[User] Please transcribe this text

[Assistant] Yeah, that looks pretty good. I could actually just use 0.1. Let's, let's reduce it to 0.05. And, you know.


[User] Please transcribe this text

[Assistant] So to summarize, if you plot a histogram with the data and find that it looks pretty non-Gaussian, it's worth playing around a little bit with different transformations like these to see if you can make your data look a little bit more Gaussian before you feed it to your learning algorithm. Although, even if you don't, it might work okay, but I usually do take the step.


[User] Please transcribe this text

[Assistant] Let's say that this is my unlabeled data, so here I have just one feature x1, and so I'm going to fit a Gaussian to this, and maybe my Gaussian that fit to my data looks like that.


[User] Please transcribe this text

[Assistant] And now, let's say I have an anomalous example. Let's say my anomalous example takes on an x value of, uh, 2.5. So I'm going to plot my anomalous example there.


[User] Please transcribe this text

[Assistant] What I would do is I would actually look at my training examples and look at what went wrong with that particular aircraft engine.


[User] Please transcribe this text

[Assistant] And see if looking at that example can inspire me to come up with a new feature X2 that helps to distinguish between this stat example compared to the rest of my, you know, red examples compared to all of my new ones.


[User] Please transcribe this text

[Assistant] Right, my x1 value is still 2.5, but maybe my x2 value, hopefully it takes on a very large value, like 3.5 over there, or a very small value.


[User] Please transcribe this text

[Assistant] But now if I model my data, I'll find that, uh, my anomaly detection algorithm gives high probability, you know, to data in the central region, slightly lower probability to that, slightly lower probability to that. An example, and then an example that's all the way out there, my algorithm will now give very low probability to that.


[User] Please transcribe this text

[Assistant] To create some new features, to find something unusual about that aircraft engine and use that to create a new feature so that, uh, with this new feature it becomes easier to distinguish the anomalies from your good examples.


[User] Please transcribe this text

[Assistant] So let's use our example again of monitoring the computers in a data center. And so, you know, lots of machines, maybe thousands or tens of thousands of machines in a data center. And we want to know if one of the machines, one of our computers is acting up, doing something strange.


[User] Please transcribe this text

[Assistant] So let's say I think, let's say I have a suspicion that one of the failure cases is if


[User] Please transcribe this text

[Assistant] So the CPU load grows but the network traffic doesn't because it's just spinning its wheels and doing a lot of CPU work, you know, stuck in some infinite loop.


[User] Please transcribe this text

[Assistant] And you can also get creative and come up with other features as well. Maybe I have a feature xx that's CPU load squared divided by network traffic.


[User] Please transcribe this text

[Assistant] And this would be another variant of the feature like X5 to try to capture anomalies where one of your machines has a very high CPU load that maybe doesn't have a commensurately large network traffic.


[User] Please transcribe this text

[Assistant] And uhm, by creating features like these, you can start to capture anomalies that correspond to particular unusual combinations of values of the features.


[User] Please transcribe this text

[Assistant] So in this video we talked about how to take a feature and maybe transform it a little bit so that it becomes a bit more Gaussian before feeding it into an anomaly detection algorithm. And also the error analysis in this process of creating features to try to capture different types of anomalies.


[User] Please transcribe this text

[Assistant] It's been great to, uh, get home, but, uh, I'm all refreshed now and ready to come back in.


[User] Please transcribe this text

[Assistant] Gastrique's bloody beautiful, uh, best tasting one I've ever done, I reckon.


[User] Please transcribe this text

[Assistant] Today I've done a lamb rack with a pea puree and a mint gastrique.


[User] Please transcribe this text

[Assistant] Made you an after dinner meal. With roasted chocolate and mint ice cream.


[User] Please transcribe this text

[Assistant] Are you okay? You always want to play.


[User] Please transcribe this text

[Assistant] She's always in the mood to play. Oh, I wouldn't worry, dear. Yeah, okay. Suppose so.


[User] Please transcribe this text

[Assistant] When an investor uses margin to buy or sell securities, they are paid for using a combination of his or her own funds, as well as money borrowed from a broker.


[User] Please transcribe this text

[Assistant] The investor receives a margin call from a broker if the securities in the portfolio decrease in value past a certain point.


[User] Please transcribe this text

[Assistant] After which the investor must either deposit more money into the account or sell some of the assets.


[User] Please transcribe this text

[Assistant] A forward contract is a contract between two individuals, you might call them counterparties, to deliver at a future date, called the exercise date or maturity date.


[User] Please transcribe this text

[Assistant] Historically, forward contracts preceded futures contracts.


[User] Please transcribe this text

[Assistant] A futures contract is a more sophisticated idea.


[User] Please transcribe this text

[Assistant] So for example, what was happening in Dojima before the 1670s, rice farmers would make a deal to sell their rice to a warehouser. Now they could just wait until the harvest is in and then bring it to Dojima and see what price they could get.


[User] Please transcribe this text

[Assistant] But they have an alternative of establishing the price earlier.


[User] Please transcribe this text

[Assistant] and locking it in. And farmers like to do this because they don't know what the price will be when they sell it. So you put in a lot of expense to produce the product, uh, and it might not pay you back all of your expenses, so you don't like that.


[User] Please transcribe this text

[Assistant] Uh, so they typically like to lock in the price. That's called, if you do it directly, and typically you would sell it to a warehouse or a grain elevator, as they're called.


[User] Please transcribe this text

[Assistant] There would be a local grain, you know, I don't know about rice, wheat or corn or any of these. In the countryside, there's a local grain elevator. And you can go to that person and say, I'm planning to harvest rice at such and such a month.


[User] Please transcribe this text

[Assistant] Because they're going to be storing. The hedging, the storage business is a tight margin business. It's very competitive. You could store a lot of grain and then not, or promise to buy grain, uh, at a price that turns out to be unprofitable for you.


[User] Please transcribe this text

[Assistant] So what happens is the stores of grain are the hedgers, and they will quote prices to farmers as a forward price.


[User] Please transcribe this text

[Assistant] There's other kinds, it's not just grains. Grains are historically the origin of forwards and futures. But let's talk about other things. You can also make a forward contract to exchange pounds for yen.


[User] Please transcribe this text

[Assistant] Why do they do it? Well, they do it because they don't want to fit into one of those standard contracts that are offered at the exchange. They might want a different delivery location, they might want a different date, uh, they might want a different quality, and so they make forward contracts are big.


[User] Please transcribe this text

[Assistant] But the problem with forward contracts is you can't get out of them. And, uh, uh, and, and you don't necessarily trust the counterparty. What if the farmer just doesn't, he gets drunk, he doesn't, he doesn't plant?


[User] Please transcribe this text

[Assistant] The advantage of futures contracts is that you don't have to worry because your counterparty is the futures exchange. It's not between the producer of wheat and the manufacturer


[User] Please transcribe this text

[Assistant] So they have to, uh, if you're going to go with forwards, you have to worry about your counterparty. And you have to check the counterparty out, uh, and you need to establish that the counterparty has good credit.


[User] Please transcribe this text

[Assistant] Uh, so they're inherently limited. Futures markets, you want to trade? Uh, you can call up a broker tomorrow and trade. Uh, the broker will do it with anyone who, who posts margin. But let me, uh, come back to that.


[User] Please transcribe this text

[Assistant] Forward exchange futures, a forward exchange contract, FX forwards, uh, can be thought of as a pair of zero coupon bonds stapled together in two different currencies.


[User] Please transcribe this text

[Assistant] Suppose I'm selling, uh, goods. I'm a, uh, a Japanese producer of goods, which I'm selling in the United States. Uh, I'm going to sell them for dollars, of course. Americans pay dollars. And then I want to repatriate the money to Japan, so I'm going to exchange it into yen.


[User] Please transcribe this text

[Assistant] Uh, but I want to have a forward contract which locks in the price today. I don't have to go through the forward mark.


[User] Please transcribe this text

[Assistant] Exchange it at the spot exchange rate today into yen.


[User] Please transcribe this text

[Assistant] Take the yen back to Japan and put them in some interest-bearing account in Japan earning the Japanese interest rate.


[User] Please transcribe this text

[Assistant] So I'm going to be charged the dollar interest rate, but I'm going to be credited the Japanese interest rate. And so I'll have...


[User] Please transcribe this text

[Assistant] An amount left at the end equal to the product of the spot exchange rate times 1 plus r yen divided by 1 plus r dollars.


[User] Please transcribe this text

[Assistant] So that, so that's what I could do without entering the forward exchange market just by trading currents, uh, by, by, uh,


[User] Please transcribe this text

[Assistant] Uh, borrowing and, and then investing in Japan. So, so this number should equal the forward exchange rate by arbitrage because there's, it's two different ways of doing the same thing. Uh, and so they should be the same number.


[User] Please transcribe this text

[Assistant] So that means that the forward exchange rate, yen dollar exchange rate, is not


[User] Please transcribe this text

[Assistant] As interesting a number as you might think. You might think that the forward exchange rate is some prediction of the spot exchange rate.


[User] Please transcribe this text

[Assistant] Well, maybe it is, but it's also just this. It reflects the relative interest rates in the two countries. If you look at, if you look at forward exchange rates and compare them with spot exchange rates and check out this formula, it works pretty well explaining the forward interest rate.


[User] Please transcribe this text

[Assistant] So that's called forward interest parity. Forward rate agreements use a formula which looks a little, uh, it's not exactly what I just showed you. Uh, they typically, uh, uh,


[User] Please transcribe this text

[Assistant] Actually just have a settlement, which is the, uh, actual interest rate on the contract date minus the contract rate times the days in the contract period times the contract amount.


