Hey Virgos, welcome back to the channel. This is Kelly from House of Virgo. If you're new, welcome. Please smash that like button and do subscribe if you enjoy the readings. Keep in mind this is a weekend general love reading, so some of the messages may or may not be for you. Take a fifth, leave the rest. This is a channel for Virgo, Sun, Moon, Rising, or Venus, but all signs are welcome. All right Virgos, so let's get into this. This is going to be for your weekend of April 21st through the 23rd. All right, Saturday is Earth Day, so happy Earth Day to all of my fellow Virgo earth signs. Okay, let's get into this. What's going on? What's going to happen here for Virgo in love? What's happening for Virgo in love over the weekend? Okay, retreat. It's time to disconnect from the world, Virgo. So it looks like this weekend is the perfect opportunity, if you can, for stepping back, retreating, disengaging, pulling your energy back, getting some rest, getting like renewing, getting, getting into some solitude or recovery. Okay, focusing on some recovery. Okay, let's see. Disconnecting from the world this weekend in whatever way that is for you. Okay, what is this? Okay, you've got deception. Someone wearing a false self mask in a relationship. Okay, maybe that's why you're going to pull back Virgo. Let's see. You might be saying to yourself, okay, somebody's deceiving or something's not right. Let me, let me pull back. You've got to express your love. Go ahead and make a romantic gesture, but also free yourself. Okay, Virgo. This is very interesting. So I get a sense that there's something going on here with like a romantic gesture gets made, but it could be tied to something not being true. And I don't know, Virgo, are you maybe deciding for yourself or is this your advice for you to just kind of step back and free yourself from something here over this weekend? Let's see. Yeah, I kind of feel like that's the energy here. Expressing yourself, but pulling back. Okay. All right, let's see what's going on. You've got the Ace of Pentacles. Okay.   Maybe you've expressed your love or something to someone and something false is occurring. I don't, I'm not sure where it's coming from or someone might say they can't do something over the weekend, but you find out they're being deceptive about that. They actually can. They might say they've got other plans or something else is going on, but actually they don't have, I don't know, I feel like you find out that somebody's not doing what they said they were going to be doing. I'm not really sure or it could be you who's telling another person that you can't because you got something going on, but you're really just trying to free yourself and you tell this person in a really nice way. You do have something going on though. Okay. You do have an opportunity. Somebody, Virgo, you might just say, you know, something else came up and this looks better for me and I think I want to do this other thing instead. Okay. Interesting. Okay. Let's see. I just get an overall feeling that over this weekend you're just kind of going off the radar. Like you can't be found. Okay. See the two of cups and the 10 of pentacles. Wow, Virgo, you're definitely making a connection with someone over this weekend. Okay. I see you meeting up with someone over this weekend. Yes, yes, yes. You step away and opportunity comes and you step away and take another opportunity somewhere. That's the biggest message. I feel like you might have two choices or something comes up and you're like, nah, because you have another option. That's what I feel is going on here. Okay. And this really feels to me like you're connecting with someone or you're meeting up with someone in particular over this weekend and it looks like it could like a friend connection that can turn into romance. Okay. And it could be somebody at work. You could have a connection with someone at work. For some of you, you are connecting with your agent in regards to a career situation or work. You can't do something. This is what I feel like another opportunity. It's either meeting someone romantically or you're making a connection with or you're meeting up with someone who's connected to your work. And this is like a, for some Virgos, it's kind of like, all right, well, do I go meet someone for drinks and have like maybe a romantic thing or do I meet this person who's got this amazing financial opportunity for me where I can make a million dollars in the next five months? Virgos like, hmm, let me see. Where's your priority, Virgo? Is it love you what or is it that million dollars that you want Virgo? That's why I kind of feel like what the two is always like a decision. Yeah. Okay. So you have the Queen of Swords here as well. Queen of Swords is making some kind of offer coming in with this offer and it's the real deal. It's like true. Okay. Let me see. Oh yeah. Listen, Virgo. Okay. So as the story unfolds, there is a financial opportunity definitely coming to you over this weekend that's connected to work or something that you're putting effort into. I feel like I do feel like the overall message is you might actually tell someone on a personal level like in a romantic thing, you know, can I get a rain check on that? Because I have this other thing that's come up, but I can't say anything about it just yet. But like, this is a phenomenal, amazing golden opportunity. And I can't like, this is a one time deal, a one time shot. I feel like a lot of you are going to put like the whole love thing on the back burner. I really do. I don't feel like you're being greedy or anything, but this is going to provide some kind of stability for you. Financial stability long term. Okay. Or give you where you can plant some roots long term. Okay. Let me see. You have the Eight of Swords. Oh, wow. Yeah. See, this offer that's coming in. Virgo, I feel like you're kind of stuck or trapped financially where you are right now. Like things could be better. That's all I'm saying. And I feel like this is something that maybe you've been thinking about or will be thinking about, you know, like, Oh, what I could do with all of these extra funds, how much like this is kind of like making like planning and dreaming and thinking these things out.  I'm not trying to focus on that right now. Too many people are deceptive. I'm focusing on my coin and I'm freeing myself, you know, and I do feel like with, go ahead and make the romantic gesture, you may tell somebody that you're really, really like, you really like them. Like if there's kindness here, you're expressing a very loving gesture to somebody, but I also feel like you're going to choose not all of you, but it's somebody here is going to choose to make a financial connection because it's going to be like a long to help you out in the long term. You know what I mean? Like you've got this panicle energy and the sword is like, it's all about like your money. It's all about your finances, your stability.  There's like isolation and restrictions are feeling like you're in prison. The only thing we have for the love here is, you know, the eight of cups and the two of cups, it's kind of like, the eight of cups is like moving on, you know, it's kind of like withdrawal and, you know, taking a temporary time out so you can focus on what you need for the long time, long term for yourself. Yeah. Wow. Virgo first time feels to me like love's just going to have to be on the back burner for now. There's things you want. You want to build your business. You want to buy a new home. You want to purchase some real estate, you know, whatever you're doing, you want to put more money in your bank. You don't want to have like feeling like you're trapped anymore. That seems to be more of a priority. It doesn't look like your priorities love. It's not your main priority. You know, you're kind of like, well, that would be really a nice benefit and that would be great if I could connect with someone. But right now I really have nothing to offer or I'm trying to build and, you know, it looks like you're passing up on getting into anything starting down that journey. Just yet you're just delaying. I feel like you're delaying. Okay. What else does Virgo need to know? This weekend is going to be prominent for that. Okay. You've got the moon. All right. Yeah. So a lot of you have set your new moon intentions for prosperity more so than anything. Okay. To be prosperous in your life. Okay. The moon and the hanged man. All right. So they're telling me this, this, so this deception card that flew out here in the beginning with the retreat Virgo, I feel like you're like taking a step back because you are, you're kind of like, you know, if it's meant for me, it'll be for me. If it's not, then I won't have to worry about it. I feel like you're just like, if someone's really interested and they want to, they'll wait and you're not really saying that to another person, you're kind of keeping things secretly, keeping it to yourself and being a little deceptive like that. Like deceptive in the way of where you're just being a little shady. Like you're not, you're not saying to somebody, Hey, you got away from me because I got other things going on. You're just kind of like, you know, I appreciate that. And right now I just got a lot of stuff to do, but that's not really the truth. I mean, it's a partial truth. I feel like for you, it's more along the lines of like, you got goals or things that you want to accomplish. And you're kind of like keeping it close to your chest and saying, you know, if this person is serious about us and they really want to pursue, they'll wait. They'll wait while I'm, you know, chasing my goals. Yeah, absolutely. Chasing your goals, your passion. You're not chasing people, you're chasing your goals. All right, Virgo. Yep, look at that. The chariot. You're taking action. Okay, so let's, let's try to look about this two cups. Who's this person? What's going on with this person? Okay, who is this person? There's definitely a soulmate connection here. Maybe you already know this person. You guys have already met, but you might be kind of like, Oh, I knew it. I just, okay, no, I didn't know it. I almost know it. Page of source.  