Okay, can everybody hear me? Omar, I didn't have the screen up.  How many of you, and be honest with me, I'm talking to the room people, not the trials here, how many of you didn't do this today? Be honest and tell me if you didn't do this today and if you didn't do it today, I want you to tell me why. You did not, you were the one that wanted me to look at this. Why didn't you do it? You came and you did it. I just want to hear from the people that didn't do it and if you didn't do it, I want to know why. I, John, it was your idea for me to look at this, your idea for me to rate this. You were the one that wanted to do it. Look at how this is moving so fast, look at that. You got scared by me because I said it was gonna be volatile but it rated good and then it swushed and then I ended up doing it. Big Thud didn't do it, the stop he thought was too large. All right, well, at least you're being honest. It's just two people didn't do it. I wasn't 100% sure that I was gonna do it because of the fact that it was gonna be very volatile but when it rated well, I said, let's watch it and then when it opened and swushed, I said, we're gonna do it. I mean, this should have been the reason that everybody should have done it and that's why I'm taking a survey. I'm taking a survey to see who didn't do it regardless of whatever I said in the morning which I said it's gonna be very volatile and I was absolutely right but the volatility worked in our favor. It went down, it swushed, which we wanna see. So when this did it, everyone should have been all in regardless of the size of the stop, regardless of the fact that I didn't even know if this would even work but we got the confirmation when it opened. So there's two things here I wanna talk about. If swushes and sets up, we should always do it. Yes, are you asking me a question or are you telling me that's what you feel, discipline Dave? The answer is yes, every time it sets up that you get a swish, you should do it. Even if you're not sure before the gap, you should do it when it does. Oh, that's what you feel and you would be right. That's how I feel. You can tell the way I speak, that's absolutely true. The way that I speak, I didn't know if I wanted to do this today, why? I said it in the morning in the room because the volatility, I knew this would be volatile today. I knew this would be volatile and I wasn't sure which direction. Then I rated it and I was surprised. I was shocked that it rated 20 points. I couldn't believe it but it did. And like I said, I highly doubt a lot of day traders probably are sure of this today but I have a system for a reason and I gotta follow it and guess what else? Nothing else rated good today. Nat was 17 points even though it worked. MCD was 19 points. I remember at 1950, 50 chance of working at a family at MCD to have worked but it really didn't meet the rating which was 20. So when something tells you that it rates well and then you get the confirmation in this setup then you do it. Regardless of the way that what I thought was gonna happen which was that it was gonna be volatile and I wasn't wrong, I was right except for the volatility played in our direction. If you didn't do it, it's okay but I want some of you to learn something from this in here. Not just the people that didn't do it but everyone. That's not what I'm gonna lecture about here in a minute. What I'm gonna lecture is which actually looks like a couple of people weren't here today, specifically Steelheart. Steelheart isn't in the room today, don't know if he, if he's doing something, he's an attorney, I'm gonna email him afterwards but BMI did not work on Friday. BMI didn't work. If you did the first train, you lost. If you didn't retake it later in the afternoon you didn't make any money on it. It did go, it did work. It ended up working on the day, the gap worked, it fell the way I said it would but if you did the first train and BMI and Friday you lost. If you didn't train today and come into the room or didn't do this because of Friday then that's what I'm gonna lecture about. And the lecture is that you gotta be here and you gotta show up and you gotta be here every day and you gotta do the same thing every day according to the reading and the setups if you wanna do well. And that is something that some of you were doing and it's something that some of you are not. And so the bottom line is when you decide to do this and you want to do this you can't pick and choose the days that you come into the room based on what happened the previous day unless you have an appointment. You have an appointment, you have to do something, you have to go to the doctor, that's different.  You gotta be here, you gotta do it. You gotta be here, you gotta do it. When I call it, when I give it, when I do it, you gotta do it with me and you gotta do it when the gap rates per the system and you gotta do it when it sets up. And not only that, you have to risk the same amount of money which some of you don't do. Some of you will not have a good day then the next day you will risk more, which is wrong. Some of you will not have a good day then the next day you will risk less and that will work and be huge and that is wrong. You have to get up and you have to do the same thing. The one thing that many, many people say, you have to trade like a robot. You have to, that's why you have to be disciplined. You have to trade like a robot, none of us are robots. That's impossible, that's ridiculous. People that teach like that and say that are insane, quite frankly, as far as I'm concerned. I'm not a robot, that person that's saying that isn't a robot and neither are you. And no computer has the intuition that I do to see something alive, time to call it and no computer ever will. So no automated system is ever better than me at what I do but I'm telling you and you once you get to know it but I'm telling you the thing that you do need to be exact about and the same every day is doing it when it rates and doing it when it sets up.  You have a heart and you have a mind and you have emotions. So stop pretending you don't but you will risk the close to the same amount of money every day, that's your plan of action.  That's it, how hard can that be? That doesn't mean you're an emotionless person but that's the discipline that's required but it's not discipline where you trade without emotion because that's idiotic. It's crazy, it doesn't make any sense, it doesn't even work.  Wrong, of course you do, look this is still going, this is hilarious. In fact it held 50, here you can do this again if you want and it's not even a kamikaze anymore. I called a kamikaze back here, I said 56.25, did anyone do it? If you did it, if you did it you could have been out. It broke 55, I missed that because I walked away. Look at this now, you could do this again. Yeah we'll go over the entry in this again, Susanna but the lecture I'm trying to make here is that it's not about trading without emotions, it's just about trading with the consistency of your plan of action with what you're doing. Your plan of action every day should be consistent with the money that you risk and that you be here every day and then you take the trades when they rate over 20. That's what has to be consistent. There are days in my voice where I definitely like something more than others and you can see that and you can tell it and you can feel it and you can hear it. This today I felt would be very wild to trade. I wasn't wrong. It worked in our favor though. I'm not against wildness if it works in our favor but I didn't know that until it opened. But when it opened it swished, there was no reason that everybody shouldn't have done it. Now it looks like everybody did but two people but some people didn't show up this morning because Mondays typically are slow, I get that because this didn't work on Friday, right? It did work, it ended up working for the reason I discussed which we lectured in the morning and I said this isn't gonna get bought today by institutions either at flat lines or it breaks and goes and it gets sold off and that is what happened. It did break and go and got sold off and it never went anywhere up but it took a long time. I hate when that happens myself. I don't like it but sometimes it does but anyways, if you make a decision based on a previous day but something happened in the past about what you're doing this week then you miss out on something. Whether it means you don't show up but you risk more, you risk less or do something different and that is where some of you have to get it right in 2017. You're either in or you're out and if you're in it doesn't mean trying to work through the emotions that you have because you have emotions. It means deciding that you're gonna just do what you say. You have a system I taught you and you don't have to think. You don't have to think. I give you the points. Then I tell you where to take the trade. You don't even have to think. You have to think about one thing which is figuring out your share size which some of you have gotten or getting better at. Some of you were good at and some of you were struggling with that last year but I think some of you were definitely getting better at it and that's good. That's a good thing. It's a great thing. JL Trader is back in a 55, 5519. Where do you have to stop? 5519? I don't know where you're getting that. If you're not consistent in your plan you'll struggle with the roller coasters of emotions. Yeah. But I'm telling you that everyone has emotions. So what do you mean? If I taught for 25 years and traded for the rest of my life it wouldn't matter. I would always have emotions. You would always have emotions.  Let me tell you something. If you paid for a system that was automated and you put your money up with that system and that system was up money you'd be happy and if that system was down money and losing you'd be upset. You're trying to tell me that if you had a system that was pressing the buttons you'd have less emotions. No, you wouldn't. You would blame the system. You'd have someone else to blame besides yourself when you lose. But you would still have emotions surrounding that automated system that is taking your trades that you purchased or bought because it has to do with money. And let me tell you something. There's a lot of emotions surrounding money. This is why a lot of things in the world sometimes sometimes your emotions surrounding money is what creates things for you. It can create good things or it can create negative things.  