If you don't have a machine, I'll probably post the link after this, so if you want to follow along at home when you have the video, your work, I want the keys back, so let me see. You have not.  Pass it along. Basically on the key, there are two directories. There's a big OVA file, and there's also a presentation holder. You can want to copy both of those. Basically, if you go to the presentations directory which you may have copied, you'll have a whole list of things that will be in the main presentation, a variety of browser based demos, which we can do even if you don't have a copied or you don't have a virtual box. And then we've got this whole virtual box thing. So, all our directors have, I guess, started the presentation, and we'll get on once you keep doing the copying thing, because we'll get to that later. Basically, we're going to be talking about deep learning. This is a hot topic. When I've done this workshop, I started this at Forsage last year. This has turned into quite a large repo, which has all opened source. There's lots of fun and experiments in it, and I've done this whole variety of things. I've actually prepared something new today, hopefully this will work. So, this is about me. I have kind of background in machine intelligence, start-ups, finance. I'm in a single port as a team. Basically, I spent 2014 reading papers and choosing to open source and basically getting back to kind of the forefront, because I didn't look like a machine learning PhD in the 90s. So, I just wanted to get back away from what I've done financially, and I want to do the machine learning thing seriously. So, since then, I've been, since 2015, it's been kind of serious. I've had a job doing proper natural language processing, deep learning, I've opened some papers, all in Singapore. So, here's a quick rundown of what can be done now with deep learning. I'm sure you've seen this a lot before, which is worse than the quick ones. So, we can do speech recognition, enterprise relation, project resolution, automated captioning, real and false learning.  This is not going very quick. Speech recognition, Evie Andrew of Phone Hasses since 2002 in the clouds, 2014 on your phone. Translation, Google phones can do this, or basically they can in-place translate text on the screen in real time, which is great. This is all deep learning. Google has been capturing house numbers from huge numbers of cameras in other countries, but also because they've got captures which people fill in to verify that they're humans, Google at the same time has collected data on what the actual house numbers are, by cross referencing these humans or supposed humans. So, not only have they got this data, they've now got a human analysis of data sets. They've now recognised this stuff better than humans. There's, I think, an image classification. Basically, we've got here, this is a container ship. This is a method. This is stuff which machines can just look at these images and tell you what they are. So, Google's got this in, say, their Photos app, whereby you're automatically letting all the signal photos. The same with Facebook, same with all the images we've got so far. They can also generate, like, detailed captions. So, over here, over here you've got someone on a motorbike, two dogs, or playing a game. So, you've got this thing where the machine can actually just spew out captions of which explain all the scenes in the picture, as well as just having a signal link there. We've also heard about reinforcement learning, where it's been applied to AlphaCo. So, actually, I've got, in the workshop folder or the repo, I've got stuff which addresses all of these things. But we're just going to go from very basics today, all the way to something that's interesting. The basic thing about neural networks is that it's been, to feel, this fairly unchanged, unmentally since the 1980s, in that you've basically got very simple units, producing very simple functions. But when you combine huge numbers of them, you get something much more complex. So, here is a single neural. And what we have here at the bottom of the look on it, inputs. So, this will be the features that, it could be the values of a pixel, or it could be the value of the temperature and humidity. This will be rising from inputs. These are then added together with weights. Then, from here, we then put them through this kind of nonlinear function to get this output. Now, this nonlinear function is just, if it's more than zero, keep it, if it's less than zero, it's zero. So, you would think you can't do very much for this, and that's true. So, we'll have a little look about what we can do with this, and why making these things, joining these things together makes a difference. So, basically, you will then move on to a multi-layer neural network. This has got the same kind of inputs at the beginning. Translate it through, each of these things, this one has got weights to all of the other inputs. Same with this one, but these weights will all be different. Similarly, this unit has now got connections only to the ones in the layer before. So, the final output is a combination of the layers before that. So, basically, each of these is a very, very simple function. This sum stuff, weighted sum, maximum is zero. So, we've only got something very, very simple. How can you actually make this turn into something useful? So, what we can now do is go to the first of the things that you'll find, which will be this tensorflow if it doesn't drain. Basically, if you've got this stuff on your machine already, you'll find it in the presentation folder. You should easily find something like this, and then you can click on this tensorflow thing. Is everyone following me here? Has someone not got a USB key yet? Are we all done with the USB keys? If you haven't got a USB key, this is actually something which Google put up on the web. It's called the TensorFlow Playground, so you can just find it anywhere. The reason I've got this on the USB key is in case the last year of the FOS agent Wi-Fi was terrible. So, everything on the key, this doesn't need a different internet connection. So, if you click on this, basically, we get to this kind of page. Does anyone fail to get to this kind of page? If you do, I'm going to move on quickly. It's already set up so that we can look at what this page is. It's a neural network playground. What the task we're trying to do is to look at this over here. This is a set of orange dots and blue dots. In these orange dots and blue dots, the idea is that we want to create something which will separate them and predict which regions of orange and which are blue. Now, what we're doing, what we have to play with here is just something which tells you how or lefty and righty it is and how or up and down it is. These are the only two features. So, we're only allowing two features here. So, if we combine these two features, the only thing we can get is basically we can do any diagonal line. So, if you click this training button, basically this will say I know I've got some weights from this one to my output from this feature. I've got another weight from this feature to this to the output. I'm going to adjust these weights until I make these regions as good as possible. So, we can start this. Well, it's completely the wrong answer. What happens here is that we've got, it actually chose the weights back and random, the wrong way around. So, when it picks one of these blue dots and says, okay, I think it should be orange. You say, well, I've got that wrong. Why did I get that wrong? Well, I paid probably too much attention to this feature here. So, you can then start saying, well, if I paid too much attention, I should be changing this weight. So, I know which weights to blame for getting this wrong. So, at every stage, if this is coloured wrong, I then feed back to these weights to adjust it. So, I prefer this. I don't want that feature. I do want this other feature. What will happen, actually fairly quickly, is that this will then train to categorise this property. So, this is a very simple blame game. Basically, we're trying to categorise all of these things in the blue region, all of these things in the orange region, and if it gets getting it wrong, it tries to de-merit the feature which it, which gave it the wrong hint, and increase it for the one which worked. Now, this is fine until we say, okay, let's place it like this. Now, this is one for which we start. So, this one is like a quadrant in the gut, like a checkerboard. And the question is, what is the best line that separates these two? Because all we can do with two features and two weights is generate a line. So, if you try to do this, basically, it's not going to do anything. There is another way you can separate this checkerboard with a single line. So, here's where you say, well, what I really should do is have some kind of hidden units. And this will then say, let's just start with two. So, if I add a layer in the middle, these hidden units can now produce a single line. This will be called a mission of two lines. So, that may be something where it could figure something out. So, it's beginning to get the idea.  