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NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | And then one, almost one last model before deep learning model is this feature or feature based model called deformable part model is where we learn parts of the object like parts of a person. | 3,096 | 3,115 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | And we learn how they configure each other, they configure in space and use a support vector machine kind of model to recognize objects like humans and bottles. | 3,115 | 3,133 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | Around this time, that's 2009, 2010, the field of computer vision is matured enough that we're working on this important and hard problem like recognizing pedestrians and recognizing cars. They're no longer a contrived problem. | 3,133 | 3,150 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | Something else was needed is benchmarking because as a field advancing enough, if we don't have good benchmark, then everybody is just publishing papers on a few set of images and it's really hard to really set global standard. | 3,150 | 3,166 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | So one of the most important benchmark is called Pascal VOC object recognition benchmark. It's by a European effort that researchers put together tens of thousands of images from 20 classes of objects. | 3,166 | 3,185 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | And these are one example per object like cats, cows, maybe no cats, dogs, cows, airplanes, bottles, horses, trains and all this. | 3,185 | 3,201 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | And then we used, and then annually our computer vision researchers and labs come to compete on the object recognition task for Pascal object recognition challenge. | 3,201 | 3,216 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | And over the past, through the years, the performance just keeps increasing. And that was when we started to feel excited about the progress of the field. | 3,216 | 3,231 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | At that time, here is a little bit of a closer story close to us is that my lab and my students were thinking, you know, the real world is not about 20 objects. The real world is a little more than 20 objects. | 3,231 | 3,248 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | So following the work of Pascal visual object recognition challenge, we put together this massive, massive project of image net. Some of you might have heard of image net in this class. | 3,248 | 3,263 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | You will be using a tiny portion of image that in some of your assignments, that image that is a data set of 50 million images all cleaned by hand and annotated over 20,000 object classes. | 3,263 | 3,282 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | Don't worry, it's not a graduate student who cleaned it. That would be very scary. It's an Amazon mechanical Turk platform, the crowd sourcing platform. And having said that, graduate student also suffered from, you know, putting together this platform. | 3,282 | 3,300 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | But it's a very exciting data set. And we started to put together competitions annually called image net competition for object recognition. | 3,300 | 3,315 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | And for example, a standard competition of image classification by image net is a thousand object classes over almost 1.5 million images and algorithms compete on the performance. | 3,315 | 3,330 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | So actually, I just heard somebody was on the social media was referring image net challenge as the Olympics of computer vision. That was very flattering. | 3,330 | 3,340 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | But here is something that here is bringing us close to the history making of deep learning. So in, in the, so the image that challenge started in 2010. | 3,340 | 3,356 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | That's actually around the time Pascal, you know, we're colleagues, they told us they're going to start to phase out their challenge of 20 objects. So we faced in the thousand object image challenge. | 3,356 | 3,369 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | And why access is error rate and we started with fairly significant error. And of course, you know, every year the error decreased. But there's a particular year the error really decreased. | 3,369 | 3,388 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | It was cutting half almost is 2012. 2012 is the year that the winning architecture of image net challenge was a convolution on your network model. | 3,388 | 3,405 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | And we'll talk about it. Convolution on your network was not invented in 2012. Despite how all the news make it sound like it's the newest thing around the block. It's not. It was invented back in the 70s, 80s. | 3,405 | 3,420 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | But having a convergence of things will talk about convolution on your network showed its massive power as a high capacity into and training architecture and won the image that challenge by a huge margin. | 3,420 | 3,437 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | And that was quite a historical moment from a mathematical point of view. It wasn't that new, but from an engineering and solving real world point of view. This was a historical moment. That piece of work was covered by, you know, New York Times and all this. | 3,437 | 3,457 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | This is the onset. This is the beginning of the deep learning revolution, if you call it. And this is the premise of this class. So at this point, I'm going to switch. | 3,457 | 3,472 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | So we went through a brief history of commuter vision for 540 million years. And now I'm going to switch to the overview of this class. Is there any other questions? | 3,472 | 3,487 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | All right. So we've talked about, even though it was kind of overwhelming, we talked a lot about many different tasks in computer vision. CS231N is