If you're thinking negative about it in a fearful way then you will create that. If you think positive about it in a happy way in a joyous way in a loving way then you will create that. And therefore you will feel happy and satisfied and have money and make money and your money will grow. But if you're in fear the money will go away and disappear. And you just gotta know that about yourself about when you make choices about things. I mean, this is about everything.  I don't know if I wanna spend this much money but I really wanna go. But I'm worrying that it's too much. Should I put it on a credit card you say to yourself?   You never really relax anyways. I mean, I could think of a hundred million things that I could relate emotions and money to. But trading is so, it's like the whole, it's like everything. It just comes all together in trading because it involves money. It's like a heightened emotional ball about money. And what I find absolutely ridiculous and hilarious and absolutely insane is how so many people teach and talk and talk and talk and talk. Traders that talk, people that teach everybody, everybody, brokers that act like all the world's problems would be solved if people would just trade without emotions. It is absolutely impossible. It is impossible.  That's like me saying tonight, this evening, I'm gonna fly to Mars. I'm gonna come back then tomorrow morning after I visited Mars and had a nice dinner and then I'm gonna trade in the morning. I'm not gonna do that. That would be impossible. I have no physical way to get to Mars. I don't even know if they're putting any money into the NASA anymore. I can't go to Mars tonight. I can't, it's impossible. I can't have dinner on Mars. I don't even know if I'd survive Mars. Even if I did, I'd never make it back to trade tomorrow morning. I'm not going to Mars tonight. That's impossible, okay? But trading isn't impossible, but it is impossible to do it without emotions. And it also is just one of these things where if you're not here doing it every day consistently, how do you expect the results to be there? They're not gonna. Your risk is now $200. Share $700 you took. You follow your risk.  It's very good and concise and easy to follow. Yes. You just manage your emotions by following a consistent plan. Well, but I'm saying it's two different things. The plan is you're gonna do this. The plan has nothing to do with your emotions. Your emotions are your emotions. You know, that's it. But the plan is you're taking the trade when it rates 20 points or more, if it sets up. And you be here money-pissing Wednesday, Thursday, Friday. Or every day I have the room open. That's the plan that has to be set. Whether you get out because you have to go somewhere or you don't want to back up or it's very volatile or it drops so hard so fast, you think it's gonna back up.  It doesn't matter. And that's the point I was trying to say earlier this morning, who cares where you get out? Who cares where you get out?  If you can consistently get it right, which I do, and take the trade entry right and be up immediately, who cares where you get out? Who cares? Who cares how many hours it is? Who cares how many dollars it drops? Who cares? If you get a made another $2,000, who cares?   That is not right. That is not good. So some of you in your head about this, and I know because when I was doing some things last year, thinking about increasing my size, I've been back and forth in my head about it. It doesn't matter. I'm less and less and less am I talking and teaching about our units. It does not matter. We get it right. Just do it. It doesn't matter how much you make its money. It's money that you didn't have at 8.30 this morning or yesterday or the day before. And now you do. Courage is about acting in spite of the fear. Yes, that's why you do need courage. That's true. Let me just see if I missed anybody's things. You took a quick loss in MSR, which is not working out. I made up for the loss with Q-Com. Good, good, good, good. Anyways, there are days where some of you have conviction and stuff that I don't. Like I didn't like this. Look at it, it's working here now very late. Again, I wouldn't have done this and I wouldn't have called it and I didn't have time to read it. I didn't have time to rate this. Look, this did set up here. It did set up. It did set up. Let's look at the market. Oops, that market's a little bit red. Anyways, this is a good lecture today. This is a good lecture today. And if you didn't do it, fine. Everybody did, but two people. MCD works, some of you did that. But the point I'm trying to make is that you gotta be here every day to get it. And you gotta follow the system. And even though I was like, I don't really know if I wanna do this today. And I said it, I'm like, oh, I know it's gonna be volatile. Do I really wanna deal with this in a Monday? Do I wanna deal with something that's a crazy, crazy in a Monday? When I really don't usually like to trade Mondays at all, as all of you know. But when it rated and I'm like, oh, geez, it rated good. I guess we're gonna watch it. Doesn't make sense to watch something that doesn't rate good over something that does. I have a system, I'm following it. And when it opened and swished, I said it in the first, this swished in the first three seconds. Three seconds into the open, I said this is swishing.  It might have been one second into the open. I noticed it swished. In three seconds, in one second, in two seconds, in a second, the stock was not down all the way here, but it was like in here. I mean, it did it. Can you find the top losers? I don't know your, you have to call the platform place. I use this Orbis because I like them the best. I just think this is very easy and simple here. Everyone should have it, but I don't know where to find it in your platform jury because I don't use it in that. I only use it in this. And I've used, I use these charts and I use this top 20 list because I like it. And actually, they just did an update here, which I have to do this week finally on the charts here. You should have it if you don't just call the customer service. Well, then you might not have it. I don't know, because I've never used it. I've never used it, I've never even asked because I have this and I get this for free. So what can I tell you? Freestockcharts.com is free. I don't know any other free services. Well, you're here in the room, though. You're here in the room, freestockcharts.com, Yahoo Finance. I don't know what other free things to tell you. Koala Bear is writing something here. Denise Scholl showing emotional areas of the brain lining up with making trading decisions. Sure, all of that. The brain is related to your emotions. I don't know exactly what you're saying there, exactly what the whole thing is, but everything, your body, your brain, your emotions, all of it, it's all tied together. It was a good day today. I guess we're gonna have a good week. I wanna say one more thing about Amazon. Does anyone know what I'm gonna say? Does anyone know what I'm gonna say before anyone says it? Or does anyone wanna guess what I'm gonna say? About Amazon? Hello, am I talking to myself? Does anyone know what I'm gonna say about Amazon? Chief says no. Options, no, I don't see anything here yet, but guess what? They changed the earnings date again. They did it last Thursday one week before. They changed it. And no, I'm not in this, but that's the second time they did it. Can you believe that? They changed the earnings date one week out in this again. And I'm not in it, but remember last year, I don't remember when it was. If it was second or third quarter last year, I don't remember what it was. It wasn't the first quarter. I don't remember when it was. They changed the earnings date one week before and I was in an option to take it into the earnings. I wasn't, I didn't see anything in here and no one should be in this. But if we were, we would be screwed because they changed the date again the second time. Can you believe that? I probably will never, never take this ever again before the earnings, unless it's after the one week. That's a second time they changed it. Can you believe that? Can you believe it? Found out Friday. I found out Friday, actually. My broker told me. I was like, you have got to be kidding because I wasn't in it. Wasn't in it. I wasn't doing anything in it. Can you believe that? That's a second time they changed it. One week before. So if we had been in something here, we would be running our, running our run around to try to get out of it today or tomorrow or we would be screwed to get it, to get it before the earnings now because I looked at it, it's crazy expensive. It doesn't make any sense. There's nothing to do here. No. But can you believe that? That, that is screwy. Twice they did that. I thought that was interesting and nobody probably even paid attention, I guess. But I've been watching these things and looking at them, I don't see anything yet. Susanna, you haven't missed any options trades. I haven't called any or done any at all. But we might have done this. We didn't. And if we had, we would be running around to get out of it. I did end up getting out of it that one time they screwed me last year.  The Monday after I found out, I get out of it that day with profit. But it, I mean, can you believe that? I was like, you've got to be kidding me. Yeah, I'll confirm with your email address to Susanna because you should be on everything. But everything got, everyone got the password today, I guess, except for Aaron. I, you know, so I think we're good for this week. All right, listen, have a good day. Everything is, looks great in our lives. Oops. And we can go on with the week. Okie doke. Oh, look at this. Here it goes. 55, 54, 53, 52, $50. Q-Con. Have a good day. You're welcome. Okie doke.


