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NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | There's more seats on the side, but people are walking in late. | 0 | 7 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | So, just to make sure you're in CS231N, the deep learning on your network class for visual recognition. | 7 | 23 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | Anybody in the wrong class? Good. All right, so welcome and happy new year, happy first day of winter break. | 23 | 34 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | So, this class CS231N, this is the second offering of this class when we have literally doubled our enrollment from 180 people last time we offered to about 350 of you signed up. | 34 | 51 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | Just a couple of words to make us all legally covered. We are video recording this class. | 51 | 60 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | So, you know, if you're uncomfortable about this, for today just go behind a camera or go to a corner that the camera is not gonna turn. | 60 | 72 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | But we are going to send out forms for you to fill out in terms of allowing a video recording. So, that's just one bit of housekeeping. | 72 | 85 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | So, all right, my name is Fei-Fei Li, I'm a professor at the computer science department. | 85 | 92 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | So, this class, I'm co-teaching with two senior graduate students and one of them is here is Andre Kapathy. Andre, can you just say hi to everybody? | 92 | 103 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | We have, well, I don't think Andre needs too much introduction. A lot of you probably know his work, follow his blog, his Twitter follower. | 103 | 114 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | Andre has way more followers than I do. He's very popular. And also Justin Johnson, who is still traveling internationally but will be back in a few days. | 114 | 126 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | So, Andre and Justin will be picking up the bulk of the lecture teaching. And today I will be giving the first lecture but as you probably can see that I'm expecting a newborn ratio speaking of weeks. | 126 | 142 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | So, you'll see more of Andre and Justin in lecture time. We will also introduce a whole team of TAs towards the end of this lecture. | 142 | 153 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | Again, people who are looking for seats, if you go out of that door and come back, there is a whole bunch of seats on this side. | 153 | 162 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | So, for this lecture we're going to give an introduction of the kind of problems we work on and the tools we'll be learning. | 162 | 176 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | So, again, welcome to CS231 and this is a vision class. It's based on a very specific modeling architecture called Neur Network and even more specifically, mostly on convolutional Neur Network. | 176 | 195 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | And a lot of you hear this term maybe through a popular press article or coverage we tend to call this the Deep Learning Network. | 195 | 208 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | But, vision is one of the fastest growing field of artificial intelligence. In fact, Cisco has estimated and we are on day four of this by 2016, which we already have arrived, more than 85% of the internet, cyber space data is in the form of pixels. | 208 | 236 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | Or what they call multimedia. So, we basically have entered an age of vision, of images and videos. And why is this so? | 236 | 250 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | Well, partly, a large extent is because of the explosion of both the internet as a carrier of data, as well as sensors. We have more sensors than the number of people on Earth these days. | 250 | 266 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | Every one of you is carrying some kind of smartphones, digital cameras and cars running on the street with the cameras. So, the sensors have really enabled the explosion of visual data on the internet. | 266 | 285 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | But, visual data or pixel data is also the hardest data to harness. So, if you have heard my previous talks and some other talks by computer vision professors, we call this the dark matter of the internet. | 285 | 308 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | Why is this the dark matter? Just like the universe is consisted of 85% dark matter, dark energy. It's this matters energy that is very hard to observe. We can infer it by mathematical models in the universe. | 308 | 324 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | On the internet, these are the matters. Pixel data are the data that we don't know. We have a hard time grasping the contents. Here's one very simple spec for you to consider. | 324 | 337 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | So, today, YouTube servers for every 60 seconds, we have more than 150 hours of videos uploaded onto YouTube servers. For every 60 seconds, think about the amount of data. | 337 | 357 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | There's no way that human eyes can sift through this massive amount of data and make annotations labeling it and describe the contents. | 357 | 373 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | So, think from the perspective of the YouTube team or Google company. If they want to help us to search, index, manage, and of course, for their purpose, put an advertisement or whatever, manipulate the content of the data, we're at loss. | 373 | 392 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | Because nobody can hand annotators. The only hope we can do this is through vision technology to be able to label the objects, find the scenes, find the frames, locate where that basketball video