[User] Please transcribe this text

[Assistant] Divided by b, which is, depending on the convention, either 360 or 365 days. Uh, we're in leap year this year, so we have 366 days. So that you know the formula won't be exact, because it's either,


[User] Please transcribe this text

[Assistant] They went back to 360 days for formulas like this because people couldn't divide. It was hard. They wanted a round number. Uh, so that's an old tradition. And 366 doesn't appeal to anybody. But that's the settlement formula.


[User] Please transcribe this text

[Assistant] Hello English learners. Welcome back to another lesson with us here at EnglishPod. My name is Marco.


[User] Please transcribe this text

[Assistant] Okay, so it's a difficult process sometimes, even in your own language, so we're gonna try and make it a little bit easier by giving you some of the common words you would need to register at a university.


[User] Please transcribe this text

[Assistant] Alright, great, a very good student, taking a lot of classes this semester.


[User] Please transcribe this text

[Assistant] Yeaah, they are. Well, she used some of those very common phrases that we would need when we are registering at university. So, let's take a look at them now in Language Takeaway.


[User] Please transcribe this text

[Assistant] Okay, so we've mentioned this word many times, so I think it would be a good idea to listen to some examples, and then we'll come back and explain it.


[User] Please transcribe this text

[Assistant] It is necessary to register on our website before joining the conference.


[User] Please transcribe this text

[Assistant] Okay, so you can register for university. What else can you register for?


[User] Please transcribe this text

[Assistant] Now she was registering for her classes and we saw these words that we know already, but it's a good idea to take a look at them. We heard that she is a business major. So a major. A major. A business major. And what is your major?


[User] Please transcribe this text

[Assistant] Okay, so major and your minor.


[User] Please transcribe this text

[Assistant] Okay, very good. And now on your degree, on your title, does it say what is your major and minor?


[User] Please transcribe this text

[Assistant] Okay, very good. So, major and minor. Now, related to this and what you were saying about your classes, it takes us to our next word, which is credits.


[User] Please transcribe this text

[Assistant] Okay, so suppose you need 100 credits to graduate.


[User] Please transcribe this text

[Assistant] Very good. So maybe the classes that are part of your major will have more credits. Alright, very good. So, and now we have our last word in this dialogue and it was tuition. Tuition. She had to pay her tuition.


[User] Please transcribe this text

[Assistant] Okay, so a tuition is the money you must pay for school only. So we've seen a lot of great words used for registering at university. So I think it's time to listen to the dialogue for the second time a little bit slower and then we'll come back and take a look at some phrases.


[User] Please transcribe this text

[Assistant] Alright, and our first phrase is actually very similar to register.


[User] Please transcribe this text

[Assistant] Alright, so moving on to our second phrase. This is very important because it's often used improperly, right?


[User] Please transcribe this text

[Assistant] Alright, so let's look at this phrase, second year.


[User] Please transcribe this text

[Assistant] My last year of college. Exactly. Very good. So now you know how you can explain to someone what year of college or university you're in. Yes, that's right. Very good. And we have one more phrase to you and it was when she was getting her class schedule.


[User] Please transcribe this text

[Assistant] Right, so it's the timetable of your day or your week.


[User] Please transcribe this text

[Assistant] Alright, and your class schedule will say all the classes that you have and what time they're at.


[User] Please transcribe this text

[Assistant] Right. So we have different types of schedules and you can use it depending on what you are doing. Those are all the phrases we have for putting it together today. And now let's listen to the dialogue for the last time and then we'll come back.


[User] Please transcribe this text

[Assistant] Alright, so we were talking about second year, first year, third year, etc. Now, there are also other names that we give to each year, both for high school and university.


[User] Please transcribe this text

[Assistant] Exactly. So you can say, yeah, I'm a sophomore at...


[User] Please transcribe this text

[Assistant] Arizona State University. Okay, so we only use this in America though, right? Yes, yes, it's very, and, uh, and not only for university, but also for high school.


[User] Please transcribe this text

[Assistant] Alright guys, that's all the time we have for today. I hope you enjoyed this lesson and if you have any questions or comments or you want to share with us your experiences in high school or university, please come to the website at EnglishPod.com.


[User] Please transcribe this text

[Assistant] You made some really good points. Well, I just wanted to say, well done. Really, you did a great job.


[User] Please transcribe this text

[Assistant] You made some really good points. Well, I just wanted to say well done. Really,


[User] Please transcribe this text

[Assistant] My roommate and I are just not compatible. All she ever does is study and I like to stay up late and party.


[User] Please transcribe this text

[Assistant] Each of these boxes can have even a small engineering team working on it. Or maybe the entire system is just built by you either way. But the question is, where should you allocate resources? Which of these boxes is most worth your effort trying to improve the performance of?


[User] Please transcribe this text

[Assistant] As I mentioned earlier, each of these boxes here, each of these machine learning components could be the work of even a small team of engineers. Or maybe it could be the whole system could be built by just one person.


[User] Please transcribe this text

[Assistant] But the question is, where should you allocate scarce resources? That is, which of these components or which one or two or maybe all three of these components is most worth your time to try to improve the performance of?


[User] Please transcribe this text

[Assistant] So here's the idea of ceiling analysis. As in the development process for other machine learning systems as well, in order to make decisions on what to do for developing the system is


[User] Please transcribe this text

[Assistant] Or you can pick some other single row number evaluation metric if you want, but you know, but let's say that for whatever evaluation metric we pick, we get that, uh, we find that the overall system currently has 72% accuracy.


[User] Please transcribe this text

[Assistant] In, for every test example, we're just gonna provide it the correct text detection outputs, right? So in other words, we're going to go to the test set and just manually tell the algorithm where the, uh, text is in each of the test examples.


[User] Please transcribe this text

[Assistant] Be part of your test set and just use these ground truth labels as what you feed in to the next stage of the pipeline, to the character segmentation pipeline.


[User] Please transcribe this text

[Assistant] And then we're going to keep going. Next, let's go to the next stage of the pipeline, to character segmentation. So again, I'm going to go to my test set and, uh, now I'm going to give it the correct.


[User] Please transcribe this text

[Assistant] Text detection output and give it the correct character segmentation output. So let's go to the test set and manually label the correct segmentations of the text into individual characters and see how much that helps. Let's say it goes up to 90% accuracy for the overall system.


[User] Please transcribe this text

[Assistant] Right, so as always the accuracy is the accuracy of the overall system. So whatever the final output of the character recognition system is, whatever the final output of the overall pipeline is going to measure the accuracy of the system.


[User] Please transcribe this text

[Assistant] So this means that if we take a current system and we spend a lot of time improving text detection.


[User] Please transcribe this text

[Assistant] That means that we could potentially improve our system's performance by 17%. It seems, seems like it's well worth our while. Whereas in contrast, when, uh, you know, going from text detection, when we gave it perfect character segmentation,


[User] Please transcribe this text

[Assistant] Performance went up only by 1%. So that's a more sobering message. It means that, you know, no matter how much time you spend on character segmentation,


[User] Please transcribe this text

[Assistant] Your performance goes up by only 1%. So that really estimates what is the ceiling or what's an upper bound on how much you can improve the performance of your system by working on one of these components.


[User] Please transcribe this text

[Assistant] Another way of thinking about this is that by going through this sort of analysis, you're trying to figure out, you know, what is the upside potential of improving each of these components, or how much could you possibly gain if one of these components became absolutely perfect? And this really places an upper bound on the performance of that system.


[User] Please transcribe this text

[Assistant] Uh, next we want to say detect the face of the person. That's usually done over learning algorithms around the sliding windows classifier to, you know, draw a box around the person's face.


[User] Please transcribe this text

[Assistant] Uhm, so segment out the eyes, and then, uh, since this, you know, will give us useful features to recognize the person. And then other parts of the face, uh, particular interest, maybe, uh, segment out the nose, uh, segment out the mouth.


[User] Please transcribe this text

[Assistant] And then having found the eyes, the nose, and the mouth, all of these give us useful features to maybe feed into a logistic regression classifier and this job with a classifier to then give us the overall label, the final label for who we think is the identity of this person.


[User] Please transcribe this text

[Assistant] So how do you go through seeding analysis for this pipeline? Well, we step through these pieces one at a time.


[User] Please transcribe this text

[Assistant] So this is a strong sign that even if you had perfect background segmentation, your performance, uh,


[User] Please transcribe this text

[Assistant] Even if they have perfect background removal, the performance of your system isn't going to go up that much. So it's maybe not worth a huge effort to work on preprocessing or background removal.


[User] Please transcribe this text

[Assistant] Then we can go to the test set, give it the correct face detection images, then again step through the eyes, nose, mouth segmentation in some order. I'm just going to pick one order. I'll just give it the correct location of the eyes, correct location of the noses, correct location of the mouth, uh, and then finally if I just give it the correct overall label, I get a hundred percent accurate.


[User] Please transcribe this text

[Assistant] And so, you know, as I go through the system and just give more and more components the correct labels in the test set, the performance of the overall system goes up.


[User] Please transcribe this text

[Assistant] And you can look at how much the performance went up on different steps. So, you know, from uh, giving it the perfect face detection, it looks like the overall performance of the system went up by 5.9%.


