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StnbUNn92vA | that we're making really good progress with that and I'm looking forward to doing some demonstrations of it as soon as as soon as I can I'm gonna get myself into trouble if I name any kind of a date because also get held to it but I would like to be demonstrating it before before the end of this year and for audience members who don't know what tera node is it is an enterprise class | 4,117 | 4,143 | https://www.youtube.com/watch?v=StnbUNn92vA&t=4117s | Bitcoin SV: The Massively Scaled Blockchain to Meet Developer Needs—Jimmy Nguyen & Steve Shadders | |
StnbUNn92vA | version of the Bitcoin SV node software that is designed to support as the name suggests terabyte size blocks you know your megabytes plus for massive massive scale Steve and team leading at first initially to reconstruct the Bitcoin node software from the ground up using a microservices architectural approach to create far more efficiencies well look for more information about that coming | 4,143 | 4,169 | https://www.youtube.com/watch?v=StnbUNn92vA&t=4143s | Bitcoin SV: The Massively Scaled Blockchain to Meet Developer Needs—Jimmy Nguyen & Steve Shadders | |
StnbUNn92vA | in the near future when will the courses for bitcoin engineer start I don't have an exact date and the but I know we're hoping to launch our partnership with a tech University for a massive online open course sometime in the fall of this year we're on October hoping and then our Bitcoin association for more online education curriculum we're hoping to get some of that play | 4,169 | 4,199 | https://www.youtube.com/watch?v=StnbUNn92vA&t=4169s | Bitcoin SV: The Massively Scaled Blockchain to Meet Developer Needs—Jimmy Nguyen & Steve Shadders | |
StnbUNn92vA | and beer do you have a timetable for change in a DAA the difficulty adjustment algorithm Tim Donovan gosh all these probe questions Steve what do you have to say about that I I get asked this one a lot and honestly scratched my head sometimes wondering why people are so interested in it because it doesn't really impact the for the day that they user experience all that much the one | 4,199 | 4,229 | https://www.youtube.com/watch?v=StnbUNn92vA&t=4199s | Bitcoin SV: The Massively Scaled Blockchain to Meet Developer Needs—Jimmy Nguyen & Steve Shadders | |
StnbUNn92vA | thing that is notable about Bitcoin SB is it actually really doesn't matter if we go for an hour or even two hours without a block because when one eventually gets found everything just gets cleared out in one one hit so but the answer to that question is it's it's dependent on transaction volume because the olds 2016 blaack difficulty adjustment algorithm creates a | 4,229 | 4,254 | https://www.youtube.com/watch?v=StnbUNn92vA&t=4229s | Bitcoin SV: The Massively Scaled Blockchain to Meet Developer Needs—Jimmy Nguyen & Steve Shadders | |
StnbUNn92vA | vulnerability for a lo hash rate chain which we call chain death attack whereby someone comes in with large amounts of hash rate from somewhere else pushes the difficulty way way way up and then goes away and if you're in a situation where it takes 24 hours to find a block well that two-week period is actually measured in blocks so it becomes 2016 times 24 hours and that's not an ideal | 4,254 | 4,280 | https://www.youtube.com/watch?v=StnbUNn92vA&t=4254s | Bitcoin SV: The Massively Scaled Blockchain to Meet Developer Needs—Jimmy Nguyen & Steve Shadders | |
StnbUNn92vA | situation to be in but when you've got large amounts of transaction volume coming in and fee revenue it changes that dynamic completely so we've been doing some studies internally on what different levels could create what sorts of scenarios in terms of if someone tried to pull off that sort of attack just to determine where the where the kind of safe level is and I've got some | 4,280 | 4,304 | https://www.youtube.com/watch?v=StnbUNn92vA&t=4280s | Bitcoin SV: The Massively Scaled Blockchain to Meet Developer Needs—Jimmy Nguyen & Steve Shadders | |
StnbUNn92vA | answers on that I'm there are security concerns which is why I'm not just bloating everything out but I know about right now and I think carefully about about how to approach this from a public discussion point of view because it doesn't in public discussion but I think their short answer is it's definitely not going to be this year my guess would be it would | 4,304 | 4,328 | https://www.youtube.com/watch?v=StnbUNn92vA&t=4304s | Bitcoin SV: The Massively Scaled Blockchain to Meet Developer Needs—Jimmy Nguyen & Steve Shadders | |
StnbUNn92vA | be probably some time probably not before middle of next year and maybe you know towards the end of year but as soon as we actually have enough data to be able to say yeah it's the it's the right time to do it or at least plan it and start setting a date then then we're talking about it publicly and getting as much public feedback as as possible we are like coming to the end of this time | 4,328 | 4,362 | https://www.youtube.com/watch?v=StnbUNn92vA&t=4328s | Bitcoin SV: The Massively Scaled Blockchain to Meet Developer Needs—Jimmy Nguyen & Steve Shadders | |
StnbUNn92vA | boxed the answer a last question or one that question then everything which comes after that is something I will forward to you and see that we get answers published so everybody can see that so on the last question is what do you see as the next steps required for the wider use of micro payments hmm steam you want to tackle that sure so a lot of the building blocks are either | 4,362 | 4,402 | https://www.youtube.com/watch?v=StnbUNn92vA&t=4362s | Bitcoin SV: The Massively Scaled Blockchain to Meet Developer Needs—Jimmy Nguyen & Steve Shadders | |
StnbUNn92vA | coming her either in place or there they're just sort of coming into place and a lot of this comes down to that animation that I showed you near the beginning of my presentation which governs the flow of a payment interaction between between two parties so there's multiple parts of that there's how do I find the person to connect to them directly which female is | 4,402 | 4,425 | https://www.youtube.com/watch?v=StnbUNn92vA&t=4402s | Bitcoin SV: The Massively Scaled Blockchain to Meet Developer Needs—Jimmy Nguyen & Steve Shadders | |