In this video I'm going to be explaining backroom's level 189 aka the bathrooms and I'll be answering the question of why you should never under any circumstance use the bathroom in the backrooms no matter how bad you have to go seriously. Alright let's get into the explanation shall we? So the 189th level inside of the backrooms has been given a classification of class two and is unsafe but kind of secure with a very low entity count at the start when this level is discovered actually but what is known is that a ton of people have entered this level at least one time by complete accident and you'll learn how later in the video. The level itself is made up of what seems to be an endless cluster of bathrooms. The bathrooms themselves well they look like bathrooms obviously I don't know what you're expecting they look like just public bathrooms you've seen before in like a store or like a bathroom center you know they look like except these have a very lonely and empty feeling to them most of them have stalls and sinks and towel dispensers and that sort of thing as well just the regular old bathroom stuff and all of the things in these rooms work like they normally should you know the paper towels come out the water runs everything works and normally each of the bathrooms has two different exit doors in them and these two doors have random chances to either lead to another bathroom room or they lead to a different level and they're the exit however those doors change and you can't really map out the level because the doors are different and the bathrooms are different as well it's sort of just a randomized cluster so there's no real way to see where you're going sorry some of the bathrooms also have windows in them that allow light to come inside and on the outside of those windows most people have reported seeing some pretty strange things to say the least so in real life when you look outside of a window you'll see just a field or a road or whatever but here on this level people have said they've seen static blue skies with really weird clouds in them or they've simply just seen a white void and nothing else that's pretty weird and it also means that this level takes place inside of something else entirely and it's not just confined to the bathrooms there is stuff outside that you technically could explore you just can't get through the windows but I probably wouldn't jump out a window into an endless white void it's just me though the air and the ambience inside of these different bathrooms is actually pretty clean and fresh at the start and actually some people have reported smelling a faint smell of air freshener which is pretty nice there's also a very very faint and subtle sound of water flowing that's constantly happening to the pipes in the ceiling in the toilets which makes the level kind of have a chill vibe to it again at the start and you'll understand why I'm saying at the start soon but like I said most of the bathrooms here just look like empty and lonely public restrooms but there's actually a very rare type of bathroom that you might find if you're lucky they've been given the name luxury bathrooms and you'll know when you're there they're pretty obvious I mean they're gold they all have golden appliances and finishes and sinks and marble and everything it just looks like a rich person's bathroom there's also a chance for whole entire showers and bathtubs and jacuzzi's to be inside of these specific luxury bathrooms as well and all of them are gold and marble everything like that but the reason these types of bathrooms are so sought after is because instead of regular old water coming out of the drains it's actually almond water here in these luxury bathrooms so you can have a pretty big source of almond water if you're lucky enough to run into one of these bathrooms and you can just fill up a bunch of bottles and leave or if you're not lucky you're stuck with the regular almond water just in the other bathrooms now from what I've explained so far you're probably thinking brookly you titled this video never use the bathrooms in the back rooms and it seems pretty safe so far are you click baiting brookly yeah well that's where you're wrong I didn't do it for the clicks I did it because a pretty unnerving phenomenon on this level is occurring it's known as the quote increasing danger theory and it goes like this according to the information we have right now the level is seemingly safe ish for the first 50 or so bathrooms that you can get to but after that it's almost impossible to find an exit door to a different level if you remember I said that each bathroom has two doors one might be an exit and the other will take you to the next bathroom yeah well eventually the exit door just doesn't exist and goes away explorers who have gone past the 50 room mark have said that they feel progressively less and less secure and they feel more and more in danger like they're being watched like something's there on top of these feelings the rooms themselves even start to decay and to break down in certain ways and they'll become more dangerous and more tumultuous to even explore one specific explorer said that he made it through 200 bathrooms before he found an exit which means he was literally there for a long time a year maybe he found that is he got stuck deeper and deeper into the maze of bathrooms the air went from smelling nice and fresh to spelling foul and rotten like a real bathroom and the toilets instead of being clean water they started to fill up with a mysterious waste material the lights also became dimmer and the bathrooms got pretty much pitch black with no light and mold grew from the walls and pipes leaked and exploded and everything like that just started to get worse and worse the deeper this guy got into it and possibly the scariest thing the dude found was behind some of the doors he said he heard weird entity noises happening back there but obviously he didn't open the door because why would you open a door if you hear like a monster behind it but there's also been reports of strange figures lurking in the shadows deeper into these bathrooms it's unknown what they are and things like that there's also some non documented entities and speaking of those creatures here you have to get past that 50-ish room mark to even see any because up until then it's pretty safe and there's not really any places for entities but if you do get past that 50 room mark you're most likely running to smilers and hounds and facelings and the normal entities like that but then like I said there's also those weird noises that have been heard so who knows what kind of disgusting monstrosities live deep in the roof and in their stalls of these bathrooms one last really weird thing that was found deep into this level was a green door with no handle when the explorer opened this door he was greeted by an entity called mark and then mark led the entity to an exit door which took that person to level four apparently mark lives behind this green door in a bathroom that is converted to like a mini house and he seems to think this level is a good place to live he tried to convince the other guy not to leave because this place has everything he needs who knows i just another quirk of the back rooms i guess to enter this level you can go to a restroom on level two and you can open a door inside or you can sit on a toilet in level two and spray air freshener in the air and you'll be sent here now you actually might want to come here to get almond water from a luxury bathroom if you're lucky enough to find one but they are very rare and like i said you're also risking getting stuck and chased by weird eldritch entities deep into the bathroom so i don't know if it's worth it or not but to exit the level you do have to get pretty lucky and you have to go through a door that exits the level and not one that takes you deeper in most of the time the door just takes you back to level two but you could also find that mark entity to be sent to level four or you can be a poor unfortunate soul that gets sent too deep into the bathrooms until they become entity-filled dark and corrupted and you'll never be seen again sounds like fun to me though thank you for watching this video i hope you enjoyed it we are on the road to one million subscribers if you all want to join and be here before that milestone make sure to drop a sub right now tell your friends family cats dogs fishes everybody about the brugley channel the brugley movement is absurd and insane i love and appreciate all of you go check out my vlog channel in the description where i upload vlogs obviously and then my cryptic channel scp channel trevor hinderson channel spoogeley in the description where i upload shorts and short videos explaining weird cryptid eldritch entities it's pretty fun the brugley empire is expanding and of course we're gonna keep expanding as always if you want to be a part of this movement so do my channels do all that stuff i'd love to have you thank you for everything and i will see you in the next video peace and love


Hey, this is Brett, and today I am super excited to go over this 2024 Jeep Wrangler four-door Willis in Hydro blue pro coat. This one has the 35 inch tire and wheel group on it It also has the two liter turbocharged four-cylinder engine and the eight-speed automatic transmission and Just excited to go over this Jeep because if anybody remembers the high-tide edition of 2023 They don't offer that anymore, and it was a really really popular package that we had on the lot And we sold every single one of them as fast as they came in Well, this is basically the high tide package with the Willis edition Because you get the 35 inch tire and wheel group and I'm gonna go over everything that the high tide package had That is exactly the same as the 2024 Willis with the 35 inch tire and wheel group starting off you do get the LED headlamps running lights and fog lights if you remember from the high tide edition those were part of that package and This one comes with that standard now The Willis package does you get the gloss black Trim around the fog light bezels and the gloss black grill as well as the updated kind of squished grill For 2024 so that is an upgrade over the high tide package, but I like the gloss black That's part of the Willis edition on there. You also get LED running lights in the fenders there the other big thing with the high tide package was that it came standard with the 35 inch extreme tire and wheel group which gives you the BF Goodrich all-terrain TA KO2 tires These are LT 3 15 70 r 17s, and you get the updated painted and polished aluminum 17-inch wheels with the new logos in the center there. You also get the suspension and what they really didn't tell you is that that spring and the Swaybar link there is actually an inch bigger than the standard Rubicon ones When you get the 35 inch tire and wheel group you also get these fenders Extensions on there so that everything's in line with the tire So that's got to be like that for a lot of highway laws The Willis package gives you the Willis stickers or decals on the hood I should say you get the jet blacked out Jeep Wrangler and trail rated badges on there the other thing That the high tide package gave you standard was the gorilla glass This one has the gorilla glass as well Standard and you get the updated antenna in the windshield. That's why they can put two trail rated badges on there now You get the T 50 on the doors Obviously use the Torx 50 bit to get those door hinges off We did a demo on how to take your doors off your Jeep Wrangler jail That video is in the upper right hand part of screen and linked in the description below The other thing the high tide package gave you was the rock rails Well, the Willis package gives you the rock rails standard as well And you also got the sport brakes with the 35 inch tire and wheel group Well, this one has the sport brakes as well And just because it's got bigger tires and wheels you gotta have that stopping power to Stop the vehicle this one has backup parking