deeplearning.ai, To view this video please enable JavaScript, and consider upgrading to a web browser that. with! Later on, Joshua Benjo, took up the idea and that's actually done quite a lot of more work on that. And use a little bit of iteration to decide whether they should really go together to make a face. And if you give it to a good student, like for example. And more recently working with Jimmy Ba, we actually got a paper in it by using fast weights for recursion like that. >> I think that at this point you more than anyone else on this planet has invented so many of the ideas behind deep learning. Tag: Geoffrey Hinton. We cover the basics of how one constructs a program from a series of simple instructions in Python. It is not a continuation or update of the original course. I'm actually curious, of all of the things you've invented, which of the ones you're still most excited about today? >> Very early word embeddings, and you're already seeing learned features of semantic meanings emerge from the training algorithm. And you have a capsule for a nose that has the parameters of the nose. And you try to make it so that things don't change as information goes around this loop. Mejora tu capacidad para tomar decisiones en los negocios con la Maestría en Inteligencia Analítica de Datos de UniAndes. And it provided the inspiration for today, tons of people use ReLU and it just works without- >> Yeah. >> So this is 1986? And so I think thoughts are just these great big vectors, and that big vectors have causal powers. There may be some subtle implementation of it. >> 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. And he showed it to people who worked with him, called the brothers, they were twins, I think. >> Yeah, I think many of the senior people in deep learning, including myself, remain very excited about it. And I have a very good principle for helping people keep at it, which is either your intuitions are good or they're not. How bright is it? And for many years it looked just like a curiosity, because it looked like it was much too slow. So how did you get involved in, going way back, how did you get involved in AI and machine learning and neural networks? Provided there's only one of them. !Neural!Networks!for!Machine!Learning!! >> Right, that's why you did all that. 1. What advice would you have for them to get into deep learning? Maybe you do, I don't feel like I do. Best Coursera Courses for Deep Learning. Anyone with moderate computer exp... Erlangen Sie mittels eines von einem Fachexperten angeleiteten interaktiven Erlebnisses in unter zwei Stunden eine berufsrelevante Kompetenz, die Sie bereits heute zum Einsatz bringen können. - Be able to build, train and apply fully connected deep neural networks So it's about 40 years later. And then to decipher whether to put them together or not, you get each of them to vote for what the parameters should be for a face. If you want to break into cutting-edge AI, this course will help you do so. So in 1987, working with Jay McClelland, I came up with the recirculation algorithm, where the idea is you send information round a loop. Explore our catalog of online degrees, certificates, Specializations, & MOOCs in data science, computer science, business, health, and dozens of other topics. Gain a Master of Computer Vision whilst working on real-world projects with industry experts. Thanks a lot for Prof Andrew and his team. Paul Werbos had published it already quite a few years earlier, but nobody paid it much attention. Kevin!Swersky! Unser Modulsystem ermöglicht es Ihnen, jederzeit online zu lernen und bei Abschluss Ihrer Kursaufgaben Punkte zu erzielen. And he was very impressed by the fact that we showed that backprop could learn representations for words. What are your, can you share your thoughts on that? His certificate of election for the Royal Society reads: Geoffrey E. Hinton is internationally distinguished for his work on artificial neural nets, especially how they can be designed to learn without the aid of a human teacher.. supports HTML5 video. Seemed to me like a really nice idea. >> You might as well trust your intuitions. If it turns out the back prop is a really good algorithm for doing learning. But using the chain rule to get derivatives was not a novel idea. But you actually find a transformation from the observables to the underlying variables where linear operations, like matrix multipliers on the underlying variables, will do the work. And he then told me later what they said, and they said, either this guy's drunk, or he's just stupid, so they really, really thought it was nonsense. 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. Geoffrey Hinton Kurse von führenden Universitäten und führenden Unternehmen in dieser Branche. Ob Sie eine neue Karriere einschlagen oder den Verlauf Ihrer aktuellen Karriere ändern möchten, Zertifikate über berufliche Qualifikationen von Coursera bereiten Sie auf Ihre jeweiligen Aufgaben vor. >> Yes. I'm hoping I can make capsules that successful, but right now generative adversarial nets, I think, have been a big breakthrough. Posted on June 11, 2018. One is about how you represent multi dimensional entities, and you can represent multi-dimensional entities by just a little backdoor activities. And you could look at those representations, which are little vectors, and you could understand the meaning of the individual features. Il fait partie de l'équipe Google Brain et est professeur au département d'informatique de l'Université de Toronto. This course aims to teach everyone the basics of programming computers using Python. And so the question was, could the learning algorithm work in something with rectified linear units? 