where Kobe Bryant is making that awesome shot. | 392 | 412 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | So, these are the problems that we are facing today. The massive amount of data and the challenges of the dark matter. | 412 | 422 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | So, computer vision is a field that touches upon many other fields of studies. So, I'm sure that even sitting here, sitting here, many of you come from computer science, but many of you come from biology, psychology, | 422 | 439 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | are specializing in natural language processing, or graphics, or robotics, or medical imaging, and so on. | 439 | 447 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | So, as a field, computer vision is really a truly interdisciplinary field. The problems we work on, the models we use, touches on engineering, physics, biology, psychology, computer science, and mathematics. | 447 | 464 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | So, just a little bit of a more personal touch. I am the director of the computer vision lab at Stanford. In our lab, I work with graduate students and postdocs, and even other graduate students on a number of topics, and most dear to our own research, who some of them, you know, the Andre Justin come from my lab, a number of TAs come from my lab. | 464 | 493 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | We work on machine learning, which is a super set of deep learning. We work a lot on cognitive science, and neuroscience, as well as the intersection between an LPN speech. | 493 | 509 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | So, that's the kind of landscape of computer vision research that my lab works in. | 509 | 518 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | So, also to put things in a little more perspective, what are the computer vision classes that we offer here at Stanford through the computer science department? | 518 | 530 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | Clearly, you're in this class, CS21N, and so, some of you who have never taken computer vision, probably have heard of computer vision for the first time, probably should have already done CS113. | 530 | 547 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | CS113, that's an intro class of previous quarter we offered, and then next quarter, which normally is offered this quarter, but this year is a little shifted. | 547 | 561 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | There's an important graduate level computer vision class called CS231A, offered by Professor Sylvia Severese, who works in robotic and 3D vision. | 561 | 573 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | And a lot of you ask us the question that, do these replace each other, this class CS231N versus CS231A, and the answer is no. | 573 | 591 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | And if you're interested in a broader coverage of tools and topics of computer vision, as well as some of the fundamental topics that relates you to 3D vision, robotic vision, and visual recognition, you should consider taking 231A, that is the more general class. | 591 | 620 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | 231N, which will go into starting today more deeply, focuses on a specific angle of both problem and model. | 620 | 631 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | The model is neural network, and the angle is visual recognition mostly. | 631 | 637 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | Of course, they have a little bit of overlap, but that's the major difference. And next quarter, we also have possibly a couple of advanced seminar level class, but that's still in the formation stage, so you just have to check the syllabus. | 637 | 659 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | So that's the kind of computer vision curriculum we offer this year at Stanford. Anything questions so far? Yes. | 659 | 671 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | Is 131A not a strict requirement for this class, but you'll soon see that if you've never heard of computer vision for the first time, I suggest you find a way to catch up because this class assumes a basic level of understanding of of of computer vision. | 671 | 695 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | You can browse the notes and so on. | 695 | 702 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | So the rest of today is that I will give a very brief broad stroke history of computer vision, and then we'll talk about 231N a little bit in terms of the organization of the class. | 702 | 716 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | I actually really care about sharing with you this brief history of computer vision because you know, you might be here primarily because of your interest in this really interesting tool called deep learning, and this is the purpose of this class, while offering you an index looking and just journey through what this deep learning model is. | 716 | 741 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | But without understanding the problem domain, without thinking deeply about what this problem is, it's very hard for you to go on to be an inventor of the next model that really solves a big problem in vision, or to be developing, | 741 | 763 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | making impactful work in solving a hard problem, and also in general problem domain and model, the modeling tools themselves are never, never fully decoupled, they inform each other, and you see through the history of deep learning a little bit, that the convolution on your network architecture come from the need to solve the problem. | 763 | 792 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | And then vision problem helps the deep learning algorithm to evolve and back and forth, so it's really important to, you know, I want you to finish this course and feel proud that your student of computer vision and of deep learning, so you have this both the tool set and the in depth understanding of how to use the tool set to tackle the problem. | 792 | 821 