All right, Virgo, they're watching, they're spying, they're curious about you. They're like trying to get more information on you. They want to know about you. Okay, very intrigued, very interested. Tell me more about this person that's trying to connect with Virgo. Okay, so Six of Pentacles. Oh, this person. All right, they've already got it in their mind. They've already made the decision that they want to go forward. This is a timing thing because I feel like somebody here is ready. They're ready to like give and receive and be reciprocal and they're very interested in you and you know, their intuition is telling me that you're the person for them. Okay, could be a cancer, could be a Pisces, could be Gemini, Query, so Libra, but yeah. Virgo, it looks to me like, I don't know if you're, let me see how you feel about or will feel about this connection because I kind of feel like for you, you're just not making it a priority right now because this other, this opportunity comes in. Either way, you're freeing yourself, you're stepping back. You're saying no to a date. You want to connect with someone but I feel like you want to, you'd rather much, you don't want to connect with someone for a love relationship first. You're not against it but it looks like you want to connect for business or work or your prosperity. Okay, yeah, Virgo. I was asking about you about this part. You're running away. You're like, sorry, gotta go. See ya. Take a rain check. Yeah, that's kind of your energy here, Virgo, over this weekend.  Huh, that's interesting. It's supposed to be a love reading and it's like, Virgo's like, I'm gonna try and, yeah, see Virgo, look, you've got the four of cups. You're just not interested. I mean, they're just clarifying what I've already said, you're just not interested. Is this person gonna stick around? Because Virgo's just like, well, they don't, they don't. But if they do, that's great. You're not chasing. Okay, a high priestess. Are they gonna wait for Virgo?  Yeah, they're gonna wait.  Yeah, so Virgo, good do your thing. I don't feel like this person is gonna hold it against you or just run away or walk away and say, well, I don't want to see you again. No, there's something, I don't know, I feel like it's making you more attractive to this person. Yes, because I feel like Virgo, this person kind of looks at you like, wow, Virgo's going after their gold. Virgo's like, that's attractive. Virgo's very independent. Virgo's self-sufficient. I'm gonna, I want to marry Virgo. This is like my person. This is the person like, Virgo's not a gold digger, but Virgo's not trying to like say, no, I just want to love you forever, never, never, and I'm never gonna work a day in my life, and I don't want to do anything with my life. I just want to be all around you all the time. And you're not doing that.  Like, I, like, not, I don't need to do anything. Virgo's like, let me back up.  I don't want to say it's chasing, but where they're just much more interested in you, because you're not making this, you're not making love a priority right now, because you either you can't or you just don't want to at this time. Do you know what I mean? Okay, that's what I'm trying to get out here. This is going to be a short reading tonight, usually Thursdays are for the weekend. But let me see what else is going on. What do I want to ask here? Okay, Virgo. All right, they're not giving me anything here. So I'm just going to be a random and pull a card. See what else comes up here. Oh, all right, they're just saying, Virgo, you're going to get to that place of abundance where you need to be. There you are. You're very, like I said, Virgo, you're very focused on your coin this weekend. What you need to do for yourself. Yeah. And I'd like, like I said, I don't feel like you're saying no, but you're not saying yes right now. You're just saying not right now. That's what I feel like is going on. So tell me, tell Virgo, basically about this new opportunity, this prosperity, what's coming in here for Virgo. Okay, got the king of wands. All right, there's a huge opportunity. Yeah. For you to be in a leadership position. Okay. Huge opportunity for you.  Like you're going to have access authority of some sort, not CEO kind of stuff. Okay. So I feel like that's kind of the description like, but you are going to have some alpha power behind you. So if this is like a promotion of some sort and it comes, which it will come with more income, it's also going to come with more responsibility. Okay. Yeah, you're going to be, I don't know why they're giving this feeling of like it's such a bummer, but you're going to be breaking some hearts this weekend. I can tell you that you're pretty much whether that's like firing some people or letting some people go or getting a new position to do that or being paid more to do this or even romantically, I feel like you're like, no, I'm sorry, but you're a heartbreaker this weekend. You are, you're, you're not only telling one person though, you might be telling a couple people no, like, sorry, I got some deadlines I got to meet and I got some goals I need to pursue this weekend. And you know, maybe another time, I feel like they just keep giving me the same message. Virgo, it doesn't even look to me like you're trying to go party or do anything.  I don't know, I keep, I feel like you keep repeating the same thing over and over again. So I'm going to stop with that. Okay. I do want to get a couple of other messages to see what else is showing up here for you. Okay. There you go. Some of you, you have children to worry about and something to do with your career or maybe grandchildren or, um, yeah, I mean, look Virgo, this is what's going on. Some of you are just trying to like be able to afford things or just be in a better financial place.  Like I got deadlines, I get stuff here, I get some opportunities. Virgo, I don't, it's okay.  Sometimes love does have to take a back seat so you can take care of business. Absolutely. You know, put your like, what is that saying? Grindstone. Put your nose to the grindstone. Yeah, some of you are working this weekend and you're taking on extra work this weekend. Yeah, some of you are trying to pay your bills. Are you going to be working from home? Some of you work taking care of children or your babysitting. I'm not really sure but it looks like something to do with home or you're trying to buy a home for you and your children or be able to pay for something for your kids as well. I don't know, you have your, yeah, new love is definitely there and for some of you, this is an unknown person. So you might get asked out over this weekend, actually for all of you, I don't see a past person. This is somebody new who's interested in you and a new person or unknown person. There's definitely a new love and it figures Virgo because you got new love coming in. You're like, can't this weekend. I got a life. I got stuff I got to do. So maybe you'll connect with them some way or talk to them on the phone or something and set something up for another day. Okay. But now you're not, yep. Virgo's like, I got to do my own thing. Okay. Could be a brunette female. Okay. That could be you. That could be another person, this new person. Okay. And for others, they have a fair male. So it could be a fair male or a fair female. They are lighter than darker or a brunette female or male, darker hair, very intense and very focused. That's either another person or you. Okay. That almost wanted to fly out. There's definitely a connection here. Someone's very like, they got their eye on you, really key, very attracted to you. Okay. Oh, courting man. Yep. Yep. Uniform professional male. Okay. Or female. Yep. You're definitely someone's courting you. This is somebody now. And I don't know, Virgo, you got to be, you're busy. You're busy. And I feel like this person will be like, okay, I'll wait for, I'll wait for you when you're ready. Definitely. Virgo, I'm not going any further. Could you know why? It's the weekend and I'm going to stretch and relax and do my thing this weekend, work on some creative projects. I'm just going to leave there with you, leave this here with you. And yeah, if you want to get a personal reading, just go to my booking site. Hopefully it's still working because something must be going on because a lot of you are emailing me trying to book with me. So I'm wondering if my website, I'm going to have to look over the weekend if it's down. If you are unable to access my website, just sit tight. If that there's a problem with it, I'll get a backup running my booking site. Okay. All right, you guys. So I'm going to leave that there for you. I hope you all have a marvelous, awesome, amazing weekend. And I'll see you all back here Sunday at 1pm. All the members, if you're not a member of my channel, do become a member for price of a cup of coffee each day. You can pop in on the Sunday live stream at 1pm Eastern and pop in that stream and ask as many questions you want. And I'll do many terror readings for you. All right, you guys. So I'll see you guys back here very soon. All right. Take care. Bye.