But, basically, it's been able to say, well, I've got the lower bit, which I'll ignore, but I'll fix up the other bit. So, this works using two lines to try and do something. Equally, we could... Equally, we could... I'll tell you that. Can I add some more lines? So, here, by... Essentially, by just putting the right blame and deciding who to, what features to use, it can then cement this thing by using this hidden layer in the middle. So, that works well. But, let's try something else. So, this is a ring around the circle. So, if we just rethink this, and we try again. So, it's had to work a bit harder. But, basically, if we now look at what it's doing, is when it's looking at these orange pieces, it's going to say, I love the stuff around this area and the stuff which has given me the wrong idea, which could be there. I'm going to have a demerit against that one. But, because of these demerits on the weights passed back here, this would say, well, I wouldn't have bought that demerit had I not been given the wrong information on my previous level. So, at each level, they get... not only has the weight changed going to them, but also how bad the information they got, how well they contributed to the whole outcome was. So, this enables each of these things to then have its own demerit system. So, this is how the error is being back-propagated through the network. So, the signal is getting forward-propagated through the network. But, because of this whole fitting and error and the blame game is actually on every weight, you can then pass back the demerits through the whole network. So, this is what the back-propagation learning is. This is a fun little thing. So, it's not clear whether this will work. It probably won't work. This is basically what this kind of network can do. It's kind of to illustrate how not only can you vary the features, because there are other features you can use, you can also create features which could be useful. This isn't going to be... So, this is just to get the idea of features and the ability to generate these internal features. So, this answers what hopefully what we've run. Basically, the goal here is to do some supervised learning. We're trying to learn to predict what the inputs mean in terms of here is blue and orange. We can also choose which input features we're going to use. We can see what the signal looks like in neural learnings, which is hardly anything. We can see how this blame game is plagued with back-propagation. And then, how the neural networks, that blame game actually, will create internal features which are useful for making this work. Those are the takeaways from this. So, let's talk about something where deep networks have had some success. This is image classification. So, through the stems of the 2000 Os, basically, there are lots of... OpenCV will be a classic image recognition or image processing library. And people would detect. They would have lots of different features that they would look for. So, if you're trying to detect... I'm not sure about if you're trying to detect a cat, you might have built a specialised third detector. You might specialise in the eye detector. You basically build an arsenal of features. You combine them to make the best possible detector. It happens in 2012. And, since then, the deep learning people, basically, instead of having hand-picked features designed by humans, they just said, basically, we should let the output drive back to the features that we want and let the machine determine every feature that we're going to put into this. So, since then, and this is one of the things which suddenly made deep learning a huge deal, is the deep learning just basically took over those competitions. So, in order to understand what's going on with these image things, is that instead of doing the orange and blue dots thing, basically, we're looking at the whole image. And this is normally considered or previously considered something which humans would have, but machines would never be able to have, because it's just so fuzzy what is in the image. But one of the nice things about images is they're actually organised. So, you've got the whole idea of up-down and upright. So that the actual pixels are related to each other, like the pixel one above is closer than the one elsewhere in the image. You've also got the idea that the cat could be anywhere in the image. Basically, images have some organisation compared to just x1, x2, x3. So, the idea for this, what it's now called, CNN, so this is a slightly different kind of neural network, but it's all the same transport, is to use the whole image as being our feature. And the parameters that we're going to twiddle are basically the elements of a Photoshop filter. So, no Photoshop filters, you can have a sharpen, you can have a blur. Basically, it takes around, I guess that, a mathematical term for these Photoshop filters would be convolutional filter or convolutional kernel. A CNN is a convolutional neural network. So, basically, the idea of a CNN filter is you have this little matrix. You have, here's your input image. And here's a little matrix. And I'm going to pass this matrix and just add this stuff up and multiply to produce an output image. So the purpose of this is this, these numbers are the parameters of my convolutional filter or the layer. So, in this, the parameters here are just translating one image into another image. And what we can do in order to see that more carefully, actually clearly, we can have a play with a convolutional filter, which is one of these. So, here is something which hopefully you've got. It's also available on the web, just that I made it last night. So, here is a simple thing where we have this is our input image and we can play with this one. This is our output image. And basically, if I start to change some of these parameters, this is a 3x3 convolutional layer. This is one convolutional layer. So, I can just change the numbers. So, you can see that by changing these things I'm going to emphasise different pieces of the image. Either I can start to make it lefty-righty or I can look at edges. I can make it blurred. I can make it sharp. But basically, it's not my job to make this happen, because I can let the neural network play the blame gate on what features it wants to have as an input. So, then that's the presentation. Here is the idea of a CNN, the workflow which is going on in a big CNN. So, basically, this is our input image. And then the first convolutional layer is basically a whole series of images which are, I'm not sure what they are, which are basically the car with different filters applied. And then what you can do is you then put this non-linearity after it. Then you do the same thing. So, you're going to say, OK, let's put another layer. Let's represent the fuzzed-up version with a sharpened version. So, I'm not sure whether you played with Photoshop, but if you blur an image and sharpen it, it's not the same image. So, you can imagine if you've got a righty image and a lefty image and a lefty image, and then you combine them, you can start producing many more different kinds of images, and you might be able to start highlighting different elements. So, if you look at this in detail, or you look at some of these pictures which you can find online, basically, you'll start here with just kind of outlines and vertical lines and different aspects of pixels. But as you start to get over here, you start to pick out shapes. These layers will be very responsive to circles, which mean car wheels. If you start to train on a whole bunch of cars, it will start to pick out features which are useful for recognising cars, and it will do this purely automatically. So, just to complete the flow, basically, you'll take all of these image pictures, and there's also these things called pooling layers. Basically, it's a squashing operation. So, you take your whole image, you just do a 2x reduction. So, basically, at the end of it, you get some rather small images which are basically saying, well, there's one wheel in this and there's a headlight in this and there's some in this. But I can't necessarily explain what terms there will be. It doesn't tell you. All it will be able to say is, in order to predict this is a car, which is the output I'm trying to strive to, I would like there to be wheels and headlights and some. Or whatever it is. So, First day you say that it's a convolutional transformation, but it's like the same filter, back to every optimization. How do you know that it's doing that? Is data adjusted? But by construction, the only way in which I'm allowing it to do is applying this filter. So, it's not going to align each pixel to reflect every pixel. It's that filter with its nine weights. So, if you're doing that, it has no option. So, this is it. So, particularly, you'll have not just one filter. You would never have just one filter. You'd have 64 filters or something. Because you want it to have a palette of different things. This is actually a three-plane thing because it's got three color channels. This would then be a 16-color image. This would then be another 16-color image. Right? Because we want to keep the we like the translation in the right side here and we know that eyes do kind of something like this. So, we're going to exploit that. So, now let's explain why this has been so successful. Basically, there are competitions to do this. And this ImageNet competition is a big deal in image processing land. It has 15 million level of images, 22,000 categories. Basically, people are playing a game to win this thing. So, it could be Google playing it. It could be Microsoft Search playing it. We'll get to that. Basically, it's now turned into that since the whole deep learning thing, I guess this is only still a four-year-old kind of issue. People are pouring money into winning this game. So, what it's doing is it's advancing the state of the art extremely quickly. So, the game, the essential game here is to take this image. And this is a visualization by a guy called Carpathian who's from Stanford and is now working at WI. This is a picture of a cop dog. Here, each strip of these are different classes which could represent of the stuff which is in the ImageNet database. So, going across here, Mae yna'r crimfyniad wedi symud i bobl ddweud, oedd hi'n dwy flynyddoedd, byddwn iawn amlwg amlwg i'n ddech. Byddai'r imiwg nesaf i gandwydurio allan o fflicer. Mae'r hwn yw hyffordd â hyffordd hyffordd â hyffordd â hyffordd, ironio i'r myfioedd i'r cyffordd. Byddai'n granteis, gallwydig yma, ac sétio'n cael ei gyfrifon o'r model. Mae'r hyffordd yn gallu i'r imiwg nesaf oeddi, i'r twfyrdd yma i ddweud. y cwmhysg yw'r cyffredin o'r cyffredinol yw'r cyffredinol. Yn yw'r cyffredinol, ond mae'n mynd i'w wneud y dyfodol yma. Wrth gwrs, mae angen i'w gweithio'r gwymau a'r gweithio'n eu cyffredinol, mae'n gwneud y 25% erioed o'r cyffredinol. A'i gilydd i'w gweithio'n gweithio'n 12, mae'n ymgylchedd y fferdd ymgylch, mae'n gweithio'n 16% erioed o'r cyffredinol yn y gweithio. Ieithwyr ydych chi'n bwysig am ymwyfyrwyr o'r perffordd o bwysig a'i gweithio'r cychwyn i'r ddweud o'r ffwrdd yn ysgolwyr o'r cyfrifnid yn yr ymwyr. Ieithwyr ydw i'w gweithio ymwyr, mae'r ddweud o'r cyfrifnid yn ddweud o'r cyfrifnid yn ysgolwyr, mae'r ddweud o'r ffwrdd yn cyfrifnid yn ysgolwyr. Dyna, mae'n ddweud i'r ddweud o'r