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[Assistant] The rise and fall of the sea level over time has helped form a mound composed of gravel and debris.


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[Assistant] This mound is 2.6 kilometers long and extends from the island towards the land.


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[Assistant] Mounds like this can be seen all along the east coast of China.


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[Assistant] However, it is very rare for a small island with an area of less than 0.2 square kilometers to have such a feature several times longer and wider than the island itself.


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[Assistant] It is known as the longest natural avenue at sea in China.


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[Assistant] As the tide retreats, the divine path reappears in the sea. Those who tread this path


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[Assistant] Seen to be walking on an infinity of water.


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[Assistant] Now we are on Seville Row, which is apparently very famous for suits that are tailor made. And I heard if you want to buy a suit here, it might cost you up to 5,000 pounds. So are we getting a suit here so I can look fresh? We are. Let's see what the mission is.


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[Assistant] It says, today your mission is to customize your own suit. Really? Where? Right over here. Pool and Co? Is this the right one? Yes. Okay, I guess we're going to be looking better than I do now in a few minutes. Let's go. Pool and Co.


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[Assistant] I wanna get a customizable suit, and I've heard the term bespoke.


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[Assistant] Okay, so for, to start, as someone who's totally new, fabric is number one.


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[Assistant] Alright, let's move on to this one. I like, I like the look of this. Okay. Now. It seems a lot hotter.


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[Assistant] Look at all the colors. That's what I like about these. The colors are more than just blue and black.


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[Assistant] Perfect. I got a lot of information about so many different fabrics. But after I decide, I make my decision, what's the next step?


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[Assistant] So the difference between single and double is what?


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[Assistant] It might cost you up to 5,000 pounds. Do you have a base price for suits?


[User] Please transcribe this text

[Assistant] The colors are more than just blue and black. Can you walk me through that?


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[Assistant] Hey agrumarians, today we're talking about compound prepositions, which are prepositions that have more than one syllable, and a syllable is like a unit of rhythmic measurement in language. So for example, you could say, compound prepositions.


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[Assistant] And it's just how many rhythmic word sounds there are, that's all it means. Uhm, and it generally tends to mean that a word is more complicated.


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[Assistant] Adding to the complication is that a lot of these words have multiple meanings.


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[Assistant] And what it means is that when you use between in a sentence, uhm, you're considering multiple objects as individuals. So between considers


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[Assistant] Individuals. I'll explain. So you could say something literal, like, the fireflies zipped between the raindrops, literally going, uh, in between each one.


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[Assistant] This describes not a physical relationship, but the relationship of ideas. You're choosing between two ideas, or making a decision between two concepts.


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[Assistant] Now, between often gets confused with the word among, and they do have distinct meanings because where between considers individuals, among is a preposition that considers, uh, collections.


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[Assistant] So, among considers collectively. Among can also have a literal physical meaning or a metaphorical meaning.


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[Assistant] We could say something like Ashley and Cyrus froliced among the daisies.


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[Assistant] And we could say this because we're not really distinguishing between each individual daisy the way that the firefly up here in this sentence is distinguishing between each individual raindrop that it flies between.


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[Assistant] I imagine Ashley and Cyrus as dancing through or frolicking through a field of daisies, and they're not distinguishing between each one. You can also use among metaphorically and say,


[User] Please transcribe this text

[Assistant] Among the ideas you've had, I like this one best.


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[Assistant] So considering the entire collection of ideas that you might have had, like say for a television show, like something called Humdinger and something called Police Cat, among those ideas, among like a multitude of ideas, I like this one.


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[Assistant] But the difference is, uh, in this, in this prepositional phrase that we're just sort of considering all of those ideas as a collective group. Let's look at some more prepositions.


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[Assistant] Around is a word that has both a literal and a metaphorical connotation. And around is, in the literal sense, is pretty...


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[Assistant] Pretty explanatory, but pretty self-explanatory. The fox ran around the tree. Alright, so we've got this tree.


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[Assistant] There goes the weird looking little foxy. But in the metaphorical sense, we can also use this connotation of going around something in a circle. You know, the mayor talked around the issue. You can use the word around as a metaphor to say that someone is avoiding something by instead of addressing it head on, just going, what, what's that, whoop, nope.


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[Assistant] Here's your little trouble box here. So you can take this physical idea and extend it into the realm of the figurative in language. Against, Ryan has this connotation of opposing or opposition.


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[Assistant] And literally, you can say something like,


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[Assistant] Or you can use it metaphorically by, by saying that you're opposed to something, you're opposed to an idea, right? Georgie campaigned against clog dancing, which for the record is a beautiful tradition.


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[Assistant] Within is a word that basically just means in or inside. And you can use it literally, you know.


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[Assistant] To say, there's a frog within the pond. Which is maybe a little ponderous, but you get the idea.


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[Assistant] But if you want to get metaphorical, you could say, non-literally, who can say what is within Ralph's heart? Right, and we're not asking, you know, what's the condition of his cardiac muscles, we're not talking about the blood, we're talking about what, you know, what spiritually or what emotionally is going on inside Ralph.


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[Assistant] Without is an interesting one because it kinda has, it used to mean something different, it used to mean...


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[Assistant] Something more akin to outside, but now it has this connotation of just not with. It's the opposite of with.


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[Assistant] I guess we're going to the candy planet without Stu, then.


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[Assistant] So inside basically means the same thing as in or within.


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[Assistant] You can use it much the same way as you would use any of those, any of those other two words. So you could use it literally.


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[Assistant] And ask, what's inside the box? Well, we can also use this kind of interiority as a metaphor and say, what's inside her mind right now? Because the difference between that and the literal sentence is you can open the box. You can't really open someone's mind, not physically anyway. You can open someone's mind by


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[Assistant] Get inside someone's head unless you're, you know, a psychic mind wizard.


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[Assistant] Finally, beyond, and beyond is a preposition that has this connotation of being far away.


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[Assistant] Like far away and past some point. And it also has a literal meaning and it has a metaphorical meaning. So for example, you could say literally,


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[Assistant] Beyond those mountains is Tyrell's kingdom. Right, we're using these mountains as like a literal...


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[Assistant] But you can also use it as a metaphor. We can use this physical relationship as a metaphor. You can say something like, this strawberry's flavor is beyond anything I've ever had before.


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[Assistant] Right, this is just like the world's most delicious strawberry. And it's, it's taste, it's flavor surpasses, it goes past, you know, over the mountains and into Torelia. That's how delicious it is. Oh my goodness, what a strawberry, you know.


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[Assistant] We're using this physical relationship as a figurative metaphor.


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[Assistant] We're using that to say that this strawberry over here is way better than anything else that we've had previously. Like this square rock. This is a, used to eat rocks before.


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[Assistant] Although we do eat rocks all the time, if you think about what salt is, technically.


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[Assistant] Anyway, these are eight of the most common compound prepositions. There are more listed in the exercises. You can learn anything. David out.


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[Assistant] To build out this database, I've gone ahead and created a new workspace page. This ensures that everyone in our workspace will have access to this Meeting Notes database. Started by giving our database a title and an icon, called it Meeting Notes, and then gave it this nice stack of books emoji.


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[Assistant] These are the things that will be available to categorize each meeting within the company and are how we'll be able to sort, filter, group, and otherwise organize our notes.


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[Assistant] These will automatically be filled based on when those rows were created. So as you can see, these were all created September 21st at 4.48pm.


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[Assistant] You can click on any page, open it and view the meeting notes.


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[Assistant] Notion's blank slate is a beautiful thing. The ultra-customizability means that it can be morphed into whatever tool you need, and the simple block structure means that, much like Legos, you can construct your own workflows and tools.


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[Assistant] In this video, we will walk through a simple planning structure to help you create the specialization that you need inside of Notion. But first, let's play a quick game. What do all of the following things have in common? Cooking with several people in the kitchen.


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[Assistant] If you guessed that they all require problem solving and analytical thinking, you would be correct. And if you enjoyed any of these activities now or as a kid, you will probably enjoy building workflows in Notion.


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[Assistant] There are a lot of different frameworks to approach building a notion, but the one we're going to explore today is four steps. Understand, plan, build, and iterate.


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[Assistant] You know you need some kind of task manager, but let's unpack that. Are you worried about giving clear assignments to team members? Deadlines? Keeping everyone on the same page?


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[Assistant] Since we're thinking about task management here, we definitely want some kind of database.


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[Assistant] Since we want this task manager to be private, we'll create it in the private section of our sidebar. That leaves just two unsolved problems. It must be simple and it must be adaptable.


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[Assistant] A calendar would be great, but it doesn't provide as much top level detail as we would want. A table would work, but it isn't visual enough.


