[User] Please transcribe this text

[Assistant] So here's what you can do. We'll go through a couple different algorithms in this video.


[User] Please transcribe this text

[Assistant] Here's a simple sentiment classification model. You can take a sentence like desserts is excellent and look up those words in your dictionary. We'll use our 10,000 word dictionary as usual. And, uh, let's build a classifier to map it to the output y that this was four stars.


[User] Please transcribe this text

[Assistant] So, given these four words, as usual, we can, uh, take these four words and, uhm, take


[User] Please transcribe this text

[Assistant] Look up the one-hot vector, so this 08928 is a one-hot vector.


[User] Please transcribe this text

[Assistant] Multiply it by the embedding matrix E, which you can learn from a much larger text corpus, so you can learn an embedding from say a billion words, a hundred billion words, and use that to extract out the embedding vector for the word D.


[User] Please transcribe this text

[Assistant] And then do the same for dessert, do the same for is, and do the same for excellent.


[User] Please transcribe this text

[Assistant] If this was trained on a very large dataset, like 100 billion words, then this allows you to take a lot of knowledge even from infrequent words and apply them to your problem, even words that weren't in your labelled training set.


[User] Please transcribe this text

[Assistant] Now, here's one way to build a class file, which is that you can take these vectors, let's say these are 300 dimensional vectors, and you could then just sum or average them.


[User] Please transcribe this text

[Assistant] Average operator here, and you could use sum or average. And this gives you a 300 dimensional feature vector that you then pass to a


[User] Please transcribe this text

[Assistant] Softmax classifier, which then outputs y hat. And so the softmax can output


[User] Please transcribe this text

[Assistant] What are the probabilities of the five possible outcomes from one star up to five stars? So this would be a soft max of the five possible outcomes to predict what is why.


[User] Please transcribe this text

[Assistant] So notice that by using the average operation here, this particular algorithm works for reviews that are short or long, because even if a review that is 100 words long, you can just sum or average all the feature vectors for 100 words, and that gives you a representation.


[User] Please transcribe this text

[Assistant] A three-dimensional feature representation that you can then pass into your SongFax classifier.


[User] Please transcribe this text

[Assistant] So this algorithm will work decently well, and what it does is it really averages the meanings of all the words, uhm, or sums the meaning of all the words in your example. And this won't work too badly.


[User] Please transcribe this text

[Assistant] So one of the problems with this algorithm is it ignores word order.


[User] Please transcribe this text

[Assistant] In particular, this is a very negative review. Completely lacking in good taste, good service, and good ambience. But the word good appears a lot. You say, wow, good, good, good. So if you use an algorithm like this that ignores word order,


[User] Please transcribe this text

[Assistant] And just sums or averages all of the embeddings for the different words, then you end up, you know, having a lot of the representation of good in your final feature vector. And your classifier will probably think this is a good review, even though this is actually very harsh. This is a one star.


[User] Please transcribe this text

[Assistant] So here's a more sophisticated model, which is that instead of just summing all of your word embeddings, you can instead use a RNN for sentiment classification.


[User] Please transcribe this text

[Assistant] So here's what you can do. You can take that, uh, review, completely lacking good taste, good service, good ambience, and, uh, find for each of them the one-hot vector.


[User] Please transcribe this text

[Assistant] And so I'm gonna just skip the one-hot vector representation, but take the one-hot vectors, multiply it by the embedding matrix E as usual, then this gives you the embedding vectors.


[User] Please transcribe this text

[Assistant] And then you can feed these into an RNN.


[User] Please transcribe this text

[Assistant] And the job of the RNN is to then compute a representation at the last time step that allows you to predict y hat. So this is an example of a many to one RNN architecture, which we saw in the previous week.


[User] Please transcribe this text

[Assistant] And, uh, with an algorithm like this, it will be much better at taking word sequence into account and realize that things are lacking in good taste as a negative review and not good as a negative review.


[User] Please transcribe this text

[Assistant] Unlike the previous algorithm, which just sums everything together into a big word vector, mush, uh, and doesn't realize that not good has a very different meaning than, um, you know, the word good, or lacking in good taste and so on.


[User] Please transcribe this text

[Assistant] And so if you train this algorithm, you end up with a pretty decent sentiment classification algorithm.


[User] Please transcribe this text

[Assistant] And because your word embeddings can be trained from a much larger data set, this will do a better job generalizing to maybe even new words not seen in your training set, such as someone else's completely absent of


[User] Please transcribe this text

[Assistant] You know, good taste, uh, good service and good ambience or something, then even if the word absent is not in your label training set, if it was in your 1 billion or 100 billion word, uh,


[User] Please transcribe this text

[Assistant] Corpus used to train the word embeddings, it might still get this right and generalize much better, even to words that were in the training set used to train the word embeddings, but not necessarily in the label training set that you had for specifically the sentiment classification problem.


[User] Please transcribe this text

[Assistant] So that's it for sentiment classification. I hope this gives you a sense of how once you've learned or downloaded from online a word embedding, this allows you to quite quickly build pretty effective NLP systems.


[User] Please transcribe this text

[Assistant] Tire Town School. Episode 41. Jeb is slow.


[User] Please transcribe this text

[Assistant] She tagged Eddie. You're it, said Poppy. She took off. Eddie started to follow.


[User] Please transcribe this text

[Assistant] I know. If the rainbow is not in the sky, then it must have disappeared.


[User] Please transcribe this text

[Assistant] I'm afraid rainbows don't stay where you want them to. Why not? Rainbows only appear when the sun comes out after the rain. But only sometimes. That's why they're so special.


[User] Please transcribe this text

[Assistant] Why don't you two go and have some fun? I'll stay here to keep an eye on the sky. Just in case the rainbow comes back.


[User] Please transcribe this text

[Assistant] But you brighten every day, even the dull ones.


[User] Please transcribe this text

[Assistant] I have promised to help Otto with his dam, so I have to go.


[User] Please transcribe this text

[Assistant] Hmm, these are good. What have you found, little nut-brown hair? Little nut-brown hair?


[User] Please transcribe this text

[Assistant] No. Are you sure you don't have a collection?


[User] Please transcribe this text

[Assistant] Maybe you do have one, but of something that you can't see.


[User] Please transcribe this text

[Assistant] You have a lot of memories.


[User] Please transcribe this text

[Assistant] That's almost like having a lot of autumn leaves or a lot of acorns.


[User] Please transcribe this text

[Assistant] That's right. And you've just shared your collection with me.


[User] Please transcribe this text

[Assistant] As much as all your wonderful memories from today?


[User] Please transcribe this text

[Assistant] However you wanna sit, wherever you wanna go, that's up to you. We're pretty lenient here. We don't care.


[User] Please transcribe this text

[Assistant] Classic survival horror game. Redone, remastered. And all thanks to you guys being a part of it.


[User] Please transcribe this text

[Assistant] tomato t-o-m-a-t-o tomato


[User] Please transcribe this text

[Assistant] tomato t o m a t o tomato


[User] Please transcribe this text

[Assistant] L-E-T-T-U-C-E. Lexus.


[User] Please transcribe this text

[Assistant] Lettuce. L-E-T-T-U-C-E. Lettuce. Lettuce.


[User] Please transcribe this text

[Assistant] Beans, b-e-a-n-s, beans. Beans, b-e-a-n-s, beans.


[User] Please transcribe this text

[Assistant] Pepper. P-E-P-E-R. Pepper.


[User] Please transcribe this text

[Assistant] Pepper. P-E-P-E-R. Pepper.


[User] Please transcribe this text

[Assistant] Pepper. P-E-P-E-R. Pepper.


[User] Please transcribe this text

[Assistant] S Q U A S H Squash


[User] Please transcribe this text

[Assistant] S Q U A S H Squash


[User] Please transcribe this text

[Assistant] g-o-u-r-d


[User] Please transcribe this text

[Assistant] C U C U M B E R Cucumber


[User] Please transcribe this text

[Assistant] C U C U M B E R Cucumber


[User] Please transcribe this text

[Assistant] Tarot. T A R O Tarot.


[User] Please transcribe this text

[Assistant] Tarot. T A R O. Tarot.


[User] Please transcribe this text

[Assistant] L A D Y apostrophe S


[User] Please transcribe this text

[Assistant] Pattern Practice by Elf Learning


[User] Please transcribe this text

[Assistant] What color are they? Are you ready? Here we go. Are they green apples? No, they're not.


[User] Please transcribe this text

[Assistant] Are they purple apples? No, they're not.


[User] Please transcribe this text

[Assistant] Are they red apples? Yes, they are.


[User] Please transcribe this text

[Assistant] Are they pink bananas? No, they're not.


[User] Please transcribe this text

[Assistant] Are they brown bananas? No, they're not.


[User] Please transcribe this text

[Assistant] Are they yellow bananas? Yes, they are.


[User] Please transcribe this text

[Assistant] Are they grey oranges? No, they're not. Are they blue oranges? No, they're not.


[User] Please transcribe this text

[Assistant] Are they orange peaches? Yes, they- Wait. No, they're not.