[User] Please transcribe this text

[Assistant] When I gave it perfect face segmentation, face detection, system went up by 5.9% performance. When I gave it perfect eye segmentation, it went up by 4%. And then my final logistic classifier, you know, well there's another 3% gap there maybe. And so this tells us maybe one of the components that were most worthwhile while working on.


[User] Please transcribe this text

[Assistant] It's because uhm, I actually know of a true story where there was a research team that actually literally had two people spend about a year and a half, spend 18 months


[User] Please transcribe this text

[Assistant] Working on back, better background removal. Actually worked out really complicated algorithms. Ended up, I think, publishing one research paper. But after all that work, they found that it just did not make a huge difference to the overall performance of the actual application they were, they were working on.


[User] Please transcribe this text

[Assistant] And, uh, if only, you know, if only, if only someone were to do a seeding analysis beforehand, maybe, uh, could have realized this, uhm.


[User] Please transcribe this text

[Assistant] My leather's been in the oven for eight minutes, I pull it out and...


[User] Please transcribe this text

[Assistant] Well if you don't, why are you watching this video? And if you do, I've got the perfect new channel for you. It's called BioArch, and I'm uploading videos over there that are just like the content you see over here on RealLifeLore.


[User] Please transcribe this text

[Assistant] Except it's all about weird animal stuff. I've already made three videos that you can go and watch right now about how orcas will eat moose, how slugs have sex, and how Noah's Ark could have worked with the power of atomic bombs.


[User] Please transcribe this text

[Assistant] More fun animal videos are coming out of Biowark all the time. So if that sounds like something you'd be interested in, it would really mean a lot if you went over there, check some videos out, and subscribe for future content.


[User] Please transcribe this text

[Assistant] I also recognize that real life lore recently surpassed 4 million subscribers, which is just completely 100% unbelievable to me.


[User] Please transcribe this text

[Assistant] Y'all are truly the best, and I'm so, so incredibly honored and humbled to have gained the trust and the following of this community.


[User] Please transcribe this text

[Assistant] I'd like to also say that I didn't build this alone. We have all built this together over the past five years since RealLifeLore was created and I'm just so grateful to all four million of you who have chosen to subscribe and the millions of more who have watched my videos over the years.


[User] Please transcribe this text

[Assistant] Because of all of you, I get to wake up every day and do what I love. Which is making videos and writing content for all of you to enjoy and hopefully throwing in a few Toyota Corolla jokes every now and then along the way.


[User] Please transcribe this text

[Assistant] Anyway, thank you for everything, I love you all, and as always, thank you for watching.


[User] Please transcribe this text

[Assistant] So, uh, this is from one of Will Getsman's books. Not the one I assigned for you.


[User] Please transcribe this text

[Assistant] Uh, it was one that he wrote with, uh, Geert Roenhorst. But it was a collection of essays, and these two Dutchmen, Gelderbloem and Janker, the three of the four are Dutch, you can tell these Dutch names.


[User] Please transcribe this text

[Assistant] Writing about the history of options. Well, the first options that are well documented come from Holland in the, maybe the 1600s, but this is an example from the year 16, 1730.


[User] Please transcribe this text

[Assistant] Uh, and unfortunately it's in Dutch. I don't know if any of you can read that, uh, but, uh,


[User] Please transcribe this text

[Assistant] Uh, this is an example of an option to buy shares in, I can't pronounce this right, this is like the General Royal India Company, which is a Dutch company in 1730.


[User] Please transcribe this text

[Assistant] Uh, and so this is the options contract. Now the other thing that's kind of neat, the Dutch were the most advanced financially people in the world in the 1600s and maybe into the 1700s.


[User] Please transcribe this text

[Assistant] Uh, this is a printed form. You see it's got, it's nicely done. This is printed. And someone has filled in the blanks in handwriting to make it an option contract.


[User] Please transcribe this text

[Assistant] That was an important, this might be the beginning of the information age. Printed forms was a way of sort of putting in a program, right? A standard program.


[User] Please transcribe this text

[Assistant] Uh, some people call it boilerplate, referring to the plates that they made the printing from. Uh, so, uh, so it says I, the undersigned, and it's


[User] Please transcribe this text

[Assistant] Uh, later, options became much more common, and they were in newspapers. Uh, this is a clipping that I took, uh, twenty years ago or so, uh, from, uh,


[User] Please transcribe this text

[Assistant] Probably the Wall Street Journal, which used to carry options prices in the printed edition. So that's all, but I took this off of the, uh, web yesterday. The CBOE, Chicago Board Options Exchange, has a website and this is, uh,


[User] Please transcribe this text

[Assistant] I just picked a company at random. This is Intel Corporation. That's the manufacturer of computer chips.


[User] Please transcribe this text

[Assistant] This is, uh, the price of the share, last quote, I guess that's closing price, uh, probably for last Friday, uh, was $31.63 per share.


[User] Please transcribe this text

[Assistant] Notice it's about $30. That's because companies do splits whenever it gets much farther than that. Because buy American, this is very American. This looks right for the price of a share. And they'll keep splitting it if it keeps going up. So it stays around there.


[User] Please transcribe this text

[Assistant] And it's paying a dividend per share of 26 cents, quarterly. That's the underlying stock.


[User] Please transcribe this text

[Assistant] Uh, now, uh, it shows, like in the newspaper column, it shows the exercise prices. Now, I think they're trying to be confusing here.


[User] Please transcribe this text

[Assistant] INTC for Intel Corporation is the ticker for Intel. What is the ticker? It's an abbreviation for the company name, which is used in financial reporting.


[User] Please transcribe this text

[Assistant] Uh, it's a four-letter ticker, which is a clue that it's NASDAQ listed, rather than New York Stock Exchange, which normally lists, stop at three letters.


[User] Please transcribe this text

[Assistant] And so it gives the, well, here is the strike prices. Uh, $27 a share, $30 a share, $32 a share, and $35 a share.


[User] Please transcribe this text

[Assistant] Uhm, and now the price per share is 31. This is a right to buy it for more than it's selling for today. So it's out of the money. So I wouldn't want, why would anyone want this option if it's a right to buy the share for more than it costs to buy it?


[User] Please transcribe this text

[Assistant] Well, I'll tell you why someone would buy this option, because, uh,


[User] Please transcribe this text

[Assistant] It might go up. The price might go up. And you could, well, you'd say, well, if you think it might go up, why don't you just buy the share? Well, but if I buy the share, I'm incurring a risk that it might go down. I could lose, I have to pay $31 to get a share. I could lose the whole thing if there was some really bad news.


[User] Please transcribe this text

[Assistant] But here I only have, when I buy the option, I'm only paying $2.36.


[User] Please transcribe this text

[Assistant] Uhm, so, uh, I, I'm not risking as much, so I, I, I'll do it. I mean, this looks like a reasonable price. Well, it probably is a reasonable price because it's the market price for the option.


[User] Please transcribe this text

[Assistant] So you'll be willing to pay $2.36 for the thought that you will, if the price of a share goes up above $35, that's not too far from where it is now, then I'll be able to buy it for only $35. So that's worth something.


[User] Please transcribe this text

[Assistant] This one is in the money, because I could buy it today, uh, for, uh, $7, uh, forget the price of buying it, I could buy it today and then exercise it at $27, and then it would be immediately worth $31. That's a difference of $4.63.


[User] Please transcribe this text

[Assistant] So would it be smart to, to buy this option today and exercise it immediately?


[User] Please transcribe this text

[Assistant] No, because you'd lose money because you paid more for the option than you'd make as profit. You'd make, uh, the difference between


[User] Please transcribe this text

[Assistant] $27.31.63, which is $4.63. But I just paid $7 for that right, so I wouldn't exercise it immediately.


[User] Please transcribe this text

[Assistant] Generally you don't want to exercise options early because they have option value. The option value is the risk, is the value that you get to the upside without so much downside.


[User] Please transcribe this text

[Assistant] Uh, so you generally want to wait to exercise enough.


[User] Please transcribe this text

[Assistant] That means, uh, demand that you buy the stock in the case of a call at the exercise price. But now they also have two other prices, bid and ask. That's because, uh, uh,


[User] Please transcribe this text

[Assistant] This is an actual last price that a trade was made, but things have changed since then. I was doing this on a Sunday, by the way. There are dealers, market makers, who are quoting prices that you can buy and sell at. So the bid price is what the, if you want to sell your option back,


[User] Please transcribe this text

[Assistant] You can buy, the dealer is offering $6.05 for the $27 strike price option.


[User] Please transcribe this text

[Assistant] Uh, or but if you want to buy the option, the market maker is offering six dollars and twenty cents. That, there's a difference, that's called a bid-ask spread. So the dealer is asking fifteen cents more per option.


[User] Please transcribe this text

[Assistant] Then, uh, the dealer is willing to pay. That's how the dealer makes money. This, uh, strike price 35 seems to be the most active. So it has the most narrow bid-ass spread. It's the most competitive market.


[User] Please transcribe this text

[Assistant] So, uh, for education right now, when student wants to pursue higher education, he takes out a student loan. So we can consider that to be a sort of say, bond that the student sort of issues. Yeah. So is it possible that, you know, we could switch to an equity model? Right.


[User] Please transcribe this text

[Assistant] Is about letting what's in front of you, inspire you.


[User] Please transcribe this text

[Assistant] With that delicious bacon and egg smoked potato foam. Memorable, yum. One of the best dishes we've seen in audition. It's yours.


[User] Please transcribe this text

[Assistant] And earlier this week that blue cheese ice cream was beautifully made and delicious. Slightly bonkers, but totally tasty.