ddweud o'r rai ffwrdd. A on ap mae'r dgweithio'r ffordd yn ôl yn 5%. A'n greu dechrau 100% er mwyn ei ddweud eich wath y mae'r cyfnod rha o bwys. Rhaid y gallwn ei ddweud eich nitw i ffwrdd, yn hollai hun. Ond mae'r ddweud bod rhywbeth i'r ddweud ymchwil, pan rwy'n credu sut yn complimentary o'r berynion o'r gweithiau, oedd ei ddweud sy'n eich ddweud. Ond mae'r ddweud yn ei ddweud. Mae'n dal i gyda mina iddo yn yr hyn yma yn gwaith yn y cerddwyr yn edrydd. Mae'n hanes gawr yn ddechrau. Mae'n rhaid o symud yma yn gweithio'n dail ar ddweud i gwych yn symud. Mae'n ddefnyddio'n gweithau yn yffernig i gwahosig i'r bwysig, ac mae'n ddweud i gweithio'n gweithio. Daeth yn peidio'r bwysig yn ddat â'r syniad gwazio. Pau'r ddoch yn ddysgu'n ddweudio. Mae'n ddweud yw'n gweithio i'r gymunedur yn'n gweithio. pan hwnna'n gweithio Gwbl Onet yn 2014. Mae'n gweithio allan o'r cyd-dweithio gyda'r Cyfnod CnN yn ddweud. Mae'r cerdd yng Nghymru yn ddweud o'r Cyfnod CnN, neu mae'r cyfnod cyfnod cedlau ar y cyfnod cedlau, ac mae'r cyfnod cedlau ar y cyfnod cedlau ar y cyfnod cedlau. Mae'r cyfnod cedlau ar y cyfnod cedlau. Felly, yn y cyfnod Cd强f, mae'n gweithio Gwbl Onet, ac fel y cerdd, If you haven't got a virtual box, then you can't press this virtual box thing. What I'll do is then import the clients. For this clients thing, I'll just get them in. So I've produced this. Cross fingers this works. So basically what's shipped here is a container through a virtual box client which will load this and run it. This VM contains Fedora 25. It has TensorFlow setup, yes, the ANOS setup. It has data sets. It has pre-trained models. It's got Jupyter, which if I do this, basically. So this is the first time I've run it. So everyone else should be able to see this. No matter what machine you have, this should be able to do it. We have to get to this login point. Let's go back to the presentation. On your localhost, you can go to... Basically, we don't have to log in. The login point is misleading. You don't need to log in. If you do want to log in and have a look around, the user name is user and the password is password. But it's not designed to be secure. It's designed to be... There's no need to go into it. There's also instructions in... Let's kill. You can also SSH into it. Now you know the user and password. You'll get a console. I don't know if there's various things around there. This runs TensorFlow as well. It does stuff. On the other hand, it can't attack your machine history. It's fine because it's nice to bring it in. Here, what we're looking for is this image net. There's a Google Run app thing. You'll see this as being this number three. Image net with Google Run app. What I'm going to do is I'm just going to run more. I could go. Normally, I would go through this in some detail. Because one of the things I've been doing is explaining how these frameworks work. The reason that people can piece together these neural networks so casually now is that people have come up. The open source movement in particular has produced not just different datasets, but these frameworks which enable you to just piece these neural networks together and have this whole blame game implement. Blame game is really the derivative chain rule. You can calculate the chain rule by any kind of real network. The frameworks can take care of this. This one's implemented in the thing called Theano, which is a Montreal project. Google thing or the MXNet or the C&K. These are newer variants produced by actual... Research seems really good, but it's basically a series of duct tape gradual projects. Whereas Google has actually started out. Other frameworks have started out with a much bigger overview of what's needed and the actual engineered product. What this does is it loads up some stuff. It imports the model. There's a model definition here. This is basically the model we're looking at here is this. This is just a rather deep neural network. This defines layers, opens this big thing which populates the layers with predefined model values. There's a way of preparing an image. This is an image I've got. There's images on the device. Basically, this is all you would need to do is to print. Basically, you get the features of the CNN and then you just say, well, what is the most likely of these classes? It's heavy capped. This is a good result. Basically, here we've got a neural network which is fully open. This whole thing is open source. You can go in and have a look. You could play around a bit. One of the disadvantages is you lose the ability of your model parameters to be predefined. The nice thing about predefined models is you can download these free, restricted, but it takes to do one of these image net things. It can take a month of GPU farming to get to a good result. It's worth making use of other stuff. There's other scripts here which enable you to drop files into these images into a directory so it can classify everything it sees. It's just called Tabbycat. It's got golf ball. This is not a great... I don't think it actually has owl as one of its training examples anyway. It basically can do... Rabbit is probably okay. Band 8.9. It's been missed there now. Muzzle of the Golden Tree will make me sign his cat. This is basically... From 2014, a state-of-the-art thing applied to mean photos I found on the internet. You can play with your own photos. Whatever it is. But wait, there's more. The thing is that the next year Google came along with Inception 3. This is a much larger network and is even better performance. There's also a pre-trained copy of Inception 3 over here. Except instead of taking half a second to run or a quarter of a second to run it's going to say five seconds to run or some extra time because there's a lot more ops going on in this. There's a lot more flops. And then there's this thing about going deeper and deeper. So you start back in 2010 at these comparatively shallow networks. This is going forward in time this way. This is the performance. So basically before the deep learning it will be at 25% errors. This thing came along down to 16 errors. The number of layers is now 8. 19. 22 layers. So this is the Google and the MEP which is sitting in a 19 layer thing. 20 layers. But most recently it hats off to Microsoft for doing this. Now it's beating human-level performance with the 152 layer of monotrosity called a Resonate which we haven't got on here because it will kill everyone to machine class. This is a really deep idea and my guess is there will be more of these ideas. One of the problems with scoring above human performance is it's very difficult to get any training data. Because who do you trust to tell you more than human performance? So this is one of the reasons why all of this stuff can get up to human level and if you then start to have committees of humans you can exceed a single human but to beat committees who do you trust? So that's a problem. So what we've seen so far is CNNs are good at images. And hopefully something is interesting. The fact that your machine from zero at the beginning of the hour to now and that Simon's cat is pretty amazing since it's very difficult to distinguish between a Simon's cat and a Tabby cat without talking about stripes and who do you trust? Which are very much more abstract human concepts than angles and pixels. So these machines can do a huge area of research also a very commercial kind of area of research and CNNs come along a huge way. So they're so good why not use them to do things they weren't meant to use to do. So one of the simple examples we're going to do here is speech recognition which is kind of not what you should be doing with this stuff but these things are so flexible why not? So basically what we're going to do is turn speech recognition into an image recognition task and then solve the image recognition task. So on your theme there'll be a there's a folder called speech under there there's a thing called data and so this thing is dividing into two pieces one of which is the thing which prepares the data sets and this is if you're into any kind of data science you know that this is the 80% problem the 20% problem is actually learning to do the thing but the data collection is a pain. So I had to admit that last weekend I did not know what I was going to do at all I was still searching around for some idea for what to do on Monday I decided let's try speech recognition Monday night I kind of figured out spectrum rounds maybe Tuesday I was collecting some speech so what I did is I have a nice little app called voice recorder on the end which produces WAV files so basically what you can do is just excuse me. So basically when you get a WAV file on your phone it sounds a bit like basically what happens is that I've rescheduled this so you can't see how so it's the graph package of this basically at the beginning you can see this is me thumbing with a phone this is me kind of thumbing a phone out I want to crop this this is the digits so this is my version of endless for audio recognition so this is 0, 1, 2, 3, 4, 5, 6 so basically I've created a crop tool which enables you to just crop this thing and basically this stuff is this speech is just a series of non it's an umpio rate it's easy to crop having done that you can then save it back then what I've got is so this is basically I'm trying to lay out how this all works so you can play with this for yourself you can play your own data it's all open it's pretty easy to get into a function which can use a spectrogram so this takes the sample, takes the mean has a hammy window FFT it's moving function so here is what I get with my kind of very naive spectrogram which is I've got my thing which is hopefully so what I've done here is so you can see so this is for the bottom there's low frequencies high frequencies this blue line is kind of the amplitude of it because I want to then crop these into simple words because rather than take the whole thing as being my image it's easier to swap the isolated words so if you're into speech you're going to know that I'm cheating I'm going to do this on isolated words which is so much easier than continuous speech because if you've got continuous speech you've got to then figure out where the word boundaries are here I've got a very simple nicely separated