[User] Please transcribe this text

[Assistant] They are orange oranges.


[User] Please transcribe this text

[Assistant] Thanks for watching! This has been ELF Learning. And, don't forget to subscribe for more ELF videos. We'll see you next time. Bye.


[User] Please transcribe this text

[Assistant] And so, they were all kept busy warming the eggs.


[User] Please transcribe this text

[Assistant] As the days passed, he and Little Field Mouse visited the chicks as often as possible. And it was always exciting and surprising.


[User] Please transcribe this text

[Assistant] So little nut brown hair decided to call for help from his friends.


[User] Please transcribe this text

[Assistant] Thank you, Little White Owl.


[User] Please transcribe this text

[Assistant] Baby, baby, I love you. Good dreams.


[User] Please transcribe this text

[Assistant] It's time to comb my hair. I comb my messy hair. Comb, comb. Uhm, uhm? Comb, comb.


[User] Please transcribe this text

[Assistant] Hello and welcome back to English with Lucy. I've got a new microphone.


[User] Please transcribe this text

[Assistant] Now, in my opinion, the absolute best way to sound more like a native speaker is to imitate other native speakers.


[User] Please transcribe this text

[Assistant] Now remember that I am speaking British English, so my pronunciation will be different to an American speaker's pronunciation. American English can be quite different, but just as beautiful. Now these words might seem quite easy to you, because they're words that we use all the time, but


[User] Please transcribe this text

[Assistant] Now, I do have the list, the complete list of the words in the description box.


[User] Please transcribe this text

[Assistant] Then you can check that and click on the right timing. Okay? Let's begin.


[User] Please transcribe this text

[Assistant] Hello everyone, this is Lucy from English with Lucy. I've got a really, really quick video answering a question that I've been getting quite a lot. And it is, how do you pronounce this word?


[User] Please transcribe this text

[Assistant] Well, there are two acceptable ways of pronouncing it.


[User] Please transcribe this text

[Assistant] Now originally in Old Old English, the only correct way was often. Often.


[User] Please transcribe this text

[Assistant] But now many, many people pronounce it often. Often.


[User] Please transcribe this text

[Assistant] And Cambridge and Oxford dictionaries both accept this pronunciation.


[User] Please transcribe this text

[Assistant] I am actually more inclined to pronounce it often as well, simply because it's something that my parents said. So both are absolutely fine and don't let anyone tell you otherwise. Big kiss to all my Lucifers and I'll see you again for another lesson very, very soon.


[User] Please transcribe this text

[Assistant] So the question is, I said that I wasted the vast majority of my time in business school, what did I actually take away that was worthwhile? Uhm, one lesson, which I could have gotten somewhere else, actually I just heard it on the wire that HPO showed the other day, is buy for a dollar, sell for two.


[User] Please transcribe this text

[Assistant] There's a key lesson that you have to sell for more than what you're buying for. And it seems like


[User] Please transcribe this text

[Assistant] That was probably sort of a key insight that I took away from it. Uhm, that you just have to appeal to people's own self-interest.


[User] Please transcribe this text

[Assistant] And that works in terms of running your company and getting customers and all of these things. I think, uh, capitalism in terms of,


[User] Please transcribe this text

[Assistant] I didn't have that narrowed down before I came to business school. I mean, it's very elementary, but sometimes the simplest things are the hardest to learn.


[User] Please transcribe this text

[Assistant] Semi outsiders, you can connect networks of people that have been through the stuff that you're about to go through.


[User] Please transcribe this text

[Assistant] So, right after we launched, I was freaking out about operations. How do you keep a cluster of servers up? We never had to do that before.


[User] Please transcribe this text

[Assistant] I mean, one of the key nuggets was that in that early launch phase when traffic is growing like crazy, you don't even really need any alerting because you're, you or somebody from the company is on the site


[User] Please transcribe this text

[Assistant] But today I thought we should take a thorough look through some words and even though they can be quite difficult, I'm pretty sure I can help you with them. So today we are looking at these four words here. Let's look at the first one. Then we have And then And finally


[User] Please transcribe this text

[Assistant] Thought, thought. Let's divide these words into two sections. Those that start with the unvoiced consonant and those that start with the voiced consonant.


[User] Please transcribe this text

[Assistant] And though starts with the v, voiced consonant.


[User] Please transcribe this text

[Assistant] And then the second syllable, the unstressed syllable is the schwa, uh, thorough, thorough, through.


[User] Please transcribe this text

[Assistant] Okay, we've got a long uuuh sound. Through, through, uuuh. With thought,


[User] Please transcribe this text

[Assistant] We've got the or sound, or, or, thought, thought, and it ends with a th.


[User] Please transcribe this text

[Assistant] Though has a diphthong. It has the o sound. Th, o, though. Okay, so we've got thorough, through, thought, though.


[User] Please transcribe this text

[Assistant] Hopefully that cleared up all your doubts. And I'm going to ask you guys a favour. Next time you see


[User] Please transcribe this text

[Assistant] Anyone with a doubt about how to pronounce any of these words, I want you to send them this video. I see people worrying about these words all the time and I know that we can help them now with this video. If you want to learn more about British English pronunciation join my free live pronunciation lesson


[User] Please transcribe this text

[Assistant] Every Sunday at 4.30 London GMT time.


[User] Please transcribe this text

[Assistant] It's on my English with Lucy Facebook page, I'm there every week, and it's great to see so many of you tuning in.


[User] Please transcribe this text

[Assistant] Don't forget to connect with me on all of my social media, and I'll see you for another lesson very, very soon.


[User] Please transcribe this text

[Assistant] This is Scott from OpticsRealm. This is the ZMax tutorial number 5. It's May 2012. So we're going to start with a paraxial double telecentric lens and then we're going to convert those paraxial lenses to real catalog parts.


[User] Please transcribe this text

[Assistant] Trace some rays in a real system, as opposed to today, we just kinda talked about the, what you need to do to set up your Zmax file.


[User] Please transcribe this text

[Assistant] So I've got a couple slides here we're gonna go through and then I'm going, I made these slides, uh, years ago. We're going to attempt to redo it live in Zmax and you'll get to see how many mistakes I make.


[User] Please transcribe this text

[Assistant] First we're going to start with a telecentric eyepiece. So here we have a paraxial lens. It's got a 20mm focal length.


[User] Please transcribe this text

[Assistant] And, uh, I've got a marginal ray solve here, and I'll show you what that is later. I haven't talked about that already. Uh, so it's, it's focused for the paraxial marginal ray, and of course this is a perfectly focused beam, no aberrations whatsoever.


[User] Please transcribe this text

[Assistant] And then actually surface one is going to be the aperture stop and it's one focal length away. That's how you get a telecentric system. So it's 20 millimeters away. A telecentric system has a


[User] Please transcribe this text

[Assistant] The layout here, the paraxial lens is your aperture stop, what I'm showing in this, uh, diagram box. So if you come to this standard and you double click on this standard line, line one, you click the surface to make it your, your surface stop and then you get this layout as I've shown here.


[User] Please transcribe this text

[Assistant] And then you insert, uh, a finite conjugate, you insert another dummy lens, another paraxial lens, and, uh, you, you make it also one focal length away. I'm gonna match these focal lengths so it's a one-to-one imager. And then I'm gonna use the field, uh, enter some field data, I'm going to put a five millimeter


[User] Please transcribe this text

[Assistant] Then I'm going to show you how to insert a lens in place of one of those paraxial lenses. And of course, I'm going to use an Edmund stock acromat. You can choose to use whatever other supplier you'd like to go to. You can click the LEN button or you can hit F5.


[User] Please transcribe this text

[Assistant] Now, you're gonna have to delete the paraxial lens after you get that inserted. So in this case, we still have a paraxial lens here.


[User] Please transcribe this text

[Assistant] This last lens I've replaced with a doublet and I've deleted the paroxial lens.


[User] Please transcribe this text

[Assistant] I've had to adjust the, uh, relative position, the distance from here to the stop, the front focal length is a little bit different than the paraxial case, to get the chief ray collimated.


[User] Please transcribe this text

[Assistant] Uhm, and again I adjusted the stop diameter, I'm using the general command, I'm doing float by stop size, and I adjusted this to maintain an F4, and you can see down here your F4. We'll show you this in Zmax in a little bit. So now we're gonna replace the front paraxial lens with that same Edmund stock lens.


[User] Please transcribe this text

[Assistant] So the way you do that, you can go tools, miscellaneous, reverse elements, or I like the hotkeys, uh, control shift B, and you could say which surfaces. So in this case, it's surface one, I've cut off the surfaces here, surface one to three, and you flip it, and you end up with this system here.


[User] Please transcribe this text

[Assistant] Now once you've got the system put together, uh, you'll look at it and you'll realize, you know, my rays at the aperture stop aren't filling the stop. And that's due to pupil aberrations. So, uhm,


[User] Please transcribe this text

[Assistant] And again, pupil, this is kind of an advanced topic. I've not talked about this in my optics video podcasting yet, but I will get to it. But the entrance pupil is the effect of, so if this is your aperture stop,


[User] Please transcribe this text

[Assistant] It's gonna be imaged through this lens, and it's gonna appear as a, kinda like when you look in a rifle scope, there's that black hole that you put your eye on, you match your eye with.