StnbUNn92vA | one of the potential will be at one of the solutions to that there's how do I get the transaction directly to the miner promptly and find out that it's definitely being accepted the merchant API is a component to that the floral of that even happens though there's the the negotiation between the the merchants are selling and the customer and that's part of it 270 so all of | 4,425 | 4,451 | https://www.youtube.com/watch?v=StnbUNn92vA&t=4425s | Bitcoin SV: The Massively Scaled Blockchain to Meet Developer Needs—Jimmy Nguyen & Steve Shadders | |
StnbUNn92vA | these components are coming together and of course they need to be implemented not just by miners or any particular service but all of the bollocks as well so they can operate happily together so completing the work on all of those steps I think basically defined what that pathway is how long have the tape and I'm not sure because it requires work to be done by a bunch of people | 4,451 | 4,474 | https://www.youtube.com/watch?v=StnbUNn92vA&t=4451s | Bitcoin SV: The Massively Scaled Blockchain to Meet Developer Needs—Jimmy Nguyen & Steve Shadders | |
StnbUNn92vA | that can't me who I don't I can't compel them but there seems to be a pretty strong appetite amongst many of the wallet applications to get on board with this and a lot of works already been done so I'm pretty happy with burgers that's the thing technical behind-the-scenes answer I think from a practical industry and because it's super effective the answer really is | 4,474 | 4,496 | https://www.youtube.com/watch?v=StnbUNn92vA&t=4474s | Bitcoin SV: The Massively Scaled Blockchain to Meet Developer Needs—Jimmy Nguyen & Steve Shadders | |
StnbUNn92vA | developers like all of you on this one program who are listening designing creating conceiving great applications has drive people to want to have some functionality that uses micropayment something like coda or developers can make money you're an API marketplace so it's the ingenuity and the creativity of developer creating really powerful applications have real utility things | 4,496 | 4,523 | https://www.youtube.com/watch?v=StnbUNn92vA&t=4496s | Bitcoin SV: The Massively Scaled Blockchain to Meet Developer Needs—Jimmy Nguyen & Steve Shadders | |
StnbUNn92vA | people want to use what do they even know it runs on Bitcoin or not that leads to real usage so we encourage people to just get building that's what we really believe in in the Bitcoin ESP world building a blockchain and the digital currency with real values we build complete useful applications that will make people want to use them and that will drive micro payments and | 4,523 | 4,546 | https://www.youtube.com/watch?v=StnbUNn92vA&t=4523s | Bitcoin SV: The Massively Scaled Blockchain to Meet Developer Needs—Jimmy Nguyen & Steve Shadders | |
StnbUNn92vA | Deitrick cool so thank you Jimmy thank you Steve for your cool presentation and for all the ideas you planted inside my head I'm sure I will be there on July 18 and 19 and see you again and hope to we'll stay in touch so for all of you who haven't already registered this could be our last chance but it's not how say bye-bye to everybody here on click continue | 4,546 | 4,591 | https://www.youtube.com/watch?v=StnbUNn92vA&t=4546s | Bitcoin SV: The Massively Scaled Blockchain to Meet Developer Needs—Jimmy Nguyen & Steve Shadders | |
733m6qBH-jI | Okay. Hey, everyone, looks like we're on. So as usual, if you have not yet, um, please enter your SUID so that we know you're here in this room. Um, so actually, can you hear me okay at the back? Is it okay? Oh, yes, is the volume okay at the back? All right. No one's responding. Yes, okay. All right. [LAUGHTER] Thank you. Okay. So, um, what I want to do today is, | 0 | 35 | https://www.youtube.com/watch?v=733m6qBH-jI&t=0s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | um, share with you two things. You know, we're approaching the end of quarter. Uh, I hope you guys are looking forward to, to the Thanksgiving break, um, next week. Um, actually and I guess we have a lot of home viewers, but those us- those of you that are viewing this from outside California, know that we're all feeling really bad air here in California. So I hope, if you're somebody watching at home you have better air wherever you are. | 35 | 56 | https://www.youtube.com/watch?v=733m6qBH-jI&t=35s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | Um, uh, but, uh, what I hope to do today is give you some advice that will set you up for the future, uh, so if even beyond the conclusion of CS230. And in particular, what I want to do today is, um, share with you some advice on how to read research papers, uh, because, you know, deep learning is evolving fast enough that even though you've learned a lot of foundations of | 56 | 81 | https://www.youtube.com/watch?v=733m6qBH-jI&t=56s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | deep learning and learned a lot of tips and tricks and probably know better than many practitioners how to actually get deep, deep learning algorithms to work already. Uh, when you're working on specific applications whether in computer vision or natural language processing or speech recognition or something else, um, for you to be able to efficiently figure out | 81 | 100 | https://www.youtube.com/watch?v=733m6qBH-jI&t=81s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | the academic literature on key parts of, uh, the, the deep learning world, will help you keep on developing and, you know, staying on top of ideas even as they evolve over the next several years or maybe decade. So first thing I wanna do is, uh, give you advice on how, uh, when say, when I'm trying to master a new body of literature, how I go about that and hope that those techniques would be | 100 | 121 | https://www.youtube.com/watch?v=733m6qBH-jI&t=100s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | useful to help you be more efficient in how you read research papers. And then a second thing is, in previous offerings of this course, one request from a lot of students was just advice for navigating a career in machine learning. And so in the second half of today, I want to share some thoughts with you on that. Okay, so it turns out that- so I guess two topics reading research papers, right? | 121 | 150 | https://www.youtube.com/watch?v=733m6qBH-jI&t=121s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | Um, and, uh, then second career advice in machine learning. So it turns out that, uh, you know, reading research papers is one of those things that a lot of P- PhD students learn by osmosis, right? Meaning that if you're a PhD student and you see, you know, a few professors or