[User] Please transcribe this text

[Assistant] I tried the dry ass trick in round one and it was an epic fail. So I really should not be doing this.


[User] Please transcribe this text

[Assistant] An option is a contract. There's both two sides to any contract. There's the buyer of the option and writer of the option. So, uh, there are two kinds of options. I've already alluded to that. A call option is a right to buy.


[User] Please transcribe this text

[Assistant] Put option is a right to sell at your discretion.


[User] Please transcribe this text

[Assistant] With a call option, you are buying the right to buy something, which is specified in the contract at a pre-determined price.


[User] Please transcribe this text

[Assistant] That means you don't have to wait to find out what the market price is of that item on the day of purchase. It's locked in in advance. But we call it an option because you don't have to purchase it. You can wait and see.


[User] Please transcribe this text

[Assistant] It's different from a forward contract to buy something, because in a forward contract, you are not only having the option to buy, you are committed to buy. You signed a contract.


[User] Please transcribe this text

[Assistant] I give a choice to the buyer of the option.


[User] Please transcribe this text

[Assistant] And at the time that the option is bought, the buyer of the option compensates the seller of the, or they call it the writer of the option.


[User] Please transcribe this text

[Assistant] Because the writer of the option is now subject to the choice in the future of the buyer of the option. And it has to be compensated for, uh, for giving that choice to the other person.


[User] Please transcribe this text

[Assistant] So what is the essence of options? Some people think it has something to do with free will. That is that you can do whatever you want and maybe you will be happy, happier in the future if you buy it or maybe you won't. You can wait and see.


[User] Please transcribe this text

[Assistant] Uh, but actually in finance, it doesn't have much to do with free will or feelings generally. You will generally buy the option if it pays to do that in finance. Most people will. So you will, if it's a call option, it's an option to buy something.


[User] Please transcribe this text

[Assistant] Is if the price of buying it through the option is less than the price of buying it in the open market. Because you will always do that even if you decide you don't want the object because you can then sell it in the open market and make a profit.


[User] Please transcribe this text

[Assistant] So options are truncated claims on asset. If it's a call option, it means that it's a claim on the price only if it rises above.


[User] Please transcribe this text

[Assistant] So that's a general idea. And so let me get a little bit more precise. The contract of the option have these terms. An exercise date or strike date.


[User] Please transcribe this text

[Assistant] That's the date, uh, when the option expires and you either have to buy if it's a call or sell if it's a put. And if you don't, then the option becomes worthless. And forever after, it's worthless. It's expired.


[User] Please transcribe this text

[Assistant] It also specifies the exercise price or strike price, uh, which is, uh, the price at which you are in a call option case have the right to buy and in the put option case where you have the right to sell.


[User] Please transcribe this text

[Assistant] The contract also has to specify what is the underlying, what is the object that has to be bought or sold.


[User] Please transcribe this text

[Assistant] How many of that, if it's, if it's shares? Now options are not just for, uh, not just for stocks. For example, suppose you are a builder, uh, and you're building a shopping center. You want to build a shopping center in, in the city.


[User] Please transcribe this text

[Assistant] Near a city. So you look around and you find, well there's some farmland here that looks well positioned. Maybe I want to build my shopping center on this guy's farm.


[User] Please transcribe this text

[Assistant] But he's a farmer, right? He doesn't know you're coming. So you walk over to him and you say, uh, I'd like to buy an option on your farm.


[User] Please transcribe this text

[Assistant] We haven't really finalized this deal to build a shopping center, but maybe we'll change our mind. But we want to know that we can get your farm. The farmer might first say, no way, why do you want my farm? And then you say, look, it's just an option. If it expires, uh, unused, you just keep the money.


[User] Please transcribe this text

[Assistant] So maybe the farmer will do that, you know, he'll think about it and say, well, this guy's offering me money right now, maybe nothing will come of it. So that's an example of an option.


[User] Please transcribe this text

[Assistant] And it, and undoubtedly has a long history. There must have been many times, going back to Sumerian times, if you listen to Will Getsman, uh, when people thought of that, you know, I'm thinking of buying something, uh, but I don't know that the seller is willing to sell. Uh, so you would buy an option.


[User] Please transcribe this text

[Assistant] So the DOM actually also includes CSS styles. And so with DOM manipulation, we can also change styles. So let's try that out now.


[User] Please transcribe this text

[Assistant] And what I want to do here is to change the background of this entire page here to a green color whenever the player guesses the right number. So when the player wins the game. And we will also make this number here, uh, wider then. So let me just demonstrate that here.


[User] Please transcribe this text

[Assistant] 17. And so you see, we want this green background and also this number here being wider. And for that, of course, we need to manipulate the styles of this page. So the CSS.


[User] Please transcribe this text

[Assistant] Okay, so that should happen here in this scenario where the player wins the game. So where the guess is equal to the secret number. And actually let's add some comments here to make this code a little bit easier to understand. So this one, when player wins,


[User] Please transcribe this text

[Assistant] So this now at least looks a little bit nicer. But anyway, let's now learn how to manipulate CSS styles. So we want to manipulate the style of this whole page. And so what we need to do is to select the whole body of this page first.


[User] Please transcribe this text

[Assistant] Okay, so the body is basically this entire visible part of the website.


[User] Please transcribe this text

[Assistant] So it's basically this whole element. Alright? So as always, we need to start by selecting the element that we want to manipulate.


[User] Please transcribe this text

[Assistant] And then the selector, and now it's just the body element. So there's no dot, because that's for classes. It's just really the element name. And since there's only one body, it's going to work just fine. So here we select the body, and now we want to change the CSS style. So here we need to write


[User] Please transcribe this text

[Assistant] And in CSS we write a background color like this. Background dash.


[User] Please transcribe this text

[Assistant] Right? However, in JavaScript, that's not allowed. And so in JavaScript, all these property names that have two words, like background color, will simply adopt the camel case notation that I already explained you before and that we've been using all the time.


[User] Please transcribe this text

[Assistant] So instead of background-color, it's gonna be background...


[User] Please transcribe this text

[Assistant] Uppercase C for color, like this. And the same rule applies to all the other property names that have multiple words. So, style.backgroundColor. And now we set it to something. And let's get the actual color from our CSS.


[User] Please transcribe this text

[Assistant] So you see, that background color here is the one that we're trying to define, and we will specify it to this color value. So this is a standard RBG value in hexadecimal.


[User] Please transcribe this text

[Assistant] And so we set it equal to, then we must write a string.


[User] Please transcribe this text

[Assistant] And then the value we want to set it to. So just like before to set anything on the DOM, we simply use the equal sign. So we have all of this. So the background color property on the style property, and we want to set it to this value. And so we set it equal.


[User] Please transcribe this text

[Assistant] Okay, so that's one part. And now let's also, uh, increase the width of this number here.


[User] Please transcribe this text

[Assistant] That's important because I want to show you something there. So document.querySelector.


[User] Please transcribe this text

[Assistant] And that is the element with the class of Number.


[User] Please transcribe this text

[Assistant] And again, we want to change the style. So dot style dot width.


[User] Please transcribe this text

[Assistant] And then again we need to specify a string. So whenever we are manipulating a style, we always need to specify a string. We cannot just write like a number, like 30. It has to be a string, and in there we need a unit. And now I already closed the file, so let's open it up again.


[User] Please transcribe this text

[Assistant] And the number that I'm looking for currently has a width of 15 rem. And now I want to double it, so 30 rem.


[User] Please transcribe this text

[Assistant] And again, it needs to be inside of a string. I cannot just write 30 rem or 30, so just a number. That would not work. We have to specify a string, which in this case contains the number plus the unit.


[User] Please transcribe this text

[Assistant] So the same would be for percentages or pixels or whatever unit we use in CSS.


[User] Please transcribe this text

[Assistant] Okay, give it a save now. And now let's actually try the correct number right away. So 12.


[User] Please transcribe this text

[Assistant] And yes, it worked. Let's actually get rid of the console here. Then the page looks a bit nicer. And we can also, by the way, get rid of the terminal here in VS Code.


[User] Please transcribe this text

[Assistant] Because even if we close this panel here, this process will still keep running. The only way we stop this process from running is by hitting here, this trash can, which will kill the terminal or by hitting, uh, control C. So let's close it because this takes up too much space, but yeah, so this worked just fine.


[User] Please transcribe this text

[Assistant] Now these styles are actually set as inline styles. So of course we are not changing the CSS file or anything like that. And let me just show that to you. So actually I will need back the DevTools here.


[User] Please transcribe this text

[Assistant] Make it a bit larger now just to show it to you. And.


[User] Please transcribe this text

[Assistant] So indeed it is an inline style. So a style that is applied directly in the HTML using this style attribute. And the same is going to be true for the body. Exactly. So we see here, this is the background color that we just applied. All right.


[User] Please transcribe this text

[Assistant] So of course not changing the CSS file or anything like that. Okay.


[User] Please transcribe this text

[Assistant] And that's it actually. That's really the fundamentals of manipulating styles in CSS. The most important thing to remember is to use the style property.


[User] Please transcribe this text

[Assistant] And then on there is where we specify the CSS property, and we need to specify that property in the camel case notation in case there is two words. And then the value always needs to be a string.


[User] Please transcribe this text

[Assistant] So that's all you need to know in order to change styles like we just did here. Awesome. We're doing great progress in this project and I hope that you're enjoying it so far and that you find it a lot of fun to be able to manipulate a webpage like this.