[User] Please transcribe this text

[Assistant] Well, there's aberrations in that. So Zmax is just launching out in object space a uniform grid of rays, and it's not filling this aperture stop. So the way around that is if you go to your general tab, J-E-N, it's this one right here, or if you do Ctrl-G and then you go to this ray aiming tab, you could go to ray aiming paraxial,


[User] Please transcribe this text

[Assistant] And what it's gonna do is it's gonna take you from this, this green rays aren't filled, to you can see here, and I'll, I'll blow this up, you can see the rays are truly filling your aperture stop.


[User] Please transcribe this text

[Assistant] So now let's attempt to do this in Zmax and hopefully I'll be able to recall exactly what it is we're doing. So in this case, uhm, let me cut out, I get a little, uh,


[User] Please transcribe this text

[Assistant] I like to keep my surfaces clean. I double click standard. I'm gonna hit the P for paraxial.


[User] Please transcribe this text

[Assistant] So here's our, here's our system. I'm gonna add some more rays just so it doesn't look too obtuse. Uhm, so my aperture stop is actually here, it's on surface, it's on the, uhm, uh, paraxial lens.


[User] Please transcribe this text

[Assistant] So you can see our aperture stop is here. I'm gonna add it to this surface. I double-click the standard to get surface properties. Make surface stop. Okay, now you really, you don't see, there's no field. So Ctrl F, or you could also do it by hitting F I E.


[User] Please transcribe this text

[Assistant] I'm going to add a real image height of 5mm just like we did in my charts. We're going to update this and, uhm,


[User] Please transcribe this text

[Assistant] Now, the reason they're clipping here is my last lens had, uhm, apertures on. So I'm going to get rid of those. You can see by this little U right here. So I'm actually going to do CTRL Z to get, I can't do that.


[User] Please transcribe this text

[Assistant] Uh, what was my aperture? I think it, let's see, uh, so 20 millimeters, F4, we want this to be 5, okay. We're gonna get rid of this one by Ctrl-Z, and it's just gonna, uh, maximize this semi-diameter from here to here as 10 millimeters. So, there is the system.


[User] Please transcribe this text

[Assistant] Your aperture stop, one focal length away, your focal length, you can see your chief ray, this green, the center ray here. This ray is collimated, uhm, let's just do marginal and chief rays. So you can see here's your marginal ray, and, uh,


[User] Please transcribe this text

[Assistant] It looks odd, but your marginal ray is going from here and it's focusing down. Your chief ray goes from here and then to here. It looks odd because the rays are the same color.


[User] Please transcribe this text

[Assistant] Now that we've got this, I'm actually going to insert two more surfaces. Well, one more. Let's also, I'm going to go to this standard, double click, hit P for paraxial. Uhm, or if you don't remember it's paraxial, you can just do this drop down. Oops, I don't want paraxial XY. That's for a toroidal.


[User] Please transcribe this text

[Assistant] So paraxial, we're going to make it 20 millimeters away, and we're going to go 20 millimeters here.


[User] Please transcribe this text

[Assistant] And I've got to change my drawing is from, uh, this aperture stop to the image. You've got to, so the way you do that is you just, you could either hit settings here or right click. So I'm going to go from there to there.


[User] Please transcribe this text

[Assistant] The focal length is wrong, that's why the ray trace looks different. Okay, so there's our doubly telecentric system. We go from object


[User] Please transcribe this text

[Assistant] To your first lens, focal length away your aperture stop.


[User] Please transcribe this text

[Assistant] Another focal length away, your second lens, and then your image. So this is our paraxial, telecentric, doubly telecentric, one-to-one imager. So I'm going to go ahead and insert a lens at this paraxial, uh, lens right here. I'm actually gonna do it afterwards. I'm gonna get rid of this marginal ray solve.


[User] Please transcribe this text

[Assistant] 20mm focal length, entrance pupil, it's the diameter is 19. We're gonna look at the layout, there's the lens, and we're gonna insert it. So if I did this right, oh, insert at surface 4. Okay. 4. Close it. I have to delete this one. I'm gonna do a marginal array solve.


[User] Please transcribe this text

[Assistant] So in theory this should update, and you can see that I'm not perfectly, I don't have my Chief Ray, it's not telecentric, so I'm going to adjust this.


[User] Please transcribe this text

[Assistant] So we're not quite telecentric. I'm sure I can adjust this space, this thickness a little bit more, but this is going to be good enough for now. Now what we want to do is insert a lens up here. Insert a surface.


[User] Please transcribe this text

[Assistant] This is, uhm, so we're gonna go ahead and go lens. It should be the same. We're going to insert. And we want it on surface one. Okay. Close.


[User] Please transcribe this text

[Assistant] So here's the lens and you can see the crown is first. We don't want that. These are designed to be infinite conjugate on this side. This is operating correctly. But I have to flip this lens. So you could, you could do the pull down. What I like to do is highlight.


[User] Please transcribe this text

[Assistant] Ctrl, Shift, V. Let's set up my right surfaces and flip.


[User] Please transcribe this text

[Assistant] So you see, and it's uh, I'm going to do unzoom, control, end.


[User] Please transcribe this text

[Assistant] So you can see now that it's uhm, it's not collimated coming out. So I need to adjust this.


[User] Please transcribe this text

[Assistant] Okay, so there's our system. We're not quite collimated. This 15, and since it's symmetric, I'm just gonna do a pick up from surface 3. So it's like 17.5.


[User] Please transcribe this text

[Assistant] And yeaah, really, I'm doing this iteratively, really you should be using the merit function.


[User] Please transcribe this text

[Assistant] That's, we've not hit that yet, so, uh, my apologies. Now we should be focused. Let's get rid of these two surfaces. We've got a marginal ray solved. So here's our optical system. We're not, well we're not collimated here. So this needs to be, let's just, let me manually do this.


[User] Please transcribe this text

[Assistant] Okay, well that's good enough. It's not quite collimated, but you can see we're pseudo-collimated. The blu-rays are somewhat collimated here.


[User] Please transcribe this text

[Assistant] So that means this is in focus. Before I address the vignetting that's going on right here, I'm actually going to address this aperture stop. So our rays coming from off axis from this point, these green rays, they're not filling the aperture stop. So how do you fix that? Well you go Ctrl G for general, you go to ray aiming,


[User] Please transcribe this text

[Assistant] And you go to paraxial. You can do real ray tracing to get the, to fill the pupil.


[User] Please transcribe this text

[Assistant] Paraxial is faster, and I've not, I generally don't play with these other terms. If you have really bizarre aberrations, you can be doing, using these other parameters. But this is usually good enough. Lo and behold, now we're going through our aperture stop. Unzoom this.


[User] Please transcribe this text

[Assistant] We still have vignetting. And that's because we have an aperture right here.


[User] Please transcribe this text

[Assistant] Clean this up a little bit. We have an aperture right here, this red surface, and it's clipping this ray. I could either cheat and cheat and say 12,


[User] Please transcribe this text

[Assistant] In which case that lens, that ray gets through but it vignettes at the next surface. I'm going to F3 and I'm going to undo it. Really what you can do is you can go to set your vignetting terms. So, uh, control F or this


[User] Please transcribe this text

[Assistant] Footprint here such that all the rays get through.


[User] Please transcribe this text

[Assistant] Click update, I double click it, you see all the rays get through. And there's your system. Of course, there's a lot of aberrations. This is a doubly telecentric with two doublets.


[User] Please transcribe this text

[Assistant] What I just showed you, both in the charts and in the live Zmax. And I've got some, uh, input here. I've changed things up. 35 millimeter, uh, focal length.


[User] Please transcribe this text

[Assistant] For the system, F3, F3 working, 10mm diameter, same I did before, magnification of, uh, 1, unity. Really, it's negative 1 because your image is inverting. And use, uh, stock doublets.


[User] Please transcribe this text

[Assistant] And put in real lenses where the paroxial lenses were. And just to warn you, you're going to get field curvature.


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[Assistant] It's gonna look out of focus and what field curvature is.


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[Assistant] Is if you look here, this is, your praxial focus is right here. And in this case, your praxial focus is here. So, you've got a curved image plane. That's field curvature. It's another topic for another section. Optics Realm is now on iTunes. We're ripping the YouTube audio. And you can listen to this in your car, on the go.


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[Assistant] The intent is to put the homework solutions there. Yeaah, I know it's difficult to show Zmax or


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[Assistant] Close your eyes and have sweet dreams.


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[Assistant] The shrewd shrew sold Sarah seven silver fish slices.


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[Assistant] The shrewd shrew sold Sarah seven silver fish slices.


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[Assistant] That's your lesson for today. I'm looking forward to receiving your feedback. Bye bye!


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[Assistant] The first rule is to keep an untroubled spirit.


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[Assistant] You have power over your mind, not outside events. Realize this, and you will find strength.


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[Assistant] People are frugal in guarding their personal property.