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Hi everyone. I'm really excited to be in Cologne. This is my first time in Cologne. I've been to a bunch of other German cities, most of all to Frankfurt. Though usually I don't leave the airport. I'm usually flying through. Very exciting to see so many people here. When I first booked this event, I was told that Cologne has a very, very small meet-up organization. This is not a very small meet-up organization, so good work. I'm going to do a general talk today. I want to talk about the basics of Bitcoin. I understand that we have mostly a beginner group here, a lot of people who are new to Bitcoin. Let me see by a show of hands. How many people here are relatively new to Bitcoin? That's almost more than half the audience. How many people have Bitcoin or have used Bitcoin? It seems like your newbies work pretty fast. As always, if you don't have Bitcoin or have not used Bitcoin, please come and talk to me afterwards. I would be happy to help you set up a wallet, and give you one or two euros in Bitcoin, not Bitcoin, or maybe one or two millibits. In order for you to experience how Bitcoin works and to try it out. Can everybody hear me in the back? Can you understand my English? Excellent. I wish I could do this in German. I cannot. Okay, so what is Bitcoin? Bitcoin is not a company. Bitcoin is not a service. Bitcoin is not an organization. In fact, if anything, I would call it a disorganization. Bitcoin is a protocol. It's an internet system. It's a system that allows anyone in the world to be able to make a payment, or run a trusted application with someone else, without having to trust the other person. If you look at it from that perspective, Bitcoin is a platform that offers trust between people who have no other basis to trust each other. They do not need to trust each other because they can trust the platform. If I had my choice in order to name this thing, I would probably not call it Bitcoin. I would call it Trustnet, but Satoshi called it Bitcoin. Here we are. Bitcoin enables us to do something we've never done in history before, which is to establish for the first time a global financial network that is completely without borders, that allows any person anywhere in the world to transact with any other person. You may hear that the magic ingredient for Bitcoin is blockchain. That is not entirely true. We are hearing that mantra a lot now, especially from financial institutions that are desperately trying to avoid being disrupted by this technology. They will say, the magic behind Bitcoin, the technology behind Bitcoin, is the blockchain. The blockchain isn't the only technology behind Bitcoin. It is one of four technologies that work together for essential ingredients that make Bitcoin what it is. What Bitcoin is, its primary characteristic, is that it is an open, borderless, neutral, and censorship-resistant platform that everyone can use. Those are the characteristics. Open means that anyone can participate in Bitcoin. You don't need permission, registration, file an application, or show identification. You simply download an app. In fact, you don't even need to download an app. You can just write an app yourself and participate in Bitcoin directly.  Just write code, join the network, done. It is open because everyone can participate not just in the payment network, not just in the currency, but can also participate in the consensus network, and can also innovate without asking for anyone's permission. In many systems that are centralized, if you want to build a new application, you have to ask permission by the central owner. Let's say you want to write an application that does better routing for Deutsche Bahn, or offers you a better mobile service with Deutsche Telekom. How do you do that? You have to ask Deutsche Telekom, can I please write an application? Will you please make that application run on your network? They will say, great, we will run a focus study. We will see how many hundreds of thousands or millions of people would like to use this application. If it is big enough, then we will spend the money required to invest in deploying this application. In Bitcoin, the process is slightly different. You say, I want an application. Does anybody else want this application? Ah, one more person. Great, we have a market. Two is all it takes. I will send stuff on port X. You receive stuff on port X. We will communicate with this protocol. We have an application. I don't need to focus study it. I don't need to ask anyone for permission. I can write an application for me and my friend. Why would I do that? Maybe my friend has special needs. Maybe my friend has a disability they can't see, or they can't read, or they have a cognitive disability. If I wanted to create an application for Deutsche Bank for my half-blind friend, or for my friend who had a cognitive disability, if Deutsche Bank didn't build it, I wouldn't get it, either with my friend. But in Bitcoin, if I care enough about writing an application for my friend, I can write a Bitcoin wallet that is for people with vision problems, with very, very big letters. I can write a Bitcoin wallet that speaks the payments to you, for blind people. I can write a Bitcoin wallet where the password is not numeric or letters, but pictures of animals, for people with cognitive disabilities, to give them security and control over their money. I can create an application for just one person. In order to run that application, all I have to do is run that application. I don't need to ask anybody's permission. This creates a very magical environment. This is what we call permissionless innovation. It ensures that innovation happens at the edge of the network, driven primarily by the users who care about that application. There's no committee, there's no focus group, there's no any way of registering the application. So then the question becomes, how many applications run on the Bitcoin platform? We have no idea. We have no idea. It's like saying, how many applications run on the internet? We have no idea. Some of the applications are invisible. Some of them we don't know exist, but they're out there and they work. We have no idea. We have no idea if there's a 12-year-old Turkish programmer writing an anti-air-to-gun Bitcoin wallet right now. I hope they are. We have no idea. They could be, right? When a country says, let's ban Bitcoin, I hope there's some 12-year-old programmer who's saying, how about we write a Bitcoin wallet in the correct language and unban it really quickly, make it available broadly? If Russia is afraid of Bitcoin, they're going to be much more afraid of a Bitcoin wallet that speaks Russian. And so this permissionless innovation creates this very vibrant environment where people can create new things. We've seen this before. It happened on the internet, and it's still happening. It's why Bitcoin itself exists. Bitcoin itself is an application that refused to ask for permission. Bitcoin itself is an application that came out of the unknown. And for many years, nobody knew it was running. But it was, getting stronger every day. So Bitcoin is this thing that is open. It allows permission to access for everyone, permission to participate for everyone, permission to innovate for everyone, without permission. It's borderless. We use that word, but it has some very important implications. The internet is borderless. The internet doesn't care which country you're in. People can try to put up firewalls, but for the most part, they can be bypassed. So the internet is a true global network. Well, Bitcoin is also a true global finance network. And this is terrifying. For many organizations, this is absolutely terrifying. The idea of a financial network that has no borders is completely unprecedented in history. The only thing that existed before that, that truly had no borders, was Roman coins. Roman coins stretched for the largest empire the world had ever known, and they were borderless. Everybody used them. Even if they weren't Roman, Bitcoin could become something like that. It is the first truly international, borderless, global, transnational financial network. The first truly borderless, global, transnational currency. What happens when you open the borders for everyone to participate? Very interesting things happen. Very scary things to some people. But we live in a world in which billions of people have no access to financial services, none whatsoever. They have access only to cash. Even those who do have access to financial services, maybe they have a bank account, but they can only use one currency. They can't import, they can't export, they can't borrow from someone in another country, they can't lend to someone in another country, they can't convert to another currency, they can't trade internationally, they can with Bitcoin. And so that changes the world in ways that we don't yet understand. We don't yet know what that will do. It will allow billions of people to join a global economy. When cell phones were first launched, I remember buying my first cell phone. In 1991, I bought a cell phone. I was wealthy enough as a student in London to buy a cell phone. It was about the size of this microphone. It had an antenna that was twice the size of this mic. And if I charged it for eight hours, I could make 20 minutes of phone calls before I run out of battery. But only if I was very near some of the central locations in London where there was actual cell coverage. And that was it. And for that privilege, I paid 25 pence a minute. And if someone called me, I paid 25 pence a minute. So when people called me, I would like, Hi, this is Andrew. Yes, quickly, faster. Yes, bye. I really can't afford to talk to you for very long. And if you looked at cell phone technology at that time, you might think, this is a tool for the rich, right? This is the kind of thing someone has in their Rolls-Royce. Someone who has an assistant. Someone who has the privileges of living in London. Even for a student, you know, that's the kind of thing that only a student in a first-world country could afford. Now who has a cell phone? Not just everyone. But the really funny thing is how it completely cross-class divides. When I had a cell phone back then, my first cell phone, it was a status symbol, right? So I would sit in the corner of my university holding my gigantic cell phone and look very, very cool. I thought it was very impressive. None of the girls did. But still, I look like a businessman. I couldn't actually afford to make phone calls, so I just talked to myself. But if you see someone on the street today and they have a Bluetooth headset, what kind of job do they do? Usually it's a plumber, a builder, a construction worker, a taxi driver. You will not see an executive with a Bluetooth headset anymore. Hell no. Because the ultimate status symbol today is to hire someone to hold your cell phone. If you are really rich, you don't hold a cell phone. You have someone coming behind you who holds the cell phone. It's weird how that flipped, right?  