[User] Please transcribe this text

[Assistant] Anyway, in the next video we will implement this, uh, reset button here basically, where we can play the game again. And so I hope to see you there soon.


[User] Please transcribe this text

[Assistant] Mains were a course of a totally different colour. Red team, your mane might have looked a little bit two-toned, but Gia delivered.


[User] Please transcribe this text

[Assistant] Aaron, who would have thought at the beginning of that challenge that you'd be able to put up that dessert? I certainly didn't.


[User] Please transcribe this text

[Assistant] Yeah, absolutely. You can see the obvious mistake spilled out.


[User] Please transcribe this text

[Assistant] Gee, how fine is that temper work on the chocolate? Beautiful, isn't it? If I walked into a pastry shop to buy it, then the answer is yes, it looks great. Shall we? Shall we cut it open?


[User] Please transcribe this text

[Assistant] Dennis, I think I know which one, uh, is gonna send their maker home. You guys? Oh yea, I've already made my mind up. I think so too. Great, let's get em in and tell em.


[User] Please transcribe this text

[Assistant] Wow, what an amazing challenge. And you had the pleasure and the pain of recreating Dennis' passion for caramel tart.


[User] Please transcribe this text

[Assistant] Today's challenge is going to take place over two rounds. The first round is going to test your knowledge of ingredients.


[User] Please transcribe this text

[Assistant] So, the rules are very simple. If you guess your chosen ingredient correctly, you stay in the game.


[User] Please transcribe this text

[Assistant] The first three of you to guess an ingredient incorrectly are going to round two.


[User] Please transcribe this text

[Assistant] Well, you push those. Did you want your apples to be as dark as that?


[User] Please transcribe this text

[Assistant] A lot of debate, but there was only one dish that we could agree unanimously on, deserve to keep its maker in the competition.


[User] Please transcribe this text

[Assistant] Great cooking this week. You get to sit back, you get to see a fantastic recipe, learn some tips and tricks from George and Gary End. You also get the man himself to show you a couple of his signature dishes I think you're going to absolutely love. So special day. What have you got planned for them?


[User] Please transcribe this text

[Assistant] Those wonderful aubergine scotch eggs. You've seen George make the codalia, which is one of my favorite comfort foods. And of course, you've seen Gary made a beautiful shortcrust pastry with the thyme, coriander seeds, parmesan.


[User] Please transcribe this text

[Assistant] I think as Adam says, there's really, one of the toughest things about MasterChef is the departures, but the great reward is memories and memories like this amazing week.


[User] Please transcribe this text

[Assistant] Choosing one ingredient for savoury and dessert. At first I thought, oh no, here we go. It's like day one. I'm nervous again.


[User] Please transcribe this text

[Assistant] But then I realised I already have a dish in mind for both sweet and savoury. Definitely oranges actually.


[User] Please transcribe this text

[Assistant] I could be going into round two based on this. I want to avoid round two at all costs. It is not where I want to be, especially not against these cooks. Come on, think clearly, think clearly.


[User] Please transcribe this text

[Assistant] Good morning everyone. Were you stuck in traffic as well, Jess? There was a huge pile up on the highway, and traffic was backed up for miles.


[User] Please transcribe this text

[Assistant] Morning everyone. Were you stuck in traffic as well, Jess? There was a huge pileup on the highway, and traffic was backed up for miles.


[User] Please transcribe this text

[Assistant] Good morning everyone. Were you stuck in traffic as well, Jess? There was a huge pile up on the highway, and traffic was backed up for miles.


[User] Please transcribe this text

[Assistant] Great for Jews and some prosciutto cuts.


[User] Please transcribe this text

[Assistant] They're very worried that I would overcook my steak. I'm confident in my method. I'm just gonna make sure that I don't mess it up. Otherwise it's just gonna look really bad.


[User] Please transcribe this text

[Assistant] Gather round everybody and listen to my song I want to tell you about Kitty and Lou My two favorite creatures in the world And if you get to meet them you'll love them too


[User] Please transcribe this text

[Assistant] You have a house! Oh, it's more of an apartment, really. Oh! Where did she go? In there.


[User] Please transcribe this text

[Assistant] And I need to take those dumplings out of the water. I just start to build the dish.


[User] Please transcribe this text

[Assistant] In the previous video, we talked about stochastic gradient descent and how that can be much faster than batch gradient descent. In this video, let's talk about another variation on these ideas called mini-batch gradient descent that can work sometimes even a bit faster than stochastic gradient descent.


[User] Please transcribe this text

[Assistant] A typical choice for the value of b might be b equals 10, let's say, and a typical range really might be anywhere from b equals 2 up to b equals 100. So that would be a pretty typical range of values for the mini batch size.


[User] Please transcribe this text

[Assistant] For my training set, so that may be some set of examples, XI, YI.


[User] Please transcribe this text

[Assistant] If it's ten examples, then the indexing would be up to x i plus nine comma y i plus nine.


[User] Please transcribe this text

[Assistant] So that's 10 examples all together. And then we'll perform essentially a gradient descent update using these 10 examples.


[User] Please transcribe this text

[Assistant] That's the learning rate times one-tenth times sum over k equals i through i


[User] Please transcribe this text

[Assistant] And then after this we will then, uh, increase, you know, i by 10. So go on to the next 10 examples and then keep looping like this.


[User] Please transcribe this text

[Assistant] In order to simplify the indexing for this, uh, for the one I'm going to write down, I'm going to assume we have a mini-bash size of 10 and a training set size of 1,000.


[User] Please transcribe this text

[Assistant] What we're going to do is have this sort of formula for i equals 1, 11, 21, so stepping in steps of 10, because we look at 10 examples at a time.


[User] Please transcribe this text

[Assistant] And then we perform this sort of, uh, gradient descent update using 10 examples at a time.


[User] Please transcribe this text

[Assistant] So this 10 and this i plus 9, those are consequence of having chosen my mini-batch size to be 10. And you know, this Altimos for loop, this ends at 991.


[User] Please transcribe this text

[Assistant] Here, because uhm, if I have a thousand training examples, then I need a hundred steps of size ten in order to get through my entire training set. So this is MeMeBatch Dread and Descend. Compared to


[User] Please transcribe this text

[Assistant] Then what we're saying is after looking at just the first ten examples, we can start to make progress in, uh, improving the parameters data. So we don't need to scan through the entire training set. We just need to look at the first ten examples and this will start letting us make progress. And then we can look at the second ten examples and multiply the parameters a little bit again and so on.


[User] Please transcribe this text

[Assistant] So that's why mini-bash gradient descent can be faster than bash gradient descent. Namely, you can start making progress in modifying the parameters after looking at just 10 examples rather than needing to wait until you've scanned through every single training example of 300 million of them.


[User] Please transcribe this text

[Assistant] So how about mini-batch gradient descent versus stochastic gradient descent? So why do we want to look at B examples at a time rather than look at just a single example at a time as in stochastic gradient descent? The answer is in vectorization. In particular,


[User] Please transcribe this text

[Assistant] 10 examples can be performed in a more vectorized way, which will allow you to


[User] Please transcribe this text

[Assistant] By using appropriate vectorization to compute the derivative terms, you can sometimes partially use the good numerical linear algebra libraries to parallelize your grading computations over the B examples.


[User] Please transcribe this text

[Assistant] Whereas if you were looking at just a single example at a time with stochastic gradient descent, then, you know, looking at just one example at a time, there isn't much to paralyze over. At least there's less to paralyze over.


[User] Please transcribe this text

[Assistant] One disadvantage of minibash gradient descent is that there's now this extra parameter b, the minibash size, which you may have to fiddle with and which may therefore take time. But if you have a good vectorized implementation, this can sometimes run even faster than stochastic gradient descent.


[User] Please transcribe this text

[Assistant] Wherever you landed today, determined what you will be cooking with. Under the cloth in front of you.


[User] Please transcribe this text

[Assistant] No open pantry. No garden. No access to other equipment.


[User] Please transcribe this text

[Assistant] You've had this kind of crazy week and we can only imagine the emotions you must have gone through.


[User] Please transcribe this text

[Assistant] I reckon that, that, that's what makes that dish. It's, um, it's got, it has all those delicious flavours of, of the sundae roast pan. It's a really lovely plate of food. And I'm glad it really pushed it.


[User] Please transcribe this text

[Assistant] The hardest thing for any cook, whether it's a home cook or whether it's a professional, to get the grass round is you don't cook to impress other people. Yeah. Would you eat this dish? I'd eat the lamb and the peas. Okay, Sam, let's take the dish of yours. Thank you guys. Cheers.


[User] Please transcribe this text

[Assistant] For you to think that clearly is a sign that you belong in this kitchen.


[User] Please transcribe this text

[Assistant] Absolutely. Okay, right. We'll taste now. Thank you very much. Cheers, right.


[User] Please transcribe this text

[Assistant] So I hope you don't go home on this dish, because we all hope for one thing with every contestant, especially at this stage, is the dish that sends you home is the best dish you could have cooked.


[User] Please transcribe this text

[Assistant] The mountains. Wow. Have you seen them?


[User] Please transcribe this text

[Assistant] Very thoughtful. Thank you. Hey, have you ever seen...


[User] Please transcribe this text

[Assistant] Imagine that one day you're summoned before a government panel. Even though you haven't committed any crime or been formally charged with one, you are repeatedly questioned about your political views, accused of disloyalty, and asked to incriminate your friends and associates.