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[Assistant] The next best option is a board, which will fulfill most of the requirements and the drag and drop feature makes it a little friendlier to adaptability.


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[Assistant] Now that we've created our plan, building is a breeze. Simply navigate to your private space, create a full page board, and start adding tasks.


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[Assistant] I'd encourage you to think about problems that you've been running into with software lately. Try using this process to build a workflow that will make your day to day life just a little bit easier.


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[Assistant] So the first concept of relational databases comes from a paper written in 1970 by Edgar F. Codd, a computer scientist working at IBM.


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[Assistant] In it, he profoundly states that future users of large data banks must be protected from having to know how the data is organized in the machine.


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[Assistant] At first glance, a database in Notion might look like something you've probably seen before. A spreadsheet.


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[Assistant] Let's take a database tour and explore the power over the next couple of videos. Starting with the name property.


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[Assistant] This is an identifier for a database entry and it's pretty similar to a page title in that it describes what you would expect to find in that row. So in our example here, every row is a person that works at Acme Inc. A pretty natural identifier.


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[Assistant] One pro tip about the name property is that it is the only property in a database that Notion requires you to have. This will be good to know as you start to build out your own databases.


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[Assistant] They are so called because they pull related bits of information together. And the same is true of Notion databases.


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[Assistant] Here you can see a number of different properties that describe the person, like their department, location, LinkedIn profile, email address, and so on.


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[Assistant] You might notice that these columns look nicer than your traditional spreadsheet, and that is because unlike a spreadsheet, relational databases require columns to have a type.


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[Assistant] Literally a data type, which you can think of just like blocks. For example, this name column is the type text. Department column is a single select.


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[Assistant] Or full page. For our purposes we'll work with an inline database, but full page databases can be great when you start to manage really complex relationships.


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[Assistant] And a general thought leadership host with our CEO.


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[Assistant] To add new entries, you can just click on the new button at the bottom of your database.


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[Assistant] In this case, we might want to use tags to group blog posts by type. So maybe we have blogs about engineering, general company news, thought leadership.


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[Assistant] Tags can be reused on multiple blog posts.


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[Assistant] Again, remember that we need to designate the type of property here. So if we want to identify the blog post author, we can first give it the name author and then select a property type of person.


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[Assistant] Ten girls have ten bugs.


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[Assistant] Ten dads have ten mugs.


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[Assistant] Mount Ermei, located in the southwest of Sichuan province, with its back to the Qinghai-Tibet Plateau and standing in the southwest edge of the Sichuan Basin, is an ancient and famous attraction in China.


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[Assistant] It has the world's highest and largest metal building complex and is also a world cultural and natural heritage site.


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[Assistant] In a commanding position, the Golden Summit, with the biggest concentration of temples and scenic spots on Mount Ermei, can be called the largest and highest place of worship of Chinese Buddhism in the world.


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[Assistant] The Buddha statue of Samantabhadra measuring 48 meters high is the highest golden Buddha in the world and is at the heart of the Golden Summit on Mount Urmai.


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[Assistant] The ten heads of the Buddha statue are divided into three layers, each with different expressions, representing the ten mentalities of ordinary people.


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[Assistant] The Golden Summit is also the perfect location to appreciate the wondrous Mount Urmai Sea of Clouds.


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[Assistant] The 72 peaks of Mount Urnay are mostly over 2,000 meters above sea level.


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[Assistant] The scenery features high peaks above the clouds and ubiquitous white mist.


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[Assistant] In such an ocean of whites, the many peaks appear to float in the clouds like islands.


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[Assistant] Formed by ever changing patches of mist and fog, they present a unique panorama.


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[Assistant] Summer and autumn are the best time to view them. At every level, the scene is different.


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[Assistant] If you're lucky, it's also possible to see Gungar Mountain, 7,590 meters above sea level, in the distance from the Golden Summit.


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[Assistant] The Chilean mountains still store 80% of the water in this land, continuously nourishing about 5 million people and 700,000 hectares of cultivated land.


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[Assistant] So today we're going to be talking about commas in space and time because commas have basically one function which is


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[Assistant] And what's neat is that we can use commas to separate stuff out, uh, in space and separate stuff out in time. Uh, so I'm gonna handle space. Page is gonna do time. It's gonna be sweet. Uh, so first of all,


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[Assistant] One way in which you can use commas to separate space is by talking about addresses, people's physical addresses in space.


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[Assistant] So, the fictitious, uh, detective, private detective, Nero Wolf, one of my very favorites.


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[Assistant] Uh, is supposed to live at, uh, 454 West 35th Street in New York. So if I wanted to write Mr. Wolf a letter, and I would write him a letter because he hates talking on the phone, I would address it like so, separating all elements of his address with commas. So, near a wolf, comma.


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[Assistant] Uhm, and then I would put the, uh, the zip code, which I think is 1-0-0-0-1, but I'm not sure if they had zip codes when those stories took place, but it doesn't matter. Uhm, you separate out all of these elements with commas.


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[Assistant] And this isn't just for writing address, this is for referring to any point in space on the planet. So if I wanted to tell you the name of my favorite city on the planet, I would say, Wagadougou.


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[Assistant] Real place, I've never been there, I would like to go some day. Uh, and that is how you use commas in space. Page, how, how are commas used in time?


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[Assistant] Because all of this is like a prepositional phrase that is modifying the word eight. True.


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[Assistant] So we want to make sure that all of it is separated out from the rest of the sentence. And we separate it out using our friend the comma.


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[Assistant] So if you want to express a kind of complicated relationship in time or in space, you want to deliver, you want to make sure a letter gets to a very specific place, you want to talk about a very specific city in a very specific country, you use commas.


[User] Please transcribe this text

[Assistant] You wanna talk about a very specific time, you use commas. That's commas in space and time, my grammarians. You can learn anything. David out.


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[Assistant] Apples can be healthy.


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[Assistant] Celery can be healthy.


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[Assistant] Nuts can be healthy.


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[Assistant] Cheese can be healthy.


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[Assistant] Carrots can be healthy.


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[Assistant] Cereal can be healthy.


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[Assistant] Uhm, and so here's basically what you need to know is that commas in dialogue


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[Assistant] Essentially function as runways. You can ramp you up to get going for an utterance and take you down and land. Uhm, Paige, would you read me this sentence?


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[Assistant] So we're starting into this sentence and we're going to use this comma as a ramp.


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[Assistant] Uh, to divide between the utterance and what's called a dialogue tag. So, Guillermo said, comma, I have no idea where I put that moonstone. This is the end of his utterance. We're gonna put a period here. However, sentence number two,


[User] Please transcribe this text

[Assistant] So here, since we're putting the dialogue tag after the reported speech, if this were otherwise going to be a period, uh, we're gonna put a comma here.


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[Assistant] They're probably pirates, comma, Roxanne said. Period.


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[Assistant] Uh, and why is this? It's because we're, we're trying to separate between the reported speech and the dialogue tag itself. So uhm,


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[Assistant] In the event that this were a question, however, you would use a question mark. Or if it were an exclamation, you would use an exclamation point. But if it were going to just be a period,


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[Assistant] You wouldn't do this. You wouldn't say they're probably pirates period Roxanne said. You would say they're probably pirates comma Roxanne said.


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[Assistant] So if, if the statement were going to end with a period, we'd use a comma in reported dialogue when the tag follows the reported speech.


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[Assistant] Otherwise we'd use all the other relevant punctuation marks.


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[Assistant] So that's how you use commas in dialogue. You can learn anything. David out.


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[Assistant] In this project, we'll consider the ever prevalent issue of change management. Someone on your team wants to make a change, they slowly gather support through meetings and Zoom calls, and suddenly it's launched. Wait, what? I didn't even know that this was happening.


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[Assistant] We need to get better at change management. And more specifically, we need a tool that will include a formal document component, create a process for feedback, and include deadlines for feedback.


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[Assistant] Let's think back to the beginning of this 101 course and remember the blocks that we have at our disposal to solve this problem. We have our basic blocks like text and headings.


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[Assistant] Our inline blocks for collaboration and dates.


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[Assistant] Database blocks, which might be helpful here to manage a large project. Media blocks, embed blocks, and our advanced blocks as well.


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[Assistant] Since pages are everywhere in Notion, it might help to organize your documents into a database where team members can go and look for new initiatives that require feedback.


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[Assistant] That way a team member can click into each project with the feedback requested tag and add comments.


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[Assistant] Creating a request for comment or RFC tag.


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[Assistant] Maybe requesting in Slack with Slack updates through Notion and changing the status to approved.


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[Assistant] Since the major goal here is accessibility across multiple teams, we can go ahead and add a page to our workspace sidebar that will be accessible to everyone in our workspace.