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[Assistant] But as soon as it comes to squandering time, they are most wasteful of one thing, in which it is right to be stingy.


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[Assistant] We've been using them, not because we needed them, but because we had them.


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[Assistant] Here is a rule to remember in future. When anything tempts you to feel bitter, not. This is misfortune, but to bear this worthily is good fortune.


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[Assistant] It's not what happens to you, but how you react to it that matters.


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[Assistant] Be content to be thought foolish and stupid.


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[Assistant] Anger, if not restrained, is frequently more hurtful to us than the injury that provokes it.


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[Assistant] The one who puts the finishing touches on their life each day is never short of time.


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[Assistant] It is a rough road that leads to the heights of greatness.


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[Assistant] Do every act of your life as though it were the very last act of your life.


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[Assistant] Do not think it impossible for anyone to accomplish.


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[Assistant] The key is to keep company only with people who uplift you, whose presence calls forth your best.


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[Assistant] Be tolerant with others and strict with yourself.


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[Assistant] Use them to throw open the windows of your soul to the sun. If you do not, the sun will soon set, and you with it.


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[Assistant] Sometimes even to live is an act of courage.


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[Assistant] How much more grievous are the consequences of anger than the causes of it?


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[Assistant] He who fears death will never do anything worthy of a man who is alive.


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[Assistant] And banquets, remember that you entertain two guests. Body and soul.


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[Assistant] He who has injured thee was either stronger or weaker than thee.


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[Assistant] Receive without conceit. Release without struggle.


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[Assistant] No man can escape his destiny. The next inquiry being how he may best live the time that he has to live.


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[Assistant] Nothing is by its own nature calamitous.


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[Assistant] Attach yourself to what is spiritually superior, regardless of what other people think or do.


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[Assistant] If you live according to what others think, you will never be rich.


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[Assistant] Man is not worried by real problems, so much as by his imagined anxieties about real problems.


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[Assistant] Hello everyone. What's in your town? Here we go. Bakery. Bakery. Bank. Bank. Bookstore. Bookstore. Bus stop. Bus stop. Cake shop. Cake shop. Coffee shop. Coffee shop. Dentist.


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[Assistant] dentist doctor's office doctor's office


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[Assistant] Alright, that was eight. Let's do eight more. Here we go. Drugstore. Drugstore. Flower shop. Flower shop. Gas station. Gas station. Hospital. Hospital. Library. Library. Movie theater. Movie theater. Park.


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[Assistant] Park. Pool. Pool. Okay. Let's do eight more. Here we go. Post office. Post office. Restaurant. Restaurant. School. School. Supermarket. Supermarket. Toy store.


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[Assistant] Toy store. Train station. Train station. Zoo. Zoo. Map. Map.


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[Assistant] Thanks very much for walking around my town with ELF learning. We'll see you next time. Goodbye everyone.


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[Assistant] Hello! This is ELF Learning with School Subjects.


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[Assistant] Some things we learn at school are interesting, and some things are fun. Some are difficult, and some are easy. Let's practice. I think art is difficult. I think art is difficult.


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[Assistant] I think English is interesting.


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[Assistant] I think English is interesting.


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[Assistant] I think geography is difficult.


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[Assistant] I think geography is difficult.


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[Assistant] I think history is interesting.


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[Assistant] I think history is interesting. I think math is easy.


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[Assistant] What do you think about your school subjects? Tell me


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[Assistant] We will see you next time. Bye.


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[Assistant] But Big Nut Brown Hare had still managed to find some green shoots for breakfast for himself. Over here! And for his son, Little Nut Brown Hare.


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[Assistant] Pattern Practice. By Elle Learning.


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[Assistant] Is it a purple heart? No, it's not.


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[Assistant] Is it a green heart? No, it's not.


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[Assistant] Is it a pink heart? Yes it is. It is a pink heart.


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[Assistant] Is it a red marker? No, it's not. Is it a blue marker? No, it's not. Is it a black marker? Yes, it is.


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[Assistant] Is it a white cloud? No, it's not.


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[Assistant] Is it an orange cloud? No, it's not. Is it a gray bird? Yes, it, wait, no, it's not.


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[Assistant] It is a gray cloud.


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[Assistant] Thanks for watching! This has been ELF Learning. And, don't forget to subscribe for more ELF videos. Bye.


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[Assistant] Okay, so for today I went back to the same store that I did a few months back when I did the Japanese curry rice video. So this store is called Washoku Goen.


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[Assistant] Uh, yeaah. Apparently, it's the number one, uhm, Japanese curry rice store in Singapore.


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[Assistant] If I were to order the same amount of food in a sit down Japanese restaurant, I think it would be like more than a hundred dollars. But this one is like just half of that. So, it's good.


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[Assistant] Okay, so what we have here today are some chicken katsu, some pork tongue katsu, some fish katsu, some potato croquette.


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[Assistant] Hang on, is it recording? Yeah, okay. Here we go.


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[Assistant] This looks like a lot of food. Okay, I'm gonna dip it in a curry.


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[Assistant] Oh yea, if you're watching this in Singapore and you're interested in this restaurant, actually it's a food store. More videos will be down in the description box below.


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[Assistant] What I like about Japanese curry is that it's not too spicy.


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[Assistant] I get a lot of comments like this, like uhm, that I'm really awkward, I'm cringe-worthy, I'm weird and all that. Yea, I get comments like this all day, every day, for the past 3 years, so, I'm used to it by now.


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[Assistant] And I kind of agree. I am awkward and creepy and weird. I'm not gonna deny that because it's, it's true. I am. If you think I'm weird now, wait till you see me in person. If you do see me in person. I can be quite elusive.


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[Assistant] Speaking of my brother, he will be coming back soon. Uh, wait, it's July now, right?


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[Assistant] So he's gonna graduate and he'll come back. Right, okay, uhm, this one is...


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[Assistant] The pork katsu, I mean pork tonkatsu, so it's pork cutlet.


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[Assistant] It's not as soft as the chicken katsu, but it's still good.


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[Assistant] I'm gonna eat a potato. This is the only time where I don't like potatoes. When it's like a one big piece like that.


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[Assistant] I like potato when it's like this. Mashed up and fried.


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[Assistant] Right, so I'm left with about one and a half chicken. One pork and one fish. My god, look how much I've left. It's really heavy, it's like a kilo of meat.


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[Assistant] But today's meal is really worth it. I mean, you get so much food for less than $40.


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[Assistant] Yeah, uhm, and I think this is like good enough for four people, so four people, forty dollars, I think it's really worth it.


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[Assistant] If you guys are in Singapore or, you know, you're planning to come to Singapore, I would recommend this store.


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[Assistant] Uhm, the only thing is I wish the curry, you know, like they have an option for spicier curry. I think that would have been better because


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[Assistant] Yeah, I just like it a little bit more spicy. Just a little bit more. But uh, yeah, that's just my preference. Well, yeah, that's it for today's video.


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[Assistant] Uhm, oh yeaah, some of you have been asking me, like, why do I always upload my videos at 1 or 2am Singapore time? Well that's because I like to film, edit and upload my video in the same day, like within one day. So usually I'll film my video at around 2pm.


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[Assistant] And then I'll edit the video and then I'll design the thumbnail and then I'll wait for the video to render and then I'll wait for the video to upload, you know. So by the time I finish all of that, it would have been.


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[Assistant] Around one or two AM usually. So that's why I always upload at that time. Yeah. So I hope that answers your question.


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[Assistant] Right, so that's about it for today. Thank you so much for watching. I hope you guys enjoyed today's video. Please give this video a thumbs up and don't forget to subscribe to my channel for more eating shows like this. So, I will see you all on, uhm, Sunday. Excuse me.


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[Assistant] Yeah, I believe it's Sunday, so until then, take care and love you! Bye!


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[Assistant] HIV is one of the best known viruses in existence. Despite this, compared to some of the other viruses we've covered, our knowledge about HIV is relatively new.


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[Assistant] Human immunodeficiency virus began circulating and infecting humans long before we knew what it was and where it came from, as with most diseases in human history. But HIV in particular is considered to be one of the most devastating infectious agents to emerge in recent history.


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[Assistant] The story begins in the early 1980s, when a mysterious disease popped up quite suddenly.


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[Assistant] This was recognized due to increasing numbers of unusual opportunistic infections and rare cancers that could be transmitted sexually.


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[Assistant] Through injection drug use, from pregnant women to their babies, or through blood transfusion.


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[Assistant] CDC identified the disease as acquired immunodeficiency syndrome, or AIDS, but it wasn't until several years later that scientists figured out that a retrovirus was causing AIDS.


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[Assistant] It took years, a few name changes, many lost lives, and a mountain of hard work from many researchers, but we now know that human immunodeficiency virus is a retrovirus that attacks the body's immune system and can lead to AIDS if not treated.


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[Assistant] Now we have heard the term retrovirus before, but what are they exactly? These viruses are enveloped, positive strand RNA viruses, and they tend to have unusual morphology and replication strategies.


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[Assistant] More specifically, retroviruses have a capsid containing two copies of a positive strand RNA genome.