see other PhD students do certain things, then you might try to pick it up by osmosis. | 150 | 175 | https://www.youtube.com/watch?v=733m6qBH-jI&t=150s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | But I hope today to accelerate your efficiency in how you acquire knowledge yourself from the, uh, from the a- academic literature, right? And so let's say that this is the area you want to become good at, let's say you want to build that, um, speech recognition, right? Let's turn this off now. Let's say you want to build that, um, speech recognition system that we talked about, | 175 | 197 | https://www.youtube.com/watch?v=733m6qBH-jI&t=175s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | the Robert turn on and the desk lamp. All right. Um, this is what I've read- this is the sequence of steps I recommend you take, uh, which is first: [NOISE] compile lists of papers and the- and by papers, I mean, both research papers often posted on arXiv, onto the Internet, but also plus Medium posts, um, [NOISE] you know, what maybe some occasional GitHub post although those are rarer. | 197 | 231 | https://www.youtube.com/watch?v=733m6qBH-jI&t=197s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | But whatever texts or learning resources you have. And then, um, what I usually do is end up skipping around the list. All right. So if I'm trying to master a new body of knowledge, say you want to learn the most speech recognition systems, this is what it feels like to read a set of papers, which is maybe you initially start off with five papers and if on the horizontal axis, | 231 | 255 | https://www.youtube.com/watch?v=733m6qBH-jI&t=231s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | I plot, you know, 0 percent to 100 percent read/understood, right? The way it feels like reading these papers is often read, you know, ten percent of each paper or try to quickly skim and understand each of these papers. And if based on that you decide that paper number two is a dud, right, other, other, other authors even cite it and say boy they, they sure got it wrong or you read it, and it just doesn't make sense. | 255 | 285 | https://www.youtube.com/watch?v=733m6qBH-jI&t=255s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | Then go ahead and forget it. And, uh, as you skip around to different papers, uh, you might decide that paper three is a really seminal one and then spend a lot of time to go ahead and read and understand the whole thing. And based on that, you might then find a sixth paper from the citations and read that and go back and flesh out your understanding on paper four. | 285 | 308 | https://www.youtube.com/watch?v=733m6qBH-jI&t=285s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | And then find a paper seven and go and read that all the way to the conclusion. Um, but this is what it feels like as you, you know, assemble a list of papers and skip around and try to, uh, um, master a body of literature around some topic that you want to learn. And I think, um, some rough guidelines, you know, if you read 15 to 20 papers I think you have a basic understanding of an- of an area like, | 308 | 333 | https://www.youtube.com/watch?v=733m6qBH-jI&t=308s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | maybe good enough to do some work, apply some algorithms. Um, if you read, um, 50 to 100 papers in an area like speech recognition and, and kind of understand a lot of it, then that's probably enough to give you a very good understanding of an area, right? You might, know- I'm, I'm always careful about when I say you're mastering a subject but you read 50 to 100 papers on speech recognition, | 333 | 355 | https://www.youtube.com/watch?v=733m6qBH-jI&t=333s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | you have a very good understanding of speech recognition. Or if you're interested in say domain adaptation, right? By the time you've read 50 or 100 papers, you have a very good understanding of, of a subject like that. But if you read 5 to 20 papers, it's probably enough for you to implement it but maybe not, not sure this is enough for you to do research or | 355 | 372 | https://www.youtube.com/watch?v=733m6qBH-jI&t=355s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | be really at the cutting edge but these are maybe some guidelines for the volume of reading you should aspire to if you want to pick up a new area. I'll take one of the subjects in CS230 and go more deeply into it, right? Um, now [NOISE] how do you read one paper? And, um, I hope most of you brought your laptops. So what I'm gonna do is describe to you how I read one paper, | 372 | 399 | https://www.youtube.com/watch?v=733m6qBH-jI&t=372s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | and then after that I'm actually going to ask all of you to, you know, download the paper online and just take, I don't know, uh, uh, take, take a few minutes to read a paper right here in class and see how far you can get understanding of a research paper in just minutes right, right here in class. Okay. So when reading one paper. So the, the, the bad way to read the paper is | 399 | 422 | https://www.youtube.com/watch?v=733m6qBH-jI&t=399s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | to go from the first word until the last word, right? This is a bad way to- when you read a paper like this. Oh, and by the way, actually here, I'll tell you what my real life is like. So, um, I actually pretty much everywhere I go, whenever I backpack this is my actual folder. I don't want to show- this is my actual folder of unread papers. So pretty much everywhere I go, | 422 | 445 | https://www.youtube.com/watch?v=733m6qBH-jI&t=422s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | I actually have a paper, you know, a stack of papers is on my personal reading list. This is actually my real life. I didn't bring this to show you. This is in my backpack all the time. Ah, and I think that- these days on my team at Landing AI and Deeplearning.ai, I personally lead a reading group where I lead a discussion about two papers a week. Uh, but to select two papers, | 445 | 466 | https://www.youtube.com/watch?v=733m6qBH-jI&t=445s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | that means I need to read like five or six papers a week to select two, you know, to present and discuss at the Landing AI and Deeplearning.ai meeting group. So this is my real life, right? And how I try to stay on top of the literature and, and I