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[Assistant] Building this workflow is relatively simple now that we've thought through the problem and planned our solution. We're creating a new page titled docs.


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[Assistant] Now we can just start to add our change management documents in here. So for example, let's go ahead and create one called New Website Font. Another called Improve Comments in App.


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[Assistant] By default, our board comes with not started, in progress, and complete tags. We can go ahead and open one of these pages. Start to delete the default tags.


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[Assistant] Maybe some bookmark tabs. And we'd want to explain this new process to our team using Slack. To set up Slack integrations with this channel, we can come to the top right hand corner, click on this three dot menu, and connect to Slack channel.


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[Assistant] You could create a calendar of projects that have been approved. Another calendar with deadlines for feedback. You could add a column for feedback required. The list goes on. Building a Notion is really as fun as you make it.


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[Assistant] Australia is the world's fourth largest exporter of agricultural products. With the exception of a handful of cities, the country is dotted with private farms.


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[Assistant] In 2016, Australia decided to offer working holiday visas to China in an effort to promote tourism. Australia is big and sparsely populated.


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[Assistant] The farms here need some helping hands, and the young backpackers need money to finance their trips. It is win-win.


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[Assistant] Today, Andy will also be a farmhand, but she has to pass a challenge first, the job interview.


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[Assistant] So, the question I wanted to start with is why is that? What has happened to science and art over these last 40 years?


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[Assistant] A thing given. It is something, uh, something that is, is given to us as a gift. It comes from the Latin word dare, which is to give.


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[Assistant] Utterly somnambulant. What the humanities profess to do is to understand everything


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[Assistant] Or perhaps getting everybody else to fit in with you.


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[Assistant] That draws scientists ever further from the phenomena they profess to study into a world of their own making.


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[Assistant] Uhm, but they're used to advertise its results.


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[Assistant] By bringing the spaceship back down to Earth. Thank you very much.


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[Assistant] Above the weapons. I had just come out of weapons, I think.


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[Assistant] That is a great question. So the definition itself is also kind of confusing, but it'll make a lot of sense when we see some examples. My older sister, Griselda, is going to college in the fall.


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[Assistant] That's true, it can be, say, at the end. So, they stopped selling my favorite snack, the cookie cat.


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[Assistant] Right, but if you, you know, don't know what my favorite snack is, then it's kinda helpful to say, or it's helpful for me to say, the cookie cat.


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[Assistant] Yeah, exactly. That's what the comma does. Man, it seems like commas can do, like, anything.


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[Assistant] The real death is that no one in the world remembers you.


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[Assistant] What do you mean? What the hell is going on? I'm the host of Tomato English. You? Ha! Look at you! And look at me!


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[Assistant] You're impossible. Am I out of the job?


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[Assistant] Man, I can't believe this. Unemployed? Actually, I'll be your new host.


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[Assistant] This is going too far. By a skeleton. By a- not even a skeleton, by a skull. Oh man. Well I guess he was pretty cool looking.


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[Assistant] We know a magic trick that can make you cooler than him. It's called Special Effects Makeup.


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[Assistant] Oh wow. So what do you think? Man.


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[Assistant] How long have you been in the special effects industry?


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[Assistant] I'm gonna show that guy. Who the boss is. Yeah.


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[Assistant] Oh, this is great. Oh, I'm so happy. Oh. Awesome, guys. I still have my job. You're gonna get your job back now.


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[Assistant] Thank you, thank you. Wow, this is great. Man, I'm so blown away by this.


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[Assistant] If I were in your shoes, I would resign. You're impossible. This is going too far. Now we're talking. Do you think that this is irritating for sensitive skin? Once you put that on, there's no going back.


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[Assistant] How long have you been in the special effects industry? This seriously goes above and beyond my expectations.


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[Assistant] Right, so that means to own something or to have something.


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[Assistant] Yeah, but that's, that's pretty complicated and can make sentences much longer than they need to be.


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[Assistant] You can say something like, the rabbit's carrot was delicious.


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[Assistant] Yeah, that, the whole idea of the rabbit owning the carrot comes from that apostrophe s.


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[Assistant] So, there's an exception to this rule of using apostrophe s for possession,


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[Assistant] Alexander Hamilton are both nouns, but there are pronouns like


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[Assistant] It or he or she where you don't use an apostrophe to show possessive.


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[Assistant] Halloween Costumes Written by Elizabeth Jane Pastilnik Focus Question


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[Assistant] I look like a tiger.


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[Assistant] Connections. Writing and art.


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[Assistant] Draw a picture and write this sentence. I look like a Social studies. When is Halloween celebrated?


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[Assistant] Of course, in blistering hot Hawaii, it's no wonder that Che Vice enjoys such huge popularity.


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[Assistant] Not only can you eat ice here, you can also go shopping. Hats, t-shirts, sunglasses. Shopping while munching on ice seems like a pretty good way to cool


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[Assistant] Look, Kevin is eagerly listening to all that juicy gossip. What he doesn't know is Amy has already sneakily brought out another mission card.


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[Assistant] First, I wanted to know if you actually knew that this script existed. There's about fifteen to twenty thousand obsessive fans. Uhm, I have seen it, yea. Are you a member?


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[Assistant] When you're abroad, what if you find bars too noisy?


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[Assistant] In China, Legends of the Three Kingdoms and Werewolf are our two most beloved games. In the West, however, there are war, business, sci-fi, art, and city-building themed games, just to name a few of the options you might find.


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[Assistant] If you want to become a pro at board games, you just need a little bit of logic, quick reaction, eloquence, emotional intelligence, and luck.


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[Assistant] Doesn't that sound so easy to make you wanna jump in immediately?


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[Assistant] So BoJack goes to some dark and controversial places. Is there anything you've nixed because you think it's gone too far?


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[Assistant] Your job is to pick out the most suitable style of coat to buy her as a gift. Good luck.


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[Assistant] I don't know what you're saying, but I'm here to prove you wrong. I'm gonna pick you out the most beautiful coat. Do you wear coats?


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[Assistant] But other than the color, I don't really see any difference between these. Is there?


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[Assistant] From my sisters and also people who shop, a lot of times the first thing you try on is the thing you buy.


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[Assistant] Oh, it looks better than the other one.


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[Assistant] Because it's black. Wow, she looks absolutely...


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[Assistant] It looks good on you. So, did I do my job?


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[Assistant] So she can have style underneath when she walks inside.


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[Assistant] So, does this coat come in any other colors?


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[Assistant] Should this be dry cleaned or hand washed? Can you iron out the creases?


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[Assistant] Since last episode, our tomato trio have come to the Atlas, ready to teach you some English that are used while shopping.


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[Assistant] And it looks like they're all pretty happy keeping themselves busy. Do you remember what we learned in our last episode?


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[Assistant] Some women say good shoes will bring you to better places. Alright, whatever you say.


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[Assistant] If you want to buy jeans, why don't you go and visit Levi's? The price of Levi's in the United States is very reasonable, and a pair of jeans will only cost about $40 here at the outlets.


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[Assistant] Well, let's give our host a chance to empty her wallet. But before leaving, I have one last thing to say. Don't forget to review today's content.


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[Assistant] Stupid mission card. It's my least favorite thing in the world right now. You guys are just straight up heartless. I don't- I'm not talking to you for two days. This is happening.


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[Assistant] Please invite a passerby to complete the mission. Uh, uh, again.


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[Assistant] Describe a Harry Potter character without saying its name. According to your description, the passerby has to guess what character it is. If they finish, you will pass. If they do not, you will not. P.S. Find a girl to do this mission. And you cannot.


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[Assistant] I know, me neither, so. Good for me.


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[Assistant] I pick up. Written by Pam Bull. Illustrated by Angela Kamstra Jacobson.


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[Assistant] Animal Caution Signs Written by Craig Frederick Focus Question


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[Assistant] What are animal caution signs, and why are they important?


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[Assistant] Caution! Watch for ducks.


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[Assistant] Caution. Watch for cattle.


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[Assistant] Caution. Watch. For. Deer.


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[Assistant] Caution. Watch. For. Moose.


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[Assistant] Caution. Watch for kangaroos.


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[Assistant] Caution! Watch for elephants.


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[Assistant] Caution. Watch for monkeys.


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[Assistant] Caution. Watch for turtles.


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[Assistant] Caution! Watch for chickens.


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[Assistant] Caution! Watch for animals.


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[Assistant] Draw a picture and label it. Caution! Watch for...


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[Assistant] Hello everybody, welcome back. Do you like to eat pancakes? Of course you do. And if you eat pancakes, you need syrup, right? You need syrup on your pancakes. It's that brown, thick liquid that's very sweet.