And now if you go to places like Brazil, rural Brazil, you go to a village on the Amazon River Basin, which is 100 miles from the nearest town by canoe. Everyone has a cell phone. They don't have electricity. They don't have running water. They don't have a clinic. They don't have a bank. But they all have a little Nokia cell phone and a little solar panel so they can communicate with the world. And there's a cell phone tower that will support their communications. Because it's easy to deploy. And this happened across the world. We call that leapfrogging. The idea that in some countries, the new technology will completely overcome the old technology. They will never see a landline. They will never see a traditional phone. For them, telephony started with Nokia and ended with Nokia. And will always be that. So what happens when an Android phone which today costs 30 to 40 euros for the cheapest version costs 10 euros? What happens when an Android phone that costs 10 euros with a very, very basic data service is a bank? What happens when that Android phone allows you to originate and terminate loans, to do wire transfers? When it gives you not just the power of a bank account, but the power of a banker? What happens when everyone with an Android phone in South Saharan Africa, in Southeast Asia, in South America can become a banker? And that's what Bitcoin is going to do. That's what technologies like Bitcoin are going to do. And that's what borderless really means. It means the possibility to create a unified world financial system that is open to everyone, without the concerns of politics, without the concerns of geography. In fact, you don't even need to be a human to own Bitcoin. Bitcoin is software money, and software money can be owned by software. Maybe a self-driving car will own its own money. Maybe your fridge one day will have a Bitcoin wallet. I don't know why, but who knows? There's probably some 12-year-old designing exactly that. So open, borderless, neutral. What does neutral mean? Neutral means that Bitcoin doesn't care who sent and who received. It doesn't care what amount is being sent or received. All it cares about is, does this transaction validate based on the rules? And if it does, it goes. And if it doesn't, it doesn't go. Simple. There's no blocking. There's no censorship. There's no opportunity to say, but the sender isn't rich enough. But the recipient is in Zimbabwe. Doesn't matter. None of it matters. You don't know who the sender is. You don't know who the recipient is. You don't know where they are. Sender, recipient, and value irrelevant. Completely neutral. A platform that can be used without concern about the humans who are behind it. Or the cars. Or the refrigerators that are behind it. And finally, censorship-resistant. Bitcoin resists censorship. That means that if I make a transaction, someone else can object to it. They can petition their government. They can sue in court. They can get a court order. Saying this transaction should not happen. And yet, it did. And now what? There's no changing that. What happens in Bitcoin happens. And once it's happened, it cannot be undone. It simply cannot be reversed. It can't be stopped. It can't be seized. It can't be frozen. It can't be reversed.  There is absolutely no way to stop it. And once it's happened, there is absolutely no way to reverse it. It becomes fact. Some people find this terrifying. What could happen in the world if anybody could do a transaction whenever they want? You know, they said the same thing about the internet. What would happen to the world if everybody could have an opinion? Because money is an opinion. There's really very little difference. What Bitcoin does is it turns money into a form of speech. And once money is a form of speech, once it's carried over the communications networks of speech, it becomes, for all practical purposes, speech. How do I know this? I put $150 million Bitcoin transaction into a photo of nine cats. And then I posted it on the internet. And the point was this. If you think you can stop Bitcoin, what you are saying is equivalent to, I can stop people from posting photos of cats on the internet. And we all know that's impossible. Because that's what the internet was made for. I've created a transaction that got converted into emojis on Skype. So you can take a transaction, 250-300 bytes, and you can convert it into 250-300 emojis.  And then you post that anywhere. Spray paints on a wall. You post it in Skype. You post it on Facebook. And anybody can then take that, decode it back to a transaction, and post it on the Bitcoin network, and the transaction will go through. And it's valid. How do you censor money when money is any form of speech we want? Picture, video, text, emoji, anything. When Bitcoin can be 12 words. How do you stop people from communicating 12 words? By the time you say, hey, don't do that, I've already said four of them. How do you stop Bitcoin from moving across borders? When a refugee can take their money, convert it into Bitcoin on the black market, memorize 12 words, and walk across the border. And even if they're completely naked, they're carrying their money in their head as 12 words. Wealth is speech, and that changes everything. This is what Bitcoin is. And in order to achieve these characteristics, open, borderless, neutral, censor-persistent, you need the recipe. And the recipe has four ingredients. It's a blockchain, yes. But it's also a system of proof of work, a consensus algorithm that is open for everyone to participate in. And in order for the blockchain to work and be decentralized, it has to have a consensus algorithm that is open for everyone to participate. Otherwise, the participants can be coerced into censoring the blockchain, into reversing its transactions. But in order for it to have an open consensus algorithm, the participants need to have some kind of reason to keep it secure and to be honest. With Bitcoin, the reason is a competition. And that competition has a reward. But the only way you can make a reward is if you have a native currency. So the consensus algorithm can only be open if you have a native currency to reward people with. And the reward only works if, when you cheat, there's a punishment. And the punishment is the extrinsic risk of energy you must commit before participating in the competition. And those are the four elements. An open blockchain can only remain open if it has an open participatory consensus algorithm, which can only work if you can reward the participants with a native currency, which will only work if they're punished through the expenditure of external energy if they try to cheat. Take any one of those elements away, and you no longer have an open, borderless, neutral, censorship-resistant network. What you have is a fancy database where the participants can be coerced to change it, to rewrite it, to censorship. And it is no longer neutral. And if it is no longer neutral, it cannot be borderless. Because you cannot take a position of neutrality if your local government is forcing you to censor. If you can, you must. And if you must, you will. And if you do, borders are back. And in order to ensure that you keep such a system healthy, you cannot make it open, because you can't secure it if the participants can censor it. So openness goes away, borderless goes away, neutral goes away, and censorship-resistant goes away. And what are you left with? Microsoft SQL Server data-centred edition with hashes. Only it's more expensive. Probably the next speaker will disagree with all of that, but we'll see. It's going to be fun. That is Bitcoin in a nutshell. It seems simple. It's very complicated. It seems like it's a form of money. It seems like a payment network. You might look at it and say, It is not. It is the fifth most radical revolution in money we have seen as a human species. It will shake the foundations of our society. It will transform everything. And the more I look into it, the more I learn about it, the more I understand how deep this invention is, how influential it will be. And it seems like such a simple proposition. Let's build a world where we have a system of finance that is open, borderless, neutral, and censorship-resistant. And yet, the very idea of saying those words together when related to finance is radical. It's revolutionary. And as one philosopher once said, at a time of universal deceit, simply speaking the truth is a revolutionary act. Bitcoin speaks the truth. Thank you.


With head men's basketball coach Gary Stewart, the men falling short today to Weidner, 69-65, take us through the last four or so minutes. Colerichard had just given you a quick five points to take a 56-53 lead and then tell me what happened after that. Well, even before that, Gary, we couldn't get any stops. We couldn't get a tourniquet on it. We talked all week about the dominant hand, guard the dominant hand. They dominant hand drove us specifically the last five minutes of the game and got to the where I'm credit them. And we didn't have much of a response. As far as your team is concerned, Colerichard with 20 overall, just talk a little bit about his game today. Yeah, Colerichard got in rhythm. And when he does that, he's a hard guard. And we did a good job of getting him the ball in some areas that he's really, really effective in. We talked this morning before the game a little bit just about what's happened to the team so far this year. You've had a lot of injuries. You had some COVID protocols, situations, et cetera, haven't been able to practice really fully as a team pretty much all year. Just talk a little bit about the season as you're winding down against Albright and then hopefully on into the playoffs. Yeah, I'm not going to offer up any excuses, Gary. I mean, the games are on the schedule. We know they're on the schedule and you play with who you have. And quite frankly, we're just poor the coach team right now. We're not doing anything well. We go 5-10 from the free throw line 50% and lose a two possession game. We're not defending very well. We're not rebounding the ball very well. Defensively, we weren't very good. And conversely, offensively, we were inept. So you put all that together. It bodes poorly for you trying to win a basketball game. And it's disappointing because at this time of year, some of the things, some of the turnover, some of the play, we should just be reading out of a different chapter in the book. And so that's disappointing. But we've got some things that we can correct and get a hold of. And we're not that far away. Like I said, we've been in one and two possession games here for weeks. And we've got to get back to executing for longer periods of time. So you've got all bright next week and then the conference tournament after that. Just take us through. What do you actually do now to get that team ready? Yeah, my fixation's not on that, Gary. I just got to get to Monday and get into the film here and try to get some things corrected. Those things will arrive when they arrive. But right now, you've got to stay in the moment. And we've got to address the play that we feel like we can make corrections on and try to get our young people to be attentive to those. Well, it sounds good. Well, thank you very much for your effort anyway. And we'll see you next week. Thank you, Gary. I appreciate it. He's Gary Stewart. I'm Gary Stein. And this is Go Mustang Sports.tv.