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[Assistant] Back in the 1970s, scientists David Baltimore and Howard Temin showed that the retroviruses encode an RNA-dependent DNA polymerase, which is also called reverse transcriptase.


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[Assistant] Essentially, this reverse transcriptase enzyme allows the virus to make copies of DNA from RNA.


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[Assistant] At the time, the central dogma of molecular biology, as it is sometimes colorfully referred to


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[Assistant] Was the idea that genetic information is passed only from DNA to RNA, and then from RNA to protein.


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[Assistant] Just as we learned when we studied transcription and translation in the biochemistry series.


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[Assistant] Baltimore and Temin's finding demonstrated that this was not exclusively the case.


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[Assistant] And their discovery ended up earning them a Nobel Prize.


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[Assistant] Among the human retroviruses, there are three subfamilies. Oncovirini, which is associated with cancer and neurologic disorders,


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[Assistant] Lentivirani, which includes HIV-1 and HIV-2,


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[Assistant] And spuma vireni, which doesn't cause clinical disease, but does cause visible changes in infected cells.


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[Assistant] The retroviruses are classified by a few factors. The disease they cause, host range, tissue tropism, virion morphology, and genetic complexity. But let's focus specifically on HIV.


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[Assistant] The human immunodeficiency virus primarily infects CD4 T cells, which are white blood cells that fight infection, but the virus can also infect macrophages, which are another type of white blood cell.


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[Assistant] The envelope of HIV-1 has proteins that form spikes on the virion's surface, and it's these glycoproteins and transmembrane proteins that attach to a host cell and help the virus get inside.


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[Assistant] In the case of CD4 T cells, the virus causes lytic infection.


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[Assistant] With macrophages, the virus causes persistent low-level productive infection. Since the virus is killing one type of host cell altogether, the CD4T cells, in order to make more copies of itself, it dramatically affects the immune response and ultimately leads to immunosuppression.


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[Assistant] There are multiple different pathways that the virus can take to trigger disease, depending on what cells it infects and where they are. And they are all very, very complicated. But the overall gist is this.


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[Assistant] HIV is very skilled at hiding from different components of the immune system, and one of the major factors contributing to this is its ability to repeatedly undergo mutation.


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[Assistant] Let's talk about the actual disease HIV causes. Stage 1 of HIV is an acute HIV infection, where an infected person might have flu-like symptoms, or might not show symptoms at all.


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[Assistant] During stage one, there is a very large amount of virus in the blood, and an infected person is very contagious.


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[Assistant] Stage two is chronic HIV infection, which is sometimes called clinical latency, or asymptomatic HIV infection.


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[Assistant] By the end of stage two, the amount of HIV in the blood, or the viral load, goes way up, while the CD4 cell count goes down.


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[Assistant] The most severe phase of HIV infection is stage 3, acquired immunodeficiency syndrome, or AIDS.


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[Assistant] By this point in the disease progression, someone infected with HIV has such a badly damaged immune system that they are more likely to get severe cases of opportunistic infections, and without treatment, people with AIDS typically survive about three years.


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[Assistant] Scientists have determined that HIV infection in humans originated from a type of chimpanzee in central Africa. The chimpanzee version of the virus, called simian immunodeficiency virus, or SIV, got passed to humans as early as the late 1800s.


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[Assistant] Over the course of the decades that followed, HIV slowly spread across the world, and we believe that HIV has existed in the United States since the 1970s.


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[Assistant] Thinking about transmission, HIV cannot be transmitted by sharing food or drink


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[Assistant] There are very specific activities that can transmit HIV, including sexual activity and needle or syringe use, though the virus can also be spread from an infected mother to their child during pregnancy, birth, or breastfeeding.


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[Assistant] As of 2018, CDC estimates that 1.2 million people in the United States have HIV, with just under 40,000 new diagnoses that year alone.


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[Assistant] Worldwide though, CDC estimates that there were 1.7 million new cases in 2018 and about 37.9 million people living with HIV.


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[Assistant] When it comes to prevention, using condoms can help, and there are a few relatively new prevention medications available, such as pre-exposure prophylaxis, or PREP, and post-exposure prophylaxis, or PEP.


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[Assistant] For those that have HIV, the most effective treatment is antiretroviral therapy, or ART.


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[Assistant] If an infected person takes this medication as prescribed,


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[Assistant] They can stay healthy and have a very low risk of transmitting the virus to a partner


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[Assistant] So while tremendous progress has been made in treating HIV since the 1980s, we do still have a ways to go before we are truly free of this virus.


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[Assistant] Thanks for watching, guys. Subscribe to my channel for more tutorials, support me on patreon so I can keep making content, and as always, feel free to email me at


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[Assistant] You always find them squirming around on a rainy day, but a worm's world is underground. Down there is home base for lubricus terrestris, the scientific name for worms. Wherever you walk, there are worms underneath you.


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[Assistant] But why do they do it? Why do worms come out of the ground when it rains? Worms are coming up and the rain's coming down. Hard. Yeah.


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[Assistant] Okay, now we're the right size to solve the squirmiest wormiest mystery of all. Why do worms come out in the rain?


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[Assistant] Alright, we need worm clues, so let's start with the worm.


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[Assistant] Maybe if we follow the worms into their holes, we'll know why they come up again when it rains.


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[Assistant] Well partner, let's just get a move on and find out then!


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[Assistant] Of course! Worms breathe through their skin, in air and in water. Slimy skin breathers! Ellie's right! Thanks, Ellie! Guess she didn't see us.


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[Assistant] What's with the wind-up toy? I could use a little help with this mystery, you know. What? I just saved you from being lunch for a large mouth. Bass? I didn't pass any bass. But we did find another clue for why worms come up in the rain. It's not because rain floods the ground with water. No matter how wet the ground gets, worms don't drown.


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[Assistant] Okay, check the scene. We've got a worm above ground. Rain? Not a drop. So worms do come above ground sometimes, even when it's not raining. But for what?


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[Assistant] That's clue number two! Worms will come above ground to get food at night. When it's cool and dark. When the surface world is more like the underground.


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[Assistant] A star-nosed mole! Oh, you sensed its digging vibrations, didn't ya?


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[Assistant] Okay, left pedal stretches, right pedal slides, and this lever steers me up. Creature clue number three, here I come! Hey, this thing handles great!


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[Assistant] Creature clue number three in the mystery of the squirmy wormy. Worms will come up on a sunny day, but only in a life or death emergency. Worms do not like the sun.


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[Assistant] Chris, we've got to get back to real size. Worm-eating monsters are following every squirm we make. Okay, okay, let's solve this mystery fast then. I've got an idea, but I have to check something. It'll just take


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[Assistant] We got fooled by a bird. The perfect trick. Robin's pecking at the ground, making vibrations just like the ones a digging mole makes. So when you're a worm, you react.


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[Assistant] Okay, first things first, I gotta fix this thing.


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[Assistant] Second thing second. Hang on Martin, here I come!


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[Assistant] Definitely not easy. Yeah, we need to get down that wormhole fast.


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[Assistant] But up here on the surface, it's way too dry and deadly.


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[Assistant] Yeah, the drying, frying sun may be the worm's deadliest enemy of all. Except if it rains.


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[Assistant] Translation? Worms can get stuck on the pavement or places that get really hot. So, why not give them a hand? A big hand. There you go, buddy.


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[Assistant] To a wet, moist place out of the hot sun. To the worm's world. You found one! Alright, now see, he's in a little puddle here. So let's get him out of there. It's so hot. Do you want to take him over to the grass? There you go! Good!


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[Assistant] After we put the food in, we take some leaves and you cover it up. And there you go. So you cover a layer with leaves. Now we cover the bin and wait. We'll break it down and turn it into dirt. Look at this! Perfect soil for your garden.


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[Assistant] A full-grown moose can weigh up to 1,800 pounds and stand seven feet tall, making it one of the largest land creatures in North America.


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[Assistant] Moose means twig eater in the Native American Algonquin language. They love twigs in park.


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[Assistant] And they're fast. A moose can run up to 35 miles per hour. Moose are one cool creature.


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[Assistant] Eating and growing. In ten days from the time he hatches, he'll grow three thousand times his original weight. If a human baby did that, he'd be the size of two and a half elephants.


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[Assistant] But it's summer now, my little nut-brown hair, and all the leaves are green.


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[Assistant] Why don't you think of all the things you know about Little Field Mouse? That should help you decide.


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[Assistant] It's never as bad as you think. There must be something else you know about your friend.


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[Assistant] Well done, little nut-brown hair, on choosing a very special gift for your friend.


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[Assistant] No, I can't guess.


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[Assistant] Good night, my happy little nut brown hair. Sleep well.


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[Assistant] Hello little field mouse. What shall we do today? Do you want to chase leaves?


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[Assistant] I can't see you. It's getting too misty. Oh, yes it is. I'm a little bit frightened little nut brown hair.


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[Assistant] It's alright, little fill mouse. Stay where you are and keep talking. I'll follow the sound of your voice. What should I say? Anything. Just keep talking. Uh, it's misty in the meadow. The flowers have gone away. It's misty in the meadow, which makes it hard to play.