sensors You get the hinge gate reinforcement, which was also part of the high tide package. You get the extended back lights With the dogs are still on there Ralph is on there for the right and this one does have the LED tail lamps as well And then on the left you got Louie He's a bulldog. He's all mad Back up parking sensors LED license plate light as well obviously matching spare tire and wheel and there's a look at that tread pattern on that tire And the Willis package is basically a It's a sport package with a couple extra things with the decals and everything But this part and the black accents and everything and then there's an axel lock for the back I believe but You know the high tide package was based on a sport package as well So and I'm I'm being a little biased because I wanted a high tide really bad But just never worked out But this would be the closest thing that you could get to the high tide package storage underneath there door hinge Roof and windshield bolt holders and you can take these hard tops off You got four bolts on each side too quick disconnects We did a demo on how to take your hard top off very popular video That's linked in the description below and in the upper right hand part of screen one little Easter egg You get seven slots on the back door here. Here's your JL Wheel base and overall length for the two-door four-door as well as water forting And then the distance in between the inside of the tires and the I should say the outside of the tires And then the middle of the tires there as well Hydra blue paint code is PBJ one of my favorite colors on the Wranglers and This one does have the black hard top and We're gonna take a look at the original window sticker in just a second so I can show you just how Similarly, this one was priced is priced to the high tides that we had heated mirrors blind spot monitoring on there This one has the enter and go system the back seats Got the gold stitching on them all weather floor mats. Those were also part of the high tide package And I got a video to the last high tide that we did I will link that in the description as well as in the upper right hand part of the screen So you can see just how similar these packages really are these headrests stay down Which is great for if nobody's sitting back here get better visibility You get the power windows back here You get the updated map lights and the updated speakers and I love those topographical all-weather floor mats I was a geography minor. So those are pretty cool child safety locks on the back doors Upfront you get the dark charcoal cloth interior the bucket seats these front seats are heated You get a heated steering wheel as well And this is really an upgrade over the high tide because you get all the new stuff for 2024 The high tide was only 2023.  Basically. It was a sport s package With the 35 inch tire and wheel group You get the seven inch LCD display digital speedometer the leather wrapped gold stitched steering wheel and Really like that steering wheel. It's heated as well You know the gorilla for the gorilla glass and then it comes with the updated radio as well This is the you connect five twelve point three inch touchscreen radio.  You get your backup camera, which they've made those crystal clear on the wranglers And then you also have all your different apps you get off-road pages. Those are really cool So I'll show you those real quick. You get your different steering angles right there As I turn that steering wheel those angles change and the wheels turn up top Transfer case if it's locked or unlocked and then your rear axle locker, which is the one thing the willis has over the high tide Is that you can lock that rear axle? latitude longitude altitude accessory gauges and pitch and roll So a lot of really cool things you can do with this radio down here are your more tactile volume tune and climate controls This is pretty much the same as the 2023 the 2024 has different air vents here And this top piece is a little bit different as well Here's your axle locker for the rear axle you get off-road plus mode and four auxiliary Upfits which is this one does have the tow package with the receiver hitch four pin and seven pin wiring Actually, I think just four pin wiring and then you get your four by four shifter eight speed automatic transmission You get a USB USB C and Augs jack there Keyless entry with remote start you get the flipper key on there always like those nice chunky key fobs You know which key bob you have in your pocket for sure you get the cloth dash I'm not sure if I'm a huge fan of that But anyways this one comes with that you also get the assistant SOS buttons up there and Home link buttons for your garage door security systems and lighting systems speaking of that lighting system I'm gonna turn on all the lights. We're gonna turn off the lights in the picture studio We're gonna do a final walk around on this Jeep I did want to show you the tow package real quick It does have seven pin wiring my bed four pin seven pin wiring and the receiver hitch right there But I'm gonna turn off all the lights in the picture studio so you can see just how bright these LED lights are on This particular Jeep Wrangler, and I hope you enjoyed the video so far. We're gonna take a look at the original window sticker It's also gonna be linked in the description below so check that out and just give me one second I'm gonna turn off all the lights. We'll do a final walk around check out that window sticker and we all set Alright, you can see just how bright those lights are. I really applaud Jeep for making the LED light package It's pretty much standard across the board besides the sport package. Those lights are super bright nice and Clear white light and they are it's a very wide area that it illuminates So really good job up for Jeep doing that. You get the LED tail lamps there very iconic looking kind of hourglass tail lights and Let's go check out the original window sticker and we'll be all set to go So here is the original window sticker Feel free to pause this.  I think it's dark charcoal 2-liter turbocharged four-cylinder engine 8-speed automatic transmission Everything on your left there is standard equipment Including that you connect five with the 12 with the 12.3 Apple CarPlay Android Auto I think those are wireless now. So that's a really neat feature Hydro blue pink Hydro blue pearl clear coat Paint is 595.  That was another thing that was on the high tides was the deep tint sunscreen windows LED fog lamps LED reflector headlamps Full-speed forward collision warning plus which this one does have adaptive cruise control Which is right there advanced brake assist all-weather floor mats and the molding bumper with gloss black receiver hitch the extreme 35 inch tire and wheel group is a $4,495 option so When I did the high tides, it was like a $13,700 option for the high tide package and I'm pretty sure that This is actually less However, this one doesn't have the v6 and all the high tides I did had the v6 anyways you get the 456 gears the 17 by 8 inch machine wheels the LT 315 70 r17 tires and the hinge gate reinforcement. You also get the sport brakes the suspension lift the wheel or the fender extensions and What else you get the Tail light extensions as well So a lot of stuff that they don't put on there that also comes with that extreme tire and wheel group convenience group is 1995 you get the seven inch driver information cluster heated seats heated steering wheel front door passive entry remote start air conditioning and emergency assistance call safety group is 1395 gives you blind spot automatic high beam control Park sense rear park assist system and LED tail lamps 8-speed automatic $2,500 and three-piece black hardtop 1695 total MSRP is 58 665 on this one if you want to get your summit price with all the rebates and incentives on this particular Jeep or one of our other Jeeps you can click the link below in the website or in the upper right hand part of the screen to get your summit pricing Tell them Brett sent you three or 36 bumper to bumper and five-year 60 power train here at your miles per gallon 20 city 22 highway and 21 average not really rated for a lot of safety except the three-star for rollover And like I said go check out that high tide video to see just how similar these two packages are and if you like this particular Wrangler Maybe not in this color. I know we have pretty much just about every color that you can get the Wranglers in in this package with The 35 inch tire and wheel group. So definitely go check our website out, which is right there www.summitauto.com We have full descriptions and pictures of every single vehicle all at summit auto.com And if you'd like to check out more HD videos you can go to youtube.com slash summit auto click the bell notifications get updates in The videos I do each and every day as well as having access to one of the largest catalogs of vehicle and specifically Jeep Wrangler walk-arounds on YouTube over 500 videos of Wranglers over 9,000 videos total and it is the best way to stay up-to-date on our current and ever-changing Jeep inventory here at Summit Auto. In fact in a second you will see a link to subscribe to my YouTube channel in the upper left a link to all the Jeep Wrangler videos I've ever done in the upper right link to this vehicle on our website in the lower left and a link to that high tide video in The lower right click those check us out. We're super excited to be offering this 2024 Jeep Wrangler four door Willis and Hydro blue Pro coat with the 35 inch tire and wheel group Thank you so much for checking out the video. Thank you so much for watching. Remember to like subscribe and share Thank you so much and have a great day


So, I gained welcome to fourth module for machine learning. This is under the Creative Commons license share and share like. And the focus for this module is on the use of neural networks for secondary structure prediction. This is a schedule. We've got about an hour. I'll have to go through this fairly quickly because we have a lot of material and apologies if I seem like I'm speeding, but there's a lot. So I'll briefly talk about secondary structures. People have never heard of it. I'll try and explain it. And then I'll show you how neural networks can be used to handle an interpret sequence data. The key thing here is we're looking at sequences. And a lot of what you have to do with machine learning is, and especially with neural nets is to figure out how to recode or encode your data so that it's more compatible with the neural net input. And some of it's thinking about it in a novel way or restructuring it or reformatting it. So this is, I think, maybe the main take home lesson for this particular module. And we'll go through a neural network for predicting secondary structure called an SSANN. And then basically we won't have enough time for the lab. So the lab will essentially be sort of homework for people to do after the class is over today. So we're talking about secondary structure that relates to proteins and that relates to polypeptides. So polypeptides are made up of amino acids, strings of amino acids that are strung together. They're sort of like chains or chain links. They can pivot around each other through certain torsion angles for proteins that consist of longer stretches of amino acids generally more than 40 amino acids. The structure of amino acids in a protein is called the primary structure. The formation of coils or spring like structures or linear strands, those are secondary structures. The three dimensional structure of a protein as it falls into collections of beta strands is called the tertiary structure. And then how proteins complex with each other form aggregates. That's called the quaternary structure. The picture is schematic diagrams of a beta sheet. This is the anti-parallel beta sheet. On the left you can see four anti-parallel, well I guess two sets of anti-parallel beta strands and one parallel beta strand. Beta strands are generally extended. They have hydrogen bonding between