[User] Please transcribe this text

[Assistant] This is exactly what happened in the United States in the 1950s as part of a campaign to expose suspected communists.


[User] Please transcribe this text

[Assistant] Named after its most notorious practitioner, the phenomenon known as McCarthyism destroyed thousands of lives and careers.


[User] Please transcribe this text

[Assistant] For over a decade, American political leaders trampled democratic freedoms in the name of protecting them.


[User] Please transcribe this text

[Assistant] During the 1930s and 1940s, there had been an active but small communist party in the United States. Its record was mixed.


[User] Please transcribe this text

[Assistant] While it played crucial roles in wider progressive struggles for labor and civil rights, it also supported the Soviet Union.


[User] Please transcribe this text

[Assistant] From the start, the American Communist Party faced attacks from conservatives and business leaders.


[User] Please transcribe this text

[Assistant] As well as from liberals who criticized its ties to the oppressive Soviet regime.


[User] Please transcribe this text

[Assistant] During World War II, when the USA and USSR were allied against Hitler, some American communists actually spied for the Russians.


[User] Please transcribe this text

[Assistant] When the Cold War escalated and this espionage became known, domestic communism came to be seen as a threat to national security.


[User] Please transcribe this text

[Assistant] But the attempt to eliminate that threat soon turned into the longest-lasting and most widespread episode of political repression in American history.


[User] Please transcribe this text

[Assistant] Spurred on by a network of bureaucrats, politicians, journalists, and businessmen, the campaign wildly exaggerated the danger of communist subversion.


[User] Please transcribe this text

[Assistant] The people behind it harassed anyone suspected of holding left-of-center political views or associating with those who did.


[User] Please transcribe this text

[Assistant] If you hung modern art on your walls, had a multiracial social circle, or signed petitions against nuclear weapons, you might just have been a communist.


[User] Please transcribe this text

[Assistant] Starting in the late 1940s, FBI Director J. Edgar Hoover used the resources of his agency to hunt down such supposed communists and eliminate them from any position of influence with an American society.


[User] Please transcribe this text

[Assistant] And the narrow criteria that Hoover and his allies used to screen federal employees spread to the rest of the country.


[User] Please transcribe this text

[Assistant] Soon, Hollywood studios, universities, car manufacturers, and thousands of other public and private employers were imposing the same political tests on the men and women who worked for them.


[User] Please transcribe this text

[Assistant] Meanwhile, Congress conducted its own witch hunt, subpoenaing hundreds of people to testify before investigative bodies like the House Un-American Activities Committee.


[User] Please transcribe this text

[Assistant] If they refused to cooperate, they could be jailed for contempt or, more commonly, fired and blacklisted.


[User] Please transcribe this text

[Assistant] Ambitious politicians like Richard Nixon and Joseph McCarthy used such hearings as a partisan weapon.


[User] Please transcribe this text

[Assistant] Accusing Democrats of being soft on communism and deliberately losing China to the communist bloc.


[User] Please transcribe this text

[Assistant] McCarthy, a Republican senator from Wisconsin, became notorious by flaunting ever-changing lists of alleged communists within the State Department.


[User] Please transcribe this text

[Assistant] Egged on by other politicians, he continued to make outrageous accusations while distorting or fabricating evidence.


[User] Please transcribe this text

[Assistant] Many citizens reviled McCarthy, while others praised him.


[User] Please transcribe this text

[Assistant] And when the Korean War broke out, McCarthy seemed vindicated.


[User] Please transcribe this text

[Assistant] Once he became chair of the Senate's Permanent Subcommittee on Investigations in 1953, McCarthy's recklessness increased.


[User] Please transcribe this text

[Assistant] It was his investigation of the army that finally turned public opinion against him and diminished his power.


[User] Please transcribe this text

[Assistant] McCarthy's colleagues in the Senate censured him, and he died less than three years later, probably from alcoholism.


[User] Please transcribe this text

[Assistant] McCarthyism ended as well. It had ruined hundreds if not thousands of lives and drastically narrowed the American political spectrum.


[User] Please transcribe this text

[Assistant] Its damage to democratic institutions would be long-lasting. In all likelihood, there were both Democrats and Republicans who knew that the anti-communist purges were deeply unjust, but feared that directly opposing them would hurt their careers.


[User] Please transcribe this text

[Assistant] Even the Supreme Court failed to stop the witch hunt, condoning serious violations of constitutional rights in the name of national security.


[User] Please transcribe this text

[Assistant] Was domestic communism an actual threat to the American government? Perhaps, though a small one.


[User] Please transcribe this text

[Assistant] And if new demagogues appeared in uncertain times to attack unpopular minorities in the name of patriotism, could it all happen again?


[User] Please transcribe this text

[Assistant] There are two of those, and they're pieced them together, then you might have a blackberry.


[User] Please transcribe this text

[Assistant] 30 of those, and then piece them together, then you might have learned.


[User] Please transcribe this text

[Assistant] It's exciting, it's intriguing, it's playful, completely, utterly satisfying on every single level. I think you've got some competition in town and she's just an amateur, man.


[User] Please transcribe this text

[Assistant] Certainly I don't know of a harder elimination challenge than the eight years I've been doing MasterChef. It takes it to a whole new level. What I've experienced the last week has been just incredible.


[User] Please transcribe this text

[Assistant] I'm gonna do a spit roasted barbecued leg of lamb.


[User] Please transcribe this text

[Assistant] Roughly the same, milk, butter, cream. Say 25, 30 grams. There's salt.


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[Assistant] What I'm gonna do here is bring this to the boil as soon as it starts to boil. As soon as it starts to boil. I'll remove it from the heat. Start the timer at six minutes.


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[Assistant] In the bottom, I've just put an upturn, just a side plate. That way, the egg falls to the bottom. It doesn't hit the direct heat of the bottom of the pan. And then I'll put salt in the water.


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[Assistant] Wow, look at that. That looks beautiful. And that is just ready for a columbaris special slice of buttered toast.


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[Assistant] I've made a brine. A brine is just salted water. There's 320 grams of salt. Mix that with 3.2 litres of water. About 30 grams of sprigs of rosemary. 20 to 30 grams of thyme. And there's two, three bay leaves. Got a head of garlic and some black peppercorns.


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[Assistant] We've got the studied lamb, and that goes into the oven, 120 degrees, till you get a core temperature of 45 degrees. Allow about 45 minutes for this.


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[Assistant] As an introduction to this theory section, I want to start with a very cool and very high level overview about the JavaScript language. Just so you get a first overview of most of the things that we need to learn in order to eventually master JavaScript.


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[Assistant] And I want to start by coming back to that first definition that I gave you on JavaScript, where I told you that JavaScript is a high-level, object-oriented, multi-paradigm programming language.


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[Assistant] JavaScript is a high-level, prototype-based, object-oriented, multi-paradigm, interpreted, or just-in-time compiled, dynamic, single-threaded, garbage-collected programming language with first-class functions and a non-blocking event loop concurrency model.


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[Assistant] Well, yeah, it kind of is, actually. I just packed as much information as possible about JavaScript into one unreadable sentence just for fun.


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[Assistant] But besides fun, this also gives us a great opportunity to unpack all of this in order to get a first peek into all the stuff that we're going to learn about JavaScript throughout the course.


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[Assistant] And this is not to intimidate you. It's really not.


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[Assistant] I just want you to get the big picture before really getting started and also introduce some topics that are just good to know. Because getting the big picture before diving deep is, in my opinion, an amazing learning technique.


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[Assistant] So, with that being said, let's unpack this now a little bit.


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[Assistant] So these are the nine JavaScript topics that we're briefly going to talk about in this lecture, slightly reordered.


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[Assistant] And starting with the high level part, as you might already know, every program that runs on your computer needs some hardware resources such as memory and a CPU to do its work.


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[Assistant] Now, there are low-level languages, such as C, where you have to manually manage these resources. For example, asking the computer for memory to create a new variable.


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[Assistant] On the other side, you have high level languages such as JavaScript and Python, where we do not have to manage resources at all.


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[Assistant] Because these languages have so-called abstractions that take all of that work away from us. This makes the language easier to learn and to use, but the downside is that programs will never be as fast or as optimized as, for example, C programs.


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[Assistant] Now, one of the powerful tools that takes memory management away from us developers is garbage collection.


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[Assistant] Which is basically an algorithm inside the JavaScript engine which automatically removes old unused objects from the computer memory in order not to clog it up with unnecessary stuff.


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[Assistant] So it's a little bit like JavaScript has a cleaning guy who cleans our memory from time to time so that we don't have to do it manually in our code.


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[Assistant] Next up, JavaScript is an interpreted or just-in-time compiled language.


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[Assistant] And again, we will learn more about this in this section, but what you need to know for now is that the computer's processor only understands zeros and ones. That's right. Ultimately, every single program needs to be written in zeros and ones, which is also called machine code.


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[Assistant] And since that's not really practical to write, is it? We simply write human readable JavaScript code, which is an abstraction over machine code. But this code eventually needs to be translated to machine code. And that step can be either compiling or interpreting.


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[Assistant] This step is necessary in every single programming language, because no one writes machine code manually. In the case of JavaScript, this happens inside the JavaScript engine.


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[Assistant] Now, one of the things that makes JavaScript so popular is the fact that it's a multi-paradigm language.


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[Assistant] In programming, a paradigm is an approach and an overall mindset of structuring our code, which will ultimately direct the coding style and technique in a project that uses a certain paradigm.