What is up everybody JCM for jazby's case breaks dot com we just sold out 2020 panini contenders football first off the line Football this is a one box break random teams number seven guys saying guys really good price on this chasing a very very big Rookie class guys still holding up today Jordan love Jalen Hurts Joe Burrell to a toggle by Loa CD lamb Justin Jefferson John and Taylor and much much more guys And obviously guys everybody gets a random team in the NFL member no veteran commons will ship out of this And then contenders contains five autographs one parallel 18 inserts on average And then look for the one short printed on-card rookie ticket rps red zone autograph and the red zone parallel and every first off the line box So here we go. Here's the dice roll. Let's roll it You guys I'll say three to four seven times one two three four five six and seven Joe down a Kyle seven One two three four five six and seven Seven times seven times Vegas Raiders down in Los Angeles Rams And let's see how it all shaped out All right, so Joe you got the Raiders Tristan with the Cowboys Joe the Bears Douglas with the Ravens D Mac with the Saints Dustin with the Falcons Johnny with the Lions Jeremy with the Texans Thomas with the bangles Douglas with the Titans Joe the bills Brad with the Panthers all over the Steelers Douglas with the Giants Ryan with the Colts Shane with the Packers Douglas with the 49ers Matthew the Broncos Johnny with the Patriots Brian with the Chiefs Douglas with my Eagles Matthew with the Bucks Justin with the Jaguars Joe the Dolphins Justin with the Browns Matthew with the Vikings and Chargers Tristan with the Cardinals Jalen with the Commanders Jeremy with the Seahawks branding with the Jets and Then Kyle Harper with the Rams So obviously if you guys want to look at the checklist You know we're going back four years Alrighty, I'll crazy to say that. All right guys, try when they're close. I'll live in you now, right? All right, guys also to just show you guys first off the line 2020 contenders. All right guys. Well, like I said, just a one-boxer See what we got here, and we'll probably go top or bottom for those round numbers. There's duplicates. We'll just split them up evenly but and what about what you know redemption hopefully that's not the RPS, but There's a chance it could be We have Marquez Callaway Now this is a burrow Jefferson round numbers. I don't remember a fissure redemption in this to be honest Is he I mean he might be live. I don't remember though You know Benjamin Supporter house steak. What's going in? Cardinals Tristan Whoa That's interesting DJ Dallas and Colby Parkinson round numbers 21 out of 99 and that's both to the Seattle Seahawks Seattle Seahawks Jeremy Port just have one more autograph to go. All right, we got this here. There's the Marlin Mac. There's the red first off the line Justin Jefferson rookie of the year. I feel like we're gonna get something here Gabe Davis nice. That's not terrible For it to be the RPS ticket. I'll take that member obviously Not all the bills anymore, correct, but this is going to Joe though, obviously Joe Lawson got the bills pretty nice. I like him Then you got a two burrow round numbers All right, let's see who this is. So this might end up being a sticker, but it's definitely be a rookie ticket though Kenneth Murray And that's gonna go to the Chargers LAC was going to Matthew Not sure if he's there anymore, but was drafted by the Chargers And isn't there checklist? All right, sweet. So I would say I mean not the greatest box But especially now four years later Not to not a bad box though It could definitely be a lot worse Then Jalen saying I'm interested in that Gabe. Where did Gabe sign again? Or where do he where do he leave to? No, he left somewhere. All right, so we're gonna go Top or bottom which would be left right on the back. So that's what we'll give him to there Oh, we went to Jacksonville Damn, oh, well, they they lost Calvin Ridley, I guess right so Jacksonville, man That's not bad weapons over there. I guess right for Trevor Lawrence. They still got a Christian Christian Christian Kirk Over there Still got anger mother tight end That's what you wanted. So was riddly would wait so Gabe got a bigger contract than Ridley I feel like Ridley's contract was pretty massive But at the same time even though he is a little bit on the older side, I Think he's right though. He kind of has fresh legs. You know, he didn't play for like two years, right, so I Guess that's not terrible. Let's go left right for the backside All right, here we go Roll it four to five nine one two three four five six seven eight nine nine times nine Right side We'll give it to the right Appreciate it guys next ones in the store if you guys want to run it back to ask these casebricks.com


But you don't grab the hose you grab this yellow ring or in that case the green ring Okay I was healthy crazy how that works be like yep, that's right This is why But don't like On and off if it's on in the opposition you won't get power You


We're now in module two and we're going to focus on decision trees. So we've got about an hour and a half or maybe an hour and 20 minutes here. We'll cover this one. It's a mixture of both a lecture and a lab. And the way that we'll do a lot of these things is we're not just going to have labs alone. We're always going to have a bit of a lecture. And then we'll allocate about 15 or 20 minutes for people to do the lab. We're not going to make you code. Otherwise, I think we would be sitting and watching all day as people are trying to figure out how to code. So a lot of the code has been given to you. And so what you're going to be doing is mostly running programs or playing around or looking at things just to sort of get a feel for what's there. I'll explain that later on. So this is a combined lecture module on decision trees. So we've seen a picture like this before. And we're talking about classification and machine learning. So we might have labeled data. Here's the red group and the blue group. We could call them Republicans and Democrats if you want. And we've done a machine classifier that now has separated them. And now we can see how they distinct they are. So, you know, machine learning, or any classification algorithm that includes things like logistic regression, principal component analysis, partially squares, discriminant SVMs, neural nets, grid markup, all those things can do classification. And they can take this, you know, label data, which lists initially looks really confusing and it'll pull them apart and separate them. So today we're going to, at least for this module, look about what is classification? What is clustering? We're going to talk about decision trees, which are a form of machine learning. And we're going to talk about how you can convert a decision tree into something that's a little more mathematical using information gain, Shannon entropy and the genie index. And then we're going to talk about feature selection and we're going to show how you can use this to do classification for flowers, which is a toy problem that's inched introduces a lot of people to decision trees and it's actually a really nice one. We're going to go through the Python code for a decision tree to separate irises, which are the flowers we're looking at. And then you guys will be able to use the colab for your first bit and we'll see how you guys do with actually running those programs that we're going to provide to you. So diving right in clustering is something that's different than classification. They are not the same. A lot of people think they are but they're different. So clustering is a way of grouping objects that are logically similar. So clustering is like matching socks after you've washed and dried them and they're all separated and now you have to match pairs of socks.  If you were blind and you couldn't see the colors you probably have to do it by their shape or size, hoping that, you know, all of them are sort of paired up to a similar size and you can tell. If you had, you know, vision and you could see color or then you could start matching the socks by color but you might have to match by size, but you have to figure that out yourself. You might use some parameter to help with that clustering. You could do the same thing with clustering balls or clustering toys clustering paintings classification is something where things are labeled objects. Essentially clustering by the label. So we have the blue and the red objects, the Democrats and the Republicans. And they're categorized based on their properties. In supervised machining which learning which is the most common thing we use algorithms to essentially perform that classification and in some cases to help with assigning class labels for new data.  So decision trees are the simplest thing to implement for machine learning. They're easy to understand. They make sense they produce a rational agreement. You can use, use decision trees for classification which is what they're primarily used for but you can also use them for curve fitting or line fitting, which is regression. You can use them for numeric data. In the machine learning approach to decision trees the computer or the model learns to split and categorize, or in the case of regression how to fit or regress the data based on decisions. So it might use a greater than or less than number that might use a yes no, and it evaluates the cost of those decisions, whether this is a good split or a bad split or a good decision or a bad decision. So the example of a decision tree was, you know, the Titanic sinking what do we do women and children first. So, we're looking at the data that was collected and we can learn from that you know how did they make their decisions and we can see that most women survived the Titanic. And then those of the men who did survive usually they were younger, and they were usually parts of siblings, or have spouses. And if you were single and male and old, you usually died in the Titanic sinking. And you can see that there are boxes which things marked, you know, gender, age, survived age cutoffs decisions and we have branches, which are the lines, those are called edges. So that's this this tree like structure in a decision tree. So seven nodes and I think six edges. So the definition of decision tree is a is a flow chart, in which each internal node represents a test and attribute. So male, female, old, young, siblings, no siblings. And each branch or node represents the outcome of that test.  And there's a leaf node that represents the class label, young, old, male, female, sibling, no sibling. So the path from the root, which is at the top of the tree to each of the nodes is sort of the classification rules. So there are two types of decision trees the classification tree which is the classic one. It's for classifying classification. It can be used once it's constructed it can then predict or classify things it could predict, you know, if there's another sinking of another boat. If there's another ship of Newfoundland, what are you supposed to do.  Same sort of thing as you know are you Republican or Democrat might decisionist and did you vote for Trump or did you not and then how old are you and are you male or female and are you college educated or not those would be decision tree points to sort of decide whether you're Republican or Democrat. The regression tree is not for classification it's it's for handling or fitting numbers and it's it's for curve fitting. And you can use decision trees for that as well. The official or formal name for a decision decision trees classification and regression tree or a cart, because it can be used for both. Most people don't use decision trees for aggression. It's a shame, because it can be very useful. So, when you build up decision tree you have to decide which features, which conditions to split the groups, and you also have to decide when to stop. You know if there are no more people left on the boat. Then, you know, you don't need to keep on deciding who should get into the rescue ship, or if you've you know got your collection of people when deciding when we're Republican or Democrat and there's no one left, and you can stop. So this decision tree we had to say, you know, is, are you male or female. Yes, no, are you greater than nine and a half, or you're less than nine and a half, do you have siblings and spouses. Yes or no. Those are the questions that are asked. So what's marked in black is the decision or condition are you male female are you young or old. So then there's leaf notes, and then this is sort of the result. If you were female, you survived as a rule if you were older than nine and a half, you generally died and if you were male. If you had family you also generally survived. And then the edges are the ones that connect the final results to each of those leaf notes. The terms and decision trees. The top note is the root note that's the entire population. So all voters in the US. That's the root node. It was all passengers on the Titanic that's the root note. And then eventually if you're doing classification it's going to be divided into these different groups. So splitting is another term and that means where we divide the node into two or more sub nodes so you can split into two, three, four if you want. There's a decision node. And that's when that's when a sub node splits into further sub nodes.  