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[Assistant] Well, in this lesson, we're going to learn where syrup comes from. This book is titled, The Maple Harvest. Two words here, maple. What is maple? Maple is a kind of tree.


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[Assistant] So we're going to talk about these trees in this book. We're going to learn more about these trees. Now we also have this word, harvest.


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[Assistant] Harvest. Harvest means when you take some food from plants that you grow. Trees are plants, right? Rice is a plant.


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[Assistant] Corn is a plant. So in the fall, usually farmers will go into their fields and take the food from the plants. They'll take the rice, or they'll take the corn, or whatever food it is.


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[Assistant] So when we learn about this subject, these are the contents. This is the table of contents. And this is what we're going to learn in this lesson. Let's go over it together.


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[Assistant] Times for the maple harvest. Remember I gave you the example, farmers go into the field in the fall, in the autumn to get the rice or to get the corn. But what about maple?


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[Assistant] Syrup. What about maple harvest? Well, there will be times. What time do they go? Do they go in the fall? Do they go in a different time of the year? We'll find out in this chapter on page two.


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[Assistant] The next one is collecting the sap. Now here we have a word that you probably don't know, sap.


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[Assistant] If you look at a plant, any plant, and you, if it's a small plant, you can break it or break it, uh, part of the plant off, you can see there's some kind of


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[Assistant] Yeah, it's some water, but there's also, it's like juice, right? Well, sap is a type of juice or water that is inside a tree, right? Now, don't do this, but if you did, if somebody cuts a tree,


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[Assistant] Once you cut into the tree, you can see that it's kind of moist. There's a little bit of water in there. But don't do that just to see, because that's not good for the tree. But anyway, there's sap inside the tree. There's some kind of juice in the tree.


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[Assistant] Then we see the sugar house. Mmm, that looks interesting, the sugar house. That's a house where they have sugar. We'll learn about that on page 6.


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[Assistant] Then we'll see how they make maple syrup. Of course we can see it has something to do with the sap, the juice inside the maple tree. How do they make maple syrup? We'll find out on page 8.


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[Assistant] Then we'll have a fun chapter, time to eat, right? Time to eat. What can we eat with maple syrup? What can we use maple syrup to make? What kinds of food can we make with maple syrup? We'll find that out in this chapter on page 10.


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[Assistant] And then of course, at the end of the book, we have our glossary, where we can review the important words we learned in this book. Okay? So let's begin.


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[Assistant] Let's go to Canada because that is where many maple trees are. In fact, did you know this important fact down here?


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[Assistant] From the maple tree is on the Canadian flag because Canada has many, many, many maple trees. So there's many maple trees in Canada. It's a great place to make maple syrup. But when? When do they make the maple syrup?


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[Assistant] It's time for the maple harvest. Time? What time?


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[Assistant] It is the end of winter in Canada. Ah, the end of winter in Canada.


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[Assistant] So the end of winter means spring is coming soon. When is the end of winter? Around the end of February, or the beginning of March. But remember, Canada has a very long winter, so maybe it's in March sometime, maybe middle March or end of March.


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[Assistant] This family, this family, these are the girls of the family.


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[Assistant] This family is going to a maple farm, right? A farm of course is where people grow food. This is a maple farm. They're, they grow maple trees and they get the syrup from the maple trees at this farm.


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[Assistant] They will see how maple syrup is made. There's our word, one of our words right there. Syrup, of course you know syrup. I told you before at the beginning of this lesson, the syrup is the hot, it's sometimes hot, it's the thick


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[Assistant] Brown sweet liquid that you pour onto your pancakes. It's thick. It's brown.


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[Assistant] And it's very, very sweet. It makes your pancakes taste very good.


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[Assistant] So they want to see how maple syrup is made. We'll go with them and we'll find out too, okay? Okay.


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[Assistant] Now before, remember I told you sap is the juice inside trees. We'll see how they get that juice out of the trees later on. The sap is harvested


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[Assistant] From sugar maple trees. This is a special type of tree. It's a sugar maple tree. It's a sweet tree. Can you imagine a tree of sugar? It's not that. That would be cool, huh? You go in the forest and you can lick the trees. No, don't do that.


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[Assistant] They are not like that. They are sweet on the inside and you have to do some many things to make it sweet. So, so don't lick the trees in Canada.


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[Assistant] But anyway, the sap is harvested. Remember that word I taught you? Harvested. It's when the farmer goes into the field and takes the food from the plants or whatever they're growing, right? So they harvest the food, they get the food from the plants. So the sap is harvested from sugar maple trees.


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[Assistant] Sugar maple trees grow well in Canada. That's what I said before. There are many, many maple trees in Canada. So many that they put the maple leaf on their flag.


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[Assistant] So they grow very well in Canada. It's the right conditions, right? Lots of land, good air, the right temperatures. They apparently like cold temperatures because Canada is quite cold. Okay.


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[Assistant] Now, collecting the sap, how do they get the sap from the tree? Look at this picture. Do you see anything strange?


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[Assistant] We see a building, we see trees, and we see something strange.


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[Assistant] What are these? These are buckets. There's buckets hanging on the trees. Okay? That's a little strange, right? But these buckets are used to collect, to collect the sap. The sap goes into the bucket and the farmer can get the sap out of the bucket.


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[Assistant] Sugar makers collect the sap.


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[Assistant] Sugar makers. Sugar makers are people. They are a certain type of farmer who collect the sap. We call them sugar makers. Okay, that's a special job title, right? Interesting job title.


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[Assistant] What do you want to be when you grow up? I want to be a sugar maker, right? Sounds interesting. Okay, sugar makers, collect the sap.


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[Assistant] They make small holes in the trees. They make small holes. So they don't make a big hole in the tree. That's not good for the tree. Just a small hole. And you can see.


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[Assistant] That they put these very small holes and maybe this is an old hole right here. It's very small hole that they drill or they put into the tree so the sap can come out.


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[Assistant] So they make small holes in the trees. They tie buckets to the trees. That's why these buckets are hanging on the trees. You have to tie them so they don't fall down. They tie buckets to the trees. When you tie something, you use wire or string or rope and you tie them.


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[Assistant] You tie your shoes, right? You tie two pieces of string or wire together so it holds together.


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[Assistant] The sap runs out the holes. So the sap runs out the holes, it fills the buckets. We can see here,


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[Assistant] The hole is here, and then you have this here. What is this? This is a sap tube. It's


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[Assistant] There's air inside, right? It's empty inside. So when the sap comes out, there's some room. It can go through the tube, right, here, and then it can go there and then into the buckets. Okay? So the sap comes out of the tree and down the tube, and then it drops into the buckets.


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[Assistant] So that is a sap tube. So it's a very, you have to have a special tool to get the sap from the trees. Today,


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[Assistant] Some sugar makers use tubes, not buckets.


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[Assistant] The tubes run from the trees to a big tank. So in this case, actually, they don't use the buckets, right? In this case, they probably just drill a hole in the tree and the sap runs out into the bucket.


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[Assistant] But other sugar makers today, they don't use the buckets. Some of them don't use the buckets.


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[Assistant] They use tubes and these tubes run out here and you can have a tube go very far and you can have a big tank over here. A big tank. What's a tank? Well, if you think about your car, your car needs gas.


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[Assistant] When you go to the gas station, the ju-yu-so, you put gas into the tank. So the tank is a big, usually metal,


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[Assistant] That's easier, right? Because if you use buckets, you have to get each bucket and pour it into a big tank. But if you've got the tubes running to the tank already,


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[Assistant] That saves a lot of work. That's pretty smart. So the sugar makers in Canada are always using new technology to make their work easier for them. Okay, let's take a break here. We'll come back and we'll learn more about how maple syrup is made.


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[Assistant] Marco, I am doing really well and I'm really looking forward to today's lesson.


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[Assistant] Well today we're talking about computer viruses.


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[Assistant] Okay, today we've got two words to preview. The first word is virus.


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[Assistant] So if your computer has a computer virus, it means your computer is sick, right?


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[Assistant] Don't worry if you don't understand everything. At the end of this podcast, you'll understand everything.


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[Assistant] In today's language takeaway, we have two words. The first word for today is infected vile.


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[Assistant] An infected file is a file in your computer that has a virus in it.


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[Assistant] Our second word is not up to date.


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[Assistant] Okay, that was pretty clear. You know, you can also use this phrase positively. You can say, up to date.


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[Assistant] Exactly, or if I bought a new computer, I could say, I just got a new computer. All my software is up to date. Okay, great. So now let's take a look at some phrases. And it's time for putting it all together. Putting it together.


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[Assistant] Okay, so Samuel said a really great phrase. He said, I'll be right up.


