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[Assistant] My mother is African-American and my father is Afro-Panamanian. I am so proudly and inextricably both. As an Afro-Latina, I'm one of millions. As an Afro-Latina curator, I'm one of very few.


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[Assistant] And bringing my whole self into the professional realm can feel like an act of bravery. And I'll admit to you that I was not always up for that challenge, whether from fear of rejection or self-preservation.


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[Assistant] In meetings, I would only speak up when I had a fully developed comment to share. No audible brainstorming or riffing off of colleagues.


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[Assistant] For a long time, I denied myself the joy of wearing my beloved hoop earrings or nameplate necklace to work thinking that they were too loud or unscholarly or unprofessional.


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[Assistant] And anyone who has felt outside of mainstream representations understands that there are basic elements just of our everyday being that can make other people uncomfortable.


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[Assistant] I've stopped presenting an inauthentic representation of myself or my work. And I have been tested. This is me pointing at my hoop earring in my office.


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[Assistant] The week of the presentation, the organization expressed concerns. They called my slides activists, and they meant that negatively.


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[Assistant] Two days before the presentation, they requested that I not show a two-minute video affirming natural hair because it, quote, may create a barrier to the learning process for some of the participants, end quote. That poem, Hair, was written and performed by Elizabeth Acevedo.


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[Assistant] I canceled the talk, explaining to them that their censorship of me and my work made me uncomfortable.


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[Assistant] The regular, the underrepresented and usually the non-white.


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[Assistant] Pride in displaying a dark-skinned Latina. A black woman.


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[Assistant] When this work was on view, it was one of our most Instagrammed pieces, and visitors told me they connected with the everyday elements of her brown skin or her rollers or her jewelry. Our collections include Celia Cruz and a rare portrait of a young Harriet Tubman.


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[Assistant] Stories of everyday women whose stories have been knowingly omitted from our national and global histories.


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[Assistant] But impactful, life-changing stories from everyday women can also look like this esmeralda in boat seats. Esmeraldas Ecuador was a maroon community.


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[Assistant] It's dense rainforest protected indigenous and African populations from Spanish colonizers.


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[Assistant] There are roads now, but there were some parts inland that are still only accessible by canoe.


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[Assistant] Frequently traveled those Ecuadorian waterways by canoe, so she had her own boat seat. Hers personalized with a spider web and a spider.


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[Assistant] Representing Anansi, a character in West African folklore. Deborah also sat on her seat at home, telling stories to her grandson, Kwan.


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[Assistant] And this intangible ritual of love in the form of intergenerational storytelling is common in communities across the African diaspora.


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[Assistant] And this everyday act sparked in Juan the desire to collect and preserve over 50,000 documents related to Afro-Indian culture. In 2005,


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[Assistant] Juan Garcia Salazar, Deborah's grandson, and by now a world-renowned Afro-Ecuadorian scholar, traveled to Washington D.C. He met with Lonnie Bunch, the director of the museum where I work, and toward the end of their conversation, Juan reached into his bag and said, I'd like to give you a present. On that day,


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[Assistant] Deborah Nassereno's humble wooden boat seat became the very first object donated to the Smithsonian National Museum of African American History and Culture. It is encased


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[Assistant] Displayed and has been seen by almost five million visitors from all over the world. I will continue to collect from extraordinary history makers. Their stories are important. But what drives me to show up today and every day is the simple passion to write our names in history, display them publicly for millions to see, and walk in the ever-present light that is woman. Thank you.


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[Assistant] Well, a dog gets up and the first thing it's usually thinking about is food.


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[Assistant] Just like the dog's able to go inside if it wants to keep warm. I can go into my flat if I want to keep warm.


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[Assistant] But while I'm very similar to the dog in some respects, I'm also quite different.


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[Assistant] Here are some ways in which my mind is different from the dog's. First of all, I can evaluate my own thoughts. So maybe I'm thinking about the party I went to last night, and I think, God, I really didn't like that guy.


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[Assistant] Which is interesting, is that we can think about things. So I've just said that we can think about thoughts. But thoughts aren't the only things we can think about. We can make plans for the future.


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[Assistant] But we can also think about things like five-legged, five-reading unicorns that don't exist. And this is a very tricky aspect of human thought.


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[Assistant] I can think about non-actual things, or I can think about things here in the room, like the tennis ball that's on the floor. But what is it that makes thoughts about things? If we're going to give a description of a thought,


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[Assistant] How are you going to capture that aboutness, that this thought is about a tennis ball?


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[Assistant] There's physical stuff, stuff that our bodies are made out of, stuff that trees are made out of, irons, metals, things in the world.


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[Assistant] It was an immaterial substance, and that was what our minds were.


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[Assistant] So Descartes thought that for every human being, there were two bits of stuff, if you like. Each human being has a physical form, which is your kind of average compounds of H2O, other chemicals, hormones that make up the human body.


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[Assistant] Including problems with the argument that he uses to argue for why minds and bodies have to be made out of fundamentally different things.


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[Assistant] In order for them to move, in order for them to affect changes in the world, there has to be some kind of physical impetus that changes the physical state of our, of our body such that we can move. Princess Elizabeth of Bohemia said, look, if thoughts are made out of immaterial substance, then that substance isn't physical. But how can it then interact with a physical body so as to affect changes in the world?


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[Assistant] Because it's a fundamental feature of thoughts that they can cause our bodies to move.


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[Assistant] Then I'll set off and I'll get my coat and I'll walk out the door and go to the shops. What caused that particular behaviour? Well, it was my desire for chocolate. It was my particular thought.


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[Assistant] Hi everybody, this is Mr Duncan in England. How are you today? Are you okay? I hope so. Are you happy?


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[Assistant] I hope so. In today's lesson, we are going to look at a part of the English language that causes such a lot of interest and observation to those learning the language and for those who speak English as their first language. In this lesson, we are going to take a look at idioms and proverbs.


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[Assistant] An idiom is a casual form of expression often used to make a point or explain a situation or express a feeling without actually using words directly related to them.


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[Assistant] They are non-literal expressions. Idioms are frequently used in spoken English and can consist of just a couple of words, right up to long sentences.


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[Assistant] However, slang is often seen as being restricted to individual groups of people, such as teenagers or cultures within a society.


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[Assistant] Idioms are often used to convey a thought quickly and succinctly.


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[Assistant] Idioms are not always to the point. They express things metaphorically. They are often described as a turn of phrase, a figure of speech, or a colloquialism.


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[Assistant] I could not really talk about idioms without giving some examples of them, so stand by for those. Remember, there are thousands of idioms in existence, and they don't just occur in English.


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[Assistant] So, here we go with a selection of English idioms.


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[Assistant] Examples of idioms Animals Ants in your pants


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[Assistant] To be restless and fidgety, you seem to have ants in your pants. A little bird told me to find out something that is a secret.


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[Assistant] A little bird told me you are getting married. The cat has got your tongue.


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[Assistant] To reveal a secret by accident or on purpose. He let the cat out of the bag.


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[Assistant] To refuse to do something because of fear. You chickened out of the fight.


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[Assistant] He was about to give his speech when he clammed up.


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[Assistant] To dress in clothes that do not suit you, or to wear too much makeup. She is dressed like a dog's dinner.


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[Assistant] A situation where many things fall or become sick at the same time.


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[Assistant] The sheep are all becoming ill. They are dropping like flies.


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[Assistant] To behave childishly and waste time. He spent all day horsing around.


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[Assistant] To be in trouble or to be scolded for something you did. The husband came home late. He is in the dog house.


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[Assistant] Make a beeline to move straight towards something without stopping.


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[Assistant] Monkey business. To be doing something bad in secret. There is some monkey business going on here.


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[Assistant] Pig out to eat lots of food greedily.


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[Assistant] Used to describe a heavy rain shower. It's raining cats and dogs outside.


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[Assistant] Till the cows come home. To do something for a very long time.


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[Assistant] I will wait here till the cows come home.


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[Assistant] Examples of idioms. Body parts.


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[Assistant] You are listening carefully to concentrate on what a person is saying.


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[Assistant] Tell me what you want. I'm all ears.


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[Assistant] Wish someone good luck, normally before giving a performance. Go out there and break a leg.


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[Assistant] Cost an arm and a leg. Something is very expensive.


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[Assistant] Do you like my new car? It cost an arm and a leg.


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[Assistant] I won the prize, so eat your heart out.