thing so this is where these other lines come in it's a detected sign with that 8 so another nice thing you can see you can see that this is 6 and 8 I've got two distinct pieces or several distinct pieces in that thing 5 this is 1, 2, 0, 1, 2, 3, 4, 5 you see this little tick this little tick here this is the I same with this this is 9 so you can see just from the spectrum at this point I'm like ok maybe this could work so you're greatly encouraged on Tuesday so I then have this kind of contiguous region detector which just then slices it up into a nice little pump here and then this is the other thing I also spend a lot of time looking around of all the open source data sets it's not so easy in speech because there's a lot of closed data or a lot of non-commercial data a lot of scripted use this is why I started doing it on my phone there are huge data sets all of these are kind of being numbered so I couldn't really hand out and go to the keys so this thing allows you to collect a spare amount of speech it's got this idea of having a prefix so these sentences are 0, 1, 2, 3, 4, 5 6, 6, 7, 8, 9 there's some animals which I've got below, that's kind of extra and then there's some other things which are kind of interesting this is the quick brown fox jumped over the lazy dog has all 26 letters of it in English but the quick beige fox jumped in the air over the thin each thin dog look out by shout before he's foiled who again created chaos but it's every phoning in the English language in kind of the shortest sentence so these would also be really interesting data sets to collect huge numbers of words for because then you'd have examples of every phoning in just one sentence and there's a whole bunch of those there's other typical data sets called Northwind there's interesting stuff to do because now I'm down to three days left there's also some time but it's something great on being so being has a very nice speech API so of course from the speech API you can get it to say creating free data for speech recognition so thank you Microsoft so there's some being on here but you can't get the thing is that there's the open source so with that it would use beautiful words but there's only one variation so now I'm going to move on to this let's have some actual Python speech features this is an actual package for people who know what they're doing so this can be better but it's my slightly worse here's the nice spectrograms this is a nice version of the spectrogram thing can see just the same style but because they're proper speech people they've made the spectrograms look like they correspond to what you hear rather than what the FFTs say and what I've wanted to do is I wanted to look like what you hear because I'm going to do a vision on it so looking alright is a good sign for me so I've got some other code which helps to build a data set so here I said this stuff 16 times or 15 times and also being has contributed Catherine, Linda, Ravi, Susan George, Sarah and Benjamin who are actually people from different what, machine people from different countries so there's another label and then I've got a thing which converts the WAVs to stamps like the long combined things combine these into stamps I've chosen I'm going to make my stamps look like that they're all going to be the same size so I can then just put a vision and I'll say Python so I'll you can see what word this is this is clearly 6 now 6 is interesting because it's got the x it's clear what's going on because it's so clear this is another good sign so here's all my 6s we're hearing as much as 6s and then I basically said well I also want to have some test data so I'll keep my test data to be separate of we can see the directory structure and so here's the digits what they look like for the test data and now it's time to train and run by the way there's also some other stuff for maybe for another day so there is insufficient time to really see what's going on here basically what we're going to do is now train our own CNN to recognise these stamps I have a label I don't have much data I have maybe 20 or 18 in different of these stamps I'm going to try and make it tell the difference and when I wrote this to the Google guy which I met I got maybe 20 examples and it's got to train in less than 5 minutes he's like definitely joking so what we've got here this is the actual definition of the CNN we've got the input features which is the stamp itself we then do a convolutional layer so you can see this is tensorflow it says layers 2D convolution and we do a poolwork layer and we do another convolution another poolwork and we do a dynx layer and this is the car recognition this is just probably 90% it's Google's code for doing N list which is a digital recognition we have a dropout layer which is another exciting example to try and call and then there's the first boilerplate to make this work so here because I said train all basically this thing has now started processing this stuff and all gradually you can see the training cycle going on so it's done 1400 steps each step is 20 examples picked a random and you can see that this loss number started at like 2 and it's now going down it turns around a bit so the next thing is as well as having this training this training data basically also with each of the training with each of the examples I store a random number between 0 and 1 and what I do is instead of having a strict partition between training and validation and mess around with lots of different variables I have one training set and I partition it based upon the value of this variable so I say I want 90% of them basically I see what is the random number more or less than 90% if you know it wasn't me to use just one data set so let's see what's happened basically after the training I then did this thing which did this evaluation basically I have an accuracy of 1 which means it guessed all of my digits correctly which is amazing but this is from my validation so let's just see how this stuff works for these predictions so basically this is from the test set so this is something you've never seen speaking, you've never seen quite a lot of me speaking but it's also got some other voices I would love to collect voices from around the room and have them all into one beautiful data set and then a bit of multi-speak I'm not sure, I don't even know whether it will do multi-speak awesome and this is kind of the matrix of this things it's 99% likely that my 0 is a 0 it's not so clear about 1 it thinks it could be a 5 so basically this is so here's the test set just illustrated so we've trained this thing it takes probably 3 minutes to train this if we trained it more obviously it certainly gets better but there's no time so because he's having a reasonable idea of most of these digits it's got number 1 it's very confused this one it would also get more but here we have so in a nutshell we have speech recognition live on your machines you can train it if you just do 2 rounds or 3 rounds this will become perfect there's another interesting thing which I started to play with but we haven't had enough time basically we've heard the animals that we eat cat dog fox bird what you can do is instead of saying well I want to have a whole training set on cat dog fox bird I would see what they look like in terms of numbers I have to say this doesn't encourage you but if I feed these birds into my numbers on your network I can see what numbers do these look like for instance bird looks like somewhere between 8 and 9 which is kind of weird but cat anyway so you can see that these are kind of what if you classify them with the wrong thing you go to like a fingerprint this is a fingerprint of errors because none of them are correct answers but the fingerprint of errors you can then start to train on so you can actually look in more detail by taking logs looking at the logents of this thing and you can then train an SPM on this thing okay so here's the answer that it's getting so I put in some training places as well so I have 5 cat dog fox birds which I then used to build mine so I've got only 5 examples of each to train the SPM on the errors and then I put in next classifying cat it thinks it's a dog dog it thinks it's a dog, fox thinks it's a fox bird thinks it's a bird so just training on the errors it's actually learnt to recognise words which it was never talked about in the initial training so this is another way we've only ever used CNNs to learn how to do speech recognition we've exploited what we did a nice train model to do stuff we never trained on which kind of opens the door to why we've used the big CNNs train for ImageNet to do recognising of T-shirts all this kind of other stuff you can do just by manipulating so that was that and that back up so deep learning may decide some height it's kind of cool what it can do the field is advancing very rapidly this is in a way the CNN thing in my mind is fairly done there's a whole bunch more they can do with speech recognition but with recursive neural networks or company neural networks tree structures there's a whole lot of interesting stuff to learn there's a lot of data out there but having a GPU would be very helpful and that's kind of one of the reasons that it needs a price to do this but you'll see from your laptops that we're sagging this is all over the source it's called deep learning workshop I should point out there's me and this guy Sam are doing a deep learning meetup group called the TensorFlow and deep learning in Singapore it's hosted by Google the next one is Monday it fills up rather quickly we found hopefully people abandon us so yeah it's hosted by Google so it's a mix of some TensorFlow which is one of these big frameworks and a lot of deep learning which is kind of the driving factor we typically want to have three things we'll have a talk for people starting out I will probably change this I will cut it down and make this into a CNN talk for me years something from a bleeding edge which I think can be generated for the serial networks coming up maybe we want to do lightning talks show what they've like shown to we've already had people who've been abusing the models that I made the previous workshops to recognise their family and this course that's a meetup.com is where