have a- I'm doing a lot. If I can find the time, if I can find the time to read a couple of papers a week, hopefully all of you can too. | 466 | 484 | https://www.youtube.com/watch?v=733m6qBH-jI&t=466s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | Uh, but when I'm reading a paper, uh, this is, this is how I recommend you go about it which is, do- do- don't go for the first word and read until the last word, uh, instead, uh, take multiple passes through the paper [NOISE]. Right? Um, and so, you know, step one is, uh, [NOISE] read the title, [NOISE] the abstract, um, [NOISE] and also the figures. Um, especially in Deep Learning, | 484 | 521 | https://www.youtube.com/watch?v=733m6qBH-jI&t=484s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | there are a lot of research papers where sort of the entire paper is summarized in one or two figures in the figure caption. So, um, so sometimes, just by reading the title, abstract and, you know, the key neural network architecture figure that just describes what the whole papers are, and maybe one or two of the experiments section. You can sometimes get a very good sense of what the whole paper is about without, | 521 | 543 | https://www.youtube.com/watch?v=733m6qBH-jI&t=521s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | you know, hardly reading any of the texts in the paper itself, right? Tha- tha- that's the first pass. Um, second pass, I would tend to read more carefully, um, [NOISE] the intro, the conclusions, um, look carefully at all the figures again, [NOISE] and then skim, um, the rest, and you know, um, I- I don't know how many of you have published academic papers, | 543 | 573 | https://www.youtube.com/watch?v=733m6qBH-jI&t=543s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | but, uh, when people publish academic papers, um, part of, you know, the publication process is, uh, convincing the reviewers that your paper is worthy for acceptance. And so what you find is that the abstract, intro and conclusion is often when the authors try to summarize their work really, really carefully, uh, to make a case, to make a very clear case to the reviewers as to why, | 573 | 595 | https://www.youtube.com/watch?v=733m6qBH-jI&t=573s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | you know, they think their paper should be accepted for publication. And so because of that, you know, maybe slightly not, slightly unusual incentive, the intro and conclusion and abstract often give a very clear summary of what's the paper actually about. Um, and depending on, [NOISE] um, and again, just to be, you know, b- bluntly honest with you guys, um, | 595 | 623 | https://www.youtube.com/watch?v=733m6qBH-jI&t=595s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | the related work section is useful if you want, sometimes is useful if you want to- to understand related work and figure out what's- what are the most important works in the papers. But the first time you read this, you might skim or even skip, skim the related work section. It turns out, unless you're already familiar with the literature, if this is a body of work that you're not that familiar with, | 623 | 642 | https://www.youtube.com/watch?v=733m6qBH-jI&t=623s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | the related work section is sometimes almost impossible to understand. Uh, and again, since I'm being very honest with you guys, sometimes, related work section is when the authors try to cite everyone that could possibly be reviewing the paper and to make them feel good, uh, uh, and then hopefully accept the paper. And so related work sessions is sometimes written in funny ways, right? | 642 | 661 | https://www.youtube.com/watch?v=733m6qBH-jI&t=642s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | Um, and then, uh, [NOISE] step 3, I would often read the paper, but, um, [NOISE] just skip the math [NOISE], right? Um, and four, read the whole thing, uh, but skip parts that don't make sense, [NOISE] right? You know, um, I think that, uh, one thing that's happened many times in the research is that, I mean, the papers will tend to be cutting edge research, | 661 | 710 | https://www.youtube.com/watch?v=733m6qBH-jI&t=661s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | and so when, uh, we publish things, we sometimes don't know what's really important and what's not important, right? So there are- there are many examples of- of well known, highly cited research papers where some of it was just great stuff and some of it, you know, turned out to be unimportant. But at the time the paper was written, the authors did not know, | 710 | 729 | https://www.youtube.com/watch?v=733m6qBH-jI&t=710s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | every- no one on the planet knew what was important and what was not important. And maybe one example. Um, the LeNet-5 paper, right? Sample paper by Yann LeCun. Part of it was phenomenal, just established a lot of the foundations of ConvNets. And so it's, uh, one of the most incredibly influential papers. But you go back and read that paper, an- another sort of, whole half of the paper was about other stuff, right? | 729 | 750 | https://www.youtube.com/watch?v=733m6qBH-jI&t=729s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | Transducers and so on that is much less used. And so- and so it's fine if you read a paper and some of it doesn't make sense because it's not that unusual, or sometimes it just happens that, um, great research means we're publishing things at the boundaries of our knowledge and sometimes, ah, uh, the stuff you see, you know, we'll realize five years in the future that that | 750 | 769 | https://www.youtube.com/watch?v=733m6qBH-jI&t=750s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | wasn't the most important thing after all, right? Or that- what was the key part of the algorithm, maybe it wasn't what the authors thought. And so sometimes the past papers don't make sense. It's okay to skim it initially and move on, right? Uh, uh, unless you're trying to do a pe- unless you're trying to do deep research and really need to master it, then go ahead and spend more time. | 769 | 787 | https://www.youtube.com/watch?v=733m6qBH-jI&t=769s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | But if you're trying to get through a lot of papers, then, you know, then- then it's just prioritizing your time, okay? Um, and so, ah, just a few last things and then I'll ask you to practice this yourself