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | So it's a brief history, but doesn't mean it's a short history, so we're going to go all the way back to 240 million years ago. | 821 | 836 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | So why did I pick this, you know, on the scale of the Earth history, this is a fairly specific range of years. Well, so I don't know if you have heard of this, but this is a very, very curious period of the Earth's history. | 836 | 856 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | And biologists call this the big band of evolution. Before 500, 40 million years ago, the Earth is a very peaceful part of water. I mean, it's pretty big part of water. | 856 | 874 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | So we have very simple organisms. These are like animals that just floats in the water and the way they eat and hang out on a daily basis is, you know, they just float and if some kind of food comes by near their mouth or whatever, they just open the mouth and grab it. | 874 | 899 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | And we don't have too many different types of animals, but something really strange happened around 540 million years. | 899 | 911 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | Suddenly, from the fossils we study, there's a huge explosion of species. The biologists call it speciation. | 911 | 922 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | It's like suddenly, for some reason, something hit the Earth that animals start to diversify and they can get really complex and they start to, you know, to, you start to have predators and prey and then they have all kind of tools to survive. | 922 | 940 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | And what was the triggering force of this was a huge question because people would say, oh, did you know, another set of whatever meteorite hit the Earth or, you know, the environment change. | 940 | 954 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | It turned out one of the most convincing theory is by this guy called Andrew Parker. He's a modern zoologist in Australia, from Australia. He studied a lot the fossils and his theory is that it was the onset of the ice. | 954 | 976 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | So, one of the first trilobites developed an eye and really, really simple light. It's almost like a pinhole camera that just catches light and makes some projections and registers some information from the environment. | 976 | 994 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | Suddenly, life is no longer so mellow because once you have the eye, the first thing you can do is you can go catch food. You actually know where food is. You're not just like blind and floating in the water. | 994 | 1,009 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | And once you can go catch food, guess what? The food better develops and to run away from you. Otherwise, they'll be gone, you know, you're, you're, so the first animal who had, had eyes were like in a, you know, | 1,009 | 1,023 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | in a limited buffet. It's like working at Google. It's, it's just like it has the best time, you know, eating everything they can. But because of this onset of the ice, what we, what the zoologist realized is the biological arms race began. | 1,023 | 1,046 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | Every single animal needs to needs to learn to develop things to survive or to, you know, you, you suddenly have praise and predators and, and all this. And the speciation began. | 1,046 | 1,059 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | So, that's when vision began. 540 million years. And not only vision began, vision was one of the major driving force of the speciation or the big band of evolution. | 1,059 | 1,075 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | Alright, so, so we're not going to follow evolution for too much detail. Another big important work that focus on engineering of vision happened around the Renaissance. | 1,075 | 1,090 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | And of course, it's attributed to this amazing guy Leonardo da Vinci. So, before Renaissance, you know, throughout human civilization, from Asia to Europe to India to Arabic world, we have seen models of cameras. | 1,090 | 1,109 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | So, Aristotle has proposed the camera through the leaves. Chinese philosopher Moots have proposed the camera through a box with a hole. But if you look at the first documentation of really a modern looking camera, it's called camera upscrewer, upscrewer. | 1,109 | 1,130 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | And that is documented by Leonardo da Vinci. I'm not going to get into the details. But this is, you know, you get the idea that there is some kind of lens or at least a hole to capture lights reflected from the real world. | 1,130 | 1,151 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | And then there is some kind of projection to capture the information of the real world image. So, that's the beginning of the modern, you know, engineering of vision. | 1,151 | 1,169 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | And then it started with wanting to copy the world and wanting to make a copy of the visual world. It hasn't got anywhere close to wanting to engineer the understanding of the visual world. Right now, we're just talking about duplicating the visual world. | 1,169 | 1,189 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | So, that's one important work to remember. And of course, after a camera upscrewer, we start to see a whole series of successful, you know, some film gets developed, you know, like Kodak was one of the first companies developing commercial cameras and then we start to have cancorders and all this. | 1,189 | 1,215 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | Another very important, important piece of work that I want you to be aware of as vision student is actually not an engineering work, but a science, science piece of science work that's starting to