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[Assistant] A person who is kind to you in person, but says bad things about you to others. My boss is so two-faced.


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[Assistant] To do something without making plans or having any expectations.


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[Assistant] A scary moment or experience. The roller coaster ride was hair raising.


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[Assistant] One foot in the grave. To appear ill or near death. You look like you have one foot in the grave.


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[Assistant] Blip service. To say nice things to someone, but not mean them. To be insincere.


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[Assistant] To keep my boss happy, I must give him lip service.


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[Assistant] To push someone away or remove them from your life, I had to give my girlfriend the elbow.


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[Assistant] Pain in the neck or pain in the ass. An annoying thing or person.


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[Assistant] My neighbour is such a pain in the neck.


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[Assistant] Hate someone's guts. To despise a person deeply. I hate my boss's guts.


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[Assistant] I would climb the mountain, but I haven't the stomach for heights.


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[Assistant] Lose your head. Blow your top.


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[Assistant] Have a fit. Throw a wobbly. To become uncontrollably angry.


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[Assistant] To sulk over something and ignore someone. In the dumps. To be sad and miserable. At a loose end. To have nothing to do. A chip off the old block. A child who resembles its parents. Hidden under a bushel.


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[Assistant] A talent or ability hidden from others.


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[Assistant] Do the same things as the local people whilst on holiday. Hit the nail on the head.


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[Assistant] To give the exact answer or solution to a problem. Butter fingers.


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[Assistant] A person who can get out of control and become a liability or problem.


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[Assistant] Used as an insult to say that a person is stupid. He's as thick as a brick.


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[Assistant] A person who talks about doing things, but never does them. He is all mouth and trousers.


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[Assistant] Talk about a serious situation concerning you and the other person. I have a bone to pick with you


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[Assistant] Within all forms of spoken English, there are thousands of idioms in existence. They express most forms of action or situation.


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[Assistant] Some idioms are playful and harmless, while others can be spiteful and malevolent.


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[Assistant] Proverbs are somewhat different to idioms by the fact that they tend to convey a direct message or meaning.


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[Assistant] Usually in the form of a phrase or sentence. The words used tend to have great meaning and are not as cryptic or hidden as those used in idioms.


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[Assistant] Proverbs are often used as guides to help us on our way through life.


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[Assistant] A proverb is often referred to as a saying or phrase. A proverb can have a religious or philosophical meaning.


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[Assistant] A proverbial sentence can provide inspiration and food for thought.


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[Assistant] Look before you leap. Think carefully before you do something or make a decision.


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[Assistant] Once bitten, twice shy. You never make the same mistake twice.


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[Assistant] A fool and his money are soon parted.


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[Assistant] Do not do something without thinking about it carefully first.


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[Assistant] Don't throw the baby out with the bathwater.


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[Assistant] During the process of making changes or a move, make sure you are not losing something useful or important.


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[Assistant] It is better to be thought of as a fool than to open your mouth and remove all doubt.


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[Assistant] Idioms and Proverbs are not exclusive to English, as they exist in many other languages too.


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[Assistant] In formal English, colloquial idioms are used much less, which often leads to them being left out of oral English education.


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[Assistant] However, they do serve as a very useful way of both expanding your word power and linking words and expressions together.


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[Assistant] You may not always hear idioms being used, but that does not mean that they are not out there.


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[Assistant] Just like slang, there are times for using them and times when they should be avoided.


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[Assistant] Proverbs are used in both formal and informal English.


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[Assistant] For English students, they can also serve as a very useful way of remembering new words and their meanings.


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[Assistant] I hope today's lesson has left you feeling tickled pink and on top of the world, and that you will drop in for another lesson real soon.


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[Assistant] This is Mr Duncan in England saying thank you for watching me teaching you. And of course, ta ta for now.


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[Assistant] In the previous video, I mentioned that broadcasting is another technique that you can use to make your Python code run faster. In this video, let's delve into how broadcasting in Python actually works.


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[Assistant] Let's motivate broadcasting with an example. In this matrix, I've shown the number of calories from carbohydrates, proteins, and fats in 100 grams of four different foods. So for example, 100 grams of apples turns out has 56 calories from carbs and much less from proteins and fats. Whereas in contrast, 100 grams of beef has, uh,


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[Assistant] 104 calories from protein, 135 calories from fat. Now let's say your goal is to calculate


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[Assistant] The percentage of calories from, uhm, carbs, proteins and fats for each of the four foods. So for example, if you look at this column and add up the numbers in that column, you get that 100 grams of apple has, I guess, 56 plus 1.2 plus 1.8. So that's 59 calories. And so as a percentage, the percentage of calories from carbohydrates in an apple would be


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[Assistant] Calories in an apple, uhm, come from calves, whereas in contrast, most of the calories of beef come from protein and fat and so on.


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[Assistant] So the calculation you want is really to sum up each of the four columns.


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[Assistant] ...of this matrix to get the total number of calories.


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[Assistant] Say this matrix equal to a 3 by 4 matrix A.


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[Assistant] And then with one line of Python code, we're going to sum down the columns. So we're going to get four numbers corresponding to the total number of calories in


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[Assistant] Uhm, these four different types of food, the hundred grams of these four different types of food. And I'm going to use a second line of Python code to divide each of the four columns by their corresponding sum. If that verbal description wasn't very clear, hopefully it will be clearer in a second when we look at the Python code.


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[Assistant] So here we are in the Jupyter notebook. I've already written this first piece of code to pre-populate


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[Assistant] The matrix A with the numbers we had just now. So let me hit shift enter and just run that. So there's the matrix A. And now here are the two lines of Python code. First, I'm going to compute cal equals A dot sum


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[Assistant] And uhm, x is equal to zero, means to sum vertically. Say more about that in a little bit. And then that's print cal, so.


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[Assistant] So with summed vertically, so as we say just now, 59 is the total number of calories in the apple, 239 was the total number of calories in the beef and eggs and potato and so on.


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[Assistant] Uhm, actually we want percentages multiplied by 100 here. Uhm, and then let's print percentage. Let's run that. And so with that command, we've taken the matrix A and divided it by this 1 by 4 matrix


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[Assistant] And this gives us the matrix of percentages. So as we worked out kind of by hand just now, you know, in the, in the apple, that was the first column, 94.9% of the calories are from carbs. Let's go back to the slides.


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[Assistant] So just to repeat the two lines of code we had, this is what we had written out in the Jupyter notebook. So add a bit of detail, this, uhm,


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[Assistant] Parameter axis equals zero means that you want Python to sum vertically, so this is axis zero, means to sum vertically, whereas the horizontal axis is axis one, so if you were to write axis one, you'll sum horizontally instead of vertically. And then this command here,


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[Assistant] This is an example of Python broadcasting where you take a matrix A, so this is a 3 by 4 matrix, and you divide it by a 1 by 4 matrix. And technically, after this first line of code,


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[Assistant] Cal, the variable cal is already a 1 by 4 matrix, so technically you don't need to call.


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[Assistant] So that's actually a little bit redundant, but uhm, when I'm writing Python codes, if I'm not entirely sure what matrix, what are the dimensions of my matrix, I often would just call a reshape command just to make sure that


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[Assistant] Uhm, is, you know, the right column vector or the row vector or whatever you want it to be. The reshape command is a constant time, it's an order one operation, so it's very cheap to call.


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[Assistant] So don't be shy about using the reshape command to make sure that your matrices are the size you need it to be.


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[Assistant] Now, let's explain in greater detail how


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[Assistant] This type of operation works, right? We had a 3 by 4 matrix and we divided it by a


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[Assistant] Let's go through a few more examples of broadcasting. If you take a 4 by 1 vector and add it to a number,


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[Assistant] Python will do is take this number and auto expand it into a 4 by 1 vector as well as follows.


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[Assistant] And so the vector 1, 2, 3, 4 plus the number 100 ends up with...


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[Assistant] That vector on the right, you're adding 100 to every element. And in fact, we use this form of broadcasting where that constant was the parameter b in an earlier video. And this type of


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[Assistant] Broadcasting works with both column vectors and row vectors. And in fact, we used a similar form of broadcasting earlier with the constant we're adding to a vector being the parameter b in logistic regression. Here's another example. Let's say you have a


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[Assistant] What Python will do is copy the matrix m times to turn this into an m by n matrix. So instead of this, uhm,


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[Assistant] One by three matrix, I'll copy it twice in this example to turn it into this also, uhm, two by three matrix. And they'll add these, so you end up with the sum on the right. Okay, so you've taken


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[Assistant] You added 100 to the first column, added 200 to the second column, added 300 to the third column.