going to focus on the visual recognition problem. | 3,487 | 3,503 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | Also, by in large, especially through most of the foundation lecture, we're going to talk about the image classification problem. But now you know everything we talk about is going to be based on that image that classification setup. | 3,503 | 3,519 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | We will get into other visual recognition scenarios, but the image classification problem is the main problem we will focus on in this class, which means please keep in mind. Visual recognition is not just image classification, right? | 3,519 | 3,536 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | There was 3D modeling, there was perception grouping and segmentation and all this. But that's what we'll focus on. And I don't need to convince you that just even application wise image classification is an extremely useful problem. | 3,536 | 3,552 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | From big commercial internet companies app point of view to startup ideas, you want to recognize objects, you want to recognize food, you want to do online shopping, you want to sort your albums. | 3,552 | 3,569 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | So image classification is can be a bread and butter task for many, many important problems. | 3,569 | 3,580 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | There is a lot of problem that's related to image classification and today I don't expect you to understand the differences. But I want you to hear that throughout this class, we'll make sure you learn to understand the neurons and the details of different flavors of visual recognition. | 3,580 | 3,601 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | What is image classification? What's object detection? What's image captioning? And these have different flavors. For example, you know, while image classification, my focus on a whole big image, object detection might tell you where things exactly are, like where the car is, the pedestrian, or the hammer. | 3,601 | 3,625 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | And where the relationship between objects and so on. So there are nuances and details that you will be learning about in this class. | 3,625 | 3,637 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | And I already said CNN or convolutional neural network is one type of deep learning architecture, but it's the overwhelmingly successful deep learning architecture. And this is the architecture we will be focusing on. And to just go back to the image that challenge. | 3,637 | 3,657 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | So I said the historical year is 2012. This is the year that Alex Kirchevsky and his advisor Jeff Hinton proposed this convolutional neural network. I think it's a seven layer convolutional neural network to win the image that challenge model. | 3,657 | 3,680 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | Before this year, it was a sift feature plus support vector machine architecture. It's still hierarchical, but it doesn't have that flavor of into and learning. | 3,680 | 3,696 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | So fast forward to 2015, the winning architecture is still a convolutional neural network. It's 151 layers by Microsoft Asia researchers and it's called the residual net. | 3,696 | 3,721 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | So I'm not so sure if we're going to cover that and definitely don't expect to know every single layer what they do. Actually, they repeat. So it's not that hard. | 3,721 | 3,733 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | But every year since 2012, the winning architecture of image and that challenge is a deep learning based architecture. | 3,733 | 3,743 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | Like I said, I also want you to respect history. CNN is not invented overnight. There is a lot of influential players today, but there are a lot of people who build the foundation. | 3,743 | 3,759 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | Actually, I don't have the slides. One important name to remember is Kunihiko Fukushima. Kunihiko Fukushima was a Japanese computer scientist who built a model called Neo-Connectron. | 3,759 | 3,773 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | And that was the beginning of the neural network architecture. And Yalankun is also a very influential person. And the groundbreaking work in my opinion of Yalankun was published in the 1990s. | 3,773 | 3,792 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | So that's when mathematicians, which Jeff Hinton, Yalankun's PhD advisor was involved, worked out the back propagation learning strategy, which if this work didn't mean anything, Andre will tell you in a couple of weeks. | 3,792 | 3,810 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | But the mathematical model was worked out in the 80s and the 90s. And this was a Yalankun was working for Bell Labs at AT&T, which is an amazing place at that time. There's no Bell Labs today anymore. | 3,810 | 3,826 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | They were working on really ambitious projects. And he needed to recognize digits because eventually that product was shipped to banks and US Post Office to recognize zip codes and checks. | 3,826 | 3,840 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | And he constructed this convolution on your network. And this is where he's inspired by Hubo and Wizo. He starts by looking at simple edge-like structures of an image. It's not like the whole letter A. It's really just edges. And then layer by layer, he filters these edges, pull them together, filters, pull. And then build this architecture. | 3,840 | 3,867 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | 2012, when Alex Krzyszewski and Jeff Hinton used almost exactly the same architecture to participate in an image-data challenge, there's very few changes. But that became the winning architecture of this. | 3,867 | 3,890 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | So, Andre will tell you more about the detail changes. The capacity, the model did grow a little bit because Moore's law helped us. There's also a very detailed function that changed a little bit