you can find that we're also looking to do a 10 week 8 to 10 week deep learning developer course this would be it's like a Udacity course but actually in person sessions we'd have some instruction but also projects because what we see is as a hiring person when someone comes to me and say well I did all these courses in university and we had this project because it's kind of meaningless whereas if you've actually done a project of your own that means something so it's like one of the benefit of people standing up and saying stuff of these open source things that they've actually done something rather than just being shefforded into doing a project cost to be decided soon it's not very easy any questions this is a fun question I have a question but I'm not sure if it makes any sense to me you have a question go ahead so the idea that the deep network finds the best features to specification but how do you find a structure or the best structure of a network? it's called the graduate gradient descent it's called graduate gradient descent you know I'm in the structure but basically you throw graduate students at a problem but certainly until you find the right structure but basically this right so I took the endless network from Google's code example it seemed a bit big so I dialed it down but I still had 10 classes I put it down so it was quicker it still kind of works really behind the conclusion that what there is a translation environment so I really have to give that in the network instead of hop for the network to guess it what insight do you have to have a hunch that this could work for speech recognition because when I look to the spectrograms I could tell a difference that's it the visualized could work that was the only intuition then so the thing is if you know what an eye looks like you could then spot that's a tick check you could then imagine passing a window even though of a continuous speech looking for eyes so one thing is to know that this thing still if you can spot it yourself then it's definitely doing more so one of the rules of thumb that Andrew Bowman has come up with for can AI can machine learning do it is if as a human you can do this yes so don't learn a task which you should be able to do in other words because the machine will be able to do that pretty soon so driving a car is going to be doable or recognising words or radiology there are these huge things which are going to be very doable I mean I have things which take a lot of pondering that's going to be much more time this stuff just back to the features question the guy who had recognised his family based upon basically he was using image net errors to recognise his family but the things it picked out about his family may have not been the shape of their face the things that you'd expect to look at may have not been maybe that his wife's eyes were particularly round or had like a nettle song it loves the nettles it doesn't care what the person is because it's going to pick his daughter will always be ahead of the serve who knows what it's really doing you can delve into it but it's not necessarily what you'd think of good features but the fact that you can spot something means it probably won't do a question because both this inception and Lenin seem to have seven structures so there were like sets of convolution drop out and pulling layers and then they have this kind of joint layer where they run these things in parallel so this is also an instance of graduate stream descent that you have got one guy who figured out that this is a good idea and he was then employed the next year like you will still be it's going to be in all the networks so this is where the Microsoft things were the resonant thing the residual network which is basically the idea is you have a fairly shallow network which does a kind of terrible job and then you kind of fix it up by stuffing in more layers but basically you're trying to pull off errors all the time which is similar to other statistical concepts so basically they've been unable to instead of training 152 layers from GEDDO they trained a very small thing and then pulled it out and each stage errors were reducing so this is the other thing to do is kind of a fake number but it is the actual number which gets executed so the Google thing they're probably a bit stuck on the nice modular structure that they have it takes someone else to come up with another brutally different idea this is one last question when we ran through the example that you had with the training set the results that you had your screen was slightly different from what I saw on night so the cat dog fox for example it got its spot on to the cat sample was the cat sample I have to say that this morning before I blew the USB keys I was still fiddling around because I thought I'd set the seeds to fix up the training regime so I'm surprised that our machine and I'm encouraged that our machine is different because it means I've missed out something my question is is there a variation between the learning that Dave's based on so basically all these networks started out with just random filters and you may have seen occasionally you'd see this even in the TensorFlow playground you may get two neurons which you've got five neurons two neurons which you've got fundamentally the same weights and these two lines will just be difficult to un-entangle so they'll just sit there on top of each other right but if they were to start off skew with then you could manipulate them both because they're sort of like co-linear you never get enough grit in the thing to actually make it happen so people with different random nationalisations could have started off all in different places and there's not enough data to force us all into the same place at the end if you had more than 200 examples you could probably get to where we'd be forced into at least local minimum which we'd similarly enough so playing with the noise element or the percentage of noise for example would be useful in some sense right, yes in fact the noise thing was kind of one of the big things which made the difference between the 90s and now in the 90s people we always used to set their weights to be like some small number like there's one of the like the footnotes just oh we'll make it point 1 but what happens is if you set all your weights as being such a small number basically from layer to layer you're shrinking the whole thing like crazy which means that all of the all of the gradients turn to be miniscule and things go horribly when this thing never learns so people learn that you need to make the initial values like certain range and then it all just works beautifully and that was a big big big difference between that's a 15 year kind of difference some it works just like choosing different random numbers one more question did you use pre-initialised networks for training or just pre-initialised? no it's just random form zero and also the data is horribly dirty I mean it's like near my phone the things it works I mean that's the really encouraging thing can you take one more question? shot for me so the accuracy is one because I've only got like 20 test examples or validation examples so either I get the right or wrong it's like a cross-validation error so it gets right if I have a huge dataset then I have much more fine grade control one of the things about the image net dataset which is huge is there's also mislabelled images there's also a human labourer fortunately because of this unless being gave me the wrong the wrong words for what I gave it because I didn't listen to them all I think I have no errors in my labels but a typical dataset will have errors in the labels too and also bad examples so big data on the other hand when you have Wikipedia which is a fairly typical machine learning natural language processing training set 6g of data so thank you


So I was invited to an event by Cutie Cinderella and during one of the breaks I sat down next to Beanans and Shroud. They started talking to me about a keyboard I built for Beanans a long time ago and how they actually really liked it. Hannah actually told me that they liked it so much that they kept typing on it because they liked the way that it sounded and felt. Now the next part to this is where it got interesting. For those of you that don't remember, Shroud had a massive collaboration with Logitech in October of 2021 for a keyboard. It was the first keyboard collab where they just straight up took a content creator's name and then put it on the product. The reason that's important because while they were telling me they liked the keyboard that I built for them, they were sad that they couldn't use it on stream. The reason for this is pretty obvious because if you have a collab with a company for a product and then you use a different product on your most visible platform, that might be an issue. And that is when Shroud hypothetically mentioned that he wishes that the keyboard that he used on stream sound and felt like the one that I built for Hannah. Logitech, in case you're watching this, this is all hypothetical. Shroud loves your keyboard. He would never change it. Now here's the thing. These keyboards that come from a major brand or anything that you can get from Best Buy or Amazon are always going to fail in comparison to a custom mechanical keyboard. Custom keyboards typically cost so much more because there is so much more that you can do with them. There are different materials, different switches, tons of modding options. But with pre-built keyboards, those options don't usually exist, usually. Now coming back to what Shroud said, I told him, you know what's actually possible for that to happen, to which he responded. Yeah, but it would probably take an unreal amount of time. No, I could probably do that in about three hours. So this is what we're doing here today. Bloodline Heroes of Lightness is a card-based fantasy RPG where you can not only collect champions, build kingdoms, but you can also create legendary heirs of your houses by combining the forces of various bloodlines, such as orcs, lichens, demons, and dragonborn.  