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[Assistant] And this is basically what we did on the previous slide, except that we use a division operation instead of an addition operation. Just one last example. What if you have a m by n matrix and you add this to an m by 1 vector, an m by 1 matrix. Then this will copy this n times horizontally, so you end up with an m by n


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[Assistant] Matrix, so as you can imagine, they'll copy it horizontally three times and add those.


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[Assistant] So when you add them, you end up with this. So we've added 100 to the first row and added 200 to the second row.


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[Assistant] Here's the more general principle of broadcasting in Python. If you have an m by n matrix,


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[Assistant] And you add or subtract or multiply or divide with a 1 by n matrix, then this will copy it m times into an m by n matrix and then apply the addition, subtraction, multiplication or division element-wise. If conversely,


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[Assistant] You were to take the m by n matrix and add subtract, multiply, or divide by an m by one matrix.


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[Assistant] Then also this will copy it now n times


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[Assistant] And turn that into an m by n matrix, and then apply the operation.


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[Assistant] Just one other form of broadcasting, which is if you have an M by 1


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[Assistant] Matrix, so that's really a column vector, like one, two, three, and you add, subtract, multiply or divide by a row number.


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[Assistant] So maybe a 1 by 1 matrix, so such as that, plus 100. Then you end up, uhm, copying this real number m times until you also get another m by 1 matrix and then


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[Assistant] You perform the operation, such as addition in this example, element twice, and something similar also works for row vectors.


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[Assistant] The fully general version of broadcasting can do even a little bit more than this. If you're interested, you can read the documentation


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[Assistant] Uh, for NumPy and look at broadcasting in that documentation. That gives an even slightly more general definition of broadcasting, but the ones on this slide are the main forms of broadcasting that you end up needing to use when you implement a neural network.


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[Assistant] Before we wrap up, just one last comment, which is for those of you that are used to programming in either MATLAB or Octave, if you've ever used the MATLAB or Octave function bsxfun,


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[Assistant] In Neural Network Programming, BSXFun does something similar, not quite the same, but is often used for a similar purpose as what we use Broadcasting in Python for. Uh, but this is really only for very advanced, non-admin-optive users. If you've not heard of this, don't worry about it. Uhm, you don't need to know it when you're coding up neural networks in Python.


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[Assistant] So that was broadcasting in Python. I hope that when you do the programming homeworks, that broadcasting will allow you to not only make your code run faster, but also help you get what you want done with fewer lines of code.


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[Assistant] Before you dive into the program exercise, I want to share with you just one more set of ideas, which is that there are some tips and tricks that I've found reduces the number of bugs in my Python code and that I hope will help you too. So with that, let's talk about that in the next video.


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[Assistant] Imagine that you are in Las Vegas in a casino.


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[Assistant] And you decide to play a game on one of the casino's computers. Just like you might play solitaire or chess.


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[Assistant] The computer can make moves in the game just like a human player. This is a coin game.


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[Assistant] It starts with the coin showing heads, and the computer will play first. It can choose to flip the coin or not, but you don't get to see the outcome. Next, it's your turn. You can also choose to flip the coin or not.


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[Assistant] Finally, the computer plays again and can flip the coin or not.


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[Assistant] And if it is heads, the computer wins. If it's tails, you win.


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[Assistant] So it's a pretty simple game, and if everybody plays honestly and the coin is fair, then you have a 50 percent chance of winning this game. And to ...


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[Assistant] Confirmed that, I asked my students to play this game on our computers. And after many, many tries, their winning rate ended up being 50 percent, or close to 50 percent as expected. Sounds like a boring game, right?


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[Assistant] Now Las Vegas casinos do not have quantum computers, as far as I know.


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[Assistant] But IBM has built a working quantum computer. Here it is. But what is a quantum computer?


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[Assistant] Well, quantum physics describes the behavior of atoms and fundamental particles, like electrons and photons. So a quantum computer operates by controlling the behavior of these particles, but in a way that is completely different from our regular computers.


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[Assistant] So a quantum computer is not just a more powerful version of our current computers.


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[Assistant] Just like a light bulb is not a more powerful candle. You cannot build a light bulb by building better and better candles.


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[Assistant] A light bulb is a different technology, based on deeper scientific understanding.


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[Assistant] Similarly, a quantum computer is a new kind of device based on the science of quantum physics. And just like a light bulb transforms society,


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[Assistant] Quantum computers have the potential to impact so many aspects of our lives, including our security needs, our health care and even the internet.


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[Assistant] So companies all around the world are working to build these devices. And to see what the excitement is all about, let's play our game on a quantum computer.


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[Assistant] So I can log into IBM's quantum computer from right here, which means I can play the game remotely, and so can you.


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[Assistant] To make this happen, you may remember getting an email ahead of time from Ted.


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[Assistant] Well actually, we asked you to choose between a circle or a square.


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[Assistant] You didn't know it, but your choice of circle meant flip the coin, and your choice of square was don't flip.


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[Assistant] We received 372 responses. Thank you.


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[Assistant] That means we can play 372 games against the quantum computer using your choices. And it's a pretty fast game to play. So I can show you the results right here. Unfortunately, you didn't do very well.


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[Assistant] It lost a few only because of operational errors in the computer.


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[Assistant] A regular computer simulates heads or tails of a coin as a bit, a zero or a one.


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[Assistant] Or a current flipping on and off inside your computer chip. A quantum computer is completely different. A quantum bit has a more fluid, non-binary identity. It can exist


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[Assistant] In a superposition or a combination of zero and one with some probability of being zero and some probability of being one. In other words, its identity is on a spectrum. For example,


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[Assistant] The key idea here is that we have to give up on precise values of zero and one and allow for some uncertainty. So during the game,


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[Assistant] The quantum computer creates this fluid combination of heads and tails, zero and one, so that no matter what the player does, flip or no flip, the superposition remains intact.


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[Assistant] It's kind of like stirring a mixture of two fluids. Whether or not you stir, the fluids remain in a mixture. But ...


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[Assistant] In its final move, the quantum computer can unmix the zero and one.


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[Assistant] Perfectly recovering heads so that you lose every time.


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[Assistant] So if you are confused by quantum, don't worry, you're getting it.


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[Assistant] We can see its very real effects in action. You've seen the data for yourself?


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[Assistant] The quantum computer won because it harnessed superposition and uncertainty.


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[Assistant] And these quantum properties are powerful, not just to win coin games.


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[Assistant] But also to build future quantum technologies. So let me give you


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[Assistant] Three examples of potential applications that could change our lives.


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[Assistant] So that hackers could not secretly copy the key perfectly because of quantum uncertainty.


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[Assistant] Today, we use more than 17 billion connected devices globally.


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[Assistant] Just imagine the impact quantum encryption could have in the future. Secondly,


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[Assistant] Quantum technologies could also transform health care and medicine.


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[Assistant] For example, the design and analysis of molecules for drug development is a challenging problem today. And that's because


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[Assistant] Exactly describing and calculating all of the quantum properties of all the atoms in the molecule is a computationally difficult task, even for our supercomputers. But a quantum computer could do better.


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[Assistant] So future large-scale quantum simulations for drug development.


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[Assistant] Could perhaps lead to treatments for diseases like Alzheimer's, which affects thousands of lives.


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[Assistant] And thirdly, my favorite quantum application is teleportation of information.


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[Assistant] From one location to another without physically transmitting the information. Sounds like sci-fi, but it is possible.


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[Assistant] Because these fluid identities of the quantum particles can get entangled across space and time in such a way that when you change something about one particle, it can impact the other. And that creates a channel for teleportation.


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[Assistant] It's already been demonstrated in research labs and could be part of a future quantum internet.


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[Assistant] We don't have such a network as yet.


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[Assistant] But my team is working on these possibilities by simulating a quantum network on a quantum computer.


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[Assistant] So we have designed and implemented some interesting new protocols, such as teleportation among different users in the network, and efficient data transmission, and even secure voting.


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[Assistant] So it's a lot of fun for me, being a quantum physicist. I highly recommend it.