amide and carbonyl residues or groups. And beta strands tend to sort of form the central core or hydrophobic core of proteins. Helices are probably more familiar to people. They look like springs. They have a gain of characteristic hydrogen bonding between the first and the fourth residue in increments all the way up. They're very stable structures. So these represent the main building blocks of secondary structure. Those secondary structures, the red and helices, yellow and beta strands can be assembled into a three dimensional structure. These are called ribbon diagrams. Some proteins are all helical. Some proteins are mostly beta sheet. Others are more mixed. The structure of proteins is something that many people have been studying for a long, long time. When the first structure is emerged in the 1960s, people immediately notice there is this periodicity and helices and beta strands. And people started noticing a relationship between sequence and secondary structure. So, very early on, people actually tried to predict protein secondary structure from sequence. And this is an effort to try and predict protein structure. And certain amino acids seem to prefer certain secondary structure elements of alanine and methionine, leucine and glutamate tended to be in helices, isoleucine, veiling, threonine tended to be in beta strands. It's been a subject that's been published about and written about for probably more than 40, 45 years. It's sort of fading from being in vogue, partly because most protein structures have been largely solved by now. But it's, I think, a nice example, because it's an application of machine learning to do something that was intrinsically very hard to do. Understanding secondary structure helps us understand three-dimensional structures. It helps with things like threading and remote sequence similarity detection. People also are using it to understand protein function. So, as most of you might gather, I'm pretty old and in fact I've been involved in protein and secondary structure prediction for the last 30 years. It's the reason actually why I got into bioinformatics. And so, again, that's one of the reasons why I'm using this as a good example. So this is an example of where we've got a string of amino acids, a sequence that's written at the top of this particular view. And then what's marked in yellow and blue or cyan are examples of secondary structures that have been predicted. So you can see one helix stretching from about residue one to residue 14, another helix stretching from about residue 33 to residue 45, and a beta strand from residue, I know, 22 to 28. And there are different predictors. One is what's called the Chu-Fasmin model. Another one is a Garnier model. One uses hydrophobic moments. Another one looks for motifs. And then you can come up with a consensus that combines these together. So this is an example of secondary structure prediction that's not using necessarily machine learning, but in this case using sort of statistical propensities. So one of these statistical propensities are identified back in the 60s by Jerry Fassman and his student Chu, and they published this, I guess, table about 1969. And it just shows that certain amino acids like alanine A has a high helical propensity 1.42. And you can see that proline P has a very high coil propensity of 1.88. So the PC is coil probability P beta is beta sheet probability P alpha is the helix. So different amino acids have different preferences or probabilities for being in certain secondary structures. So for example, you can kind of come up with a simple mathematical one. This is not machine learning, but it's a mathematical one where you say take a stretch of amino acids, seven amino acids. Calculate the numbers for helix beta sheet coil. Determine the average assigned the middle residue, residue number four to that value. You can see the helix for each of the alphas, the betas and the coils. And then you can slide this window of seven residues, the entire length of the protein sequence. And then you can generate a plot, which is essentially the helical beta sheet and coil propensity. So here's a protein that's got maybe 61 or 62 residues, and we can see the plot with the green as a beta sheet at the beginning because that's the highest value. We can see a blue, that's the helix, and then we can see a red that starts around 18 and goes to about residue. I know 37 that's a coil region, and there's another beta sheet, then there's another helix and then there's another coil. And so this is just simply a plot and then use a sort of a threshold to say if it's above this it's helix if it's above that it's a coil, which would whichever value is highest. That's the winner. So that that's, that's an example of heuristic method, one that can be calculated with a conventional computer, even with an Excel spreadsheet to predict secondary structure. The two fast-man method is really old, 50 years old, at least. And it doesn't take into account really long range information, doesn't take into the fact that some proteins have a preference for being in a structural class. And it assumes secondary structure probabilities are additive. It doesn't look for certain patterns of amino acids that are known, they're known to be helical caps and they're known to be helical ends. And so with all those limitations, the method is only about 50% accurate. And a random method would be about 33% accurate so it's, you know, it's better than random but it's not great. So secondary structure prediction took a giant leap forward in the 1980s, late 80s, early 90s when people started using neural networks and a program called PhD was developed and this one basically took sequences. So sequence alignment or sequence profiles ran it through a fairly simple neural network and predicted secondary structure and its performance went from, you know, at the time standard was around 50, 55% up to around 65 or 70% accurate. And that was, that was massive. And that's what actually got me interested in neural nets and machine learning. The way that secondary structure is evaluated is sort of like a multiple choice exam, where if you had answers A, B and C, and you count the number of correct answers from A's and B's and C's. So in secondary structure we have B for beta sheet, C for coil, and H for helix. And so you can have essentially confusion matrix compare between the predicted and observed values. So you can have in this case the diagonals the beta sheet was predicted 77% of the time coil 81% of the time helix 88% of the time correctly. And then we can compare where that's over predicted or under predicted in terms of true positives and false positives. The result combining of the beta sheet coil and helix prediction is called a Q3 score. And then what I'm showing is another method which is called the confusion matrix. So the idea of using artificial neural nets to predict secondary structure and identify patterns of residues is getting on to be about 25 years old now. And we've talked about neural nets already so I won't belabor the point just saying that they are ways of performing both classification and regression. For secondary structure prediction. What you're trying to do is take your sequence data and you might have many examples of sequences. So we've got AC's, G's and A. It could be DNA, it could be protein, whatever. In this case it's protein. We're passing it as an input or through an input layer. We have a hidden layer. We have an output and the output here is secondary structures. In this case these sequences predict to be mostly beta sheet. The net connections between those nodes are the weight matrices and just as we've learned before, it's trying to modify those weight matrices that allows us to make the predictions. So we think about encoding. So we have a sequence and we might one hot encode A's, C's and G's just like we did with flowers like 001010. And if we only had an alphabet of three letters, we could encode it this way. So think of having a sliding window where maybe we're taking three or seven residues at a time so we then concatenate those, those three amino acids together to produce an input vector which is three times three or nine elements long. So we have another output where we indicate the secondary structure is coded through B, C and H. And we might have a decision of what the central residue should be in terms of the desired output, whether it's in this case a coil. So the CGA, the central residue G probably is indicated to be let's say coil or something like that. So there's a formatting where we're modifying how we encode how we do one hot encoding. And this is critical to actually having a successful neural net. If you don't do that proper kind of encoding and sometimes you need to be creative or inventive, then your neural net may not work out as well as you want. I think we've shown this sort of same structure before where we have a vector or set of numbers that codes the sequence we have a weight matrix. In this case, the weight matrix has to be of the same number of rows as the length as the input vector. This is just general linear algebra. Seven or two, four, six, eight, a nine by one matrix multiplied by nine by three matrix, which gives us a three by one matrix and we multiply a three by one matrix by a three by two. And that gives us a two by one matrix and that gives us let's say your desired output. So here, you know what our initial feed forward calculation is at point two four point seven four, we compare it with the preferred or desired output in this case say a zero one. And we can see we're slightly off so we have to do some back propagation to adjust those numbers. We recalculate saying kind of input vector we find that we've changed things the it's dropped a little better and the first digit and the last numbers increased so it's a little closer to zero one. And so after a few rounds of training, we think it's converged. We can carry on. We can put in a new input we can modify and compare and iterate and just as I showed before after many iterations we create in this case to generalized weight matrices which maybe allow us to predict secondary structure with say high accuracy. So that's the concept behind using neural nets for taking sequence and predicting an output. So that could be secondary structure, it could be binding. It could be the location of a promoter it could be a gene. So all kinds of things can be done through neural nets or through hidden markup models where we're taking sequence data and converting it. So we're going to try a real example and this is one where we're going to try and predict secondary structure from protein sequence data. That's our problem, then now we're going to construct our data set. So we're going to take a data set that we compiled many years ago called the protein property prediction and testing database. It's a website where we have sequence information and the secondary structure for thousands of proteins. So you can see the amino acid sequence is using the standard one letter code secondary structure uses Cs for coils Bs for beta sheet and H for helices. So, you can take that data download it and it has all the information about the protein name, the sequence, the secondary structure. And so you've got, you know, in this case hundreds if not thousands of examples. So that's our data set. It's considered a gold standard it uses fairly sophisticated computational methods to identify the secondary structure again it's not machine learning it's it's calculating certain features and summing them together but it's a robust useful data set. So, we're going to try using the neural net and this training set to see if we can predict secondary structure. So as before, we could, you know, pretend to code but in this case all we're going to do is open up the module for Python code. Again it's also been written in our. And then you can open up the secondary structure SS AN and and work with Google cloud. So it's very similar in design to the iris neural net program, we have to read data check for missing data