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[Assistant] How about we play, I don't spy with my little eye. Hmmmm.


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[Assistant] I've never played that before. That's because I just made it up. Okay, how does it work?


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[Assistant] We take it in turns thinking of something that we can't see, but we know we'll be there when the mist rises.


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[Assistant] A little blue beetle. Oh, hehe. My turn.


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[Assistant] I don't spy, with my little eye, something orange. Orange? A Dahlia flower? No.


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[Assistant] I'm going to dream about running through the flowers.


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[Assistant] The highest activity a human being can attain is learning for understanding. Because to understand is to be free.


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[Assistant] No matter how thin you slice it, there will always be two sides.


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[Assistant] If you want the present to be different from the past, study the past.


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[Assistant] In so far as the mind sees things in their eternal aspect, it participates in eternity.


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[Assistant] Ceases to be suffering as soon as we form a clear and precise picture of it.


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[Assistant] In practical life, we are compelled to follow what is most probable. In speculative thought, we are compelled to follow truth.


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[Assistant] We feel and experience ourselves to be eternal.


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[Assistant] Minds, however, are conquered not by arms, but by love and nobility.


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[Assistant] He alone is free who lives with free consent under the entire guidance of reason.


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[Assistant] The only way to avoid death is to make art.


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[Assistant] Blessedness is not the reward of virtue, but virtue itself.


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[Assistant] A poor man is better than a rich man because he has a clear conscience.


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[Assistant] A man is obliged to cry only in one case, from happiness.


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[Assistant] Only a fool craves money. A smart one craves opportunities.


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[Assistant] I call him freedom, who is led solely by reason.


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[Assistant] The mind of God is all the mentality that is scattered over space and time.


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[Assistant] Self-preservation is the primary and only foundation of virtue.


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[Assistant] Little grey squirrel and I can find some treats. Where will we have the feast?


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[Assistant] Is something wrong? We need your help. We're looking for a special place to celebrate feast day. I can tell you a story.


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[Assistant] Pink and green. Sparkling pink and green. And there was dancing in the sky.


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[Assistant] But close? Yes, this must be the place in the story. Hooray! But, there's no dancing in the sky. That's true. Maybe we haven't found it after all. Where shall we look now?


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[Assistant] This might have to do. Never mind, little nut-brown hair. At least we've got the feast. I suppose so.


[User] Please transcribe this text

[Assistant] Oh, they're good, but- Hello everyone!


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[Assistant] Well, it's just, we don't have much of a feast, and we couldn't find Little White House's special place. I'm sorry. Feast day is ruined.


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[Assistant] That was a bigger feast than I thought.


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[Assistant] Sparkling pink and green all around.


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[Assistant] That evening, I collected many, many treasures. But the one I wanted most of all.


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[Assistant] Is the most special treasure of all. What happened next little white owl? I made a wish. A wish? Star light, star bright. The first star I see tonight. I wish I may, I wish I might.


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[Assistant] This is a great game! Come on little field mouse, it's your turn!


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[Assistant] But, you would have won all the time. Wouldn't you have liked that?


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[Assistant] And roll the snowballs instead of throwing them. Then the twins could play too. That's a great idea, little nut-brown hair.


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[Assistant] Just roll the snowball as close to the middle as you can, okay? Ready?


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[Assistant] And I want to make sure that you've actually given this a go before you look through the solution because you can watch me typing code all day long and it's still not going to get you where you want to be. So make sure you've actually practiced and you've struggled.


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[Assistant] Even if you get stuck, it's still better than just watching the videos and seeing the code written. You have to write code to get good at writing code.


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[Assistant] All right, enough motivation. Now I'm going to take the normal starting point to guide you through step-by-step. Now notice how in the starting project I've included a bunch of to-dos. And one of the nice things about PyCharm is you can view all of the to-dos found in the file and you can delete them as you check them off. And then you'll be able to see what is remaining.


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[Assistant] And when you click on it, it takes you directly to that particular to do's location, which is really neat.


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[Assistant] Let's start from the top. We want to be able to use the alphaadvantage.co's APIs in order to get yesterday's closing stock price. So that's the first thing.


[User] Please transcribe this text

[Assistant] How do we do that? Well, Alpha Vantage is a stock market API.


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[Assistant] You can select yourself as a student and then your organization if you have one and then enter your email.


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[Assistant] Now once you've filled in the form, you can click on get a free API key and you should see your API key show up below.


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[Assistant] Make sure that you copy this API key and save it in your project. So I'm going to head over here and right at the top, I'm going to add my stock API key as a constant like this. That way I won't forget it because it's actually quite hard to navigate back to this page and get your API key.


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[Assistant] Now for those of you guys who chose the hard and extra hard versions, I didn't really point you towards which part of the API documentation is most relevant, because I hope that if you had chosen to give yourself a challenge, you would appreciate having the opportunity to dig through all the different things you can do with the API.


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[Assistant] Now in our case, what we actually want is the daily data.


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[Assistant] So this API will return the daily data, which will give us the open price,


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[Assistant] The highest price of the day, the lowest price of the day and also the closing price of the day, which is what we're mostly interested in.


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[Assistant] There's a couple of API parameters which are required, the function, which in this case is time series daily, and also the symbol, which is the name of the equity of your choice. In our case, we've already defined that at the top. It's the stock name right here. So let's see how we can fetch yesterday's closing stock price.


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[Assistant] To start, we of course need our trusty request module.


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[Assistant] From the stock endpoint. So you can see that in their example, the endpoint is everything up to and not including the question mark.


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[Assistant] This should be all pretty familiar to you, but you could also just type in simply the one that I've already included, this stock endpoint. Now, next we're going to need to add some parameters. So let's create something called our stock params.


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[Assistant] And let's see what we need to add. So we have to add the function, which is equal to time series daily. I'm going to copy that so I don't make any typos. So let's create a Python dictionary and this is going to be the key and value.


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[Assistant] Next, we have to add our symbol and that is going to be the name of our stock or equity.


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[Assistant] The symbol in this case is simply going to be our stock name, TSLA.


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[Assistant] And stock symbols tend to have a maximum number of four letters. So that's why you see it abbreviated like snap for Snapchat or TSLA for Tesla.


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[Assistant] Now the final required parameter is our API key.


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[Assistant] The whole thing is not going to work unless we authenticate ourselves using the API key that we got just now, which is saved as our stock API key.


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[Assistant] Now that we've got all three parameters in there, we can add it to our request method and we'll just add it as the stock params. Now let's save this as the response and let's print out what the response actually look like when it's in the format of a JSON. So let's run this code.


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[Assistant] And I'm getting a error that says indentation error, unexpected indent. And that's because my stock params here really should.


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[Assistant] Actually be within the same indentation as the starting point like this.


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[Assistant] So be careful that when you have a to-do that's indented, every line that you start afterwards is going to be indented as well. So it probably helps to just unindent everything so that they will start at the margin here. So let's try that again.


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[Assistant] And this time it looks like we're getting back actual data in the format of a JSON. Let's view it using our JSON viewer.


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[Assistant] You can see that we've got some metadata which basically just describes what we told the API.


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[Assistant] For example, we wanted the Tesla symbol and we wanted the daily price.


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[Assistant] But what we're actually interested is in the time series daily data.


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[Assistant] So you can see it's giving us the data for all of the dates prior to today. So currently it's 22nd of July. So we can see the complete data for the 21st of July and the 20th of July.


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[Assistant] Right now, what we're interested in is yesterday's closing stock price. So this is yesterday and this is the closing stock price. So it's 1,568. How do we get hold of it? Well, we have to step through a number of keys in order to get to this particular value.


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[Assistant] First we need to pass in our time series daily and notice how this has brackets and everything. We have to include all of the spaces, all of the brackets in our key.


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[Assistant] Let's say that our data is equal to the response.json. Let's go ahead and add a square bracket and then we can provide the key, which I'm actually just going to copy from down here.


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[Assistant] So I don't make any typos, make sure that I preserve all of the spaces and all of the symbols. So basically everything in between the quotation marks like this. Now, if we print our data,


[User] Please transcribe this text

[Assistant] You can see it's stepped into that particular dictionary and it's now giving us all of this data.


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[Assistant] But this is not a list. As you can see, the data starts out with a curly bracket. In fact, it's a really big dictionary with the key being the date.


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[Assistant] Now while I can tap into yesterday's data by simply providing this as the key, this is going to be pretty hard coded because once I get to tomorrow, when it becomes the 23rd, then yesterday's date is going to change.


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[Assistant] So we have to figure out another way of getting hold of that first value. How about turning this large dictionary into a list?


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[Assistant] Where we get each of the pieces of data that's associated with each of the keys.


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[Assistant] To do that, we're going to need to do a bit of list comprehension.


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[Assistant] So I'm going to create a variable called data list, and then I'm going to use my list comprehension keywords.


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[Assistant] We did list comprehensions a while back, but this might be a good time to remind yourself of how to use it.