of a shape from a sigmoid to a more rectified linear shape. | 3,890 | 3,913 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | And whatever, there's a couple of small changes. But really by enlarged, nothing had changed. Mathematically. But two important things did change. And that drove the deep learning architecture back into its renaissance. One is like as Moore's law. | 3,913 | 3,935 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | And hardware made a huge difference because these are extremely high capacity models. When Yalaku was doing this, it's just painfully slow. | 3,935 | 3,947 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | Because of the bottleneck of computation, he couldn't build this model too big. And once you cannot build it too big, it cannot fully realize its potential. | 3,947 | 3,957 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | From machine learning standpoint, there's overfitting and all these problems you can all solve. But now we have a much faster and bigger capacity microchips and GPUs from Nvidia. | 3,957 | 3,974 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | Nvidia made a huge difference in deep learning history. That we can now train these models in a reasonable amount of time, even if they're huge. | 3,974 | 3,984 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | Another thing I think we do need to take credit for is data. The availability of data. That was the big data itself. It doesn't mean anything if you don't know how to use it. | 3,984 | 4,000 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | But in this deep learning architecture, data become the driving force for high capacity model to enable the end to end training to help avoid overfitting when you have enough data. | 4,000 | 4,014 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | If you look at the number of pixels that machine learning people had in 2012 versus Yalaku had in 1998, it's a huge difference. Orders of magnitude. | 4,014 | 4,029 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | So this is the focus of 231M. But it's also important. One last time I'm going to drill in this idea that visual intelligence does go beyond object recognition. | 4,029 | 4,045 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | I don't want any of you coming out of this course thinking we've done everything. We've solved vision and image net challenge defined the entire space of visual recognition. It's not true. There are still a lot of cool problems to solve. | 4,045 | 4,062 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | For example, dense labeling of an entire scene with perceptual groupings. I know where every single pixel belongs to. That's still an ongoing problem. | 4,062 | 4,076 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | Combining recognition with 3D is a really there's a lot of excitement happening at the intersection of vision and robotics. This is definitely one area of that. And then anything to do with motion, affordance, and this is another big open area of research. | 4,076 | 4,100 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | I put this here because just in this heavily involved in this work, beyond just putting labels on a scene, you actually want deeply understand a picture what people are doing, what are the relationship between objects. | 4,100 | 4,119 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | And we start getting into the relation between objects. This is an ongoing project called Visual Genome in MyLab that just in the number of my students are involved. And this goes far beyond image classification we talked about. | 4,119 | 4,137 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | And what is one of our holy grails? Well, one of the holy grails of the community is to be able to tell a story of a scene. Right? So think about you as a human. You open your eye. The moment you open your eye, you're able to describe what you see. | 4,137 | 4,156 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | And in fact, in psychology experiments, we find that even if you show people this picture for only 500 milliseconds, that's literally half of a second. People can write essays about it. | 4,156 | 4,173 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | We paid them $10 an hour, so they did it. | 4,173 | 4,177 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | It wasn't that long, but I figure if we took place longer, more money, we could write longer essays. But the point is that our visual system is extremely powerful. We can tell stories. | 4,177 | 4,192 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | And I would dream, this is my challenge to Andres' dissertation, that can we give a computer one picture and outcomes a description like this? You know, I work getting there. You'll see work that you give the computer one picture, it gives you one sentence. | 4,192 | 4,212 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | Or you give the computer one picture, it gives you a bunch of short sentences. But we're not here yet, but that's one of the holy grails. And another holy grail is continuing this. | 4,212 | 4,224 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | Continuing this, I think, is summarized really well by Andres' blog is, you know, take a picture like this, right? | 4,224 | 4,233 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | The stories are so refined, there's so much nuance in this picture that you get to enjoy. Not only you recognize the global scene, it would be very boring. | 4,233 | 4,246 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | If all computer can tell you is, man, man, man, room, you know, room, scale, mirror, whatever cabinet locker, that's it. | 4,246 | 4,256 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | You know, here you recognize who they are, you recognize the trick Obama is doing, you recognize the kind of interaction, you recognize the humor, you recognize, there's so much nuance that this is what visual world is about. | 4,256 | 4,270 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | We use our ability of visual understanding to not only survive, navigate, manipulate, but we use it to socialize, to entertain, to understand, to learn the world. And this is where vision, you know, the grand goals of vision is. | 4,270 | 4,290 