check for invalid amino acids, split things in the training and test set. We have to do some encoding, we have to convert amino acids to some of the numerical for that we have to convert the secondary structure to some kind of numeric encoding so this is where we do the sort of one hot encoding. So we have windows of about, I think it's 21 amino acids, we have to make sure that we have, I guess padded sequences at the beginning and at the end, because you want to be able to have your window run to the end, and not start 10 or 11 residues into a sequence. So we put extra fake amino acids at the beginning and at the end, so that our window can slide through so we introduce what are called null amino acids. So we've, we encode the amino acids we encode the secondary structure. We define some functions for those. We also have to define our activation function, just like we did before a sigmoidal and softmax functions. And just like we did before we initialize weights and biases, we determine the batches that you do forward propagation error calculation back propagation update and then iterate over many hundreds of epochs. The first numpy and pandas distance we've done before for most of our other programs. Then we have to read our data. So this is still in a CSV format. The reading is a little different. It's not the same as the iris data anymore. But this is what the data looks like in terms of the sequence and the secondary structure. So we can parse that out. So modify this a little bit to look for or ensure that we have standard amino acids, whether they have the right letters if there's any X's in particular, and make sure that those are either cleared up or identified and sorted out. We're also going to check for any missing values. And the game is just a standard thing to make sure that we don't have any missing sequences or missing secondary structure elements. If we do it flags that. So again, this is very standard. We're using a training data set the training and testing data set of about 1400 proteins. There's more than 100,000 in the protein database, but this is just to keep things reasonable. In this case, we've set out our data size. There's a data fraction and then a training fraction of 70% and then 30%. So we're going to still split it out. So that we have a reasonable set. So this is, you know, just sort of deregure set up or just reading our data, setting up our training and testing set, working with a data size just to make sure we don't flood the computer and have to wait hours. So key to neural nets and we emphasize this before with the iris one, they have to do the same thing with sequence data is encoding one hot encoding so the input can be manipulated using matrix calculations using dot products and vector products. We have to change our amino acids from a and C and D, which are single last letter characters to, and we'll call this amino acid binary. So, in this case there's 20 or 21 locations, because there's 20 amino acids. And then we've introduced a special amino acid called the null amino acid. And this is sort of the invisible amino acid that we put at the beginning at the end of the sequence. And this is typically done whenever you're having to do some kind of windowing function where you're spanning through sequences. Because your window function kind of runs off the front and runs off the end. So we have one hot encode secondary structure. We're setting the B to be one zero zero C to be zero one zero H to be zero zero one same sort of thing that we did with Satosa, Virginia con and whatever the other one versus color. So, we have to make sure we convert our first of our amino acids into a sort of a binary number for each amino acid. And so this is what we're doing instead of the encoding. So we've got these 21 letters for the 21 amino acids standard 20 plus the null amino acid. And the structures are also going to convert that to BC and H and create numbers that are going to be zero zero one zero one zero zero. We have a windowing function as I said this is another thing where you use intuition. So that when you're trying to build out or create some kind of predictor, you take what you know, you take what perhaps has worked for other people and certainly in cases secondary structure people know that when you collect from data with nearest neighbors it helps with the quality of the secondary structure prediction. So we're trying to capture these pair wise or distant interactions and so we group them into windows, and we're going to try and calculate the secondary structure for the residue at the center of these windows. So, first part as I said, this is padding, where we've got a protein that begins with proline ends with proline. And then we're putting these null amino acids at the beginning and at the end, so that we can still allow our window to start and pass through the entire sequence for the protein. And what we're doing is we're taking sequence and then this is this window, and then we're predicting the secondary structure for the central residue in that window. So we've got this window of about 10 residues or 11 I guess the central residue is glutamate that's the E, and it is predicted to be a helix in that center in that central residue. And then slide our window along by one residue so we've moved from E to P, and then we'll take the prediction. And that also is predicted to be helix. So we can just slide this along the length of the entire sequence. So here we're encoding the sequence. The sequence starts with in this case with isoleucine glutamate glutamate glutamate, leucine leucine.  These are null amino acids, and we're encoding them as 0000 all the way to one. Isoleucine has the encoding of 00001 0000. Glutamine has an encoding where there's about 15 ones or zeros and then a one. So this is how we are encoding. Now, the other thing that we're doing is we're taking all these sort of binary readouts that have 21 zeros or ones, that's for each amino acid. And then we're taking a window of 17, which means that it'll be the ninth residue, which is the middle residue of the window from 17 is being predicted. So we've, I've drawn a box of covering 17 amino acids at the very beginning. I have eight null residues. The ninth residue is the first residue. We're getting a table that's 17 by 21, which is 17 amino acids and 21 binary values for amino acids. I'm going to flatten this. I'm going to take the 17 by 21, which is 357 and make it into a single vector of 357 bits. So this is another trick that's used a lot in neural networks for sequence data. This is what flattening looks like. So I've taken all of those zero zero zero zero ones and from all of those sequences, and I've just moved them all up into one very, very, very long 357 bit vector. And so when I'm doing the calculations, I got my window of 17. I move it down by one. So I've moved from isoleucine to glutamine, glutamate, which is a new center. And this is now residue two. And then I convert things, flatten things and carry on. So I do this for the entire length of the protein. So the proteins 300 residues long. And I'm going to have a lot of these really long vectors 357 shown 300 times. And so we go this all the way to the very end. And this is the last residue, the tyrosine. And then I padded it with another eight no amino acids. So here's the whole length of the protein, let's say 350 residues and I now have 357 bits for each of the proteins are each of the amino acids which corresponds to information about the surrounding sequence. So I also encode not only the amino acid data, which is on the left, on the right I'm including the output data. So each amino acid has a secondary structure. And so I feel that in as well. And recall the encoding that we use for secondary structures of 0100 for helix 010 for beta sheet and 001 for coil I guess. I'm just shown in slides what essentially we're encoding here we're producing this a in code. So it creates this window size we create first set of null, we pad it based on the size of the window. So how many columns we're going to have, how many residues the length, and how we bit ties everything. We also make sure that we've encoded it can calculate those padding sequences to the beginning at the end. And then now that we've encoded the padded protein sequence, then we start flattening it. So we convert this to this 357 bit representation of the 17 amino acids and 21 amino acid calls. And then take the secondary structure which again I was showing previously but this is how we encoded it. So this is a function called sst three encodes of the secondary structure encoding. We have hc and b three different types and then we're going to encode that all the way through to the protein whole length, and then also convert that to zeros and ones that's needed. So now that we've encoded. And unlike say the iris data, where we have to worry about, you know, numeric data regarding the length and width, we don't have numeric data we have basically zeros and ones so we don't have to do any more normalization, we don't have to do L one normalization The data is pretty randomized. It's ranging between zero and one. So we get to avoid the normalization process. So here's what our model architecture looks like. We've got 357 inputs. I'm not going to show 357 in the diagram. And we have three outputs. And we have a hidden layer with the hidden layer size being somewhat very what we want for architecture. Just like we did for the iris data iris problem, we have to choose a an activation function. So we're using the sigmoid function and the sigmoid derivative function which works really well. Again, this is just some math reminders about the sigmoid function also about the softmax function, which is used for layer two. Again, this is the definition of the softmax function. We went through this last time so I'm not going to repeat it but this is again just setting things up so that we can use our appropriate activation functions for each of the layers. Just as before we have to initialize our weights and biases. So we call up some random numbers and create those random weights all the way through. We're going to calculate the number of batches. So we've got 1400 proteins, I guess, and we have to choose how many batches we're going to train. And so that's partly decided by the user, but we have to make sure that the batch sizes are come up to whole numbers. The training loop is exactly the same. This idea of looking at these different batches do the forward propagation error determination back propagation update the weights and biases. We do this for batch one two three to batch and once we've completed that then we, we've completed one epoch and then we repeat for hundreds of epochs to make sure the training is thorough and complete. It's very similar to the slides we saw and in many respects this is just recycling a lot of the same architecture. We have to worry about the learning rate batch size. We want outputs which return the trained weights the biases and the error measurements. The forward propagation is largely the same. Again, it's now we're dealing with sort of a window size and the number of amino acids. And the hidden layer size and we have the dot product and we're calculating the activation function with layer one using a sigmital function and second layer using the soft max function. Just as before we have to determine the error once we've done the forward propagation. This is the difference between the output and the observed or known output. So the predicted versus observed and then we change or propagate that delta all the way through to the other layers. The back propagation goes from layer two to layer one layer one to layer zero. Again, these are the same methods models approaches. In terms of both the waiting in the bias. That we talked about the last time. So I'm not going to go into a whole lot of detail. The back propagation, as I say, still continues through the different layers. We're looking at the sigmoid derivative, we're looking at cost. And that function which is marked red. And highlighted to this to