You can choose your path by raising heirs with true companions from a vast fantasy world. Customize your champions by marrying different bloodlines until you find a unique combination of any five powerful abilities out of dozens of traits that suit your own need for gameplay. You also get to build your own kingdom and economy. The best part is the game is available on both Android and iOS. Download the game now. You can scan the QR code on screen or you can go to my link in the description, which I recommend because if you use the download link or scan the QR code, you will get an exclusive starter pack worth more than $30. You will receive 200 diamonds, two summoning crystals, and five stamina potions. Thanks again to Bloodline Heroes of Lightness for sponsoring the video. Now let's get back to the rest of it. So I might have lied to Shroud. I told him that it would only take me three hours to fix this keyboard, and if someone who was actually good with keyboards was in charge, it would probably actually only take three hours. Look, I'm not the best when it comes to keyboards.  We're filling this bad boy with some Play-Doh. I don't know if it would work long term or ruin your board long term, but I felt like doing it, so here we are. However, I did watch one of those nerd videos, and you will not believe the secret ingredient that it takes to make a keyboard sound better. Yup, that is glue all right. So there is a keyboard content creator named Keyboard, who is a part of a keyboard competition with other keyboard content creators, and they were all given cheap keyboards and incredibly small budgets to see if they could turn those cheap keyboards into something that sounded and felt like they were worth a lot more. Now, while keyboard didn't win for the best final product, he did create something that sounds just as good, if not better, than some of my $500 to $1,000 keyboards, which is kind of frustrating when I think about it. Sounds the best is keyboard D. Oh, wow. So what did he do? He put glue in his keyboard.  Now, if you watch his video, he will give you a perfect explanation for why he does this and why certain sounds make the sounds that they do. But essentially, by making the cheaper, thin keycaps artificially thicker with glue, he gets rid of a lot of the cheap and hollow sound that exists in almost every prebuilt keyboard. That's one of my biggest issues with Logitech, Corsair, Razer, or any of these other mass producing keyboard companies is that they don't put a lot of time or thought into the design or materials used for these keyboards, specifically when it comes to higher end sound or feel. That's why Cherry MX Reds and Blues are still even a thing. Don't get me wrong. Saying people don't get into custom mechanical keyboards, but if you're someone who plays video games, you probably spend a lot of time at your computer, right? If you drive your car a lot, it would make sense for you to get a nicer car. So why not do the same thing for people that spend time at their computer? Anyways, unfortunately, I can't just put glue inside of the keyboard. The entire point of this project is to make it look exactly the same while improving the way it sounds and feels. One of the challenges specifically with Stroud's keyboard is that if I were to just put glue inside of the keycaps, it would block out the RGB from shining through. Fortunately, they make see-through glue. The bad news is is that the video I watched to even learn about this, it didn't cover what actually happens when you type on a keyboard that uses glue. There's glue falling out. The problem is if it cracks out and falls out like that, almost like it's a cookie out of the oven coming out of like a baking sheet or something, then it's definitely gonna do that after use. Fuck, man. Just when I thought I figured everything out. So now I need to figure out something sticky that will stay hard without cracking. That sounded weird, resin. I'm not sure why I didn't think about this earlier. Resin keycaps are actually really popular in the keyboard world. I will warn anyone trying to replicate this. Before you get your breaking bat on, you will need to do this outside or in a well-ventilated room. I did neither.  So after letting the resin dry, this is our final product. Okay, well, hold on. I did a couple other things. I put foam under the PCB. I lubed the plate stabilizers, put vinyl tape between the PCB and plate, and swapped the Cherry MX Reds together on Oil Kings. If you're new to keyboards and you really wanna know what the rest of that will do for your keyboard, or you wanna know how to do it, stop by my stream or join my Discord. The links are in the description and you can ask any of those questions there. This is our final product. Now, you might be thinking to yourself, wow, Chris, that was actually quite an improvement. What does Shrout think? I don't know, dude, it's in their mailbox. By the time you watch this, he's probably already tried it. Stop by my stream and ask me there, because YouTube comments are cringe anyways. But yeah, as you can see, we did fix his keyboard and for any of you that might wanna try this for yourself, check to see if your keyboard is able to be cracked open because you might be able to do it for you too. However, if you're really lazy and you don't wanna do any of that and you would rather just buy something that is already made really nice, I have been working on a project for the last year. A keyboard that I've been really, really passionate about that I've gone through at least four prototypes for now is almost ready and I'm actually going to be selling them for people in stock. The reason that's a big deal is because typically with really nice products in the custom mechanical keyboard world, you have to put in a group by order that you might receive years after you actually buy the product. If you want to know when I will be selling them, follow me on Twitter because I will be announcing it there very soon. Anyways, I hope you guys enjoyed the video and I will see you very soon in the next one with my incredibly consistent upload schedule. Goodbye.


Well, no Okay, hello guys welcome back to another stream. Hopefully you guys are doing great. Let's get started with today Interesting day yesterday. We ended up there sliding down as well. I mean Again came back to the 50 which is sort of first area. We were looking for potential Long area. I mean we saw a long slide down on the open came back up reach back into here projected pushes back down Yeah, doing great man. How you doing reset my book map? So I was getting issues. Let's change the volume dot I don't know if you guys prefer 2d guys prefer 3d. I like 2d. It's it contrasts against the sort of background The bubbles kind of irritate me. I don't know why just get some just I don't know. It just gets to me But I mean it's a small leaky side from my father. Let's get started Okay, so If you got all the slide from yesterday 16 yeah, pretty much was everything 16 you can see the slide down came back up large cell large cell the session rainfall in profile and We'll add the Delta In super easy to add Delta of the P and we can make it chart range. So it's what we're looking at right now So again chart range split on the Delta you can see Never have anything set up. Do I I have just ultimately and always always behind schedule Perfect perfection does I showed up 25. So did I want 160 hours to 160 hours for you. We will be nice I've got brownie trades and you have issues I think it's just random bug. I don't know. I don't know why I was giving me issues It should it should be all for the most part up and running now I need to fix a few things here and there but Again, just proverbially always behind on things. So you should start seeing stuff come through But yeah, I don't know why it's giving me issues But there we go So we sort of see that pent here So I've got this area of short that we've collect collated here from this area here all the way and constantly sort of Leveling out here. So we know that at this point. We are always having these areas here We've potentially have accumulated a bunch of stops now on either side from this these points here So this is sort of a general point where we'd like to focus our interest on and we're seeing this sort of slide down here Was this large push down starting to finally see these sellers sort of sort of trade out and sort of Sort of come into a point where they're saying, you know, well, we no longer feel like selling too much We had this massive bit entered in a sort of large pressure wise and we have this sort of constant wall from 2700 still be active. So there's still sort of Some desire to visit here at some point Whether it may be today or much later as yet to be seen It is a new month so first of months is you when they usually like to test test out sort of people's People's wits, I'd say with what yeah people's with with wit Jesus. I have no wit People's wit so they chat and so patience with this sort of day. So you might see sort of active day We didn't get really in too much of an active day yesterday As you can know here, so we slayed came back up Climbed it on here one two three. They said, you know what? We'll drop it is the drop came back in this came back in And then you have the slide down on here as you can see we're constantly repeating The visits here and constantly And and and always seeing sort of shorts being applied to this area. We've we've taken out liquidity so that could also be The stops being taken as well as liquidation. So there's two folds from from this area here But that has culminated in a sort of large sort of heavy that Delta on a people in short So it's a potential sort of to see this as a long at least to take the liquidity on the Higher side because we still haven't taken any sort of liquidity from that side as you can see every time we came to It's just sort of stayed stagnant and said, you know what? We're not we're not going to take stops here We're not going to take the stops here and and it's just at infinity Let me see this sort of level and if I were if I were a Retail shade I would sort of say this and say, okay, cool. There's my trend man I buy this and they bought this and then they declined so Hey presto Retail does it again. I mean that's something that you expect them to learn won't you? But nevertheless thing they never learned but as you can see this is heavy long We sort of