But then for males, there are lots of other decisions about whether you're young enough or old enough. And related to the Titanic we also call things nodes, parent nodes and child nodes, not passengers and Titanic but it's just the terminology and trees. So the root node is typically a parent node. Then later on, among the male nodes, then we divided those into other groups. And it also was a parent node that was then broken down into are you young or old. So decision trees are pretty easy to understand. I think most everyone should have understood how we did the Titanic separations women and children first, and how decisions were made and this is actually from real data that they collected after the Titanic and evaluated who survived and but so that you know you can see the decisions were made it wasn't a black box it wasn't a neural net. So we call things white boxes, which means that you can understand it, it makes sense. Decision trees and the examples I've been giving are mostly categorical but you can do it for numbers. You don't have to do data transformation you don't have to do data normalization you don't have to do data scaling. It mimics our mirrors help humans think, and so we learn, and a lot of learning and approaches that we have to life really do involve decisions, and we make choices and we assess the costs of those choices. So our decision trees have kind of a built in feature selection so you can kind of have all kinds of garbage data, and it will do the feature selection on its own. And as I said you just don't need to normalize or do any statistical fixes and decision trees are not the most robust machine learning method. The decision tree surrounding forest is pretty robust, but just a decision tree on its own is not as good as an LSTM or a GNN. You can have small changes in the training data set and that kind of screw up the whole decision tree. There are tricks called bagging and boosting the work it. It's kind of a heuristic method. There are robust functions evaluations called the Gini coefficients and entropy gain. It can be prone to overfitting. It's not going to give the best solution. And this is where random forests can fix that. So again these are sort of mathematically proven, because it's called a greedy algorithm. So we need to sort of keep on categorizing into some kinds of ridiculous categories. So it's not as maybe intuitive as we might like. So, we can kind of understand the Titanic model and decision tree approach there. Someone told us the rule women and children first. But how do you learn a decision tree. This is a little different. So the formal way of learning a decision tree is called recursive binary splitting. And it's also called iterative dichotomization. So I've taken a tree, turned it upside down the roots in the in the pot, and there's the root node and then you can see the leaf nodes. So recursive binary splitting is split two ways. So you start off with all the features. And you start doing different splits and say, okay, should I just, you know, split by age first versus male and female. Should I split by, you know, their zodiac sign. Maybe that was how they actually decided he was going to be saved or not. And then you test and see, you know, how well did I do in predicting or determining how the Titanic survivors were selected. So the split with the lowest cost. That's one thing or the highest information gain is the one that's selected. So from our decision tree, if you chose choose gender first, that's probably it was the most information. That's how they initially group people and say, okay, all women this site.  If we have, you know, three different features of the age, family size, gender or sex, then there are potentially three candidate splits. So, okay, do we split by age first to be split by gender to be split by family size or do we split by zodiac sign. Those are things. And then in each case we calculate the cost of that split. And that could be the information gain or entropy loss or Shannon entropy or genie index, those are all things that calculate that cost so we repeat that calculation for other splits and for other reasons, as we keep on cutting through this recursive binary splitting. For people sitting long silences that evolve on the sleep. Okay. Information gain or IG is based on entropy, which is a sort of something if you go from physics is uncertainty or disorder. And the entropy is called Shannon entropy which is something that was developed for information theory. So entropy is the probability of being in a cost and in a class. So entropy is equal to the sum of these different probabilities of minus P log P. And you can calculate the inch information gain for an entire data set and you can calculate information gain for a specific feature. And so when you subtract the two from each other, you calculate the information gain for specific feature. So is it age, gender, number of siblings. So this information is given to which feature which attribute. In Titanic case it was age or gender or number family size, which is given the maximum amount about how to class or how to perform a class or split things. In Shannon entropy all the probabilities typically have to have a value that's one or less. You have to have a negative sign because logs, things that are less than one will produce a negative number. So if you have two classes, the likelihood of being in any one below Ms, you know, one half. So you can calculate the sum of the entropy for two classes is one half log two of one half, and the summit over two states. So it's minus one half plus minus one half multiplied by a negative member. That gives you an entropy of one. So this is a calculation for four classes. This is now one quarter, really log two, one eighth. And you'll get maximum values depending on the classes of, you know, two, three or four. So I'm going to take an example. This one's not from the Titanic, but this is one from buying cars. And we're talking about cars where they're old cars and recent cars. They're low mileage cars and high mileage cars, and they're cars that have been road tested where this person has driven them, not driven them. And there are recommendations that are associated with this. In these cases, if it's a recent car, low mileage and road tested, you should buy it. If it's a recent car, even with high mileage, and it's been road tested, you should buy it. But if it's an old car and it hasn't been road tested and they're still low mileage, the recommendation is don't buy. And same way with a recent car, high mileage, but hasn't been road tested, don't buy. So these are, this is the table of data. And you're going to try and then come up with a decision tree that would learn this so that if you came up with another example and said, okay, what if I have a an old car with a high mileage and has been road tested, which they do. And this is, you know, the training day that we build our decision tree with. So we have to start doing some math, because we have to in this case calculate the Shannon entropy using that formula we saw before. And the first thing we're going to do is calculating the calculate the entropy for the root node, which is buy or don't buy. And we take the number of cases that we're, we buy it's two out of four and the number of cases where we don't buy that's two out of four. And so we can plug in the numbers with the remembering that log of point five is minus one and log of one is zero. And we can calculate that entropy for the root node and it's, it's one. And from that root node, we're going to look at age of car. And we can see that there are three recent cars and there's one old car. And of those, you know, some of the recent cars we recommended buying but one of them we didn't recommend an old car we also said don't buy. And what we can do when we split this node based on age, we can calculate the entropy between, you know, using recent and using old as the split decision or age if you want. And we can calculate based on the number of instances, we can plug it into the log P. I've done those calculations at the bottom. But the, you know, recent entropy gives you some information knowledge it's 0.98188 and the entropy for identifying old doesn't give us a whole lot of information. We can calculate the information gain by taking the entropy. That's given from the parent node, which we have one, and then we can calculate the average of those two child nodes. One had an entropy of zero and the average other one had a point nine one eight. We could do a one half one half but in this case we waited because three of the instances out of the four was recent and one of the four was old so we waited a little differently. And so we take an average value of 0.688 in that group, and we can say the information gain to use age as a discriminator was one minus 0.688, which is 0.3. Not great, but it's it's there's information there. Okay, so what if we tried mileage is our first choice to split things. In this case we've got information about low mileage and high mileage. And in this case it was kind of random. Some cases it was low mileage we buy have the cases low mileage we don't buy. So we can calculate based on the instances we can calculate the entropy for each of them. So in the mileage case, the entropy for the low mileage was one. And the entropy for the high mileage is one and the information gain from the weighted average of the children child node entropy is one. The information gain is one minus one, which is zero. So this information is completely useless. There's no information with mileage playing a role in our decisions. Okay, so this one won't be a good child node. And then there's a road test. And you'll notice that in the cases of road testing. If we road tested we would buy and if we didn't road test we wouldn't buy. So you can calculate the entropy for road testing. In this case, using that formula, we get a zero entropy for road testing and we get a zero entropy for not road testing. So we can calculate the weighted average of those at zero. So the information gain from parent node which is one minus the child node is zero, we get an entropy gain or information gain of one. So this is perfect. This one actually fully separates these things and is probably should be our first choice as the decision tree. I just wrote down the information gains for these things road testing got an information gain of one mileage and information gain of zero, no information, and age tells us a little bit of information but not as much as road testing. So the maximum information gain is for the feature road testing so this would be the, well, not necessarily there. I'll just do the real note. And so this will be how we distinguish them. And do we road test. Yes, road test no, and that's what we buy or don't buy. So information gain using Shannon entropy is formally correct, but involves calculating logs and logs are expensive to calculate on a computer. So you can use something called the genie index, which is used by actually economist to measure things like, you know, wealth disparity, but it's also used to measure variables being wrongly classified. So like Shannon entropy can range between zero and one. But it's the genie index, which is a GI, instead of information gain, which is IG. So it'll get a little confusing, but the GI is zero when everything wants to certain class, and one, when things are random. So in this case, if you gain IG, high information gain is good genie index, low index as good and high index, high GI is bad. So it's similar we use probabilities, you know, just like the PI in the genie and the information gain or entropy method. It's not using logs, it's just p squareds. So different people, different algorithms will use either information gain slash entropy or the genie