those points. So this is a game very much similar to what we did. Or the back propagation continues all the way from two to one to zero. So this is a game with the deltas for both weight and the bias. And now we're looking from layer one to layer zero. After we've completed the back propagation step, we're updating all of the weights. And we're multiplying this by our learning rate. Which is the Greek letter, Ada, and the delta that's learning weight delta that's marked here. So we have a different layers marked and different weights marked. So the weights are updated, the biases are updated. And we're looking at a kind of formula, same slides, basically the same interpretation that we used for the Iris model. So once you've done one neural network, you've kind of done them all. There's subtle changes to format, but the back propagation steps, the activation steps, the bias and weighting adjustments. They're all pretty much the same. This is the same coding that you saw with the Iris model. It's taking a batch, doing forward propagation, calculating errors, calculating the derivatives, doing the back propagation and the weight and bias updating and repeating that over and over and over again for each epoch. So this is a little different. So this is an animation of what's happening with the different input layers. So the 17 input layers we're seeing across the graph is the 17 and they're sort of concatenated or condensed so that instead of seeing 357 we're only seeing 17 units. So you can see they're weighting or the numbers that are sort of the average over these things. We have input units, we're seeing the epochs being changed or going from first epoch to 170, 180, 190. We also have the output units. And what their value is. So as we're training, you can see numbers are changing. Some initially change quite quickly, but as we get towards the end of, I don't know, I think it's about 1000 epochs. The numbers are only changing subtly. They're 0.1 here, minus 0.1 or 0.01. So it's starting to settle. You can see how the colors change as you went from the beginning to the end and back again. So there's quite a bit of change that happens at the beginning first 200 and then it settles quite nicely around the last 100 or so of the 1000 epochs that it runs through. So this is just sort of showing sort of the specific weights. 17, I guess, illustrated as the inputs coming in. We've got within the single hidden layer, we've got these five hidden units and then we've got three output units. So it's kind of a pseudo 17, five and three, although it's 357, five and three is the real input that we just can't show 357. So we're showing the weightings and the various connections based on where they are, the numbers and the strengths of the weightings sort of in terms of the lines. We could show darker thicker blue lines and thinner blue lines based on their overall weights. We could color them so that we have the appropriate colored weights, but the light, light green would be lower weights the dark blue or black heavier weights in this diagram. So, in addition to the weights changing and eventually converging. The error also is converging and the error plot is pretty generic over the number of epochs we trained for 1000 error drops quite significantly by factor probably 40 or more in terms of what we see. And so that's telling us that as it settles out where we're getting good convergence and that the program is is performing well. We have two versions of the secondary structure. And one written in Python, the other one written in our one in Python uses NumPy and pandas. You can see that the Python one is much more compact in terms of the number of lines that code. They run about the same as a general rule our programs run a little slower. And once we've essentially assembled the program trained it on our training set, then we can start testing it on the on the on the test data technically. And this is essentially how we do the testing forward propagation. So what we've got is a training set. There's 497 sequences and a test set of 213. So a grand total of I guess 710. So we used about 10% of the entire data set, which had 7100 sequences in it. So we kept this small just so that the program could perform in your lifetime, just given how slow some of the collab code can be. So what we've done here is we've calculated the q3 score for the training. And we've assessed it on the number of amino acids. What you can see is a diagonal so we've got the confusion matrix and so it predicts beta sheets 48% of the time correctly coil 69% of the time correct the helix 63%. That's on the training set the testing set which is about one third the size. The performance is 466965. It's not a perfect prediction it's not like what we saw with the. Iris data set where the errors for mostly zero or 0.05. The iris data set is almost a trivial one secondary structure prediction is non trivial. So this is more typical of what you'll see in, I guess we'll call it a difficult prediction program or prediction challenge. So we have, you know, larger off diagonal elements, and the diagonal elements are not 1.00 or 0.99. They hover around 65%. Overall, the q3 score, which is sort of what you would get on a three question multiple choice tester there's three answers in each question is 61%. Both the training and testing are consistent in terms of the numbers. So we can say this is not over trained. So we're confident in terms of its performance. So essentially what we have is is a neural network program written in pure Python also one written in our we've trained it on fairly large data sets. It took a fair bit of time for us to find the right training size set. So the first time we did it was too large second time was too small. So we're not showing you all the challenges we faced in terms of, you know, choosing a good training set size, but in principle, this could be used, not only for secondary structure prediction but you could use it to predict in spanning regions you could predict signal site prediction, the same concept could even be applied to DNA for gene prediction and in fact we'll show you how to do this so similar ideas. Just with the way that we've encoded sequence data the way that we've mapped sequence to some output information that it's a concept that can be reused in many, many formats. So what you can do with this obviously is is actually play around with it and I think what I wanted to do because of like the time was sort of give people the opportunity to work in the lab either with homework. So that's the end of the day for many people, or if those of you who are ambitious. You could just follow the slides along where it's the same sort of thing go to module for download SS and and or Sam, as we call it. You can also take the R code if you prefer that over the Python code as before sort of just look at the code as I've illustrated and explained. We have a data set. This is called converted data. So it's a much larger data set than what we have in the iris. And as before you can run the program. So it'll take a while to execute. You can change things and make adjustments to an assessment performance in terms of helix of coil and beta sheet prediction, adjust your data fraction just your training fraction. There are some other variations you can do and change your learning rate. You can plot your error plot see how it performs. You can upload different sequences and see how they predict. So any number of things that you could do with this in terms of testing it out trying it out. And given that we still have a few minutes that I'd certainly encourage people to give it a try.


I haven't been on this bike for a while, the tire pressures feel a bit soft, but yeah look after it, won't you? Bye darling, I shall miss you. Oh great, it actually gets ridden. And I could bring the video and get a few of your customers talking about it. Yeah cool. Next Friday. What time do you have beers from?  There's an UNO card in here as well. Just one UNO card. That's how we play UNO at my house. Does this have a special button? A lot of brands are trying to represent a community and I think with Curve what makes us really different is perhaps that we actually are all riders and we're all testing our products and ensuring that anything that we produce we've ridden and we wouldn't release anything that we wouldn't ride ourselves. Or outside looking in it's like man this is like a really strange cult with all these people kind of riding curves and going on these silly adventures. Some can just, they just sound crazy. It's like what do you mean Jesse Cross the US in 18 days? An average 400 kilometers a day? Yeah. Like that just, I wanted to bring you along a little visit. I'm at 36 Bond Street in Abbotsford. This is Curve Cycling headquarters. About six months ago on the BikeChase YouTube channel we did a full comprehensive piece on the Handmade Bicycle show showing off a number of the local brands here in Australia that are building handmade bikes. And in that video I asked our audience which bike, Bram, would you like us to approach and do a comprehensive review on BikeChaser. Curve Cycling, they won the vote. So today I'm going to introduce you to the crew here, a little bit about Curve Cycling and I'm also going to be taking home Rhino's bike. Let's go have a chat. How are you mate? Practically scripted. Yes. Hello. So what are we going here? This is a good looking joint. This is Rhino's house. Yeah right. Yeah. How long have you been here for? It's nearly a year. So we've kind of moved out of our shed. We've grown a bit in the last three years. Kind of a steady growth and a change in the business. So Curve kind of started off as a as a wheel manufacturer and trying to understand carbon and why it's so expensive and why there were only a few players in the market. And then two and a bit years thereafter we started making our titanium and steel frames. So it was kind of the pursuit of all these adventures we were going on and testing the products in those. So like the tour guide, we tested our first mountain bike rim which is pretty much North America over the Rockies from Banff all the way down into the finish which is New Mexico. And Jesse came second in that and we were like yeah we've got a great product here. The carbon's holding up really well in a really difficult environment and then started making road rims as well. And then Jesse took our first wheel set into the transamerica. So it was nearly 7,000 k's across the US and he won that. And the wheels held up again really well. And we've just kind of it's been an evolution of that kind of principle of we're all riders, we all love cycling and just kind of taking on adventures and sometimes those adventures require us to build like a new bike. So this is one I'm going to take away, which is yours. Yeah this is the one you kind of did a thing at the Handmade Bike Show all those months ago. Yeah and it was bike chase the audience voted. They wanted me to do a curve review. That's right. So how do you feel? So here we are.  Your wish is pretty much being granted. Thank you. Yeah I'm happy with you riding my bike. I mean we're pretty much the same size. It's actually a bit small for me because this is a prototype. So final production has just been agreed upon. So there are a few tweaks to things but yeah don't break it. Look after it. So before we go there you need to quickly show us like what's going on. You've got to beast up here. What's going on here? My special princess which has just come back from Ares Rock. As you can see she's been ridden hard and long. Like it's three and a half thousand k's off road. This year it started in Tasmania. Does that sort of thing excite you? That dirt and mud and just it's been taken for a proper ride? It excites me. It's frustrating when it's not you that gets to go on these adventures but I've been very lucky and have gone on some great rides over the last couple of years. So I was very happy to see Adam get out on the bike. Yeah this is a gravel monster cross bike so it's actually something our engineer, Niam had a lot to do with and Jesse because everyone was kind of making mountain bikes and then just putting a drop bar on them rather than actually thinking a bit more about the geometry and what's required for these like ultra kind of off-road races. Hey I switched my phone off man. This is all you buddy. That's my wife she's calling from the US. Are we sure? Yeah. How'd you want to get it? No, no, that's all right. Yeah so this is a gravel monster cross bike and it's something that we're actually further kind of developing around a three inch tire which is going to be pretty exciting.  