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. >> Yes, it was a huge advance. I think it'd be very good at getting the changes in viewpoint, very good at doing segmentation. >> I see, right, so rather than FIFO learning, supervised learning, you can learn this in some different way. Nothing can get better than this course from Professor Andrew Ng. If you want to produce the image from another viewpoint, what you should do is go from the pixels to coordinates. It's weird because I do understand markov models somehow so I thought it wouldn't be so hard, but it doesn't look like Geoffrey explained what is a RNN. So I think that's the most beautiful thing. I think what's happened is, most departments have been very slow to understand the kind of revolution that's going on. The basic idea is right, but you shouldn't go for features that don't change, you should go for features that change in predictable ways. I'm actually really curious, how has your thinking, your understanding of AI changed over these years? And by showing the rectified linear units were almost exactly equivalent to a stack of logistic units, we showed that all the math would go through. And in psychology they had very, very simple theories, and it seemed to me it was sort of hopelessly inadequate to explaining what the brain was doing. >> That was one of the cases where actually the math was important to the development of the idea. His aim is to discover a learning procedure that is efficient at finding complex structure in large, high-dimensional datasets and to show that this is how the brain learns … 485 People Used View all course ›› in Management from the University of Illinois, and learn critical leadership and business skills for the next step in your executive career path. >> I see, and last one on advice for learners, how do you feel about people entering a PhD program? And because of that, strings of words are the obvious way to represent things. And then I gave up on that and tried to do philosophy, because I thought that might give me more insight. And to capture a concept, you'd have to do something like a graph structure or maybe a semantic net. If you are looking for a job in AI, after this course you will also be able to answer basic interview questions. And you want to know if you should put them together to make one thing. So my department refuses to acknowledge that it should have lots and lots of people doing this. Hinton was elected a Fellow of the Royal Society (FRS) in 1998. >> You worked in deep learning for several decades. And it was a lot of fun there, in particular collaborating with David Rumelhart was great. And what you want, you want to train an autoencoder, but you want to train it without having to do backpropagation. I usually advise people to not just read, but replicate published papers. And the weights that is used for actually knowledge get re-used in the recursive core. >> And the idea is a capsule is able to represent an instance of a feature, but only one. So we managed to make EN work a whole lot better by showing you didn't need to do a perfect E step. 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. >> So I think the most beautiful one is the work I do with Terry Sejnowski on Boltzmann machines. It feels like your paper marked an inflection in the acceptance of this algorithm, whoever accepted it. because you used the neurons for the recursive core. >> Yes, so actually, that goes back to my first years of graduate student. You take your measurements, and you're applying nonlinear transformations to your measurements until you get to a representation as a state vector in which the action is linear. >> I was really curious about that. >> I see, why do you think it was your paper that helped so much the community latch on to backprop? >> Over the years I've heard you talk a lot about the brain. >> Right, yes, well, as you know, that was because you invited me to do the MOOC. It's just none of us really have almost any idea how to do it yet. You look at it and it just doesn't feel right. But you have to sort of face reality. >> I eventually got a PhD in AI, and then I couldn't get a job in Britain. 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. How fast is it moving? Inspiring advice, might as well go for it. And they don't understand that sort of, this showing computers is going to be as big as programming computers. Learn to address the challenges of a complex world with a Master of Public Health degree. What color is it? >> I'm actually working on a paper on that right now. As far as I know, their first deep learning MOOC was actually yours taught on Coursera, back in 2012, as well. So what advice would you have? One fun fact about RMSprop, it was actually first proposed not in an academic research paper, but in a Coursera course that Jeff Hinton had taught on Coursera many years ago. The first talk I ever gave was about using what I called fast weights. This 5-course certificate, developed by Google, includes innovative curriculum designed to prepare you for an entry-level role in IT support. Geoffrey Hinton with Nitish Srivastava Kevin Swersky . And then when people tell you, that's no good, just keep at it. 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. So to begin with, in the mid 80s, we were using it for discriminative learning and it was working well.
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