index, both can be used. Highest information gain is good. Lowest genie index is good. Those are the ones that are placed at the root of the decision tree and that's how you decide which ones are ranked and then you rank things subsequently below that. So if you have one split, you got information gain of 0.9 or a genie index of 0.1. That's the one that's, you know, the root node and then you might get another one with an information gain of 0.4 or a genie index of 0.3. So that might be your second one and then you might have another node which has an information gain of 0.2 and a genie index of 0.8 that would be your third layer of your decision tree. It's a way of doing feature selection. It's a way of getting rid of things that are useless. So in the case of the car classification, it was the mileage I think was useless. And this is, as I said, it's an automatic way of doing feature selection and essentially pruning the tree. So just the mathematical functions about the information gain which is shown in green or the genie index. There's something called the impurity index so they are slightly different in terms of both their shape. But they're close enough. So both are totally valid. Now, when you're running machine learning algorithms. And also make sure that they're not producing trees that are too complicated. So this is called pruning, getting rid of branches that are of low importance. And pruning actually improves the decision tree performance. It is known to reduce overfitting. Obviously it makes it less complicated. And it's things called reduced error pruning and weakest link printing. These are methods that are applied to decision trees to clean them up. So, you could also, in essence, what these are just looking at all the calculated information gains. So if there are seven ways of getting information, you know, evaluated all of them. And only include the high information gains, anything with an information gain of about 0.4 or a genie index of below 0.3. In the case of the Titanic one, there were just three features that they eventually decided on. They probably had other things that they could have chosen. So, the label was there's passenger ID that's kind of useless. So that's not a useful feature. The survival was what you're trying to predict. And then the other features were are you male zero is, if you're not one is if you are your age and the size of the family. So, in terms of, you know, this is the the actual data, there's 1317 passengers on the Titanic. All of them were have the information age male female and family size. Now, if you had data that was also on the zodiac sign that they were born on. Well zodiac signs, I think that would be a useless feature and have no information and therefore no information gain. And so you could determine from your Shannon entropy that zodiac had nothing to do, whereas age or male female, in terms of the ranking male female have the highest utility age was second and subsize was third. So our feature selection, this is what the genie indices were, or in terms of information gain they sort of one minus those. So lowest genie index best was sex. Next best was age last was subsized but zodiac had a genie index of 0.98 or an information gain of pretty much zero. So, this is, you know, how you're making your feature selections it's how you're deciding where your nodes will be placed which ones the first node the second node the third node. You have to have a number of training inputs and identify the number of objects that are affected so you know if you have a decision tree that splits, you know, three objects into three different categories. It's probably not a very useful thing. Sorry, I think Angel has her head up. She wants to ask a question. Sure. Thanks, maybe this is a silly question but I was wondering, suppose on the Titanic if there were a lot more children, would the genie index for the age have gone down. Like if it became more important to classify the age before classifying the sex just because there were so many more kids. Yeah, again, I think, I don't know if I can do the math in my head I suspect. If, you know, if you had a different population than what was currently in the Titanic, or what was known to be in the Titanic, then I think, yeah I suspect the genie index. I don't know if it's the genie index for children would have gone down, but again, it's based on the historic data that you're. I understand I'm just trying to understand how. Yeah, that would factor into the model but yeah, thanks. So you decide the number of inputs you want to put in.  The depth of a tree should be small, you know, two to three, maybe maximum four layers. There's no hard and fast rule but you'll see most decision trees typically have a depth of three or four. You can help and discern which which depth you want to have by feature selection. So we're going to take a real example here. And we're going to follow the workflow that I described last time, there's these six steps. And the six steps are define your problem, suggest a solution.  Once you construct your data set then you have to do data transformation feature selection. Then you choose your model, is it a decision tree on your own that or whatever. Then you test and validate your model and then you say okay models ready, you release it and say you know, let's let's use it so in this case it's how do I classify iris flowers in my area of the country. Based on the floral dimensions. So it's a well defined description. It's a problem. Maybe it's not one that is earth shattering but it is one that we have data for. So, what we need to do is get some data to train and test. So this is a data set that was introduced by Ronald Fisher. Fisher is developed, basically single handedly developed all those statistics. And he used it to use to describe linear discriminant analysis or LDA something that I talked about before. So we have data from three different flowers, purple flowers, iris Satosa, virginica and versa color. And these are examples of these different flowers. So they have essentially six pedals, if you want, one is called the pedal, and the other is called the sepal, and the sepal is generally longer and more fancy. Has relatively large pedals and I guess moderately sized sepals. Satosa has tiny pedals and giant long sepals virginica I think also the same. This the species can be differentiated by their pedal and sepal dimensions. And this is showing length and width so there's four dimensions to for pedal size and to for sepal size. And this is how they classify those species. This data was actually collected by a Canadian Edgar Anderson, and it was published in 1936 and it's showing actual measurements of the iris Satosa versa color and virginica. And you can see that the sepal length for iris, the versa color and virginica is generally long sepal width is about the same for the two of them. But the pedal is quite distinct. So the pedal length is very long for virginica, moderately long for versa color and very short for Satosa. The width is also kind of intermediate so that have tiny pedal width for Satosa, moderate pedal width for versa color and fairly long width wide width for virginica. So that's highlighted here versa color kind of sits in the intermediate virginica is the big one. Satosa is the small one. So you can kind of look at it yourself. You don't have to be a botanist or machine learning expert kind of kind of see a trend here. This is what's called a toy problem. Most machine learning ones the data's too many separation is too complicated to noisy. So this is why we can basically do in this machine learning example with just 150 examples. Why we can do it. But because it's so trivial, you really don't need machine learning. And this is, you know, when to use machine learning. Well, typically when the answer is, is not obvious, or when the data set is so large that you just can't figure it out. So we're going to take the data set in fact we took the tables from the published paper and then entered all of them. So we have now 150 rows, 50 of the Satosa 50 of the versa color 50 of the virginica with all their sepal pedal went lengths and widths. So we've got the data. Now we can try and choose our model and we decided to do a decision tree. And so what we're supposed to do is move to Google Colab, which you guys learned how to do and open a file there. And then you have a notebook, which is where you're going to be typing your program. And then you can all start coding this. Now, we don't have enough time for you guys to do the coding. So to save time, we've got you guys with Python code that's already been written. This is written a couple years ago by TAs who developed this help develop this course back in 2020. So you can do this right now. If you want, you can go to module two, which is what we're at. You can go to Python code and the CBW learning near Google Drive. If you don't want to do that right now, we can save that for later. But this shows you how to navigate it. And I think I'd like people at least to try this. And I'll continue lecturing but just, you know, find your Python code. Don't choose the R code and look at module two. And you're going to click on which is the iris decision tree version for Python. And you're going to open it with a Google Colab. Now, it's going to pop up some code and it's going to be more than 100 lines of code. And the general algorithm for this decision tree is fairly simple. It's going to read your data. In this case, this is the 150 iris dimensions. You know, it's a table of four or five columns and 150 rows. You're going to check your data. This is something you always have to do with machine learning because usually there's lots of data. You want to make sure it's clean. You're going to do a training and testing data split. You know, two thirds so that it's going to be training or 70% is going to be training and 30% is going to be testing. Then you have to create your splitting function. Because this is how you're going to say do I split them into, you know, three groups, two groups, one group. So I decide on petal length, on sepal length. And so I have to have a splitting function. And then I have to, I'm going to use the genie index because it's faster to calculate than the entropy one. And so I'm going to put in a genie index function to calculate things and decide, you know, how do I decide on petal length and sepal length and everything else. And then I have to do a split function. So it's an optimal split function. So I decide based on the genie index where to cut. I have to also have a terminal node function. So when do I stop because I've finished everything. So I have to have a function that says have I finished. And then I also have to sort of do a recursive splitting function. So these are all functions that I have to create to do this. They have names with underscores. And then once I've created all of these things to do that, I still have to have a way of calling it. Because by then if I've trained it, then I want to be able to use it so that if I have some more flowers with more dimensions, I can classify them and see how well I do. And that's actually what I'm going to do with my testing set and say, okay, train and train and train. Well, does it do if I take some unseen data? And does it classify things into Satosa or VersaColor or Virginica properly? So with Python, you, because we're going to do some math, you have to import the NumPy library. You have to handle arrays or matrices. And then pandas to do what's called data frame framing or giving you some data framing capabilities. So these are almost always used in Python anytime you're doing. So we've imported, you're going to have code to import these two libraries or functions. We also have our code. This is the code for reading the data. So if you're looking at this, and I've just blown things up, but you've got this function for reading, and it's in a data one CSV file. And then we commented called this is the data head and then it's just giving the positions of where these data sets off. So that's the reading. So this is the data verification. And we're just trying to see if we have to determine if there's any missing values. You know, we've got 600 different values. What if we've only got 598. So it's looking through all of the data columns and trying to find out if there's no data in them. And if there's, if everything checks out, it'll print out, you know, data says complete no missing value. And this is always good to have for any machine learning algorithm because you, you can have, as I say, 10s of 1000s of data sets, and just