There are. Oh there he goes. Oh no. Oh wow. That's heavy duty. Look at that guy.  Big beast. I feel so light because it's not loaded. Yeah. What were you calling it? I was calling it Dr. Deal Good or Mad Max. Something to do with Mad Max. Maybe call it Mel. Just call it Mel. Yeah. Mel Gibson. The little little brother of that which we're pretty excited. It's the steel Kevin so we've made made Kevin which is these guys. Yeah okay. So you can run like a 650B wheel up to 2.2 or you can run 700C by 45. So you can see here like these two are set up in 700C mode and a bit of gravel and then that dude over there is in kind of Kevin party mode which is 650B 2.2 and there's something magical about steel bikes that especially that one in 650B like it just feels really really right. It's just a lot of fun. It's so that's doing really well. We did a small batch of production in steel and then we've kind of put in our second batch already hopefully ready for Christmas. So if you're looking for ideas yeah I think steel Kevin should be on on the shopping list. I like this little Kevin you've got here. And you can see you've got a huge amount of clearance. We designed our own forks as well. So what could you fit in there? Oh you can go 650B 2.2 or 700 by 45. So who does your paint jobs? They're all very nice looking. Yeah most of our paint done in Australia is Bikes by Steve. He's more of a small independent painter and he kind of does a lot of work with Bastion, a lot of work with Prova and we're very lucky to have him work with Curve as well. So I actually did a piece on Bikes by Steve. Yeah I'm bike chaser. It gets about 400 unique page views a month. A lot of people searching for him these days. But I'll link to that below this video if anyone's interested in Bikes by Steve. He's a very very good painter and to get someone that really understands how to how to paint titanium is quite hard. His quality of work has really kind of been recognised by a lot of manufacturers and a lot of frame builders. So yeah thanks Steve we appreciate you mate. Yeah and then we've got another air. So it's a similar one that you're going to take out but this is kind of in mechanical jewellery format and with a different kind of wheel build a bit of a shadow rim. Steve this is your beast isn't it? So Steve what do you do here? I am the brand founder and I help with products. Yeah okay. Products design through to production. All the production relationships are my relationships. So how did you come up with the design for this bike? Is it 24? Is that right? It's 23. There's a bit of a story as to how many there are, why it's here and how it got its name and but yeah this is prototype number one and it started off as being my birthday present. Oh okay. All right so I'm born on the 23rd of December and originally I wanted something that wasn't quite part of the curved lineup. That was going to be special to me because I've been working with the bashing guys for the last pretty much since they started really since three two years now so I started letting this paint booth a lot of the the bikes that were and are painted are like they're both my paint jobs. I used to do before other Steve took over and started doing all the paint because I used to do painting as well. Yeah so yeah. Oh you were in another box by Steve? Yeah it's passed by Steve's so yeah and yeah originally I was speaking to James the head engineer there and Ben from Bastion that I wanted to do a special project and I wanted them to be involved and it actually started off there's a few this is actually the idea actually came from the original Cannondales the system six that was aluminium and carbon okay but we kind of took the Belly disc which is that bike yeah kind of just knocked it up a notch basically try to show you a bit of weight out of it try to add a few design cues that I wanted to see on the next version of the Belly disc. The topper and the seat post are by Bastion. Yeah that's where they come into it so I presented the idea. So this is very Bastion-y isn't it so this is what you're talking about? Yeah so yep and you can see their logo just here and with curves. There's quite a few details in it so I like the collaboration. The design was 100% me using fabricator in China that we know really well and trust and rely on. They actually developed their own method of building a frame with a void in it basically and still have it square and true and so what's this bike going to be like to ride? It's pretty racy yeah yeah it's racy and comfortable at the same time it's a fast road bike straight bridgeless seat stage that was kind of an aesthetic thing that was brought across and a lot of people already have highlighted that aesthetically it looks tidy. So now we have a semi-scalloped chopped and welded chainring clearance built in dimple essentially. Yeah right. The first bike we've had with the integrated head tube. Because there's two two carbon tube sets that you can kind of build a bike up from Bastion so you can either go with an intermediate or a quite stiff and it's your lucky day because you get the super stiff ride. So she's even racier I find. It's interesting because the great thing about the bikes is you can kind of tweak every element from the wheel set and its depth to the stiffness of the the carbon which is quite cool as well because no rider is the same and there's still a few design things that weren't finalized on these two bikes which was essentially kind of just the tube tubing here we were speaking with the engineer and with James I think and they realized that you didn't need this huge area of adhesive so that allowed us to kind of just make it aesthetically a bit prettier and actually the final design will actually be chopped even further. Same angle as the seat stay right and the same and then down tube same angle down tube down here so just a few little things takes out a little bit of weight like this here it goes just the stamp of it being the curve fashion project here and it's number one so what started off as birthday present to myself yep is now the first limited edition product which is kind of cool yeah a lucky 23 there's a few people who say oh it's such an air Jordan thing this and that air yeah 23 yeah it kind of just ended up that way yeah and air because the original frame construction during had a void in it had nothing but air in between the the main triangle so to me it just made sense to call it the Belgium yeah all right what about what about these ones here this is a Kevin in party mode as you can see he's got chunky tires on and he's ready for a bit of fun this is where this is like Kevin's grandfather like this is where Kevin started life was the version one and the version two of the grovel and then we made a cyclocross bike and then this is the natural evolution of things so it's a bit more bike packing and adventuring orientated but you can also run it on the road so that's that's what that one is so the steel one we showed you earlier this is just the titanium version yeah okay and then this is a bike that we it's our very first belgium the classic that Jesse and Sarah both rode in the transamerica and the only variance was the disc this is actually even earlier version which was our rim brake and lb the people's champ you can see he's kind of got the people's champ kind of logo on on his bike and he rests at the australian road nationals a few a few times yeah right so we kind of wanted to make it a bit special for him and as you can see he's got a super super pro long stem and kind of really aggressive position on the bike but it just shows you that in the kind of elite pro peloton there is still some of the some of the guys riding on on tie which was pretty cool this is a cool story but we we kind of phasing out rim brakes over the next year so we kind of i was hearing they're not going to be making rim brakes all manufacturers from 2020 i don't know if that's just an industry thing or yeah i think like for us carbon delamination is always an issue we've had a lot of riders that unfortunately the the carbon rim brakes do fail it's just that they're fundamental failing is heat and delaminations occurring obviously when it goes over like 220 degrees and then you have a separation of that resin really that bonds the fibers i mean carbon's incredibly strong and that's why we love working with it in wheels and in forks it's the same with my bike like i i rode that across australia and that was the first time i raced the indie pack this was um this is the bike i took and i even took etap across australia when it was first released did you when you had to charge it every sort of other day well every what was it 1200 you're not changing gear very often um and in the first when you're going across the desert you're pretty much staying in one gear so yeah i just charged it off a dynamo i had one one spare battery and i think i only charged them twice so yeah five and a half thousand k's she's she's still going she's done many great adventures together i remember elby and i both taking these bikes down the back of buffalo on the mountain bike kind of gravel tracks just showing how strong carbon is but also how comfortable titanium is and how resilient both are together so yeah all our bikes have got cool little stories um if they could talk they'd they'd never shut up really so quickly on that one of the things that i've sort of picked up with kerb i'm very much on the outer sanctum not on the inner sanctum but the inner sanctum with kerb is is very very strong and it's it's almost like a cult cult following i don't know if that's the right word but uh can you tell me a little bit about about the the custom about the cult the cult please tell me about the cult yeah well um what's that each night we pray we pray on a friday their shed beers we we just kind of really invested in is this what like this area is about people coming in yeah they just come out and i mean it's a bit of a mess because that Adam's kind of come back from race to the rock and we usually have friday night you know we clock off work in the in the australian tradition we clock off work a bit a bit a bit earlier kind of roll out a few eskies turn up the music have a bit of a a yarn as they say yeah um what's that i think we've got a barbecue out the front so we throw throw a few snags yeah on the barbecue yeah i'm from south african so i'm like they don't say that they're like yeah let's uh let's fire up the bribe we're going to eat some uh eat some meat but um before we wrap up so your bike that i'm about to take is there any last thing that i need to know about that before i take it off your hands for a week yeah not there's not a hell of a lot really to to be mindful of it's at the end of the day it's just it's just a bike you know go out and have some fun on it


The grants will actually offset a lot of our budget. We operate with about a $3 million budget of that. The biggest part of it is put towards trail maintenance. These grants will allow us to do regular maintenance that we otherwise might not be able to do. The primary use of that grant funding will be to get our dozers and our guys out there to keep the brush cut back, to actually install drainage systems, to do long-term maintenance on the trails. As the riders come in from all over the country, they have a lot of wear and tear on the soil, we'll use these grants to repair those trails and get them back up so that the next group of riders come in on an ice groomed system.