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[Assistant] And this definition still sounds kind of abstract, but don't worry, it will become more clear as we move on.


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[Assistant] Now, three popular paradigms are procedural, object-oriented and functional programming.


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[Assistant] So procedural programming is what we've been doing so far, which is basically just organizing the code in a very linear way, and then with some functions in between.


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[Assistant] Now about object-oriented programming and functional programming, I will talk about them in a second. Also, we can classify paradigms as imperative or as declarative. But again, more on that later.


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[Assistant] Now, many languages are only procedural or only object-oriented or only functional. But JavaScript does all of it.


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[Assistant] So it's really flexible and versatile, and so we can do really whatever we want with it. It's our choice. We can use whatever paradigm we want. And in this course, I will show you how to use all of them.


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[Assistant] So, about the object-oriented nature of JavaScript, it is a prototype-based, object-oriented approach. Now what does that mean?


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[Assistant] Well, first, almost everything in JavaScript is an object, except for primitive values, such as numbers, strings, etc. But arrays, for example, are just object. And we already saw that in practice, right?


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[Assistant] Now, have you ever wondered why we can create an array and then use the push method on it, for example?


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[Assistant] Well, it's because of prototypal inheritance. Basically, we create arrays from an array blueprint, which is like a template, and this is called the prototype.


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[Assistant] This prototype contains all the array methods. And the arrays that we create in our code then inherit the methods from the blueprint so that we can use them on the arrays.


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[Assistant] And this, what I just explained to you, is actually a huge oversimplification, which might still sound confusing, and that's the reason why there is a whole section on object-oriented programming with JavaScript later in the course.


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[Assistant] But since this is a big overview lecture, I wanted to first introduce this topic right here.


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[Assistant] Next up, JavaScript is a language with first class functions, which simply means that functions are treated just as regular variables.


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[Assistant] So, we can pass functions into other functions, and we can even return functions from functions.


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[Assistant] And this is extremely powerful because it allows us to use a lot of powerful techniques and also allows for functional programming, which is one of the paradigms that we just talked about before.


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[Assistant] And in fact, we have already used the power of first-class functions without knowing that they are called first-class functions.


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[Assistant] So remember this piece of code that we wrote for closing the modal window that we built before. So right here we passed the closeModal function into the addEventListener function as if it was just a regular variable. Right?


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[Assistant] And actually, not all languages have first-class functions. But JavaScript has, and it is amazing. Believe me, it's really, really helpful.


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[Assistant] Next, I said that JavaScript is a dynamic language. And dynamic actually means dynamically typed. So as we've already seen, in JavaScript we don't assign data types to variables.


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[Assistant] Instead, they only became known when the JavaScript engine executes our code.


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[Assistant] Also, the type of variables can easily be changed as we reassign variables. And this is basically what dynamically typed means. Now, there is a lot of controversy if this is good or bad, but this is just how it works.


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[Assistant] Now the same is not true for most other programming languages, where we have to manually assign types to variables.


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[Assistant] And this then usually prevents bugs from happening, which is the reason why many people say that JavaScript should be a strongly typed language as well. And if you yourself want to use JavaScript with types, then you can always look into TypeScript.


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[Assistant] But anyway, let's now finally talk about the single thread and the non-blocking event loop concurrency model.


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[Assistant] Now this is a really complex topic and probably the most complex one of the whole course, which is why it's kind of at the end of the course. And therefore I'm not going to go into specifics here just yet, but instead I will just define some things here.


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[Assistant] First, what actually is a concurrency model?


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[Assistant] Well, it's just a fancy term that means how the JavaScript engine handles multiple tasks happening at the same time.


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[Assistant] Okay, that's cool, but why do we need that? Well, because JavaScript itself runs in one single thread, which means that it can only do one thing at a time. And therefore we need a way of handling multiple things happening at the same time.


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[Assistant] And by the way, in computing, a thread is like a set of instructions that is executed in the computer's CPU. So basically, the thread is where our code is actually executed in the machine's processor. Alright.


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[Assistant] What if there is a long-running task, like fetching data from a remote server?


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[Assistant] Well, it sounds like that would block the single thread where the code is running, right? But of course, we don't want that. What we want is so-called non-blocking behavior. And how do we achieve that?


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[Assistant] Well, by using a so-called event loop. The event loop takes long-running tasks, executes them in the background, and then puts them back in the main thread once they are finished.


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[Assistant] And this is, in a nutshell, JavaScript's non-blocking event loop concurrency model with a single thread. It sounds like a mouthful for sure, but in the end, it really just compresses to this.


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[Assistant] Just keep in mind that, again, this is a huge oversimplification that we will come back to.


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[Assistant] Alright, and this actually rounds up this overview lecture. I know there was a ton of stuff to take in, but I hope that you still learned a thing or two here.


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[Assistant] And equally important, how do you tune the learning rates alpha for stochastic gradient descent? In this video, we'll talk about some techniques for doing these things, for making sure it's converging, and for picking the learning rates alpha.


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[Assistant] Cost function and we would make sure that this cost function is decreasing on every iteration.


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[Assistant] When the training set size was small, we could do that because we could compute the sum pretty efficiently. But when you have a massive training set size,


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[Assistant] Then you don't want to have to pause your algorithm periodically. You don't want to have to pause the cost gradient descent periodically in order to compute this cost function since it requires a sum over your entire training set size.


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[Assistant] And the whole point of stochastic gradient descent was that you wanted to start to make progress after looking at just a single example without needing to occasionally scan through your entire training set right in the middle of the algorithm just to compute things like the cost function over your entire training set.


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[Assistant] Right before, we train on a specific example. So in stochastic gradient descent, we're going to look at the examples xi, yi in order, and then sort of take a little update with respect to this example, and then go on to the next example, xi plus one, yi plus one, and so on. Right? That's what stochastic gradient descent does.


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[Assistant] Just to say the same thing again but using slightly different words, as the costly gradient descent is scanning through our training set,


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[Assistant] Right before we have updated theta using a particular training example x i comma y i, let's compute how well our hypothesis is doing on that training example.


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[Assistant] And we want to do this before updating theta because if we've just updated theta using that training example, you know, then it might be doing better on that example than would be representative.


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[Assistant] Say every thousand innovations, we can plot these costs that we've been computing in the previous step. We can plot those costs average over say the last thousand examples processed by the algorithm.


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[Assistant] So in contrast to computing J train periodically, which needed to scan through the entire training set, with this sort of procedure, well as part of stochastic gradient descent, it doesn't cost much to compute these costs as well.


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[Assistant] Right before updating to prime to theta, and all we're doing is every thousand iterations or so, we just average the last one thousand costs that we computed and we plot that.


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[Assistant] And uh, by looking at those plots, this will allow us to check if stochastic grain descent is converging.


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[Assistant] You get a figure that looks like this, you know, that would be a pretty decent run of the algorithm, maybe, where it looks like the cost has gone down and then it's plateaued out and it's kind of flattened out, you know, starting from around that point. So it looks like, if this is what your cost looks like, then maybe your learning algorithm has converged.


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[Assistant] If you were to try using a smaller learning rate, something that you might see is that the algorithm may initially learn more slowly, so the cost goes down more slowly.


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[Assistant] But then eventually with a smaller learning rate it's actually possible for the algorithm to end up at a maybe very slightly better solution. So the red line may represent the behavior of stochastic gradient descent using a slower, using a smaller learning rate.


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[Assistant] And the reason this is the case is because you remember stochastic gradient descent doesn't just converge to the global minimum, is that what it does is the parameters will oscillate a bit around the global minimum. And so by using a smaller learning rate, you end up with smaller oscillations. And uh, sometimes this little difference will be negligible.


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[Assistant] And, uh, sometimes with a smaller learning rate, you can get a slightly better, um, value for the parentheses.


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[Assistant] Here are some other things that might happen. Let's say you run stochastic gradient descent and you average over a thousand examples, uh, when, when plotting these costs. So, you know, here it might be a, a result of another one of these plots. And again, it kind of looks like it's converged.


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[Assistant] The disadvantage of making this too big of course is that now you get one data point only every 5,000 examples. And so the feedback you get on how well your learning algorithm is doing is sort of, maybe it's more delayed because you get one data point on your plot only every 5,000 examples rather than every 1,000 examples. Along a similar vein,


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[Assistant] Sometimes you may run stochastic gradient descent and end up with a plot that looks like this. And with a plot that looks like this, you know, it looks like...


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[Assistant] The cost just is not decreasing at all. It looks like the algorithm is just not learning. It just looks like basically a flat curve and it's just, the cost is just not decreasing.


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[Assistant] To averaging over a larger number of examples, it's possible that you see something like this red line. It looks like the cost actually is decreasing. It's just that the blue line, averaging over two few examples, the blue line was too noisy, so you couldn't see the actual trend in the cost actually decreasing.


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[Assistant] You need to either change the learning rates or change the features or change something else about the algorithm.


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[Assistant] Finally, one last thing that you might see would be if you were to plot these curves and you see a curve that looks like this, where it actually looks like it's increasing.


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[Assistant] And if that's the case, then this is a sign that the algorithm is diverging. And what you really should do is use a smaller value of the learning rate alpha.


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[Assistant] So hopefully this gives you a sense of the range of phenomena you might see when you plot these costs average over some range of examples, as well as suggest the sorts of things you might try to do in response to seeing different plots.


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[Assistant] So if the plot looks too noisy, if it wiggles up and down too much, then try increasing the number of examples you're averaging over so you can see the overall trend in the plot better. And if you see that the errors are actually increasing, the costs are actually increasing, try using a smaller value of alpha.