with a paper, right? Um, you know, I think that when you've read and understood the paper, um, [NOISE] these are questions to try to keep in mind. And when you read a paper in a few minutes, | 787 | 816 | https://www.youtube.com/watch?v=733m6qBH-jI&t=787s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | maybe try to answer these questions: what do the authors try to accomplish? And what I hope to do in a few minutes is ask you to, uh, download a paper off the Internet, read it, and then, um, try to answer these questions and discuss your answer to these questions with- with- with your peers, right? With others in the class, okay? Um, what were the key elements, [NOISE] | 816 | 860 | https://www.youtube.com/watch?v=733m6qBH-jI&t=816s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | what can you use yourself, and um, [NOISE] okay? So I think if you can answer these questions, hopefully that will reflect that you have a pretty good understanding of the paper, okay? Um, and so what I would like you to do is, um, pull up your laptop and then so you- there- there's actually a- so I think on the, uh, ConvNet videos, right? On, um, the- the different AI ConvNet videos on Coursera, | 860 | 907 | https://www.youtube.com/watch?v=733m6qBH-jI&t=860s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | you learned a bit about, um, ah, well, various neural network architectures up to ResNets. And it turns out that there's another, uh, follow-on piece of work that maybe builds on some of the ideas of ResNets, which is called DenseNets. So, what I'd like you to do is, um, oh, and- and so what I'd like you to do is actually try this. [NOISE] And when I'm reading a paper, | 907 | 928 | https://www.youtube.com/watch?v=733m6qBH-jI&t=907s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | [NOISE] again, in the earlier stages, don't get stuck on the math, just go ahead and skim the math, and read the English text where you get through faster. Ah, and maybe one of the principles is, go from the very efficient high information content first, and then go to the harder material later, right? That's why often I just skim the math and I don't- if I don't understand some of the equation just move on, | 928 | 947 | https://www.youtube.com/watch?v=733m6qBH-jI&t=928s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | and then only later go back and, and really try to figure out the math more carefully, okay? So what I'd like you to do is take on a- I want you to take, um, uh, uh, wonder if, uh, let's- let's- let's try, let's- let's- have you take seven minutes. I was thinking maybe one- one minute per page is quite fast and, um, [NOISE] search for this paper, [NOISE] Densely Connected Convolutional Neural Net- Networks, | 947 | 978 | https://www.youtube.com/watch?v=733m6qBH-jI&t=947s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | by Gao Huang et al, okay? I want you guys to take out your laptops, uh, search for this paper, er, download it. You should find this on arXiv, um, A-R-X-I-V, right? And, uh, and this is also, so sometimes we also call this Dense Nets, I guess. And go ahead and, uh, take, why don't you take like seven minutes to read this paper and I'll let you know when the time is passed, | 978 | 1,011 | https://www.youtube.com/watch?v=733m6qBH-jI&t=978s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | and then after that time, um, I'd like you to, you know, discuss with your, with, with the others, right, what, wha- what you think are the answers, especially the first two. Because the other two you can leave for later. Why don't you go ahead and take a few minutes to do that now, and then I'll let you know when, um, sort of like, seven minutes have passed and then you can | 1,011 | 1,031 | https://www.youtube.com/watch?v=733m6qBH-jI&t=1011s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | discuss your answers to these with your friends, okay? [NOISE] All right guys. So, um, anyone with any thoughts or insights, surprises, or thoughts from this? So, now you've spent 11 minutes on this paper, right? Seven minutes reading, four minutes discussing. It was a very, very short period of time, but any, any thoughts? What do you think of the paper? Come on, you-all, you-all just spent a lot of time sitting around, discussing with each other. | 1,031 | 1,065 | https://www.youtube.com/watch?v=733m6qBH-jI&t=1031s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | Wha- wha- what did people think about the time you spent trying to read the paper? Actually, did you feel you, how, actually, r- raise your hand if you feel, you know, you've kind of understood the main concepts in the paper just a bit. Okay, yeah, like, two-thirds of you, many of you. And, actually, what did you think of the figures? Wow, people are really less energetic today than usual [inaudible] | 1,065 | 1,109 | https://www.youtube.com/watch?v=733m6qBH-jI&t=1065s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | So I think this is one of those papers where the, the paper is almost entirely summarized in figures one and two, all right. I think if you [inaudible] um, if you look at Figure One and the caption there and Figure Two on page three and the caption there and understand those two figures, those really convey, you know, 80 percent of the idea of the paper, right? | 1,109 | 1,133 | https://www.youtube.com/watch?v=733m6qBH-jI&t=1109s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | Um, and I think that, uh, um, couple of other tips. So, um, it turns out that as you read these papers with practice, you do get faster. So, um, for example, Table One, uh, on page four, right, the, you know, this mess of the table on top. This is a pretty common format or a format like this is how a lot of authors use to describe a network architecture, especially in computer vision. | 1,133 | 1,162 | https://www.youtube.com/watch?v=733m6qBH-jI&t=1133s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | So one of the things you find as well is that, um, the first time you see something like Table One it just looks really complicated. But by the time you've read a few papers in a similar format, you will look at Table One and go, "Oh, yeah, got it." You know, this is, this is, this is the DenseNet-121 versus DenseNet-169 architecture, and you will more quickly pick up those things. | 1,162 | 1,182 | https://www.youtube.com/watch?v=733m6qBH-jI&t=1162s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | And so another