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[Assistant] Yeah, you know that phrase, I'll be right out. I used to hear this all the time when I was young.


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[Assistant] It turns out that this is a great phrase. You can use it in many different situations. Marco, can you give us an example?


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[Assistant] Yep, or I could say, oh, I've been feeling really sick recently. Why?


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[Assistant] Influency Builder, we give you some great, useful phrases to help you express your ideas clearly.


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[Assistant] I love this phrase, it's acting up. It just sounds really natural.


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[Assistant] It'll be at the normal speed. Try and see if you can hear these keywords.


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[Assistant] Because I have been turning on my computer and then suddenly it turns off by itself. It shuts down by itself.


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[Assistant] Firefly, written by Harriet Rosenblum. Illustrated by Kirsten Egan.


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[Assistant] I tried catching one Tuesday. Ooh.


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[Assistant] I tried catching one Wednesday. Uuuh.


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[Assistant] I tried catching one Thursday. uuuh, uuuh.


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[Assistant] I tried catching one rye-day.


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[Assistant] I tried catching one Saturday. Uuuh.


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[Assistant] I tried catching one Sunday.


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[Assistant] I caught one and let it go.


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[Assistant] When I first started, it was mostly like a bunch of white guys from Europe in very like sad looking competition rooms.


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[Assistant] Starts mid-sentence. Information effectively by using humor.


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[Assistant] So then it's basically like you're reading something instead of looking at all these numbers. So 539 is sag. And the next triplet, 166, becomes TBB.


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[Assistant] This saggy, half-naked person is covered in like tabbouleh rice and because it's disgusting, I remember it more. Anything that has like visceral or like very emotional things, your brain is like, ugh. She translates the rest of the digits the same way. Gimli from Lord of the Rings, he is running for office. For me, Malik buying boots. My spleen turns into the lube.


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[Assistant] Alpaca llama, and it's like eating this tube of like melted cheese.


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[Assistant] Wired to remember that than to remember random sets of digits. In general, we're like emotional and visual learners. And storytellers.


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[Assistant] So the source code can be found at github.com slash William Fize slash data dash structures


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[Assistant] Make sure you understood part one and two of the stack series before continuing. So you actually know how we implement a stack using a linked list.


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[Assistant] Also, if you like this video series and the implementation of the stack data structure,


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[Assistant] This is the link list they provide in their package java.util that I will be using today instead of the one that I created in


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[Assistant] So to get to do that, we return the size of the internal length list with all the elements are stacked easy.


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[Assistant] So to do this, we check if the stack is empty, if it is, then we throw an empty stack exception because we can't pop an element off of an empty stack, that doesn't make sense.


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[Assistant] And lastly, we can return an iterator to allow the user to iterate through our stack


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[Assistant] This iterator returns an iterator for our linked list, which supports concurrent modification errors. So this was a really, really quick implementation of a stack, it's only like 50 lines of code. However, if you do have any questions, drop me a comment below.


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[Assistant] I can't believe it took us two hours to get here. The traffic in New York is unbelievable.


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[Assistant] I don't know. Look at that line. It must be a mile long. There's no way I'm waiting for another two hours. Honey, don't.


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[Assistant] I can't believe it took us two hours to get here. The traffic in New York is unbelievable.


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[Assistant] I don't know. Look at that line. It must be a mile long. There's no way I'm waiting for another two hours.


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[Assistant] Alright, now that we were done with abstract data types, we need to have a quick look at the wild world of computational complexity to understand the performance that our data structures are providing.


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[Assistant] So as programmers, we often find ourselves asking the same two questions over and over and over again. That is how much time


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[Assistant] So if your program takes a lifetime of the universe to finish, then it's no good.


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[Assistant] Similarly, if your program runs in constant time, but requires a space equal to the sum of all the bytes of all the files on the internet, internet, your algorithm is also useless.


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[Assistant] So to standardize a way of talking about how much time and how much space is required for


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[Assistant] Amongst other things like big theta, big omega, and so on. But we're interested in big O because it tells us about the worst case.


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[Assistant] Big O notation only cares about the worst case. So if your algorithm sorts numbers, imagine the input is the worst possible arrangement of numbers for your particular sorting algorithm.


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[Assistant] For that particular example, the time complexity would be linear with respect to the number of elements in the size of your list, because you have to traverse every single element until you find seven.


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[Assistant] The same concept applies to space. You could just consider the worst possible amount of space your algorithm is going to need for that particular input.


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[Assistant] There's also the fact that big O really only cares about what happens when your input becomes


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[Assistant] For this reason, you'll see that we ignore things like constants and multiplicative factors.


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[Assistant] So in our big O notation, you'll often see these coming up again and again. So to clarify,


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[Assistant] If it takes like quadratic time or cubic time, then we say that's n raised to that power.


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[Assistant] Um, usually this is going to be something like 2 to the n, 3 to the n, any number b greater than 1 to the n, and then we also have n factorial.


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[Assistant] How and why we get the first two properties.


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[Assistant] Uh, the first that we can simply remove constant values added to our big O notation.


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[Assistant] So we can ignore constants. But of course, this is all theoretical. In the real world, if your constant is of size 2 billion, probably that's going to have a substantial impact on the running time of your algorithm.


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[Assistant] However, let us look at a function f which I've defined over some input size and if f of n is seven log


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[Assistant] Alright, now let's look at some concrete examples of how big O is used.


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[Assistant] So on the left, when we're just adding or doing some mathematical formula, yes, that's constant time. And on the right,


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[Assistant] Okay, we're doing a loop, but the loop itself doesn't depend on n. So it runs also in a constant amount of time. So as our input size gets arbitrarily large,


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[Assistant] Because we do a constant amount of work and times.


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[Assistant] So f of n is n. And clearly when we wrap this in a big O, we get a big O of n.


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[Assistant] So we're going to finish that loop three times faster. So f of n is n over three. So our second example is


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[Assistant] Two algorithms that run in quadratic time. So the first one seems obvious, we do n work n times. So n times as big of n squared


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[Assistant] Observe the second one, I changed the zero with an eye. So pause the video and try to figure out maybe why that's big of n squared.


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[Assistant] So first, just focus on the second loop. The first loop isn't as important. So since I go from zero to n,


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[Assistant] This is a well known identity, and it turns out to be n times n plus one divided by two. So if we wrap this in a big O, we split our equation, we can see that this is big O of n squared. Now let's look at perhaps a more complicated example.


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[Assistant] Then it selects a midpoint between the two and checks if the value


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[Assistant] We're looking for was found at the midpoint and then has either founded or not.


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[Assistant] If it has found it, it stops otherwise it discards one half of the array and adjusts either the high or the low pointer.


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[Assistant] Remark that even in the worst case we're still discarding half of the array each


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[Assistant] That does n work. Then notice that there are two inner loops, one that does three n work and another one that does two n work.


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[Assistant] So the rule we use to determine the complexity of this algorithm is to multiply loops on


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[Assistant] Different levels and add those that are on the same, generally speaking.


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[Assistant] Uh, so, so using the rule above, we can see that it takes n work to do the outer loop,


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[Assistant] Three n plus two n for both inner loops, which gives us five n squared, which is big O of n squared.


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[Assistant] And cubed, but j is j equals j plus two, so it's accelerated a little. So we're going to finish that loop a little faster. So we're going to get on the inside 40


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[Assistant] It's going to give us big O of n to the fourth, because n to the fourth is the dominant term in our function f of n.


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[Assistant] There's some other classic examples of big O. So if we have to find all the subsets of a set, that takes an exponential amount of time, because there are to the end subsets, finding all permutations of a string takes big O of n factorial.


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[Assistant] And if we have to iterate over all the cells of an array of size n by m, we have big of nm time.


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[Assistant] Here we go, the mission is... Hi Connor. The Tomato English crew are all feeling really sick today. I knew this. Please be our hero and save us. That's why I'm here. You have to go and buy us all the correct medicines.


[User] Please transcribe this text

[Assistant] Since I am the savior, I will go to the nearest pharmacy. ASAP. Let's go.


[User] Please transcribe this text

[Assistant] Okay, here we are. Constipation is my first medication.


[User] Please transcribe this text

[Assistant] I don't usually come to pharmacies. Double action.


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[Assistant] She doesn't want to diarrhea, but just eliminate naturally. Does that make sense? Cause I know some of the medicine can be very strong. Yes. So we want something not so strong. Yes. I guess. Yeah. Could you show me?


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[Assistant] 12 hours. Okay. It should be best taken at night. Okay.


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[Assistant] He said, he said no. But some are fine with it. And it's cultural. It's cultural.