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[Assistant] We must tread carefully and responsibly as we build our quantum future. And for me personally,


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[Assistant] I don't see quantum physics as a tool just to build quantum computers. I see quantum computers as a way for us to probe the mysteries of nature and reveal more about this hidden world outside of our experiences. How amazing that we humans, with our relatively limited access to the universe,


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[Assistant] Can still see far beyond our horizons just using our imagination and our ingenuity.


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[Assistant] And the universe rewards us by showing us how incredibly interesting and surprising it is. The future is fundamentally uncertain, and to me, that is certainly exciting. Thank you.


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[Assistant] When you build your neural network, one of the choices you get to make is what activation function to use in the hidden layers as well as at the output units of your neural network.


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[Assistant] So far we've just been using the sigmoid activation function, but sometimes other choices can work much better. Let's take a look at some of the options. In the forward propagation steps for the neural network, we had these two steps where we used the sigmoid function here. So that sigmoid is called an activation function. And uhm, here's the familiar sigmoid function, a equals one over one plus e to the negative z.


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[Assistant] So in the more general case, we can have a different function, g of z, which is going to be right here, where g could be a nonlinear function that may not be the sigmoid function. So for example, the sigmoid function goes between 0 and 1,


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[Assistant] An activation function that almost always works better than the sigmoid function is the tanh function or the, uhm, hyperbolic tangent function. So this is z, this is a, this is a equals tanh of z and this goes between plus 1 and minus 1. The formula for the tanh function is e to the


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[Assistant] Z minus e to the negative z over their sum. And uhm, it's actually mathematically a shifted version of the sigmoid function. So as a, you know, sigmoid function,


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[Assistant] Just like that, but shift it so that it now


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[Assistant] Crosses a zero zero point and rescales, so it goes between minus one and plus one.


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[Assistant] This almost always works better than the sigmoid function because with values between plus one and minus one, the mean of the activations that come out of your hidden layer are closer to having a zero mean. And so just as sometimes when you train a learning algorithm, you might center the data and have your data have zero mean.


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[Assistant] Using a tanh instead of a sigmoid function kind of has the effect of


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[Assistant] Centering your data so that the mean of your data is closer to zero rather than maybe 0.5 and this actually makes learning for the next layer a little bit easier.


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[Assistant] We'll say more about this in the second course when we talk about optimization algorithms as well. But one takeaway is that I pretty much never use the sigmoid activation function anymore. The tanh function is almost always strictly superior. The one exception is for the output layer, because if y is either 0 or 1, then it makes sense for y hat.


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[Assistant] To be a number that you want to output that's between


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[Assistant] Zero and one, rather than between minus one and one. So the one exception


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[Assistant] Where I would use the sigmoid activation function is when you're using binary classification, in which case you might use the sigmoid


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[Assistant] Activation function for the output layer. So g of z2 here is equal to sigma of z2.


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[Assistant] And so what you see in this example is where you might have a tanh activation function for the hidden layer and sigmoid.


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[Assistant] So the activation functions can be different for different layers. And sometimes to denote that the activation functions are different for different layers,


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[Assistant] We might use these uhm, square brackets, superscripts as well to indicate that.


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[Assistant] G of square bracket 1 may be different than G of square bracket 2, but again, square bracket 1, superscript refers to this layer, and superscript square bracket 2 refers to the upper layer.


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[Assistant] Now, one of the downsides of both the sigmoid function and the tanh function is that if z is either very large or very small, then the gradient or the derivative or the slope of this function becomes very small. So if z is very large or z is very small, the slope of the function, you know, ends up, uhm,


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[Assistant] Being close to zero, and so this can slow down gradient descent. So one other choice that is very popular in machine learning is what's called the rectified linear unit. So the relu function looks like this.


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[Assistant] So the derivative is 1, so long as z is positive.


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[Assistant] And the derivative, or the slope, is zero when z is negative. If you're implementing this, technically the derivative when z is exactly zero is not well defined.


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[Assistant] But when you implement this in the computer, the odds that you get exactly z equals zero zero zero zero zero zero zero zero zero zero zero is very small, so you don't need to worry about it. In practice, you could


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[Assistant] Pretend the derivative when z is equal to 0, uh, you can pretend it's either 1 or 0, uhm, and, and your code will work just fine. So the fact that it's not differentiable, the fact that


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[Assistant] So here are some rules of thumb for choosing activation functions.


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[Assistant] If your output is 0, 1 value, if you're using binary classification, then the sigmoid activation function is a very natural choice for the output layer. And then for all other units, um, value of the


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[Assistant] Is increasingly the default choice of activation function. So if you're not sure what to use,


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[Assistant] Uhm, for your hidden layer, I would just use the


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[Assistant] ReLU activation function is what you see most people using these days, although sometimes people also use the tannish activation function.


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[Assistant] One disadvantage of the ReLU is that the derivative is equal to zero when z is negative. In practice this works just fine, but uhm, there is another version of the ReLU called the Leaky ReLU. We'll give you the formula on the next slide, but instead of it being zero,


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[Assistant] When z is negative, it just takes a slight slope like so. So this is called the leaky relu.


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[Assistant] This usually works better than the relu activation function, although it's just not used as much in practice. Either one should be fine, although if you had to pick one,


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[Assistant] I usually just use the ReLU. And the advantage of both the ReLU and the leaky ReLU is that for a lot of the space of z, the derivative of the activation function, the slope of the activation function is very


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[Assistant] And so in practice, using the ReLU activation function, your neural network will often learn much faster than using the tanh or the sigmoid activation function. And the main reason is that there's less of this effect of the slope of the function going to zero, which slows down learning.


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[Assistant] And I know that for half of the range of z, the slope for ReLU is zero, but in practice, enough of your hidden units will have z greater than zero, so learning can still be quite vast for most training examples.


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[Assistant] So let's just quickly recap the pros and cons of different activation functions. Here's a sigmoid activation function. Uhm, I would say never use this except for the output layer if you are doing binary classification, or maybe almost never use this.


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[Assistant] Uhm, and the reason I almost never use this is because the 10H is pretty much strictly superior. So the 10H activation function is this.


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[Assistant] And then, uhm, the default, the most commonly used activation function is the relu, which is this. So if you're not sure what else to use, use this one. Uhm, and maybe, you know, feel free also to try the leaky relu, where, uhm, might be 0.01z.


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[Assistant] So a is the max of 0.01 times z and z. So that gives you this, uhm, bend in the function. And you might say, you know, why is that constant 0.01? Well, uhm, you can also make that


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[Assistant] Another parameter of the learning algorithm, and some people say that works even better, but I hardly see people do that, so.


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[Assistant] But if you feel like trying it in your application, you know, please feel free to do so and you can just see how it works and how well it works and stick with it if it gives you a good result.


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[Assistant] So I hope that gives you a sense of some of the choices of activation functions you can use in your neural network.


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[Assistant] One of the themes we'll see in deep learning is that you often have a lot of different choices in how you build your neural network, ranging from the number of hidden units, to the choice of activation function, uhm, to how you initialize the ways, which we'll see later, a lot of choices like this.


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[Assistant] And it turns out that it's sometimes difficult to get good guidelines for exactly what would work best for your problem.


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[Assistant] So throughout these three courses, I'll keep on giving you a sense of what I see in the industry in terms of what's more or less popular. But for your application, with your application's idiosyncrasies, it's actually very difficult to know in advance exactly what will work best. So a common piece of advice would be,


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[Assistant] If you're not sure which one of these activation functions work best, you know, try them all and evaluate on like a holdout validation set or like a development set, which we'll talk about later, and see which one works better and then go with that. And I think that by testing these different choices for your application,


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[Assistant] Better at future-proofing your neural network architecture.


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[Assistant] Uhm, against the idiosyncrasies of the problem as well as evolutions of the algorithms rather than, uhm, you know, if I were to tell you always use a value activation and don't use anything else that just may or may not apply for whatever problem you end up working on, you know, either in the near future or in the distant future.


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[Assistant] Alright, so that was a choice of activation functions, and you've seen the most popular activation functions. There's one other question that, uhm, sometimes we get asked, which is why do you even need to use an activation function at all? Why not just do away with that?


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[Assistant] So let's talk about that in the next video and where you see why neural networks do need some sort of nonlinear activation function.


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[Assistant] Oh, there you are, daughter. Are you and Miss Patel ready?


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[Assistant] Ready, Professor? To dance? It's traditional that the three champions, well in this case four, are the first to dance. Surely I told you that? No. Oh, well now you know.