to make sure that's clean. Now, if it's not cleaner if there's a lot of data missing. We didn't put in a lot of fixes for it. In fact, it's specific to each problem. Now to impute it. If it was repeats, again, you might want to check to see if there are repeats. If there's nonsensical values. Data birth and date of death and if the date of death is before the date of birth, that's something you should fix. Those are things that, that happen or need to be done, partly, you know, manually, or with someone getting familiar with the data and say, you know, what's what makes sense here. In some cases people use imputing. Sometimes the value is, you know, too low to measure. And so you'll give a lower estimate or an upper estimate. In some cases you might have data that's fairly consistent. You know, you've got a child that's age five, it's a boy, but you're missing the weight. You can probably use, you know, the average weight for a five year old and you're probably pretty good. So that's a form of imputing. So effectively we've done these first three steps we've defined our machine learning problem we've constructed our data set we didn't really have to select features because this is a decision tree. We've chosen for a cursive binary selection decision tree model that's already done. And so if that's what we're going to do with every machine learning program, you have to divide your things into your training and testing stuff. So there's 150 flowers, 50 of each species, and we've decided to do a sort of a 70% for training and 30% for testing. The, it's a whole that says technically it's not threefold cross validation because cross validation is typically done with the with the 70. So it's done and you can see how the code is written there's green is the comments and the function name is defined as deaf and then we talked about how we've divided it and we've multiplied 0.7 by 150. And the length of the data set and then we've got another one which is the testing data so there's training data, and there's the testing data. So we've divided things into the testing and training, then we have to call that genie index that's the genie function. And this is where we're, you know, actually doing the calculation of the genie index. And we're trying to find the minimum genie that's the good thing in genie index remember information gain high information is good thing genie index low. And if we're using petal length, we can come to, you know, choose different points is a two centimeter cut off good one at three centimeter cut off a good one at four centimeter cut off a good one. And by calculating, roughly every point one centimeter interval, all the way through, you can come up with a minimum genie index, where you get a perfect separation between the setosis, and both the virginica and versa color. So, at least you get one group perfect. And then you still have to classify versa color and virginica separately. And that's the petal length and the number is 2.4 2.5 centimeters. And you can plot the genie index as you increment through point one values starting at one centimeter 1.1 1.2 2.1 2.4 2.5 3 all the way on plotting out your genie index. And this is where you're going to do this for going with sepal length petal length sepal width, petal width, and determine which one gives you your best genie score. And in this case, the minimum and which one gives you the worst genie score. So, before you calculate a genie index over the different split points you have to figure out how to split things. And this is where we call this function that I talked about in the algorithm outline called test split. And these are the lists will contain the split.  These are two nodes that you call a left node and right node. And we based on that, you know, decide, okay, how many are going to be in the left node and how many are going to be in the right node. So this is a simple function just to make sure that we've got these two groups partitioned. They could be badly group groups or nicely group groups, but at least that separates these groups. Then, now we can split things we're going to calculate the genie index. And so this is the genie index function, genie underscore index. And it's going to count all the samples at the split point. Some it's going to calculate the genie index. And this is part one of it. So, we have these input classes, because there's three different types of flowers.  So that's caveat that's put in the code. The second part because this is a fairly long function. This is where we actually calculate the genie index. Summations perform each of the class values. And then we return this genie calculation and you can see the formula from the genie calculation is in the earlier slides and then this is just calculated here. So it's the p squared, summing over them. So that's the end of the genie function. Then there's the get split function. And this is determining optimal splits, split points, which uses both the test split function and the genie index functions that we previously written. And we have to figure out, you know, your minimum and maximum values to determine what was best, where to get the split. In this case, the split was at 2.45 centimeters, although this one only increments in 0.1 intervals, so it would be 2.4 centimeters. And as I said, this is this instrument where we're stepping through this. So we're stepping by 0.1 instead of 0.05 centimeters. So this is going through this as we're determining the satosa versus the versicolor versus virginica. And looking at whether it's a petal length, petal width, sepal length, sepal width. And we're calculating the genie index all the way through. And so this is what we're doing with these functions. We're stepwise moving through that. And you can see where the genie index starts bottoming out and then it starts climbing. We're determining those optimal splits to that point, one centimeter step. And then we have to decide when to stop growing the tree, when we've reached the maximum depth. So it's a number of nodes from the root node. And this is the two terminal function. It has to be able to accommodate a few things when the tree stops growing, when the class is there, and that things, if they're not split perfectly, we have to choose a common class value. And then there's the recursive splitting function, which we talked about that was another function that was described in the algorithm. So we have to write this one. And here's the split function, or get split, we get the left and right ones. And we process and repeat, process and repeat. And then we determine or use a minimum size to force a terminal node if there's too few samples. So we split, there's nothing left. That's a terminal node. The split function has certain components into it. If we reached an empty group, we see if this is a terminal node. If our maximum depth has already been reached, then we have to force terminal nodes and stop doing the splitting. If we still haven't reached the maximum depth, we can still force to do more splitting. And so split and get split, get called as needed. There's a question from Lance, I guess, specifically, do the slides correspond with the Iris TT4 Python code? Some of the functions in the slides are not in this notebook. They should. I think, I mean, just checking. You guys should check because that was supposed to be done for the check. It's there. If you're running for the Iris code, Iris code runs and that's where you're checking and helping a couple of people to run to get the data imported in. So yeah, if Mark and Sagan, if you guys can also be on the chat room, please, yeah, I'm in the comment chat. Yeah, I think in some cases, the comments may have been added for this specifically may not be in the original code. So the green stuff may not be in all the code. But all the elements are supposed to be there. Well, like, for example, the get split function, I couldn't find that. And it doesn't quite follow as to what we're showing in the PowerPoint. Yeah. So that will have to double check that one but it's in some cases the code's been slightly modified. But there should be if we look back at the original algorithm. So it's supposed to be the test split get split terminal node and split and genie index. So those are the, I think there's five functions that should be there. So something's been changed that it shouldn't have been but this is the way that the code was written and should have been the way that you guys got it in your files. And it might be that that somebody renaming has happened, which I didn't know about, but in the end there should be still these five functions. So, once you've got this program written you still have to be able to call it so that you can take new data and predict with it. So this is the predict function and so it allows you to do this analysis and to make your splitting and decisions within the data set. So the entire program and if it's different from what you guys got was supposed to be 123 lines. There's 30 common lines and 91 coding lines. Because a small data set, it should work very quickly. So it should only take about a second to train, and then to test on the test data set 45 samples. So we have another second with the whole thing. With this three set of species, we actually have a confusion matrix which is a three by three rather than a two by two. So Satosa, Virginica and VersaColor. And we want to figure out what percentage and this is should be 100% 0% or the number that were there so the program and it was trained on this training set of 105. The performance was perfect. So from the training, it learned. And the question is, has it over trained. So this is where we have to test our model with the whole data set. And the whole that set is these 45. On that on that program. We find out that we get almost a perfect set but it kind of gets confused between virginica and VersaColor. And what should have happened if it was perfect we get, you know, one one one along the diagonal and zero everywhere else. But we can see that the VersaColor and virginica are confused. So we have this summer virginica being classified as VersaColor. So, ideally, and this is something that people need to be aware of, you want to have your training, you know, as good as you can get in this case it's perfect. But even if it wasn't perfect, you know, supposedly it's better than what you could do manually or what other people have reported. And then you want to say have I over trained. And if it's over trained, sometimes a little hard to tell I mean you could have also got a result with your testing say data set, where it also looks identical to your training. That's a good sign. It says it's robust. If your performance on your testing data set is maybe five or 6% lower overall. In this case we're going to calculate the average of 100, 193 so divided by three, the average performance is like 96% 97%. So, this is within that five or 6% range. If I had done it on the test and I got on the diagonal point three five point three two point four six. That's probably an average of 35 or 40%. That's terrible. And it tells me that my, I've over trained that I've somehow either decided to use features or hadn't had enough training data and maybe my testing data is so different than the training that that's why it's doing so terrible. I have to evaluate but in this case, the training and testing performance is within that five or 6% threshold and so we can be confident that it hasn't been over trained. So, if we've evaluated and you know we've created the program we've got our data set we've chosen a model written a program. There's some discrepancy between the program that's on the slides and what you guys get and I hope that Sagan and Mark and Vasu can kind of sort that one out because the code wasn't supposed to change from the slide. And then you can test and validate the model and then once you've done that and we've done that we can use this to make predictions. So we've basically got a decision tree program. It's in Python, and it predicts iris fire classes we tested on a training set of 105 hours. So we trained it and then we tested on a holdout of 45. And it's quite generic uses a general genie index uses general numbers it has. So we can actually use it for classification of patients cases and controls with maybe different levels of gene expression. Protein expression or tablet like levels or snips. And, and so in this case, we're going to sort of dive a little bit into using the code. Now, I've shown the stuff in Python. We've written this in our and so people are more comfortable with our understanding are they can use.