thing you'll find is that, um, reading these papers actually gets better with practice, because you see different authors use different ways or similar ways of expressing themselves, and you get used to that. You'll actually be faster and faster at, uh, implementing these, um, at, at, at understanding these ideas. And I think, I know these days when I'm reading a paper like this, | 1,182 | 1,201 | https://www.youtube.com/watch?v=733m6qBH-jI&t=1182s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | it maybe takes me about half an hour to, to feel like, and I, I know I gave you guys seven minutes when I thought I would need maybe half an hour to figure out a paper like this. Uh, um, uh, and I think, uh, for a more c- uh, I find that, uh, it's not unusual for people relatively new to machine learning to need maybe an hour to kind of, you know, really understand a paper like this. | 1,201 | 1,221 | https://www.youtube.com/watch?v=733m6qBH-jI&t=1201s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | Um, and then I know I'm pretty experienced in machine learning, so I'm down to maybe half an hour for a paper like this, maybe even 20 minutes if it's a really easy one. But there are some outliers, so I have some colleagues who sometimes stumble across a really difficult paper. You need to chase down all the references and learn a lot of other stuff. So sometimes you come across a paper that takes you three or | 1,221 | 1,241 | https://www.youtube.com/watch?v=733m6qBH-jI&t=1221s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | four hours or even longer to really understand it, but, uh, but I think depending on how much time you want to spend per week reading papers, um, you could actually learn, you know, learn a lot, right, um, uh, doing what you just did by maybe spending half an hour per paper, an hour a paper rather than seven minutes, right? Um, so, all right. I feel like, uh, yeah, | 1,241 | 1,265 | https://www.youtube.com/watch?v=733m6qBH-jI&t=1241s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | and so, I, I think it's great, and, and, and notice that I've actually not said anything about the content of this paper, right? So whatever you guys just learned, that was all you. I had nothing to do with it. So, yeah, like you have the ability to go and learn this stuff by yourself. You don't need me anymore, right? [LAUGHTER] Um, so just the last few comments. | 1,265 | 1,287 | https://www.youtube.com/watch?v=733m6qBH-jI&t=1265s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | Um, let's see. So the other things I get asked, questions I get is, uh, you know, where, where do you go? The deep learning field evolves so rapidly. So where, where do you go, uh, to? So if you're trying to master a new body of knowledge, definitely do web searches, and there are often good blog posts on, you know, here are the most important papers in speech recognition. | 1,287 | 1,308 | https://www.youtube.com/watch?v=733m6qBH-jI&t=1287s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | There are lots of great resources there. And then the other thing you, I don't know, a lot of people try, want to do is try to keep up with the state of the art of deep learning even as it's evolving rapidly. And so, um, I, I- I'll just tell you where I go to keep up with, um, you know, discussions, announcements. And surprisingly, Twitter is becoming an impo- surprisingly important place for researchers to find out about, um, new things. | 1,308 | 1,332 | https://www.youtube.com/watch?v=733m6qBH-jI&t=1308s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | Um, there's an ML Subreddit, it is actually pretty good. Um, lot of noise, but many important pieces of work do get mentioned there. Uh, some of the top machine-learning con- conferences are NIPS, ICML, and ICLR, right? And so whenever these conferences come around, take a look and glance throughout these, the titles, see if there's something that interests you. | 1,332 | 1,356 | https://www.youtube.com/watch?v=733m6qBH-jI&t=1332s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | And then I think I'm, I'm fortunate I guess to have, um, friends, you know, uh, both colleagues here at Stanford as well as colleagues in several other teams I work with that, um, uh, that just tell me when there's a cool paper, I guess. But I think with, here within Stanford or among your workplace, for those of you taking this at SCPD, you can form a community that shares interesting papers. | 1,356 | 1,378 | https://www.youtube.com/watch?v=733m6qBH-jI&t=1356s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | So a lot of the groups I have on Slack and we regularly Slack each other or send, send each other, uh, text messages on the Slack messaging system, where you find interesting papers, and tha- tha- that's been great for me actually. Um, yeah, oh, and, and, and Twitter, let's see. Kian is, I follow Kian, you can follow him too. Uh, This is me, Andrew Y Ng, right? | 1,378 | 1,403 | https://www.youtube.com/watch?v=733m6qBH-jI&t=1378s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | Um, I probably don't Slack up papers as often as I do. But if you look at, and you can also look at who we follow, and there are a lot of good researchers, uh, that, that will share all these things online. Oh, and, um, there, there are, there's a bunch of people that also use a website called Arxiv Sanity. Um, I don't as much sometimes, um, but there's lots of resources like that, all right? Um. | 1,403 | 1,428 | https://www.youtube.com/watch?v=733m6qBH-jI&t=1403s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | All right. Cool. So just two last tips for how to read papers and get good at this. Um, so to more deeply understand the paper, uh, some of the papers will have math in it. Uh, and, actually, if you read the, I don't know, you all learned about Batch Norm, right? In the second module's videos. If you read the Batch Norm paper, it's actually one of the harder papers to read. | 1,428 | 1,457 | https://www.youtube.com/watch?v=733m6qBH-jI&t=1428s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | There's a lot of math in the derivation of Batch Norm but there are papers like that. And if you want to make sure you understand the math here's what I would recommend, which is, read through it, take detailed notes and then see if you can re-derive it from scratch. So if you want to deeply understand the math of an algorithm from like, you know, Batch Norm or the details