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[Assistant] Ready, Professor? To dance? It's traditional that the three champions, well in this case four, are the first to dance. Surely I told you that? No. Oh, well now you know.


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[Assistant] So, substance dualism posited two types of stuff. Immaterial stuff and physical stuff. And the problem of causation says, well, how can this immaterial stuff interact with physical stuff? As I said, there are other problems with substance dualism, but this is a particularly pressing one. Now, perhaps the easiest way to get around this problem is to get rid of the immaterial stuff. Just say, there's only physical stuff in the world.


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[Assistant] So my thoughts about going on holiday to Paris, or my desires to have a nice lunch later on.


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[Assistant] So this mix of chemicals and hormones and H2O make up my thoughts.


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[Assistant] Now, there are different ways of spelling out the view that is physicalism. Now we're going to look at two today.


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[Assistant] So imagine you have two organisms and you can't tell them apart just looking by their physical features.


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[Assistant] Because if she's saying that mental states just are physical states, then she has to say that if two things are physically indiscernible, then they're going to be psychologically indiscernible. Identity theory is also a reductionist view.


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[Assistant] It's reducing the psychological thoughts that we have to the physical. It's saying that if we think very carefully, the thought that I'm going to go to Paris next week can be re-described in purely physical terms.


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[Assistant] Be re-described in terms of a mix of chemistry and hormones and atomic structures that are going on in that, in my body right now.


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[Assistant] Individual dogs were there at Crufts. So I might say, oh, 30,000. Or I might take you to be asking me how many species of dogs there were at Crufts. In which case I might say, uhm, 300.


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[Assistant] If I was asking about how many individual dogs there were at Crufts, I'm asking about what philosophers call tokens.


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[Assistant] Then I'd be asking about types. I'd be asking, uhm, I'd be being asked about how many types of dogs there were at Crufts. This is known as a type relation.


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[Assistant] Are identical with particular types of physical states.


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[Assistant] So for example, the neural realization of pains.


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[Assistant] It's saying that there's no immaterial substance in the world. That's crazy talk. All we have is physical stuff. It's a reductionist view. It's saying that psychological states...


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[Assistant] If we look at brains and we look at bodies, then we'll find out more about psychological states. However, the philosopher Hilary Putnam, in an iconic paper, 1967, pointed out some problems with identity theory.


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[Assistant] The main problem is that he thought that it was too chauvinistic. It's too narrow. Let's think about it in this way. Think about an octopus. Octopuses, octopi, are very different from humans.


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[Assistant] Their brains are probably made up of slightly different chemicals to human brains.


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[Assistant] And yet, we don't want to say that octopuses can't feel pain. That seems a very odd claim to make. Just cause your brain isn't like my brain.


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[Assistant] Does it mean that you can't feel the same things that I feel? Why is this a problem for identity theory?


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[Assistant] Identity theory is saying that each type of psychological state can be reduced to a particular brain state. So we're saying that pain just is the stimulation of C fibers.


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[Assistant] But their brains were made up of totally different things to us. They're sort of not even comparable.


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[Assistant] They avoid certain things because they say that it gives them an unpleasant sensation. They tell us that they can feel pain.


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[Assistant] So this seems that we have to qualify the types of identity that we're using. We're saying that pain and other psychological states are identical with particular states of the body. But the states of the body that they're identical with could vary from species to species.


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[Assistant] Well, this greatly reduces the strength of the identity theory. Now, this isn't a killer blow for the identity theory. This doesn't completely rule it out altogether.


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[Assistant] But it does raise a very interesting question about how we should think about the identity relation between psychological states and their physical realisers.


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[Assistant] Putnam says that this points to a more fundamental feature of psychological states. When we're thinking about psychological states, we should think about what they do and not what they're made of.


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[Assistant] Can have different physical realisers, can be made of different physical stuff, dependent on whether you're a human, whether you're an octopus, or whether you're an alien.


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[Assistant] Even though the stuff that makes up those pains, the stuff that the identity theorist says the pain is identical with, is completely different.


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[Assistant] So this is known as multiple realisability because the physical state that realises the psychological state of pain.


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[Assistant] Ah yeah, Harry Potter fam! I'm back with another special lesson for you! Today, we'll see Harry and his classmates in Herbology class, in their second year at Hogwarts.


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[Assistant] Herbology is a real science that studies herbs and medicinal plants. In the Wizarding World, it studies magical plants, as we'll see in today's scene.


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[Assistant] This will be a great opportunity for you to learn some specific vocabulary in English about plants and gardening.


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[Assistant] As you probably already know, this is one of the great things about learning English with different movies and series. They give you all sorts of real-life situations that can be useful in gaining English fluency. Are you ready for some fun? Let's dive in!


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[Assistant] So, here we meet the Herbology professor at Hogwarts, Professor Pomona Sprout.


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[Assistant] Sprout is her surname, but it is also a word in English.


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[Assistant] In the real world, professor is a title normally given to a teacher at a university or college because they have more studies than a normal teacher.


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[Assistant] Teacher is a more general word, which refers to the professional who educates others.


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[Assistant] However, in the world of Harry Potter, the teachers at Hogwarts are called professors even though they teach young children.


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[Assistant] By the way, the children tell her good morning, but did you notice that her greeting before was simply morning? Morning everyone!


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[Assistant] It's extremely common for natives to drop the word good in these common greetings. So instead of good morning, good afternoon, good evening, good night, we will simply say morning, afternoon, evening, night. Example. Evening. Do you know what you would like to drink? Check out these other examples.


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[Assistant] As we can hear Professor Sprout say, this herbology class is taking place at greenhouse number three. A greenhouse is a building that usually looks like a house but that has roofs and sides made of glass used for growing plants that need warmth and protection.


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[Assistant] So Professor Sprout is asking the students to gather around her so she can start the class.


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[Assistant] Also, to gather around is a common phrasal verb, and it can normally be heard as a command by itself. Example.


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[Assistant] The root of a plant is the part that grows down into the earth to get water and nutrients and holds the plant firmly into the ground.


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[Assistant] The professor is asking about the properties, that is, the characteristics or attributes of a mandrake. And this is what a real mandrake root looks like.


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[Assistant] Now, in the Wizarding World, the Mandrake Root is magical, and it looks alive and moves just like a human baby.


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[Assistant] In this class, the students are going to read pot mandrakes. To pot is a specific verb used in gardening.


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[Assistant] Which means to put a plant or seed into a pot with soil.


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[Assistant] So to repot just means to move a plant that has already been potted into a new pot.


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[Assistant] This is normally done in order to give plants more space to grow as they change in sizes.


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[Assistant] In fantasy stories, if somebody or something is petrified, they or it is turned into stone.


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[Assistant] On the other hand, Avada Kedavra is a fatal spell, as it is used for killing the target.


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[Assistant] A seedling is a very young plant, still in its initial phase of growth.


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[Assistant] For example, the stunning spell Stupify can be used to knock out opponents. Or in a boxing match, you lose if you get knocked out. This is also abbreviated to a KO.


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[Assistant] In the scene, she's saying that these Mandrakes are still very young, so hearing their cry will not kill you, but it will leave you unconscious.


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[Assistant] As you can already see the students wearing them, earmuffs are a pair of ear coverings such as these, which protect your ears against loud noises or cold temperatures.


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[Assistant] Auditory is an adjective more used in medical contexts that defines anything involving hearing, like an auditory disease or auditory equipment. When talking about clothing or items that you can wear, to put something on simply means to start wearing something.


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[Assistant] to watch closely is to pay attention to a situation that interests or worries you as to see how it develops example


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[Assistant] The presentation will only be explained once, so watch it closely.


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[Assistant] Another useful and practical definition of this word means to understand something.


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[Assistant] I didn't really grasp all the parts of the professor's lecture. Can you help me to understand it?


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[Assistant] To pull something out is to take something out of the interior of something, like to pull your notebook out of your backpack or to pull your wallet out of your pocket.


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[Assistant] One of the possible meanings of the verb to get is to understand, much like the verb grasp that we saw before.


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[Assistant] To dunk something into a place such as a cup, jar, or pot is simply to put something inside of that recipient in a vigorous way.


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[Assistant] For example, you might dunk cookies in milk or coffee


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[Assistant] To pour something is normally used to refer to making a substance flow out of a container and into another container. For example, I spilled some milk while pouring it into the bowl.