ask the question is, how does vision work in our biological brain? | 1,215 | 1,237 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | You know, we now know that it took 540 million years of evolution to get a really fantastic visual system in mammals and in humans. But what did evolution do during this time? | 1,237 | 1,255 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | And the kind of architecture did it develop from that simple trilobite to today yours and mine? Well, a very important piece of work happened at Harvard by two at that time young, two very young ambitious postdoc, Hugo and the visual. | 1,255 | 1,275 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | And the thing that I wanted to do is that they used a wake but anesthetized cats and then there was enough technology to build this little needle called electrode to push the electrode into the, the, the, the, the skull is open into the brain of the cat into an area what we already know, | 1,275 | 1,300 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | visual cortex primary visual cortex is an area that neurons do a lot of things for, for visual processing. | 1,300 | 1,309 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | But before you were a visual, we don't really know what primary visual cortex is doing. We just know it's one of the earliest stage other than your eyes, of course, but earliest stage for visual processing and there's tons and tons of neurons working on vision. | 1,309 | 1,327 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | And then we really ought to know what this is because that's the beginning of vision visual process in the brain. | 1,327 | 1,335 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | So they put this electrode into the primary visual cortex and interestingly this is another interesting fact, if I don't drop all my stuff I'll show you. | 1,335 | 1,348 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | Primary visual cortex, the first stage or second, depending on where you come from, I'm being very, very rough, rough here. | 1,348 | 1,358 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | First stage of your cortical visual processing stage is in the back of your brain, not near your eye. | 1,358 | 1,365 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | Okay, it's very interesting because your old factory cortical processing is right behind your nose, your auditory is right behind your ear, but your primary visual cortex is the furthest from your eye. | 1,365 | 1,383 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | And another very interesting fact, in fact, not only the primary, there's a huge area working on vision, almost 50% of your brain is involved in vision. | 1,383 | 1,394 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | Vision is the hardest and most important sensory perceptual cognitive system in the brain. | 1,394 | 1,401 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | You know, I'm not saying anything else doesn't, it's not, useful clearly, but you know, it takes nature this long to develop this sensory system and it takes nature this much real estate space to be used for this system. | 1,401 | 1,419 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | Why? Because it's so important and it's so damn hard. That's why we need to use this much space. | 1,419 | 1,426 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | Okay, back to human viso, they were really ambitious. They want to know what primary visual cortex is doing, because this is the beginning of our knowledge for deep learning neural network. | 1,426 | 1,439 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | So, they were showing cats, so they put the cats in this room and they were recording neural activities. And when I say recording neural activity, they're basically trying to see, you know, if I put the neural electrode here, like to the neurons, to the neurons fire when they see something. | 1,439 | 1,459 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | So, for example, if they show cats, their ideas, if I show this cat a fish, you know, apparently at that time cats eat fish rather than these beings. | 1,459 | 1,473 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | With the cats, neural, like, you know, they're happy and start sending spikes. And the funny thing here is a story of scientific discovery. A scientific discovery takes both luck and care and thoughtfulness. | 1,473 | 1,490 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | They were showing this cat fish, whatever mouse, flower, it just doesn't work. | 1,490 | 1,497 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | The cats, neural, in the primary visual cortex was silent. There was no spiking, a very little spiking, and they were really frustrated. | 1,497 | 1,506 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | But the good news is that there was no computer at that time. So, what they have to do when they show this cat, these stimuli, is they have to use a slight projector. | 1,506 | 1,520 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | So, they put a slide of a fish and then wait till the neuron spike. If the neuron doesn't spike, they take the slide out and put in another slide. | 1,520 | 1,529 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | And then they notice, every time they change slide, like this, this, like, you know, this squareish film, I don't even remember if they use glass or film, but whatever. | 1,529 | 1,541 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | The neuron spikes. That's weird, you know, like the actual mouse and fish and flower didn't drive the neuron, excite the neuron. But the movement of taking the slide out or putting a sliding did excite the neuron. | 1,541 | 1,560 |
NfnWJUyUJYU | CS231n Winter 2016: Lecture1: Introduction and Historical Context | https://youtu.be/NfnWJUyUJYU | 2016-01-04T00:00:00.000000 | It can be the cat is thinking, finally, they're changing the new object for me. So, it turned out there's an edge that's created by the slide that they're changing, right? | 1,560 | 1,575 |
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