offsetting that and the numbers still hasn't have Incombinated in saying that this is all all even. This is all sort of Pushing down. We still have this sort of major sort of area here And so that's I think that's why this they're testing this slowly to the downside They're seeing if they can get rid of that while accumulating short and they can potentially punch that up So you look you're looking for that Maybe and then you sort of a way for that was a baby breath and and you can see the effect of what trend Mines can do and how they sort of trade there as you can see people buy these up and the stops get hit here So this is a way you'd potentially look to buy and then potentially a recon a confirmation of your bias here Maybe look for that on the opposite side Hey, hey, ah, do you know why I forgot to put up bookmark chat me quickly find bookmark YouTube chat Spare with me Don't want to leave those guys Unanswered just in case they do have questions don't want to be unfair to everybody Okay, now that's up So now again free field to ask questions wherever you are watching if you're watching in discord free to ask on the discord Crypto chat you're watching on YouTube feel free to ask in YouTube chat and then so on more than happy to Answer any questions pertaining to trading So I mean right now the inventory is looking a bit weak. We're pulling down I mean if we look at yes, I mean, this is where to reset But if you look at that, it's also sort of find the walls below here and if you go and But go into Chrome and sort of open up Trend spider and can see where spider ended and and where our sort of areas of interest offer spider So as you can see we came up and we we saw that sort of rise up on the last And I can't pull that up because I did reset my data. So I'm not I'm not privy to that data anymore But we did see that rise from yesterday But it did end up just coming to and culminated in for 400 Rejections so we got that on open slides up Comes in here on open and says, you know what from here on out We just want to go down so they just constantly trade it down take our stops We could see this sort of come into a sort of and being a W. So but we have sort of Bits here and support here. So we sort of holding out and saying, you know What if they've already sort of dropped this to this level here? They're about somewhere They could potentially just slide and why me my side just drop it I'm excited. It just means smooth motion with self into it and just move out into that So maybe looking for turning 3900 if anything on on the sort of week but Yeah, we go but I'm not the week open this weekend month beginning. Oh god damn.  So you want to see above? 4650 no Jesus not 46 49 60 6 5 5 Yeah, I'm up this number and you guys can read it out. There we go. That's the number. I can't do reading Okay, but that aside I'm back into that and sort of pull out and see where sort of Bitcoin Wants to move if it tries to move down So does have does Bitcoin have space to move down and yes, it does Would it like to move down? That's the thing I don't think it would like to move down, but it definitely can see some Sort of desire to see that and if we come back to sort of yesterday and what we saw what we saw again new months So a new sort of profile being sort of presented We saw that large PM. We said, you know what this is not really a good shape and that being where we saw these Aggressive longs we came in with this below. No one was willing to buy certain book back that In a book map that will show buyer exhaustion So we had buyer exhaustion here and Willing I'm willing this from buyers. So they just pulled it down said, you know what we trapped more here We trapped a lot here We pull those down and now we're seeing a bunch of shorts and we saw that on book map We had a large shorts here. We have a lot of shorts here just this middle where we have some longs So we could definitely and do with some Some trickery and maybe taking out the stop So maybe you look for the stops to be taken out here just because if if I were a retail trader and I were to enter along here on the daily metric I would place stops at this point, which would seem like the most logical point to play stops and And it would seem like that would is where they would place them So maybe look for a targeted stop on at the 19-5-50 level so when you get that target stop on we could potentially look for that to rise off of that So there is a level and room for price to move down and then that sort of collates with what yes It's sort of setting up potential to move down because we do have this sort of large buy up Coming in taking out sort of the shorts here. I mean we sort of came into this wall So I need to change this. This is just irritating There we go We saw these come in sell into that stop buying up start shorting the shorts got taken out for a run And now we're seeing sort of a dangerous amount of shorts open up as well. So let's also add a Delta Go chart range Make this Delta There we go as you can see but a lot of longs but the same time we've got shorts starting to stack up here So I kind of want to see where they take this and which way they move So if they start moving up, you can start maybe saying on open if they grab enough longs They drive drive them down And sort of a print that because again, we're now just printing bases on where the month would start But we're also ending our week. So we're sort of in an awkward position I don't really like weekends and months months starts when they when they come in together just because It's a fool's it's a fool's game. Hey, hey, how you doing? So but if we do end up going up again, that's that second inch go into that There we go. We've already got a green bar coming in slightly strong 2,300 comes in short takes out the liquidity here and And and catches retail traders out. So if we catch retail traders out, you're potentially looking for that as a short to then Drop down a lower Just because we see that cluster of even trades and you can see this person has just bit is present everywhere He's everywhere. You just see the single trader And these traders are just large larger trader is just entering positions everywhere And you can see that with the yellow mark that I've just added where they are roughly So you can see the shape the they sort of preference Prefer to sort of get filled here. They've got a small sort of hump here But I think we're want to see some shorts opened up or long so opened up into this short to then pull down Take out the sort of monthly Months stop take out these longs and we can sort of present that was a better sort of view on Longing it over a period of time Again, we've got that double top is just a slow. So please you can see here It's nice double top here nice double top here same thing here, but we're sort of moving into sort of this sort of down So not really a downtrend, but sort of slope Sloping down into this area here back to our sort of 50% range So we can potentially make some arguments again where I did sort of state that this could be a potential Long area if we're sort of stuck in in between this area But again, we're just looking at orders and we're just seeing if there's a a Desire from retail traders or market makers to bring it up Just tuned in Could you check out just a chance and a chart? Yeah, cool, he's in the main Yeah, yeah, I talked about that right in the beginning. Okay. Yeah, sure dig in So, yeah, I mean we saw we saw that liquidity and we see that liquidity into here from here this large sort of slope and you can see that was retail traders and and we saw off and I would say around here We saw maybe a target that and we see that in bug map where we see the large large liquidity To catch these stops. So there's there's I know for a fact, there will be resting stops here and and and there's extension around here, so we'd sort of look for that as a Bait of breath if we sort of pull out into that area here and just sort of show you Does that there you go. That's your 2300 always 2200. So if you sort of mark that out we go 2200 comes in at the peak start off starts here as soon as we take out the trend always 2300 Coming here, but just above this area here as you can see If you put it down Make exact there we go 2330 so slight overhang But so maybe little target in the 2300 which is just here So you can see they round that up here So we've got liquidity here to catch that catch out any sort of stops and what that will end up culminating in is a large sort of green dot Arising from all the stops getting hit and then we'll probably see a decline after that for the most part for the most part Not always, but for the most part Generally see seeing this fall.   This is your You and they say, oh, look, it'll do this it'll go up and then they get tricked into sort of And seeing this is a potential upward movement We're in fact is just trading to take stops to then fall down So you could potentially sort of come about in that direction It's all about trying to recognize sort of patterns and and and what sort of is What sort of is coming out from that? So then the reason why patterns print in the first place is is because it's easy liquidity to print patterns If someone recognize something they're like, they won't think to say two other thoughts. They say, you know what? I recognize that. Let me take that Exactly exactly Way to catch out retail traders. Can I show eat up or 8 to 5? I'll check for you ether It's always a nightmare to look at just because it's everywhere But we'll get it. I mean, it's a nightmare to look at in the books and the charts is less of a nightmare I think the spot and there's very much There's very little differentiation between spot and futures, which is usually a recurrence. I Would say possibly you can Just looking at it as I glancing out where great sort of taking out sort of traders And then where it's as you can see the sharp turner and of events here and here into this sort of sliding down motion But looking further again, you see the small tip nice Or to teardrop, but this again was just nice sort of sharp area here Just say, you know what this here is trapped liquidity again You're potentially looking for this to be the same if it revisits here Just it'll be a revisit to be a double top or if it