of back-prop or something the good practice. | 1,457 | 1,483 | https://www.youtube.com/watch?v=733m6qBH-jI&t=1457s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | And I think a lot of sort of a theory- theoretical science and mathematics Ph.D students will use a practice like this. You just go ahead and read the paper. Make sure you understand it and then to make sure you really, really understand it put, put, put aside the results and try to re-derive the math yourself from scratch. And if you can start from a blank piece of paper | 1,483 | 1,504 | https://www.youtube.com/watch?v=733m6qBH-jI&t=1483s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | and re-derive one of these algorithms from scratch, then that's a good sign that you really understood it. When I was a Ph. D student I did this a lot, right? That you know I would read a textbook or read a paper or something and then put aside whatever I read and see if I could re-derive it from scratch starting from a blank piece of paper as only if I could do that, | 1,504 | 1,522 | https://www.youtube.com/watch?v=733m6qBH-jI&t=1504s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | and I would you know feel like yep, I think I understand this piece of math. And it turns out if you want to do this type of math yourself is your ability to derive this type of math, re-derive this type of math, that gives you the ability to generalize, to derive new novel pieces of math yourself. So I think I actually learned a lot of math, for several machine learning by doing this. | 1,522 | 1,542 | https://www.youtube.com/watch?v=733m6qBH-jI&t=1522s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | And just by re-deriving other people's work that allowed me to learn how to derive my own novel algorithms. And actually sometimes you go to the art galleries, right? They go to the Smithsonian. You see these art students, you know, sitting on the floor copying the great artworks, the great paintings you know, painted by the masters centuries ago. And so I think just as today there are art students sitting in or the de Young Museum | 1,542 | 1,568 | https://www.youtube.com/watch?v=733m6qBH-jI&t=1542s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | or whatever or and I was at the Getty Museum in LA a few months ago. You actually see these art students you know, copying the work of the masters. And I think a lot of the ways that you want to become good at the math of machine learning yourself, this is a good way to do it. It's time-consuming but then you can become good at it that way. And same thing for codes, right? | 1,568 | 1,588 | https://www.youtube.com/watch?v=733m6qBH-jI&t=1568s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | I think the simple lightweight version one of learning would be to download and run the open source code if you can find it, and a deeper way to learn this material is to re-implement it from scratch. Right, it is easy to download an open sourcing and run it and say ooh, it works. But if you can re-implement one of these algorithms from scratch then that's a strong sign that you've really understood this algorithm. | 1,588 | 1,621 | https://www.youtube.com/watch?v=733m6qBH-jI&t=1588s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | Okay? Um, alright. And then longer term advice. Right. You know, for user keep on learning and keep on getting better and better, the more important thing is for you to learn steadily not for you to have a focus intense activity you know, like over Thanksgiving you read 50 papers over Thanksgiving and then you're done for the rest your life. It doesn't work like that, right? | 1,621 | 1,661 | https://www.youtube.com/watch?v=733m6qBH-jI&t=1621s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | And I think you're actually much better off reading two or three papers a week for the next year than you know, cramming everything right over, over one long weekend or something. Actually in education we actually know that spaced repetition works better than cramming so the same same thing, same body of learning. If you learn a bit every week and space it out | 1,661 | 1,680 | https://www.youtube.com/watch?v=733m6qBH-jI&t=1661s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | you actually have much better long-term retention than if you try to cram everything in short-term so there's, there's a very solid result that we know from pedagogy and how the human brain works. So, so if you're able to- so so again the way I, my life is my backpack. I just always have a few papers with me. And I find that I can, I read almost everything on the tablet. | 1,680 | 1,700 | https://www.youtube.com/watch?v=733m6qBH-jI&t=1680s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | Almost everything on iPad, but I find that research papers one of the things where the ability to flip between pages and skim I still find more efficient on paper. So I read almost nothing on paper these days except for research papers, but that's just me. Your mileage may vary. Maybe something else will work better for you. Okay? All right. So let's see, that's it for reading research papers, | 1,700 | 1,722 | https://www.youtube.com/watch?v=733m6qBH-jI&t=1700s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | I hope that while you're in CS230, you know, if some of you find some cool papers or if you go further for the DenseNet paper and find an interesting result there. Go ahead and post on Piazza if any of you want to start a reading group of other friends here at Stanford you know, encourage you to look around class, find, find, find a group here on campus or with among | 1,722 | 1,744 | https://www.youtube.com/watch?v=733m6qBH-jI&t=1722s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | your CS230 classmates or your work colleagues. For those of you taking this on SCPD so that you can all keep studying the literature and learning and helping each other along. Okay? So that's it for reading papers. The second thing we're gonna do today is just give some longer-term advice on navigating a career in machine learning, right? Any questions about this before I move on? | 1,744 | 1,773 | https://www.youtube.com/watch?v=733m6qBH-jI&t=1744s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | Okay. Cool. All right. But I hope that was useful. Some of this I wish I had known when I was a first-year PhD student but c'est la vie. Alright. Let's see. Can we turn on the lights please? Alright. So kind of