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[Assistant] In the case of this scene she means that she's taking the soil from one pot and adding it to another.


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[Assistant] A sprinkling is a small amount of drops or particles of a substance. For example, sprinklings of water or sand.


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[Assistant] This comes from the verb to sprinkle, which means to drop small amounts of something onto or into something else. So we could also say that Professor Sprout is sprinkling soil into the pot.


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[Assistant] If you are responsible for something and you neglect it, that means you do not give it the necessary attention or care.


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[Assistant] In this case, the professor thinks Neville did not take care to wear his earmuffs properly, and because of that, he became unconscious.


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[Assistant] If there's plenty of something, there's an amount that is enough or more than enough


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[Assistant] For example, if you go to a party and there's plenty of food, that means there's lots of food and that it should be enough to feed everyone there.


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[Assistant] I would have taken her myself if she wasn't so bloody proud.


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[Assistant] Uhm, I dunno. I can fly, I mean I'm a fair flyer, but I...


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[Assistant] Can, uh, I should never reveal the best of you sirs.


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[Assistant] Wait, look, it all sounds great when you say it like that, but the truth is most of that was just luck. I didn't know what I was doing half the time, I nearly always had help.


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[Assistant] Facing this stuff in real life is not like school.


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[Assistant] In school, if you make a mistake, you can just try again tomorrow, but out there, when you're a second away from being murdered, or watching a friend die right before your eyes,


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[Assistant] It all sounds great when you say it like that, but the truth is most of that was just luck. I didn't know what I was doing half the time, I nearly always had help.


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[Assistant] In school, if you make a mistake, you can just try again tomorrow, but out there, when you're a second away from being murdered, or watching a friend die right before your eyes,


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[Assistant] This is mad. Who'd want to be taught by me? I'm a nutter, remember?


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[Assistant] Look, it all sounds great when you say it like that, but the truth is most of that was just luck. I didn't know what I was doing half the time, I nearly always had help.


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[Assistant] Facing this stuff in real life is not like school.


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[Assistant] In school, if you make a mistake, you can just try again tomorrow, but out there, when you're a second away from being murdered, or watching a friend die right before your eyes,


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[Assistant] Today you are one of the world's most visible deep learning researchers. Let us share a bit about your personal story. So how do you end up doing this work that you now do?


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[Assistant] And then uhm, indeed, uh, and, and I remember, uh, Reject Reyna's very old GPU paper, right, acknowledges you for having done a lot of early work.


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[Assistant] Uhm, and then today one of the, you know, things that's really taken the deep learning world by storm is your invention of GANs. So how did you come up with that?


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[Assistant] That's great that when you thought you might be dying soon, you're just thinking how to get the research done. Yeah. Yeah. That, that, that, that, that's commitment. Yeah. Yeah.


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[Assistant] So today you're still at the center of a lot of the activities with GANs, with Generative Adversarial Networks. So tell me how you see the future of GANs.


[User] Please transcribe this text

[Assistant] I see, cool. Great. Oh, and, uh, so, so just as a lot of people, they joined deep learning about 10 years ago, such as yourself, wound up being pioneers. Maybe the people that joined GANs today, if it works out, could end up the early pioneers.


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[Assistant] So in addition to all your research, you also co-authored a book on deep learning. How's that going?


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[Assistant] So even someone whose map, you know, is a little shaky or haven't seen the map for a few years, we're going to start from the beginning of your book and get that background and get into deep learning.


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[Assistant] Uhm, as someone that's worked in deep learning for a long time, uhm, I'd be curious if you look back over the years, tell me a bit about how your thinking of AI and deep learning has evolved over the years.


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[Assistant] And so today there are a lot of people wanting to get into AI. So what advice would you have for someone like that?


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[Assistant] Cool. So sort of, you know, read your book, practice the materials and post on GitHub and maybe on Archive.


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[Assistant] Thank you. That was great. There was a lot about your story that I thought was fascinating and that despite having known you for years, I didn't actually know. So thank you for sharing all that.


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[Assistant] Many purebloods believe it is their birthright to rule. Not only our world, but the non-magic world as well.


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[Assistant] And though doctors assure us that Mrs. Chesnick is no longer contagious, the cafeteria nachos bar will be closed for the remainder of the week. And now, what you've all been waiting for.


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[Assistant] Emma and I have always loved each other, really.


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[Assistant] Cynthia Fuller is a pillar of the community. She knows what the town needs.


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[Assistant] Emma and I have always loved each other, really.


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[Assistant] I think I can tell the wrong sort for myself, thanks.


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[Assistant] And said that each had earned a prize for having been clever enough to evade him.


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[Assistant] But to do this, that student must survive three tasks. Three!


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[Assistant] Aww yeah, you asked for it and you got it. Today we are bringing you another Harry Potter lesson. And this time we'll be learning with the potion master at Hogwarts, Professor Severus Snape.


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[Assistant] In fantasy stories, potions are liquids that are put inside of bottles such as this one and that have magical effects for the person who drinks it. So this is how you'll learn with today's lesson.


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[Assistant] First, you will watch some clips with subtitles, then we'll teach you all the most important vocabulary, pronunciation, and cultural context before you test everything you learn by watching it a final time without subtitles and answering some quiz questions. Plus, we have this free PDF and MP3 lesson that you can download to take your learning to the next level.


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[Assistant] I'll tell you more about that later. For now, let's watch Harry in his first class with Professor Snape.


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[Assistant] If something is considered foolish or silly it appears to be stupid and not serious.


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[Assistant] A wand is the object a wizard or witch uses for doing magic. If you wave an object, you move it from side to side while holding it in your hand.


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[Assistant] As you probably have seen, a wizard has to wave his wand for casting spells or incantations, or the words a wizard says to cast a magic spell.


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[Assistant] In the scene, Snape transforms the phrase to wave a wand into a noun. Wand waving.


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[Assistant] English is flexible in allowing you to manipulate words and turn them into different parts of speech. Noun, adjective, verb, etc.


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[Assistant] For example, if an athlete breaks records, we could say he is a record breaking athlete.


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[Assistant] Or if you can make potions well, you are good at potion making.


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[Assistant] Can you think up another example making a verb into a noun like Snape did here? Practice it by writing it down in the comments.


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[Assistant] To appreciate is to understand or recognize the full value of something or somebody.


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[Assistant] And it can also be used as a way to thank somebody, meaning that you understand and see how good something or somebody was to you.


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[Assistant] In other words, Snape means that potion making is an activity that requires attention to detail and intelligence, and he is saying he doesn't expect many students to understand the value of this subject.


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[Assistant] For example, if you're a student at Hogwarts, you are not allowed to walk around the castle at night.


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[Assistant] Just click up at the top or down in the description below to get your freebies for this lesson with Harry Potter and join the masterclass now!


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[Assistant] To possess something is the same as to have something, to own it. For example, if you possess a car, you probably have to pay taxes for it. However, we also use this for intangible qualities, like in this example from Chamber of Secrets.


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[Assistant] If you have a predisposition to something, you can learn the skill more easily. It is natural for you.


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[Assistant] For example, if you have a predisposition to cooking, it means you could easily be a chef.


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[Assistant] To bewitch something or somebody is to cast a spell or use magic to control them or it.


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[Assistant] More figuratively, if someone or something bewitches you, you're so attracted to them that you cannot think about anything else.


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[Assistant] To ensnare is to catch or capture somebody or something, like in this picture.


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[Assistant] So Snape is saying that he can teach his students how to take control of somebody's mind and senses.


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[Assistant] Here, Snape is talking figuratively about the magic art of potion making.


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[Assistant] To brew is to prepare a drink with hot water, like brewing coffee or beer.


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[Assistant] That is, making potions. They can help somebody attain fame and glory by drinking them.


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[Assistant] To put a stopper on something is to cause something to stop, to make it stop.


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[Assistant] This is also a bit of a play on words because in order to close a bottle, you might use a stopper, which looks like this.


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[Assistant] Then again is an expression used before saying something that is different from or the opposite of what you have just said, similar to on the other hand. For example,


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[Assistant] If you call something or somebody formidable, you mean that they are great or respectful in some way. This is a formal adjective and is not very common to hear in daily conversations between native speakers.


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[Assistant] In this scene, Snape is telling his students he can teach them how to do incredible things with potions. Then he says with sarcasm that on the other hand,


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[Assistant] Some students might already be so great that they feel confident enough to not pay attention to the class.