[User] Please transcribe this text

[Assistant] Next will be Rachel with a cold. I saw the word cold here earlier. Let's see. Here we go. Cold relief absols. One option. And then they have


[User] Please transcribe this text

[Assistant] Shoot, they have so many different options. Lookit, chesty, cold, powder, they have syrup for a dry cough and sore throat. She also said she has a sore throat. Uh, what else? Migraine.


[User] Please transcribe this text

[Assistant] I'm a little bit confused because I don't usually shop at stores like this.


[User] Please transcribe this text

[Assistant] My friend Rachel actually has a cold.


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[Assistant] You know, she has a really runny nose to start. And when I feel her head, it kind of feels like she has a little bit of a fever or a temperature, but I'm not sure. So to start, where, which one would you go for?


[User] Please transcribe this text

[Assistant] It's not only just a cold, I think she has a sore throat and even coughing up mucus. Yes.


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[Assistant] What is the difference between


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[Assistant] Okay, for Rachel's cold, I have decided to go with doctor's orders of day nurse because he said it takes care of all of the symptoms. Not just runny nose, not just sore throat. I think it's best for her.


[User] Please transcribe this text

[Assistant] Grace is taken care of. Rachel is taken care of.


[User] Please transcribe this text

[Assistant] Alicia has some lady problems.


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[Assistant] It says relief from period pain and menstrual cramps. Something I know nothing about. Thankfully. But do you have-


[User] Please transcribe this text

[Assistant] With food? Yes. So you recommend taking with food? Yes. Okay, always take with food.


[User] Please transcribe this text

[Assistant] Alright, so you gotta take with food, otherwise it will irritate your stomach. This is the one I'm getting. Feminax. 250 milligrams. Maximum strength.


[User] Please transcribe this text

[Assistant] Thank you. Thank you, I appreciate it. We saved lives today. And the vacation. We did, but I need to know one more thing.


[User] Please transcribe this text

[Assistant] How many tablets should they take per day?


[User] Please transcribe this text

[Assistant] And how much per time, I guess, because this is important.


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[Assistant] So anytime you buy something, make sure to ask the pharmacist how much to take, but if you're confused, it will say on the back.


[User] Please transcribe this text

[Assistant] How bad it is. Yes. Okay, how constipated you are. One or two at night. Maybe three. Okay, so I'm gonna buy- Maybe three? No.


[User] Please transcribe this text

[Assistant] A person who doesn't work here. I would listen to the man who works here. You're a professional.


[User] Please transcribe this text

[Assistant] She doesn't want to diarrhea, but just eliminate naturally. About how long does it take for the medicine to work?


[User] Please transcribe this text

[Assistant] If I need something more intense, what would I use?


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[Assistant] How many tablets should they take per day? And how much per time?


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[Assistant] So, we're going abseiling. So what abseiling is, is you're descending down a rope that's fixed to something. So it could be down a cliff, could be down a tree, could be down a building or a waterfall, but in our case today it's down a cliff.


[User] Please transcribe this text

[Assistant] So uhm, what we're going to do today is go to a place with a few different cliffs of


[User] Please transcribe this text

[Assistant] Cool. Okay, so we're good to go. So what I'm going to do now, Andy, is give you a bit of a demonstration of how to abseil.


[User] Please transcribe this text

[Assistant] Uhm, what you need to do and I guess what to avoid doing as well.


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[Assistant] So a couple of main things with the abseiling. One of them is what you're doing with your hands. So because you're also right handed, your right hand is the one that controls your speed. Okay. So, so behind my back.


[User] Please transcribe this text

[Assistant] So, if you have your feet nice and flat and wide apart, that'll be the best spot to be in. So when you start to get into this steeper bit,


[User] Please transcribe this text

[Assistant] What you're aiming for is more like a capital L shape with your body. So you can see right here, my legs, they're not quite horizontal, but that's what I'm aiming for. My upper body is more vertical, so it should look a bit like this.


[User] Please transcribe this text

[Assistant] No, no, it's dumb. Just keep stepping those feet. Oh, I have to move my feet. Do I need


[User] Please transcribe this text

[Assistant] Perfect, okay. So just step across here, be careful of the ropes.


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[Assistant] Draw a picture and write about it. Social studies. How do community workers help your community? Share your answer


[User] Please transcribe this text

[Assistant] I'm just so afraid of heights, so maybe... I'm just...


[User] Please transcribe this text

[Assistant] This food is your food.


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[Assistant] Let's have a look at some of the union fine source code. So


[User] Please transcribe this text

[Assistant] Here's a link to the source code. You can find it on my GitHub repository at github.com


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[Assistant] Here we are inside the code. And I have a class called union find, and it's got a few instance variables. So let's go over them. The first is size is just how many elements we have in our union find.


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[Assistant] Then I have these two arrays, one called ID and one called size. So the interest, well, the more interesting ones ID and idea I is that array I talked about, which at index I points to the parent node of I.


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[Assistant] And if ID at I is equal to I, then we know that I is a root node. So we're essentially keeping track of all these like, tree like structures right inside an array, which is practical.


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[Assistant] And also because we create a bijection between our elements, and some numbers, this is how we're able to access them through this ID array.


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[Assistant] And just for convenience, I tracked the number of components. That's sometimes some useful information you might want.


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[Assistant] So if you create union find, well, you need to know how many elements are going to be inside your unit find. And I make sure that we have a positive number of elements. Otherwise, I throw an exception.


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[Assistant] Then I go ahead and initialize some instance variables. And I initialize ID to equal I. So initially, everyone is a root node. And every component has a size of one.


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[Assistant] So find is pretty simple. It's given a a node, it finds which component it belongs to and also does path compression along the way.


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[Assistant] So if we're at P and want to find the root node of P, what we're going to do is we're going to find the root node using this one while loop.


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[Assistant] So we initialize a new variable called root to P. And then we loop until the root is not equal to ID at root. So aka, this is a root node or a self loop that we found. And so we can stop and the root is stored in the variable called root. And then what we do is we do the path compression. This is what I talked about in the last video.


[User] Please transcribe this text

[Assistant] So starting back at P, we assign everything from ID at P to equal the root. And this is what compresses the path and gives us that nice amortized time complexity.


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[Assistant] You can also do this recursively, but I don't like having the overhead and doing things iteratively can be slightly faster.


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[Assistant] We have these simple methods we can call. So if p and q are inside the same component, this will return true because the root p and the root q will be equal.


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[Assistant] Otherwise, this will return false. And just calling find will do path compression. So even if we're just checking if two components are connected,


[User] Please transcribe this text

[Assistant] And it calls the find method and we're compressing the path. Same thing here. If we decide to find the component size relative to some index p


[User] Please transcribe this text

[Assistant] Then when we index into the size and call find, we'll find


[User] Please transcribe this text

[Assistant] Well, we'll find the root, but at the same time, we'll also do path compression, which is nice. And I would just like to note that


[User] Please transcribe this text

[Assistant] The, the root nodes are the ones who will always contain the size because they're the ones that are found well, if you want to think about it like at the end of the chain.


[User] Please transcribe this text

[Assistant] Size just returns the number of components. Now you can find this joint set, components, number of components, that's pretty self explanatory.


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[Assistant] And the unified method is the last interesting method. So this is the one that


[User] Please transcribe this text

[Assistant] Uh, well it unifies two components together. So


[User] Please transcribe this text

[Assistant] So first thing we do is we find what the root node


[User] Please transcribe this text

[Assistant] For p is and what the root node for q is.


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[Assistant] Otherwise, by convention, I just merge the smaller group into the larger group. Although, I know some people like to keep


[User] Please transcribe this text

[Assistant] The theoretical largest path in a component, and then merge according to that, that may be slightly more efficient, but it's more work. So I just like to merge the smaller group into the larger group.


[User] Please transcribe this text

[Assistant] And also, since the roots are different, and we're merging, we know that the number of components or sets must have decreased by one.


[User] Please transcribe this text

[Assistant] So that's why at the bottom here, I say the number of components, uh, subtract that by one, because we know we're going to have one less component.


[User] Please transcribe this text

[Assistant] So this whole time, inside this class, I've been treating p and q as integers and not as elements, like letters that we saw in our in the slides.


[User] Please transcribe this text

[Assistant] And this is because of that bijection, I would do a lookup to find out what maps to the element P that should give me an integer and what maps to the element Q and then I could use those


[User] Please transcribe this text

[Assistant] Uh, with this, uh, union fine data structure I created and turn everything into the realm of numbers instead of dealing with objects and having, uh,


[User] Please transcribe this text

[Assistant] All this more complexity. It's much easier to have an array based unit find. You could also have a pointer based unit find with node objects. But this is really nice and it's a really clean implementation.