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[Assistant] It's going to be new item for item in list. Our list is actually not a list. It's in fact a dictionary. So we're going to tap into our data, which is the dictionary, and we're going to call


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[Assistant] The items method on it. And this is going to give us not just the item, but the key and the value.


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[Assistant] So we're actually only interested in the values. So we can put that in here so that each item in this new list is composed of only the values and not the keys. Now, if I print this data list,


[User] Please transcribe this text

[Assistant] You can see I've now got a list denoted by the square brackets and each item in the list is a dictionary with each day's data.


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[Assistant] Using that, I can get hold of yesterday data by simply tapping to our data list and getting the object at index zero.


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[Assistant] Now once I've got yesterday's data, then it's pretty simple to get yesterday's closing price.


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[Assistant] Because that is simply stored under this particular key. Now again, notice how the entire key is this. It's everything in between the quotation marks, including the number and the dot and all of that.


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[Assistant] So this is a bit of a strange way of formatting your keys to be honest. I haven't seen many APIs that do this because it's a little bit confusing to have spaces and dots and numbers.


[User] Please transcribe this text

[Assistant] That's just the way that they decided to do it. So in order to get the closing price, we can tap into yesterday's data, add a set of square brackets, pass in this particular key.


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[Assistant] So now if we print out yesterday's closing price, you can see we've narrowed down on the exact price of the Tesla stock at yesterday market close.


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[Assistant] That's to do number one completed. So we can delete the to do.


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[Assistant] Now the next one is to get the day before yesterday's closing stock price. So now that we've actually got this data list, it's pretty simple to do that. Day before yesterday data.


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[Assistant] Is going to be equal to the data list at index one, because remember everything in the list is ordered going backwards from today's date. So that is going to be the day before yesterday's data.


[User] Please transcribe this text

[Assistant] And then the day before yesterday's closing price, we can fetch that in the same way by passing in that key. Four dot space close.


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[Assistant] And just to confirm everything works, let's print out both of the closing prices. So you can see that yesterday it closed at $1,568 and then the day before it closed at $1,643. So that's actually quite a big drop, almost $100.


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[Assistant] That's to do number two done. We've managed to get the day before yesterday's closing stock price.


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[Assistant] To do number three involves finding the positive difference between step one and two. So yesterday's closing price and day before yesterday's closing price. And it gives us a hint.


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[Assistant] Now the hint takes us to the Python ABS function, which we've also seen before.


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[Assistant] It's a function that returns the absolute value of a number. So it basically just gets rid of any negative signs in front of a number.


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[Assistant] And this is what we need in order to complete to do number three.


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[Assistant] The difference is going to be yesterday's closing price subtract by the day before yesterday's closing price.


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[Assistant] Now because these two are strings, if we actually run the code right now, you're going to get an error.


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[Assistant] And the error tells you that you can't subtract between a string and a string.


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[Assistant] We know that these numbers look like this, so we can quite easily convert them into floating point numbers just by wrapping the float function around them.


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[Assistant] If we print that out, you can see that it's actually going to be a negative number. And the reason is because this is yesterday's price. This is the day before yesterday's price. The price actually went down by $74.


[User] Please transcribe this text

[Assistant] Well, this is where the abs or absolute value function comes in handy. If we wrap all of that around the absolute function, now if I run it, you can see we're getting a positive value instead.


[User] Please transcribe this text

[Assistant] Now that step three completed, we found the positive difference between yesterday's closing price and the day before yesterday's closing price. Now let's tackle step number four.


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[Assistant] We want to be able to work out the percentage difference in price between the closing price yesterday and the closing price the day before yesterday.


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[Assistant] The diff percent is going to be the difference divided by the closing price yesterday.


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[Assistant] And in order to turn this into a percentage, we're going to have to multiply it by 100.


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[Assistant] But again, notice that this is a floating point number because it was calculated here, but this is still the string.


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[Assistant] So we're going to have to turn that into a float for this calculation to actually go through. And once it does, we're going to print it out.


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[Assistant] Which is if the percentage here is greater than five, then we're going to print get news.


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[Assistant] This is pretty simple, all we have to do is check if the diff percent is greater than 5, in which case we're going to print get news.


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[Assistant] Now depending on the time when you're completing this project, the stock price of Tesla may not show a difference that's greater than five.


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[Assistant] Because in order to continue testing our code, we're going to need this to be true, then we can change this number to a smaller number just so that we can actually get it to work.


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[Assistant] So in my case, the difference is 4.75, so I'm actually just gonna change this down to 4.


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[Assistant] And now if I run all of this, then you can see it's going to print get news. So that is what we're going to do on the next lesson in the second part of the solution.


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[Assistant] Hey, I need to fix Riko, right?


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[Assistant] Not that.


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[Assistant] I think you'll find out the toughest animal in Africa is the leopard, by the way.


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[Assistant] Uh, guys? Where are you going? To the Kalahari! But you're already in the Kalahari!


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[Assistant] Honey Guide? Honey Badger? This could make a dynamic duo of creature power suits. What else you got on him, Koki? Hmmmm.


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[Assistant] Whoa, that skeleton is rigged for some serious muscles. Wild claws! Huh.


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[Assistant] I think she thought she did when she was in high school, but eventually it was true. I'm honored to be here. I thank you for the degree.


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[Assistant] The phenomenal success you have in producing women surgeons, which I am impressed with.


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[Assistant] Is the national leadership Howard has shown across all these disciplines and all the ones I didn't mention.


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[Assistant] Medical schools committed to totally retrain the healthcare workforce in Rwanda. And why this is important is


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[Assistant] And so they have to not only have hospitals and people trying to work there,


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[Assistant] Schools including Howard is committed to doing this work.


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[Assistant] I congratulate all of you who are here, your parents, your friends.


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[Assistant] We were like this on the lawn at Georgetown just across town.


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[Assistant] When you leave here, it is important for you to remember


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[Assistant] That you have been empowered to do something most people who ever lived could not do.


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[Assistant] Hundreds of millions, even billions of people on the face of the earth today never had that choice. They simply did whatever was at hand in whatever form it presented itself to put food on the table and if they had a family to support their families.


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[Assistant] You have a choice. The only bit of personal advice I have is this. Try to do something that'll make you happy.


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[Assistant] You're going out into a world that is the most interdependent world in history. You can see it not just in trade, but travel in increasing diversity.


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[Assistant] The world has many challenges. You know them. There's too much inequality.


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[Assistant] It's manifested for most college students in the fact that they haul out of college with too much debt and wonder if they're going to be able to repay it.


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[Assistant] The only way their lives can have meaning is to take some other people's lives away.


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[Assistant] To face failure in the face and pick ourselves up and go back to work and make something good happen. Uh, and I hope you will do that.


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[Assistant] We have our differences, we have our arguments, you have election, it's over, and people join hands and work together.


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[Assistant] The Christian Bible says you should love your neighbor as yourself. And it is the second most important commandment ad for loving God.


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[Assistant] We now know that it is scientifically true. I spent three billion dollars of your money


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[Assistant] Gabe Einstein, the biggest brain ever measured. He made pretty good use of it. It's a good thing.


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[Assistant] The last point I want to make is it's very good economics.


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[Assistant] And the person with the highest measured IQ here in this entire crowd, the biggest genius here, went into another room.


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[Assistant] It is more likely than not that even if you start out fast, something will happen that you will find frustrating.


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[Assistant] All their cooperation options were closed and the only way they could say, hey, look at me, I matter, I mean something is to take somebody else down. If you were going to avoid that, you have to be able to begin again. The thing I liked about being here today is that you still like to laugh. It's a part of our culture, you know, and it's essential thing in life.


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[Assistant] And I hope you will remember, try to do something that makes you happy. Try to find a way to serve. Try never to forget what we have in common is more important than our interesting and profoundly important differences. And don't give up on yourself or your life.


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[Assistant] One of the most rewarding experiences that I had or have had since I left the White House is the opportunity to work in South Asia after that horrible tsunami


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[Assistant] But in every disaster, as you remember from Katrina and as we now see in Haiti where I'm working, the housing is always the hardest thing to fix. So there was one


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[Assistant] So when I started the tour with the father, the president of the camp,


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[Assistant] This man who had lost nine of his ten children led me through that camp with a smile on his face and never talked about anything but what those other people needed.


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[Assistant] And she was carrying the baby, not the mother. I, I hesitate to tell you all this, but in their culture, when a mother gives birth to a new baby, she gets to go to bed for 40 days and be waited on.


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[Assistant] That you will ever face anything as awful as what happened to that mother and father. They somehow found the strength to treasure the one who remained and to serve others. That is the heritage that you take with you from Howard.


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[Assistant] That is what your people are doing in Rwanda half a world away. If you do what makes you happy and you don't give up and you keep serving,


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[Assistant] Uh, ladies and gentlemen, I'm so happy to be here tonight and I feel honored to finally have one of these in my hand.


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[Assistant] Well, a brown furry animal with spots.


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[Assistant] Oh, they don't live in our valley, but they do visit in winter, looking for food.


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[Assistant] Spring had sprung, and Little Nutbrownhair was busy discovering tasty treasures with his father. Big Nutbrownhair.