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | And I don't need to convince you that computer visual technology will make our world a better place, despite some scary talks out there. | 4,290 | 4,304 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | You know, even going on today in industry, as well as research world, we're using computer vision to build better robots, to save lives, to go deep exploring and all this. | 4,304 | 4,318 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | Now, okay, so I have like two minutes, three minutes, five minutes left, great time. Let me introduce the team, and Andre and Justin are the co-instructors with me. T.A. is please stand up, we'll just say hi to everybody. | 4,318 | 4,336 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | Can you like to say your name quickly and what year, don't give a speech, but start with you, your name? 50 or what? | 4,336 | 4,349 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | Okay. I'm a talker, 50s to the belly. I'm part of the fourth year of PhD student in your science. And now I'm setting your master's student in your science? | 4,349 | 4,365 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | I'm 39 and the third year of my PhD with P.K. | 4,365 | 4,368 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | Cool, so these are the heroes behind the scene. | 4,368 | 4,374 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | And so please stay in touch with us, they're two really the best way, and almost I almost wanted to say the only way, and I'll tell you what's the exception, is stay in touch through Piazza, as well as the staff meeting list. | 4,374 | 4,389 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | Anything course related, please do not send any of us personal email, because I'm just going to say this, if you don't hear replies or your issue is not taken care of, because you send a personal email, I'm really sorry, because this is a 300 plus people class. | 4,389 | 4,408 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | Our, this mailing list actually tags our email and help us to process. The only time I respect you to send a personal email mostly to me and Andre and Justin, is confidential personal issues. | 4,408 | 4,425 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | And I understand if you don't want that to be broadcasted to a team of 10 TAs, that's okay, but that should be really, really minimal, the only time that you send us an email. | 4,425 | 4,440 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | And also, you know, just again, I'm going on maternity leave for a few weeks, starting the end of January, so please, if you decide you just want to send an email to me, and it's my like, do you day for a baby? | 4,440 | 4,456 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | I'm not likely going to reply you promptly, sorry about that, priorities. | 4,456 | 4,467 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | So, a couple words about our philosophy, this is, we're not going to get into the details, we really want this to be a very hands-on project. | 4,467 | 4,477 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | And this is really, I give a lot of credit to Justin and Andre, they are extremely good at walking through these hands-on details with you, so that when you come out of this class, you not only have a high level understanding, but you have a thorough, you have a really good ability to | 4,477 | 4,496 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | build your own deep learning code, we want you to be exposed to state-of-the-art material, you're going to be learning things really that's as fresh as 2015, and it'll be fun, you'll get to do things like this, not all the time, but, you know, like turn a picture into a van Gogh or this weird looking thing. | 4,496 | 4,520 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | So, it'll be a fun class in addition to all the important tasks you learn. | 4,520 | 4,529 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | We do have grading policies, these are all on our website, I'm not going to iterate this. | 4,529 | 4,535 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | Again, one thing I want to be very clear, actually two things, what is late policy? | 4,535 | 4,542 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | You are grown-ups, we treat you like grown-ups, we do not take anything at the end of the course, as a whole not professors want me to go to this conference, and I have to have like three more late days. | 4,542 | 4,556 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | No, you are responsible for using your total late days, you have seven late days, you can use them in whatever way you want with zero penalty. | 4,556 | 4,568 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | Beyond those, you have to take a penalty. Again, if there's like really, really exceptional medical family emergency, talk to us on an individual basis, but anything else, conference, deadline, other final exams, you know, like missing cat or whatever, we budgeted that into the seven days. | 4,568 | 4,595 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | Another thing is on our code, this is one thing I have to say with a really straight face, you are in such a privileged institution, you are grown-ups, I want you to be responsible for on our code. | 4,595 | 4,611 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | Every single Stanford student taking this class should know the on our code, if you don't, there's no excuse, you should go back, we take collaboration extremely seriously. | 4,611 | 4,622 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | I almost hate to say that statistically given the class this big, we're going to have a few cases, but I also want you to be an exceptional class. | 4,622 | 4,632 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | Even with the size this big, we do not want to see anything that infringes on academic on our code. So read the collaboration policies and respect that. This is really respecting yourself. | 4,632 | 4,648 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | I think I'm done with, you know, this prerec, you can read it. I'm done with anything I want to say, is there any birdie questions that you feel worth asking? Yes. | 4,648 | 4,662 |