[User] Please transcribe this text

[Assistant] Alright, that's it for the unit find. Definitely my favorite data structure. And if you guys have any questions, just drop a comment and I'll try to get back to you. So thank you for watching and I'll catch you next time.


[User] Please transcribe this text

[Assistant] We have two words today in our vocabulary preview and the first one is recession.


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[Assistant] Recession. Everyone's talking about it right now. And what does it mean?


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[Assistant] Okay, recession is a period of time when the economy of a country is doing badly.


[User] Please transcribe this text

[Assistant] Because, uh, companies might close or go bankrupt.


[User] Please transcribe this text

[Assistant] That's true. And the stock prices of companies, uhm, fall.


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[Assistant] This dialogue is about a man who is in debt. He's broke. Let's find out what happens.


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[Assistant] Alright, so for today's language takeaway, we have four words from the dialogue which we think are really important.


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[Assistant] Okay, when you borrow money, you get a loan.


[User] Please transcribe this text

[Assistant] Basically it's a loan that helps you to buy a house.


[User] Please transcribe this text

[Assistant] Yeah, so this is a funny word. I mean, there's this strange mortgage with a T.


[User] Please transcribe this text

[Assistant] Hit me pretty hard. I think we have some examples that will help us understand this phrase.


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[Assistant] The news of Fluffy's death hit me pretty hard. I've been crying for days.


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[Assistant] So this is the money you have to pay to your school so that you can study there.


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[Assistant] Okay, so I think a lot of the key language we just talked about is a lot more clear now. But you know, I also noticed some really interesting phrases in this dialogue.


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[Assistant] Fluency Builder is a tool that we use to help you sound more native.


[User] Please transcribe this text

[Assistant] So we take a really common phrase and we show you some great natural ways of expressing that same idea.


[User] Please transcribe this text

[Assistant] So this phrase we use when we want to ask somebody, can I help you?


[User] Please transcribe this text

[Assistant] Yeah, that sounds really, really polite and really natural.


[User] Please transcribe this text

[Assistant] So let's move to our second phrase. This phrase we use when we need something. When we want to say, I'm sorry to bother you.


[User] Please transcribe this text

[Assistant] I'm sorry to trouble you, this phrase might be a little bit more difficult to understand how it's used, uhm, but we've got some examples. Yes, so let's listen.


[User] Please transcribe this text

[Assistant] That's true, you could say. So today was a really great day. It was my birthday and all my friends came to visit me and on top of all that, I got a million presents!


[User] Please transcribe this text

[Assistant] Today's dialogue was all about being in debt, right? Yes. And different people have different attitudes or different viewpoints on being in debt.


[User] Please transcribe this text

[Assistant] That's right, unless you have a history of being in debt and regularly paying off your debts,


[User] Please transcribe this text

[Assistant] You can't actually borrow money, you can't get a mortgage, you can't get a car loan so you can buy a new car.


[User] Please transcribe this text

[Assistant] Yeah, and you know the funny thing is, uhm, when I was in university, maybe I was 18 years old, the first thing that happened at university was, you know, banks came to our university and started offering you credit cards. So you can get a credit card when you're 18, no problem.


[User] Please transcribe this text

[Assistant] Yeah, but you know what? It's totally different in Europe, for example. I used to live in Switzerland, and there, they're really careful with debts. Like, it's a bad thing to be in debt.


[User] Please transcribe this text

[Assistant] For example, if I want to have a credit card in Switzerland, and let's say I want to have a limit of, uhm,


[User] Please transcribe this text

[Assistant] 500 francs that I can spend every month. I need to put in my bank account 700 francs before I can even spend that credit card money. So it's totally opposite from North America.


[User] Please transcribe this text

[Assistant] Okay everyone, thanks for listening. Now remember, if you have any questions or comments, please visit our website at EnglishPod.com.


[User] Please transcribe this text

[Assistant] Ah, but you've been taught basketball by Daddy Coach!


[User] Please transcribe this text

[Assistant] Yeah, yeah, it was really interesting. You know, I'm in a bit of a hurry, but here's my card. We should definitely meet up again and continue our discussion.


[User] Please transcribe this text

[Assistant] You know what? This is really embarrassing, but your name has just slipped my mind. Can you remind me?


[User] Please transcribe this text

[Assistant] Actually, my office is right around the corner.


[User] Please transcribe this text

[Assistant] Yeah, yeah, it was really interesting.


[User] Please transcribe this text

[Assistant] You know, I'm in a bit of a hurry, but here's my card.


[User] Please transcribe this text

[Assistant] We should definitely meet up again and continue our discussion.


[User] Please transcribe this text

[Assistant] This is really embarrassing, but your name has just slipped my mind. Can you remind me?


[User] Please transcribe this text

[Assistant] Yeah, yeah, it was really interesting. You know, I'm in a bit of a hurry, but here's my card. We should definitely meet up again and continue our discussion.


[User] Please transcribe this text

[Assistant] You know what? This is really embarrassing, but your name has just slipped my mind. Can you remind me?


[User] Please transcribe this text

[Assistant] The Great Barrier Reef, off the coast of northeastern Australia.


[User] Please transcribe this text

[Assistant] Is the world's biggest and longest coral reef. And a marvel of nature that the Aussies are most proud of.


[User] Please transcribe this text

[Assistant] The multitude of marine species and the pristine water make it a paradise for divers from all over the world.


[User] Please transcribe this text

[Assistant] Today, Andy will catch a yacht at Karen's, learn a few diving tips, and get a really close look at the Great Barrier Reef.


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[Assistant] Wake Up Rooster Written by Nigel Pepperhouse


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[Assistant] Illustrated by Sholto Walker. Focus Question.


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[Assistant] Roosters slept too long today.


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[Assistant] So rat woke up cat.


[User] Please transcribe this text

[Assistant] So horse woke up farmer.


[User] Please transcribe this text

[Assistant] I know, I know. Tomato English has filmed a special on supermarkets before. But you must realize, my friends, Costco is no near supermarket. In fact, it might very well be a miniaturized replica of American life itself.


[User] Please transcribe this text

[Assistant] Actually, young single American men rarely shop at Costco. Why?


[User] Please transcribe this text

[Assistant] As a typical wholesale supermarket, the volume of everything sold in Costco is by crate.


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[Assistant] Alright, welcome everybody. Today we're going to be talking about hash tables, one of the most remarkable data structures of all times. If I had a subtitle, it would be one data structure to rule them all.


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[Assistant] So let's get started. There's going to be a lot of things to cover in the hash table series. We're going to start off with what's the hash table and what the heck is a hash function? And why do we need one? Then we're going to talk about some popular


[User] Please transcribe this text

[Assistant] Collision resolution methods in particular separate chaining open addressing those are the two most popular ones although there's a ton more.


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[Assistant] Then we're going to separate chaining with linked list just because it's a really popular implementation. And there's a ton of stuff in open address and that needs to get covered. So we're going to be talking about linear probing and quadratic probing how that's done.


[User] Please transcribe this text

[Assistant] I'm only giving lots and lots of examples for those just because it's not super obvious how they work.


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[Assistant] And to finish off we're going to be going over double hashing.


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[Assistant] And finally removing elements from the open addressing scheme because that's not so obvious either. All right. So to begin with, what is a hash table?


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[Assistant] So the hash table is a data structure that lets us construct a mapping from some keys to some values via technique called hashing, which we'll talk about shortly.


[User] Please transcribe this text

[Assistant] So for instance, keys could be names, which are people names and the value could be someone's favorite color. So Williams favorite color is green.


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[Assistant] Micah's is purple, Catherine's is yellow, and so on.


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[Assistant] And we call these key value pairs. So each key is associated with a value.


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[Assistant] So all the keys have to be unique, the values don't have to be unique.


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[Assistant] For example, Micah's favorite color is purple, but also Leah's favorite color is purple.


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[Assistant] We often use hash tables to track item frequencies.


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[Assistant] For example, below is a frequency table of the number of times a certain word appeared in Shakespeare's Hamlet, which I parsed and obtained this table.


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[Assistant] So you can see Hamlet appeared 114 times the word Lord 223, but the word cabbage did not appear. So hash tables are often used as to track frequencies, which is really handy.


[User] Please transcribe this text

[Assistant] You can really construct a mapping between


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[Assistant] Any key value pairs given that the keys are hashable. This is something we're talking about shortly.


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[Assistant] So to be able to understand how we construct the mapping within the hash table, we need to understand what a hash function is.


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[Assistant] So simply put a hash function, which I will denote from now on as h of x for some key x is a function that maps x to a whole number in some fixed range. So that's pretty simple.