in response to requests from early- students in earlier versions of the class, before we, you know as we approach the end of the quarter, want to give some advice to how to navigate a career in machine learning, right? | 1,773 | 1,803 | https://www.youtube.com/watch?v=733m6qBH-jI&t=1773s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | So today machine learning there are so many options to do, so many exciting things. So how do you, you know, what do you want to do? So I'm going to assume that most of you will want to do one of two things, right? At some point you know you want to get the job, right? Maybe a job that does work in machine learning and including a faculty position for those of you who want to be a professor. | 1,803 | 1,832 | https://www.youtube.com/watch?v=733m6qBH-jI&t=1803s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | But I guess eventually most people end up with a job I think I guess there are other alternatives but but and some of you want to go on to more advanced graduate studies although even after you get your PhD at some point, most people do get a job after the PhD. And by job I mean either in a big company, you know, or a or a startup, right? But regardless of the details of this, | 1,832 | 1,859 | https://www.youtube.com/watch?v=733m6qBH-jI&t=1832s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | I think- I hope most of you want to do important work. Okay. So what I'd like to do today is break, you know, this into, how do you find a job or join a Ph.D program or whatever that lets you do important work. And I want to break this discussion into two steps. One is just how do you get a position? How do you get that job offer or how do you get that offer of | 1,859 | 1,894 | https://www.youtube.com/watch?v=733m6qBH-jI&t=1859s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | admission to the Ph.D program or admission to the master's program or whatever you wanna do. And then two is selecting a position. Between going to this university versus that university or between taking on the job in this company versus that company. What are the ones that will tend to set you up for success, for long-term personal success and career success? | 1,894 | 1,916 | https://www.youtube.com/watch?v=733m6qBH-jI&t=1894s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | And really I hope that, by the way, I hope that all of these are just tactics to let you do important work right and this, I hope that's what you want to do. So you know, what do recruiters look for? And I think just to keep the language simpler I'm going to pretend that, I'm just gonna talk about finding a job. And but a lot of that very similar things apply for PhD programs | 1,916 | 1,942 | https://www.youtube.com/watch?v=733m6qBH-jI&t=1916s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | is just instead of saying recruiters I would say admissions committees right then it's actually some of this is, but let me just focus on the job scenario. So most recruiters look for technical skills. So for example, there are a lot of machine learning interviews that will ask you questions like, you know, where would you use gradient descent or batch gradient descent or | 1,942 | 1,964 | https://www.youtube.com/watch?v=733m6qBH-jI&t=1942s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | stochastic gradient descent, you know, descent and what happens when the mean batch size is too large or too small, right? So there are companies, many companies today asking questions like that in the interview process. Or can you explain difference between an LCM and GIGO and when would you use GIGO? So you really get questions like that in many job interviews today. | 1,964 | 1,981 | https://www.youtube.com/watch?v=733m6qBH-jI&t=1964s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | And so recruiters looking for ML skills as well as, and so you will often be quizzed on ML skills as well as your coding ability, right? And then beyond your- and I think Silicon Valley's become quite good at giving people the assessments to test for real skill in machine learning engineering and in software engineering. And then the other thing that recruiters will look for, | 1,981 | 2,008 | https://www.youtube.com/watch?v=733m6qBH-jI&t=1981s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | that many recruiters will look for is meaningful work. And in particular, um, uh, you know, there are some candidates that apply for jobs that have very, um, theoreticals. They're very academic skills meaning you can answer all the quiz questions about, you know, what is Batch Norm? Can you derive the [inaudible] for this? But unless you've actually shown that you can apply this in a meaningful setting, | 2,008 | 2,040 | https://www.youtube.com/watch?v=733m6qBH-jI&t=2008s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | it's harder to convince a company or a recruiter that you know not just the theory, but that you know how to actually make this stuff work. And so, um, having done meaningful work using machine learning is a very strong, is a very desirable candidate, I think, to a lot of companies. Kind of work experience. And I think really, whether you've done, whether you've done something meaningful, | 2,040 | 2,061 | https://www.youtube.com/watch?v=733m6qBH-jI&t=2040s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | um, reassures that, you know, that you can actually do work, right? There's not just you can answer quiz questions, that you know how to implement learning algorithms that work. Um, and, and maybe, um, uh, yeah, right. Um, and then many recruiters actually look for your ability to keep on learning new skills and stay on top of machine learning even as it evolves as well. | 2,061 | 2,084 | https://www.youtube.com/watch?v=733m6qBH-jI&t=2061s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers | |
733m6qBH-jI | Okay. And so a very common pattern for the, um, successful, you know, AI engineers, say, machine learning engineers, would be the following, where if on the horizontal axis, I plot different areas. So, you might learn about machine learning. Learn about deep learning. Learn about probabilistic graphical models. Learn about NLP. Learn about computer vision and so on for other areas of AI and machine learning. | 2,084 | 2,113 | https://www.youtube.com/watch?v=733m6qBH-jI&t=2084s | Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers |
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