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[Assistant] He's obviously talking about Harry, who appears to be writing at Snape Talks instead of looking at him.


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[Assistant] Even though we see he is paying close attention by taking notes.


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[Assistant] In English, we often link the sounds of different words that come together, as you can hear here with the words then again. In this case,


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[Assistant] Check out these other examples from the TV series Friends.


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[Assistant] Many of the following definitions are different plants and objects that even native speakers wouldn't recognize the names of. However, we will still define them for you. Asphodel is a plant like this one.


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[Assistant] And root refers to the part of a plant that grows down into the earth to get water and nutrients and holds the plant firmly into the ground.


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[Assistant] Infusion is just another way to call a drink made by leaving tea leaves or herbs in hot water. Wormwood is another plant like this one.


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[Assistant] Hey Wizarding World fan, did you know that there is actually an interesting fan theory about this line from Snape, which is the first thing he says to Harry?


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[Assistant] According to Victorian flower language, asphodel is a type of lily, meaning my regrets follow you to the grave.


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[Assistant] And wormwood typically symbolizes bitter sorrow.


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[Assistant] By combining the two, it creates a secret message that would be something like, What do you think? Are there any other fan theories about Snape that you love? Let's talk about it in the comments.


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[Assistant] A bezoar is a stone or hard mass that forms in the stomach and intestines of animals, which in the past was thought to be an antidote to poisons.


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[Assistant] In the world of Harry Potter, wizards actually use it as an ingredient for making potions.


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[Assistant] Sir is a noun used as a formal and very polite way of speaking to a man, especially one who you are providing a service to or who is in a position of authority or older than you. So, for example, a waiter at a restaurant might call a male customer, sir.


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[Assistant] Or, as in the case of this scene, a student might call their male teacher sir. Many learners overuse this word.


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[Assistant] Monkswood and Wolfsbane are different names for the same plant.


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[Assistant] However, if you say that something is a pity, as Snape does in this scene, then you mean that you feel disappointment or regret about it. Example. It's a pity to waste all this food. Did you hear the cancel the concert? Pity, I really wanted to see that band live.


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[Assistant] Clearly is another way to say obviously or without question. Example.


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[Assistant] Hey, remember you have a free pdf and mp3 to take your learning to the next level with this scene.


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[Assistant] Commodore Norrington's going to be very pleased with this. Do pass my compliments on to your master.


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[Assistant] You're lying! Not just to me, that doesn't matter. To yourself as well. That's what a fool does.


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[Assistant] It's 4 a.m. You've been awake for 40 hours when you unlock a puzzle containing this video.


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[Assistant] Of some kind of dance-off between a chicken and a roller-skating beaver.


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[Assistant] The confusion and delight you're experiencing is a typical moment at the MIT Mystery Hunt, which is basically the Olympics meets Burning Man for a specific type of nerd.


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[Assistant] Today I'm going to take you inside this strange, intellectually masochistic and incredibly joyful world. But first I have to explain what I mean when I say puzzle.


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[Assistant] A puzzle hunt style puzzle is a data set. It can be a grid of letters, a sudoku, a video, an audio. It can be anything that contains hidden information.


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[Assistant] That can eventually resolve into an answer that is a word or a phrase. So to give you an example, this is a puzzle called Masterpieces. It consists of 10 images of LEGO people looking at piles of LEGOs. And to save us some time, I'm going to explain what's going on here


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[Assistant] Each of the piles of Legos is a deconstructed work of art in the style of a famous artist. So does anybody recognize the one, the artist on the left? They used a lot of red. I heard Rothko, yeah.


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[Assistant] Yeah, Clint, I heard it. Well done. The color is the biggest clue there. So...


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[Assistant] The puzzle has various clues that tell you what matters here are the artists, not the specific works of art.


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[Assistant] And what you need to do is then look at what you haven't used yet, which is the number of LEGO people in each painting.


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[Assistant] And you can count them and then count into the artist's last names by the same number of letters. So there's three people in front of the Rothko on the left. So you take the third letter, which is a T. There's only one in front of the Mondrian, so you take the first letter, M.


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[Assistant] And there's three again in front of Klimt, so you take the third letter, I.


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[Assistant] You do that for all 10 of the original artists and put them in the order, and you get the answer, which is illuminate.


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[Assistant] But where I'm trying to be as clear as possible for you now, puzzles have to navigate the line between abstraction and clarity.


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[Assistant] They have to be obtuse enough to make you work for it, but elegant enough so that you can get to the aha moment, where everything clicks into place. Puzzle solvers are junkies for this aha moment. It feels like a brief high in an instant of pristine clarity.


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[Assistant] And there's also a deeper fulfillment at play here, which is that humans are innate problem solvers. It's why we love crosswords and escape rooms and figuring out how to explore the bottom of the ocean.


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[Assistant] Solving deviously difficult puzzles expands our minds in new directions, and it also helps us come at problems from diverse perspectives.


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[Assistant] These puzzles come in various puzzle hunts, which come in various shapes and sizes. There's one-hour ones designed for novices, 24-hour road rallies and the puzzle hunt of puzzle hunts, the MIT Mystery Hunt.


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[Assistant] This is an event that takes place once a year and has thousands of, around 2,000 people descending on MIT's campus and solving puzzles in teams that range from a single person to over a hundred.


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[Assistant] My team has 60 people on it. That includes a national crossword puzzle tournament champion, a particle physicist, a composer, an actual deep sea explorer, and me feeling like Mr. Bean goes to Bletchley Park.


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[Assistant] That's actually an apt comparison because one year involved a puzzle where you had to construct a working Enigma machine out of pieces of cardboard.


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[Assistant] Each mystery hunt has a theme. Past ones have included The Matrix and Alice in Wonderland. It's often pop culture and literary based themes. And the goal is to find the coin that's been hidden somewhere on MIT's campus. And in order to get there, you have to solve around 150 puzzles and do various events and challenges.


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[Assistant] I had done this for about 10 years without ever dreaming of winning. Until January of 2016, we're 53 hours into a hunt whose theme is the movie Inception. We haven't slept in days, and so everything is hilarious.


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[Assistant] The tables are covered in piles of papers of our notes and completed puzzles. The whiteboards are an unintelligible mess of three days' worth of insights.


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[Assistant] And we're stuck on two puzzles. If we could crack them, we would get into the end game. And after hours of work, in a magical moment, they both fall within 10 seconds of each other. And soon we're on the final runaround, a series of clues that will lead us to the coin.


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[Assistant] And in claiming it, we win the MIT Mystery Hunt by a tiny margin of five minutes.


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[Assistant] What I didn't mention before is that the prize for winning is that you get to construct the whole hunt for the following year.


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[Assistant] The punishment for winning is that you have to construct the whole hunt for the following year.


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[Assistant] At the beginning of 2016, I had never constructed a puzzle before. I had solved plenty of puzzles, but constructing and solving are entirely different beasts. But once again, I was lucky to be on a team full of brilliant mentors and collaborators.


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[Assistant] So from a constructor's point of view, a puzzle is where I have an idea, and instead of telling you what it is, I'm going to leave a trail of breadcrumbs so you can figure it out for yourself and have the joy and experience of the aha moment. This is another way of looking at the aha moment.


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[Assistant] And what's incredible to me is that this experience, which is very emotional and kind of almost physical, is something that can be carefully designed.


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[Assistant] So to show you what I mean, this is a puzzle I co-constructed with my friend Matt Gruskin. It's a text adventure.


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[Assistant] Which is the old-school adventure game format, where you're exploring going north, east, south and west, picking up items and using them, and you can get to the end of the game part, but you won't have solved the puzzle.


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[Assistant] In order to do so, you have to recognize a hidden layer of information, and the easiest way of seeing it is by mapping the game out. That looks something like this. Does anybody recognize what this is?


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[Assistant] Yeah, exactly. This is, uh, this text adventure takes place within Settlers of Catan. Who here knows what Settlers is? Nerds.


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[Assistant] If you don't know, Settlers is a board game where you're competing against other people to collect resources and use them to build structures.


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[Assistant] And within the game, within the text adventure, we hid information in various ways with which you could reconstruct an entire game. You could figure out the roads, the cities, the towns, the resources, the numbers on the tiles, even the dice rolls. You put all that information together and you could extract an answer in a way that's too complicated to explain right now. But find me afterwards if you really want to know.