[User] Please transcribe this text

[Assistant] Let's have a look at an arbitrary hash function. So if our hash function is h of x equals


[User] Please transcribe this text

[Assistant] Some polynomial x squared minus six x plus nine modulo 10. Well all the integer keys get mapped to the range zero to nine inclusive.


[User] Please transcribe this text

[Assistant] No matter what integer number I plug into h of x. So below I'm plugging in certain values, or keys, if you will, into the hash function and getting an output.


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[Assistant] So notice that the output is not unique in that there could be two values that I plug into the hash function which yield the same value for instance


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[Assistant] h of four and h of two map to the same thing and that's completely fine.


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[Assistant] So we can also define arbitrary hash functions not just on integers, but on arbitrary objects, which makes this really powerful.


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[Assistant] For instance, suppose we have a string s, and we're going to say h of s be the hash function that I have defined below.


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[Assistant] So this hash function, all it does is it sums the ASCII values of the characters within the string and then at the end says module 50.


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[Assistant] So that will for any given string just output a number and that's a hash function.


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[Assistant] So for example, some simple inputs, so h of BB gets 66 plus 66. And then we mod that by 50. So we have 32. The empty string gets zero because we don't even execute the loop and so on. So we've effectively converted a string to a number. Great. All right, here's a mini challenge for you. Suppose you have a database of people below.


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[Assistant] Three fields, they have an age and name and a sex. I want you to define a hash function


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[Assistant] H of a person where you're going to be given a person object as an argument.


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[Assistant] Now I want you to create a hash function that's going to map a person to the set of values zero through five inclusive, you can pause the video, just create any hash function that does this given the three fields.


[User] Please transcribe this text

[Assistant] Alright, so here's my attempt at creating such a hash function. Turns out there is an infinite amount of possible hash functions we could define for the hash of the person. And here's a simple one. So I'm gonna say okay, first, let's start with the age


[User] Please transcribe this text

[Assistant] So whatever there is, that's the starting value hash, then I'm going to add on the length of the person's name. Again, an arbitrary choice, I'm going to increment by one if there are males, and then mod by six. So


[User] Please transcribe this text

[Assistant] As you can see, hash functions are a bit arbitrarily defined at this point, we're going to get into some more sophisticated hash functions later. So


[User] Please transcribe this text

[Assistant] This particular hash function yields those values.


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[Assistant] Alright, something very important, very, very important when you talk about our properties of hash functions.


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[Assistant] So if we have a hash function and two objects say x and y, and their hash values are equal,


[User] Please transcribe this text

[Assistant] Then those two objects might be equal. We don't know for sure. We have to explicitly check x against y.


[User] Please transcribe this text

[Assistant] Yes, the hash function tells us that there was a collision right there.


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[Assistant] But if the hash functions are not equal, then x and y are certainly not equal.


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[Assistant] Here's a good question. How can we use this to our advantage to speed up object comparisons?


[User] Please transcribe this text

[Assistant] The answer is that instead of comparing x and y directly, if we've already computed their hash values, then first let's compare the hash values of x and y instead of comparing x and y explicitly. And in this next example, you'll see why that's important.


[User] Please transcribe this text

[Assistant] Consider the problem of trying to determine if two very large files have the same contents. This is something you want to do in an operating system context all the time.


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[Assistant] So if we've computed the hash values for file one and file two, first, we should compare the hash values together to see if they match or don't match because this requires


[User] Please transcribe this text

[Assistant] If possible, we don't want to open the files at all. This would be comparing x and y directly or file one against file two, but we will have to compare them


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[Assistant] Byte per byte if their hash values are equal.


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[Assistant] So actually hash functions for files are much more sophisticated than those that we use for hash tables.


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[Assistant] Instead, we use very sophisticated hashing algorithms called cryptographic hash functions and checksums.


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[Assistant] Alright, another property of hash functions is that they must absolutely be deterministic.


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[Assistant] This means that if h of x produces y, then h of x must always, always always produce y and never another value. This is super critical, because this will screw up our hash table completely if this happens.


[User] Please transcribe this text

[Assistant] We do not want this. So an example of a non deterministic hash function is something that introduces say a global variable or some kind of randomness. You don't want that.


[User] Please transcribe this text

[Assistant] So in this particular hash function, the first time I run it, the hash value for five is six. But then if I call it again, we've incremented the counter and now the hash value is seven. Oh, not good.


[User] Please transcribe this text

[Assistant] Something else about hash functions is we try very hard to make them uniform.


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[Assistant] To minimize the number of hash collisions, we'll get into why hash collisions are bad shortly.


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[Assistant] But a hash collision is when two objects x and y hash to the same value. That is h of x equals h of y.


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[Assistant] And the reason why to be uniform is so that all the values of our hash function are hit so that we can fill the table uniformly.


[User] Please transcribe this text

[Assistant] So for example, the table we generated earlier,


[User] Please transcribe this text

[Assistant] Uh, William and Kate, they have a hash collision. Uh oh.


[User] Please transcribe this text

[Assistant] Okay, so now we're able to answer a central question about the types of keys that we're allowed to put in our hash table. So what makes a key of type t hashable? Here's the answer.


[User] Please transcribe this text

[Assistant] Since we're going to implement our hash table using these hash


[User] Please transcribe this text

[Assistant] functions, we need those hash functions to be deterministic. And to enforce that behavior,


[User] Please transcribe this text

[Assistant] You'll see a lot of programming languages enforce that the keys you use be immutable, meaning you can't change them. They're fixed. They're constants.


[User] Please transcribe this text

[Assistant] So they're things like immutable strings, integers, but not things like sets or lists or things you can add or remove things from their immutable.


[User] Please transcribe this text

[Assistant] And if we have that condition and we have a hash function.


[User] Please transcribe this text

[Assistant] That is defined for all keys of type T, then we say that that that type is hashable. Okay,


[User] Please transcribe this text

[Assistant] Well, ideally we want our hash function to have really quick insertion lookup and removal times.


[User] Please transcribe this text

[Assistant] And remarkably, we can achieve all of that.


[User] Please transcribe this text

[Assistant] Using the hash function as a way to index into a hash table in a hash table.


[User] Please transcribe this text

[Assistant] It's just a fancy word for an array. Think of it like that. Every time I say hashable, think array.


[User] Please transcribe this text

[Assistant] So again, we use the hash function.


[User] Please transcribe this text

[Assistant] gives us an index to go look up inside the table. And yes, we can do this in constant time.


[User] Please transcribe this text

[Assistant] Given that we have a uniform hash function, super essential. Okay.


[User] Please transcribe this text

[Assistant] Think of the hash table on the right as an indexable block of memory. So as I said, an array, we can access these entries with the hash function h of x. So suppose we're going to be inserting


[User] Please transcribe this text

[Assistant] Integer string key value pairs into the table that are going to represent rankings of users to their user names, say in an online programming competition. And this is the arbitrary hash function that we chose x squared plus three mod 10. All right, let's get started.


[User] Please transcribe this text

[Assistant] So if I want to insert the key value pair three by either


[User] Please transcribe this text

[Assistant] So in the contest, the user whose username is bite eater got a rank of three and we want to insert that into the hash table. Then what we do is we hash the key which is three and we get


[User] Please transcribe this text

[Assistant] H of three being three squared plus three mod 10 or two. So in our hash table or array index two, we're going to put


[User] Please transcribe this text

[Assistant] The key be three and the value to be bite either.


[User] Please transcribe this text

[Assistant] All right. Now, this next user will fizz a, which is usually what I call myself an online programming competitions. He is ranked one


[User] Please transcribe this text

[Assistant] And if we hash one, then we get four. So at index for


[User] Please transcribe this text

[Assistant] Then we're going to put the key in the value for William or will.feeze.


[User] Please transcribe this text

[Assistant] 425, we've got rank 32. And we hash 32. And we put her in the table where she goes. Then we can do the same thing for ternary wizard has rank five. And insert orange night, again, by hashing the key, finding out where it goes.


[User] Please transcribe this text

[Assistant] So as we keep doing this, we keep filling the table, but eventually we're bound to have a collision. Now, in the event that we want to do a lookup,


[User] Please transcribe this text

[Assistant] For a user that has a rank R, all we need to do is compute the hash value for R and then look inside your hash table to see where this user is. So say I want to find the user with rank 10,


[User] Please transcribe this text

[Assistant] My hash 10 figure out its indexes three and then I get the value which is the orange night has rank 10. Great. However, how do we handle hash collisions, for example, users who have ranks two and eight


[User] Please transcribe this text

[Assistant] Have the same value. This is problematic.


[User] Please transcribe this text

[Assistant] Because we don't have another place to put them inside our table. They would index into the same position. Yeah.


[User] Please transcribe this text

[Assistant] There are many, but the two most popular ones are separate chaining and open addressing.