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[Assistant] And so, as the spring moon rose into the night sky, the nut brown hares shared the wonderful treasures they had found.


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[Assistant] Please, no one can tell stories like you.


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[Assistant] A story of my own? I wouldn't know where to start.


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[Assistant] Maybe I will. I'll make up a story for you little white hair.


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[Assistant] Big pile of them! Well, I would, but I'm trying to think up an idea for a story.


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[Assistant] To tell little White Owl, I promised her. Can you think of one? Oh, I can try. Uh, I know! Let's go to the river!


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[Assistant] A strawberry was as big and as pink as, uhm, a strawberry and, uhm.


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[Assistant] Yes! Why don't we go to the flower fields?


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[Assistant] I always go there when I have to think hard. Yes, we're sure to think of an idea there. Once upon a time, long, long ago,


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[Assistant] Have you come to enjoy the lavender too? Sort of. We're trying to think up a story. We need to think of one by sunset. Can you think of one?


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[Assistant] Nope, I can't think of any stories. Oh dear, it's getting late and I still haven't got an idea. Little White Tower will be so disappointed.


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[Assistant] I promised Little White Owl a story, and I haven't got one to tell her. And soon she'll wake up because, look! It's already sunset!


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[Assistant] Yes! Once upon a time.


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[Assistant] The little nut-brown hair saw the sunset was full of red and pink and purple so bright that you could almost


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[Assistant] Because he had sweet raspberries as red as the sunsets. Oh, he had slurpy strawberries as pink as the sunsets.


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[Assistant] One autumn morning, little nut-brown hair, big nut-brown hair, and little field mouse sat bathed in the early morning light, fascinated by one of Little White Owl's stories.


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[Assistant] But there was something little nut-brown hair wanted to tell his father.


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[Assistant] Well, good morning. Thanks for, uh, joining us today. I think, uhm,


[User] Please transcribe this text

[Assistant] I think going forward, uh, there's a lot of new tech, uh, new technologies and, uhm, ideas in terms of collaborating together to share data.


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[Assistant] Uh, industry data sets that we all use, uh, like ImageNet that we talked about in the video. Uh, I think there's a lot of ideas that we can talk back and forth about, um, in terms of collaborating together. So I encourage you to, uh, to, uh, to take that to heart and, uh, and to work together, uh, with us and challenge us, um, with the best way we can collaborate together as,


[User] Please transcribe this text

[Assistant] We have about 33,000 people participated last year and we expect thousands to see this material as well. So I think there's lots of opportunities going forward. With that, I actually want to


[User] Please transcribe this text

[Assistant] He'll be joining us today, uh, but I think he'll do a great, uh, session and we'll have a little Q&A with, uh, with Bill and Rick. So thank you for coming and please welcome Microsoft Chairman Bill Gates.


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[Assistant] She was very shy. We must be quiet and still.


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[Assistant] I did see her! I really wish you'd seen her too.


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[Assistant] Bluebird, I saw a little white fawn. She was dancing in the moonlight and she was beautiful. I'm sure she was.


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[Assistant] I promise I really did see a little white fawn.


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[Assistant] Sticks? Did you try to trick us? No!


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[Assistant] I'm sorry, I, I just wanted to help. It didn't help. It made everything worse. Because now I know, you don't believe me either.


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[Assistant] Little Not Brown Hair, I have an idea! Why don't we visit Little White Owl? She's been everywhere! All the way from Cloudy Mountain to the sea! Wow!


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[Assistant] Hello. You caught me just before I fell fast asleep. Little white owl, have you ever seen a pure white deer? Well.


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[Assistant] Little white owl, please wake up. Is this a true story?


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[Assistant] Little White Owl's story has made Little Grey Squirrel and Little Red Wood Fox believe, and they're coming too!


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[Assistant] They are not very good at seeking. Can't be far from here.


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[Assistant] Come on little redwood fox, seeing little white thorn will be worth it. Oh no! The path is blocked!


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[Assistant] And it's getting cold. I think I saw a place we can climb over. Little white fawn could be just over there.


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[Assistant] Little field mouse, I'm going to use the winter berries. Ah, okay. Follow me.


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[Assistant] It did look like a brown fawn lit on up brown hair. Ahh, it wasn't.


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[Assistant] Game? Which game? Hide and seek of course. You were seeking me and I was hiding from you.


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[Assistant] Thank you. We don't really have a herd, but this is my father, and these are all my friends. A red feather? Is that for me?


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[Assistant] No, Big Nub Brown Hair, I love you all the way to the moon and back. Hmm.


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[Assistant] The report is a compilation of observations and it's part of an ongoing research project. It's not a final statement, it's very much a work in progress. But it contains a big warning about the future of the global system.


[User] Please transcribe this text

[Assistant] And the trend that was already present prior to this crisis towards authoritarian government.


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[Assistant] There is no reason for believing, nothing inevitable, however, but no reason for believing that this will change in the post-virus world, this existing trend to authoritarian government.


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[Assistant] And at best we can say we're at a pivot point in history.


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[Assistant] A hinge moment where history could go in a number of different directions where reactionary and progressive outcomes are both possible, uh, in this current conjuncture.


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[Assistant] And while the future is certainly uncertain, we can have a good idea about it from studying the current trajectories of the international system. And I think we can broaden this out.


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[Assistant] A little bit more of this argument by saying that, well, all of the big moments of social transformation we've seen in the world system over the last three or four decades, 1989, 2001, 2008, for example, the peaceful revolutions in the Soviet Union, 9-11,


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[Assistant] What happens at each of those crises, I would argue, is that the balance of forces and the ideas lying around shapes what happens next.


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[Assistant] And so they're not, uh, moments where the past is one thing and then you get a year zero afterwards and a completely new and different direction.


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[Assistant] So that's why I think we should be particularly concerned right now in the COVID-19 coronavirus crisis about the trend that has developed over the last decade or so, and that's towards, as I said, a very authoritarian government.


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[Assistant] And what the report tries to do is lay out what some of the drivers of change will be across this moment. And the first one I think is the changing relationship of capitalist elites to the state.


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[Assistant] And I think you can see this as a kind of end of free market economics, if you like, and the emergent of a very, very state dependent capitalism.


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[Assistant] The capitalism that we have now is incredibly dependent on state subsidy.


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[Assistant] This fact, I think, has been clear since the 2008 financial crisis, but it's undeniable in the face of the coronavirus crisis of 2020. In 2008, we had bank bailouts, we had quantitative easing, and I would argue that these


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[Assistant] Now this in itself is not unusual in neoliberalism.


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[Assistant] From the 1980s onwards, I think you can always see these trends to what Guy Standing and others call a rentier capitalism.


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[Assistant] It is always been, despite the language of free markets, very dependent on the state. But I think 2008 and even more so 2020,


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[Assistant] Take this to a whole new and different level, to the point where I think it's quite reasonable to talk about the end of neoliberalism as a result of the policies that we've seen now.


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[Assistant] Now here's the caveat for those of us who are quite keen on the idea of ending neoliberalism. At the end of neoliberalism is not necessarily a good thing. So we have, I think, two clear choices that are laid out as a result of this crisis now.


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[Assistant] One choice could be that we take advantage of this crisis to restore


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[Assistant] And where we radically restructure our economy to put climate justice and social justice at the centre of it.


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[Assistant] And that would mean turning on its head the pickety problem of capital benefiting in a asymmetric way compared to labour.


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[Assistant] Or we have another option ahead of us in this situation too, and that is that we could mobilize.


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[Assistant] Colossal amounts of state intervention.


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[Assistant] And this is what we call in the report state dependent financialization. You keep the whole show on the road by mobilizing massive amounts of state resources.


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[Assistant] Now here's where the link to, I think, authoritarianism comes in. So say that you want to defend the existing model and you're looking around to the political system for allies to do so. Well, who would you look to? I think the answer


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[Assistant] And we know this, I think, from the experience of the, of the last decade that we see an uneasy alliance that is increasingly forming around that defence. Because there are forces out there who don't talk very much, if at all, about regulating international finance and putting greater control on capital.


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[Assistant] The political scientist John Keynes calls monetary democracy the capacity of the state to regulate itself.


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[Assistant] And they do all this and they've done all this in a range of states through mobilizing discourses of ethnic nationalism and toxic masculinity.


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[Assistant] And the real risk then with COVID-19 is that you can find lots of raw material for authoritarianism.


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[Assistant] In the current crisis, because say if you were an authoritarian and you wanted to promote your political project in the face of this crisis, you would say, well look, the first thing that cultural globalisation did for us was mass immigration.


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[Assistant] The second thing was, uh, the 2008, uh, financial crisis and the third thing was coronavirus. So they have a ready-made, if you like, arsenal and toolkit ideolo- ideologically to respond.


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[Assistant] What do I think are the implications of these two choices that we essentially face?


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[Assistant] Well, I think one thing we can say about them, either choice, is that actually they both involve quite a lot of retrenchment away from globalisation. This is already happening.


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[Assistant] Uh, organically in the way that capitalism is working, things like financial flows are down, have never recovered through their 2008 level.