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[Assistant] But what this puzzle emphasized for me is the value of perspective shifts in inspiring an aha. So in this puzzle, you go from experiencing the world on the ground as a character to looking down on it from above as if you're playing a board game, and in that shift, you completely reframe all the information you've been given.


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[Assistant] Fortunately, the world is a torrent of ideas and information. I've seen fantastic puzzles constructed out of the waggle dances of bees and the remarkable coincidence that the 88 keys of a piano can be perfectly mapped to the 88 constellations in the sky.


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[Assistant] Once you find that out, you can't not construct the puzzle, and it's going to be about having the solvers make that connection in their own minds, whether you give them stars on a keyboard or play the celestial music of the cosmos, you're getting them there one way or another.


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[Assistant] Before long, you find yourself staring at a turtle and asking yourself, is this a puzzle?


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[Assistant] And also staring at a turtle and saying, I never appreciated what multitudes this contains in its shell alone. This might be a familiar experience to you if you've ever been watching a TED Talk and asked yourself, is this a puzzle?


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[Assistant] I'm not telling, but what I will say is that puzzles can be found in the most unexpected of places. That brings us back to one of my favorite puzzles of all time, which was constructed by Tripp Payne, and this time I'm going to play it for you with the sound on, so get ready to name that tune.


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[Assistant] Who knows what that is? Yeah, you make me feel like a natural woman.


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[Assistant] So you can identify that in seven other songs and clips, and then look at the videos themselves for clues, where the way that they are filmed and edited together, plus things like the cutaways to the panel of five people sitting at a table, which is reminiscent of a panel of judges, all of this can suggest reality competition show.


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[Assistant] And either through internet research or from just recognizing this, you can get to the aha, which is that these clips are shot-for-shot recreations of lip-sync battles from RuPaul's Drag Race.


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[Assistant] But I think it also improves our lives in various ways. Uh, being able to solve puzzles has, when I'm confronted with a challenge, has allowed me to explore it from multiple perspectives before I lock in an approach.


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[Assistant] It's also, the process of solving is great training for working with a team, knowing when to listen, when to share, and how to recognize and celebrate insight. And being able to construct aha's is a very powerful tool. Think of how powerful and exciting and convincing an idea is that comes from your own mind, where you make all the connections yourself. So in January of 2017,


[User] Please transcribe this text

[Assistant] After about tens of thousands of hours of work, we finally run our mystery hunt. And it's a different sort of satisfaction than the quick high of an aha moment. Instead, it's the slow burn of saying something through perplexing abstraction, yet being understood.


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[Assistant] And when it was all over, in our exhaustion, we turned to each other and the world and we said, we're never doing this again. It's too much work. It's really fun, but no more winning. One year later, in January of 2018, we won the MIT Mystery Hunt again.


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[Assistant] So welcome to this interview Richard. Thank you. My name's Andrew and this is my colleague Joshua. So to start off, can you tell me about yourself?


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[Assistant] Oh, I'm stuck, I'm stuck, I can't leave. Oh, there we go. On the map, it shows the first objective, right? The first objective, and then you head to the first objective, and you find out what you need.


[User] Please transcribe this text

[Assistant] And this time around you need the gears and the gas tank. Mechanical and-


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[Assistant] Yeah, yeah, and now, and now we need to collect, collect, yeah, you could craft stuff with, uhm, one wood and one stone.


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[Assistant] Yeah, yeah, yeah, there's a crafting bench in the cabin.


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[Assistant] Oh wait, if you hold left click, that's probably the fastest way. Get the max strength and then... Yeah. FALCON PUNCH!


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[Assistant] Yes, baby. And, uhm, if you go into these bunkers- There's moose! Bunker, bunker.


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[Assistant] Okay, and we're, we're trying to, we're trying to just do the, do the game. Or play the game so we know what's going on. Okay. Okay. And then, press this.


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[Assistant] Yeah, there you go, there you go. And we needed mechanical scraps and fuel. Oh my gosh, Fidana's like a scraper.


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[Assistant] Alright, good start. Did y'all get mechanical scraps?


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[Assistant] Alright, uh, well, Pomo, if you're looking from, uh, out there, uh, you're supposed to collect some mechanical scraps now because we got enough of the fuel.


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[Assistant] I think you has one. But yea, come this way, come this way.


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[Assistant] Press E, yeaah, and then put it in. Oh, Nina's using it.


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[Assistant] I'm gonna, okay, nice, nice. Nice! Alright, and then after this, we get, we get the sec- if you go back to the map, it indicates locations where the second objective would be.


[User] Please transcribe this text

[Assistant] They could, they could sabotage, uh, what do you call it? The, the objectives we have, they could, uh, just straight up shoot us with guns and stuff, uhm.


[User] Please transcribe this text

[Assistant] Oh, if you're a trader, you could use the hatch to teleport to a location.


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[Assistant] The second objective is a helipad.


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[Assistant] Oh, this is a trader chest. Yeah, only traders open this. So if you see it open and if there's a trader nearby, like, it's kind of sus, right?


[User] Please transcribe this text

[Assistant] Uh, you, there's like different things you could do. You could sabotage the...


[User] Please transcribe this text

[Assistant] You could also use it to swap like, you know, those like signs on the, on the floor, like this one.


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[Assistant] Reimu, do you see the, the, the gear? With the...


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[Assistant] Oh, it's Manka, but like, it's a little like, more Manka, I guess.


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[Assistant] Oh, by the way, we need mechanical scraps. Dead bruskull emoji.


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[Assistant] Yeah, if you, if you're, if you're, if you're a task speedrunner in Among Us, this helps, it helps the speedrun.


[User] Please transcribe this text

[Assistant] But yea, I, I, I hope the rules got across. Puma died early, but... We need three, right?


[User] Please transcribe this text

[Assistant] I'm sure Pong will figure it out. Sus. Susj. Oh, that's Luca in Discord. Sus.


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[Assistant] If anybody else is around, hello? Hello, anyone? Hello?


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[Assistant] I dont have wood. That's fine. We should just head over to fix that.


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[Assistant] We're gonna fix the, the helipad, guys.


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[Assistant] Yeah, it's kinda hard to do tasks when you're alone. It can't really get carried in these kind of games.


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[Assistant] This is a power station? I don't even know who's alive yet.


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[Assistant] Where is everyone? They're probably dead. Hello?


[User] Please transcribe this text

[Assistant] Uh, no way. Uuuuuuuuuuuuuuuuuuuuuuuuh.


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[Assistant] In the earlier videos from this week, as well as from the videos from the past several weeks, you've already seen the basic building blocks of forward propagation and back propagation, the key components you need to implement a deep neural network. Let's see how you can put these components together to build your deep net.


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[Assistant] Here's a network with a few layers. Let's pick one layer.


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[Assistant] And look at the computations focusing on just that layer for now.


[User] Please transcribe this text

[Assistant] L, uhm, you have some parameters WL and BL, and for the forward prop, you will input deactivations A, L minus 1 from the previous layer, and, uhm, output AL.


[User] Please transcribe this text

[Assistant] So the way we did this previously was you compute ZL equals WL times AL minus 1 plus BL, uhm, and then AL equals G of ZL. Right?


[User] Please transcribe this text

[Assistant] So that's how you go from the input AL minus 1 to the output AL. And it turns out that for later use, it will be useful to also cache the value


[User] Please transcribe this text

[Assistant] ZL. So let me include this, uhm, cache as well because storing the value ZL will be useful for backwards, for the back propagation step later.


[User] Please transcribe this text

[Assistant] And then for the backward step, really for the back propagation step, again focusing on the computation for this layer L.


[User] Please transcribe this text

[Assistant] You're going to implement a function that inputs the AFL


[User] Please transcribe this text

[Assistant] Uhm, and just a flash of the details, the input is actually DAFL as well as the cache. So you have available to you the value of ZL that you computed.


[User] Please transcribe this text

[Assistant] And then in addition to outputting DAL minus 1, you will output


[User] Please transcribe this text

[Assistant] Structure of how you implement this forward step.


[User] Please transcribe this text

[Assistant] I'm going to call the forward function as well as this backward step, which I'm going to call a backward function. So just to summarize, in layer L, you're going to have, you know, the forward step, or the forward prop of the forward function, input AL minus 1, and output AL, and, uh,


[User] Please transcribe this text

[Assistant] In order to make this computation, you need to use wl and bl. Uhm, and also I'll put a cache which contains zl.