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[Assistant] We saw that when you run stochastic rain descent, the algorithm will start here and sort of neander towards the minimum.


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[Assistant] And so you end up with a parameter value that is hopefully close to the global minimum that won't be exactly at the global minimum.


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[Assistant] In most typical implementations of stochastic gradient descent, the learning rate alpha is typically held constant, and so what you typically end up with is exactly a picture like this.


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[Assistant] If you want stochastic grain descent to actually converge to a global minimum, there's one thing which you can do, which is you can slowly decrease the learning rate alpha over time.


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[Assistant] One of the reasons people tend not to do this is because you end up needing to spend time playing with these two extra parameters, cons1 and cons2. And so this makes the algorithm more finicky. It's just more parameters that you need to fiddle with in order to make the algorithm work well.


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[Assistant] But if you manage to tune the parameters well, then the picture you can get is that the algorithm will actually meander around.


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[Assistant] Towards the minimum, but as it gets closer, because you're decreasing the learning rate, the meanderings will get smaller and smaller until it pretty much just converges to the global minimum.


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[Assistant] I hope this makes sense, right? And the reason this formula makes sense is because as the algorithm runs, the iteration number becomes large and so alpha will slowly become small. And so you take smaller and smaller steps until it, you know, hopefully converges to the global minimum.


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[Assistant] And, uh, because frankly usually we're pretty happy with any parameter value that's, you know, pretty close to global minimum. Typically this process of decreasing alpha slowly is usually not done and keeping the learning rate alpha constant is the more common application of stochastic gradient descent. Although you will see people use either version.


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[Assistant] This episode is brought to you by Nature's Find, a fungi-based food company for optimists. Click the link in the description to discover the science behind Nature's Find fungi-based foods.


[User] Please transcribe this text

[Assistant] Can also make a world of difference. Some famous chefs swear by one of those classic metals for some of their pots and pans, copper.


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[Assistant] Which historians think humans have been cooking with for thousands of years. But at hundreds of dollars or more for just a piece of cookware,


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[Assistant] Copper is a lot pricier than other materials like stainless steel or aluminum, but some people insist it's well worth the dough. One of the major functions of cookware is thermal conductivity.


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[Assistant] And this unpaired electron gets to wander free in the pure copper. And when you apply heat, the atom closest to the heat source starts vibrating and bumping into the free electrons nearby, transferring the heat. So copper is really effective at thermal conductivity.


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[Assistant] As a side note, this also applies to its electrical conductivity, which is why we use it in wires. But in the kitchen, we often see pans made out of stainless steel or cast iron, which, compared to copper, take a long time to heat up.


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[Assistant] In large part because it's an alloy, or a mixture of metals. Compared to these alloys, copper is 5 times as conductive as cast iron and 25 times as conductive as stainless steel. Because pure copper provides smooth sailing for its electrons.


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[Assistant] But when you mix copper with other metals to make stainless steel, you're blending atoms of different sizes, forming an irregular alloy framework, which makes the road pretty bumpy for electrons to travel and transfer heat. Another great property of copper is thermal diffusivity. That means that these pans don't only heat up fast, they also spread heat evenly throughout the material.


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[Assistant] So you don't have to worry about hot and cold spots due to the uneven distribution of heat. In addition to its excellent heat conduction and diffusion, copper is also really responsive to heat. Its electrons get moving right away when you put it over a hot stove.


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[Assistant] But they also chill really fast when you take the heat source away. So it's great for stuff that needs to heat up fast and also cool down quickly in the same pan, like caramel or delicate sauces. But copper's ready-to-go electrons also make it finicky in some ways. Like, it's prone to oxidation, a process where it loses its electrons and sometimes gains oxygen.


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[Assistant] That's what's going on with the blue-green coating, or patina, that we see on the Statue of Liberty, which has a wrought iron framework under her thin outer copper skin. And acids speed up this process. They're happy to take electrons from copper.


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[Assistant] To prevent this, some copper pans are lined with more stable metals or alloys like tin or stainless steel to make them safe for contact with those foods and easier to clean. And in some countries, like Canada, it's actually not possible to buy copper pots


[User] Please transcribe this text

[Assistant] And that has to do with the capacity of different metals to store heat per unit of mass. Stainless steel, for instance, requires about 25% more energy to heat up than the same mass of copper.


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[Assistant] So its free electrons are a little more free to move around and transfer heat. But silver pans are obviously way more expensive. They're known as the chef's version of a Rolex. So the answer to whether you need a copper pot depends on


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[Assistant] What your needs are. Copper has its perks, but you can totally go to town in the kitchen with an alloy like stainless steel or another material. But now you know how electrons move in different pans and heat up its contents, so hooray for informed decision-making. Or if all of this chemistry chalk just has you beat, you can always consider outsourcing the electron vibing and just order some takeouts.


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[Assistant] Or you can use whatever kind of pot you prefer to cook a protein-rich vegan meal with nature's fun.


[User] Please transcribe this text

[Assistant] From a microbe first discovered by NASA-funded research, Nature's Find uses fermentation to grow nutritional fungi protein that's more sustainable than traditional agriculture. I really enjoyed getting to try Nature's Find meatless breakfast patties. It's honestly pretty impressive what they're able to accomplish with fungi.


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[Assistant] The seasoning, the flavours, the preparation of the fish, how everything's laid out.


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[Assistant] If I leave any bones in this fish, then I'm toast. So I've gotta take my time over this. It's gotta be right.


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[Assistant] There's a reason why every single element's on a dish, so I'm determined I'm not going to miss off a single element.


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[Assistant] I think I took the top of the black pudding a bit far as well, but I'm really happy that I've got every single element up.


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[Assistant] He's gone. He's gone to the indescribable.


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[Assistant] Come on guys, stop being such cowards. It's a mystery and an adventure.


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[Assistant] That laugh came from this room. Let's go and check it out.


[User] Please transcribe this text

[Assistant] Hi. Thanks for watching all the way to the end of the video. I hope you enjoyed it. If you want even more great content, we've got plenty more videos on the channel. So, subscribe and you'll never miss a thing. I'll see you there. Bye.


[User] Please transcribe this text

[Assistant] But not all fillings come in runny form. What's a Christmas turkey without stuffing? Where would the dumplings be without that tasty meatiness cradled inside it?


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[Assistant] But just that little peek out in the garden out there, pacing backs and forth, is a top professional chef and he wants to know the same thing. We want to know who is going to take him on in the second round. Who is going to have the chance to win a pin?


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[Assistant] Love, love the saltiness of the walnut crumb. Love the, the colour that you've got in the brioche around the edge.


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[Assistant] We have picked the cook who'll be cooking off against professional four. Potentially only the second pin of the year.


[User] Please transcribe this text

[Assistant] Please welcome from Cabarita Beach, in the state's sunny north, Paper Daisies' Ben Devlin!


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[Assistant] So, Ben, look, I think we all know you from Noma and from the top restaurants you worked in. How do you think you'd affair in this kitchen then?


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[Assistant] And Sarah is forced to be reckoned with. Uhm, she's on a bit of a roll, so...


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[Assistant] A little distraction. But it's a really delicious plate of food. Delicious.


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[Assistant] But then there's also that question, there's something very reassuring about the first broth.


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[Assistant] The prudent man has been changed to the prudent person. But regulators have wondered, how can we define what good behavior is among investment managers?


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[Assistant] In 1974, ERISA, the Employment Retirees Income Security Act, defined a prudent man and required that investment managers behave like a prudent man, a prudent person.


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[Assistant] And that is quoting the act with the care, skill, prudence, and diligence under the circumstances then prevailing that a prudent man acting in like capacity and familiar with such matters would use in the conduct of an enterprise of a like character and with like aims.


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[Assistant] It's the strangest regulatory demand. What it's telling investment managers to do is do what somebody else would do in this circumstance. Uh, well that's maybe overstating it a little bit. But, uh, you don't have to do exactly what they're doing, but it has to be prudent.


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[Assistant] This prudent man rule has been a bugbear for investment managers ever since 1974. The Dodd-Frank Act doesn't ever use prudent person.


[User] Please transcribe this text

[Assistant] Uh, we've updated it, now we can't say man, it has to be gender neutral. But beyond that, they've dropped person out completely, and they call that prudential standards. That appears 34 times in the Dodd-Frank Act.


[User] Please transcribe this text

[Assistant] Uhm, and we're moving away from the reliance on the prudent person rule into having more concrete regulatory standards. However, there's so many details. If you move away from the prudent person standard, uh,


[User] Please transcribe this text

[Assistant] What do you substitute? Now we have to be explicit and the financial world is so complicated. A financial advisor


[User] Please transcribe this text

[Assistant] I have to not claim that I am, uh, we're only getting general education and not tailored advice. Financial advisors listen to your situation and give advice that's tailored to your situation.


[User] Please transcribe this text

[Assistant] Uh, it also excludes broker-dealer. So now we're trying to get past the prudent person. We have to regulate all of these people. We have to define their different categories. A broker-dealer is a company that you would go to to buy and sell securities.


[User] Please transcribe this text

[Assistant] And when you call your broker, you could just say, I want to place a, you know, a limit order to buy, uh, this at, uh, this share at such and such a price.


[User] Please transcribe this text

[Assistant] That would be a pure broker-dealer interaction. But typically people, when they call their broker, they'll also say, I'm thinking of buying these shares. What do you think?