slides higher will be a stop-punt into a drop Let's see where our orders are and where our traders sentiment is. So let's see. Oh Looks like three nine hundred is a prior to that. We've got already this area Sentry falling on its foot. So It'll be interesting to see if it falls and all of its lands through this I know it's generally it looks like it's been pretty decent to look at Coming out slide down Shopping below this day below this which is nice. There you go staying below this area here the 1589 level season I saw blue doji we can mark off for ourselves and so same sort of Sort of what's going The same sort of reasoning Bitcoin has as you see this sort of area pins down we sort of trade lower We finally have a nice rise We trade back this will might be sort of a nice area to sort of say we've trapped retail shorts Into areas where prior longs were so we could see this as a rise You could see if it ends up trapping liquidity here as it could be I think this is what a cup looks like as you can see we've drawn a nice sort of If I still like this, I think a nice little cup and Not not not nice looking but it's it's all shaving up to be with internal traps And then we trade to the midpoint we definitely look for this to try trade up If it takes this area here We'd sort of wait it and see where we end up being with liquidity definitely look to drop it But the key is what what Jim Kramer is feeling today if he's feeling bearish today We we are very bullish if he's feeling bullish today. We are very bearish I'm joking. No, I'm not But now again coming up to liquidity so you want to see if they We slam through this equity and if we look at sort of where the candles are There's a reasoning to this area Yeah, but it's like reasoning to have 2700 as a stock gap Sliding all the way for it. We've got this larger area from 20 26 9 to 27 Check I'm trying to struggle this price 27 100. There you go 27 200. Yeah, so around where our 50% will lie. So let's say if they grab all the the grab may made people buy all the way to 27 27 to and decide to start moving it down from there You can definitely see just a short throw and just say, you know what? There's your short throw and you put it down Yeah, I want to see where this with walls started coming in and then how long it's been there then it will sort of tell me Jesus that's really sure and it sort of tell me sort of how how how they developed and how they Plan to sort of do you see it from the looks of things doesn't look like it was there for too long As if I want to see it try coming at least here Just sort of say there's your social area and it may tries It almost makes sense as you can see this area of large sort of longs entering into here and We can get rid of the numbers and just show the color of the bars just because there's too many numbers Well softwares on the left-hand side. It looks really cool. It's bookmark. You know that That my little no One's transpired and one's booked, you know You should know that by now. It's spicy Holy, that's too many orders. Ah Yeah, it's just probably chugging my system long like and I like to mind you I have a really really powerful system was 64 gigs of RAM. So it shouldn't be chugging this badly I think the discord link is in the description If I'm not mistaken On the book map side one question about book map, when you look at Binance futures You look at them simultaneously and you sort of I mean, it's an example from my side because I don't have both of them up, but You want to look at my simultaneously could just cause So and here's how I look at and here's how I justify it if So future contracts and it's just for making profit and it's just for making money and he has no sort of monetary gain So if you sort of look at so and sort of say, okay You know what is Binance futures and and everyone in Binance futures is trading to turn into US currency and everyone on spot side thought this is a Binance side everyone on Binance spot FBS everyone who's in BS Is accumulating or show is it's sharing stocks? So it's a shelf. It's shares shares being exchanged This is uh is uh is money being exchanged So you look at so and sort of can identify if there's a large amount of buying and uh And a large amount of shakeouts in in spore They're looking to uh exchange shares into sort of strong hands and it comes to just the argument of strong hands Uh versus weak hands. So the strong Or uh strong hands and weak hands exchanging sort of shares between each other because there there is no such exchange in futures Other than uh the exchange of of of of currency and and the desire to make money of these who are liquidated So you see that there is sort of There's you have beneficiaries and there is cause to look at both uh because they both uh Define different things and if you see a dive and and there's there's ways to look at divergence between the two And if you if one is buying more than the other and you can say, okay They're they're buying more in spots. So they're buying to accumulate or you see more buying in futures So they're buying at a higher price. You see, okay, there's a potential liquidations Uh but and and so on PC noob No, no, it's it's heavier on ram. I can I can show you man. I can My GPU not issue man My GPU not issue look memory Memory is an issue. It's using 26 things of RAM CPU uh The GPU nothing man breeze man 3080. It's a breeze It's a breeze man RAM Oh, no, help me then sort of uh Other than the uh jokes aside That is sort of the differentiations between the two And sort of that's why you want to look at both and and I see people looking at one or looking at the other and and there's sort of They get blindsided by a move because it doesn't show up and they get sort of like, oh, no Bookmap doesn't really help. Oh, I didn't it bounced it around a phantom wall It's because it was accumulating and it was showing something on on futures Uh and not showing on spot or the other way around and you missed it because you weren't looking at it I don't know most people. I don't I don't know who who who else does this except for me. I I'm not saying I pioneered it, but I pioneered No, no, anyone was uh with two brain cells can figure that there's differentiations between Between the two and and this cause and reason to look at the other and you can make up your own explanations Why it's important to look at the other? As long as it fits Yeah, I don't know that it's called showing but okay. There you go Uh Okay, coming down it looks like they're short for it too much. I think uh, I think you don't want to short this to uh Do you want to short this early? I don't think you want to short this early. Just I mean you've seen this come down Oh terrible There you go. I mean are they still will they get into and said, you know, we're done for today and Are they done for today? I doubt they are And there you go They they came up and I think they came up as this week and that week came into this and they said as soon as it Starts pulling down we start seeing this bit come in and just saying nope And now we're sort of pulling away already Yeah, I kind of want to still want to see so actually, you know, are they playing with this they're moving around beds But looks like they're still staying this area here So kind of I kind of want to see slightly higher I want to see the thicker we'll sort of area here just get hit just just a Thing just for necessity. No, not even necessity just for greed's sake Is greed be And you can see yes pulled back into this area here. So we're already sort of pulling down Uh, if we pull down early, you know to reverse it, you know to hit that switch And no no no no no no I mean we still haven't hit the weekly uh outside the weekly range We've hit monday out on tuesday. We haven't hit the entire week, which is monday and tuesday combined out Uh, so considering that we haven't hit monday and tuesday out still waiting for that to get uh get hit Out and I like if it's on this side start pulling pulling into it if it's 50 I mean, yeah, we can make some arguments about 50, but let me see if this is the weekly range Uh, it's sort of uh, let's all look at in golden not a golden pocket. So that's your golden pocket for the week Here you go. Uh, I mean for both you trade golden pockets Really my style, but it's sort of your dca levels again That's by sort of mark the higher 50 because it comes in sort of uh, yeah So your dollar cost average levels coming in If we can start moving up we can start showing potency to at least trade to here And trade above the 50 Of the golden line So I'll zoom that in you guys can see that clearly now the rain Just nine o'clock. Oh, it's nine or six actually We're coming up half hour nine o'clock.  It's slightly higher as well. This sort of extends all the way here, but the start of it is is here So you want to see if they target that internal sort of uh, range liquidity here and 15 minutes is just red bar To the swig and if they do they might just cascade it all the way to uh, this uh, these stop areas here But generally speaking, uh I mean if they've got collected enough sort of long So if they want to drop it, they'll probably drop it around here after coming to it That's why I want to see it. It's just like you hire from ethereum to see if they can pull it Uh, pull some more orders, uh, after they get more to say you go after they get more orders See if they can pull it uh from there So that's that there's just more rise if you're just if you're trading very micro moves Can I see one or when I want to see or can I want to see can I want to see Kind of kind of want to see if uh, we pull up on here. So there's your large cell into this, uh, Oh Quick bit Let's take out I want to see if we can take out this entire social liquidity If that short liquidity ends up being the point where they just reject off of or if they just trade, uh, this Until open if they trade up until open you can sort of kiss your Um, majority of your chances. Goodbye of going that high In got these beds load loaded up waiting Yes, that you try to stop me right now on some short Let me know to a uh I mean Here's what it looks like on sort of a 15 minute scale where we ended It's still it's still isn't that Jesus my thought is wrong with it I I've reset it. I've done everything to it. It's just it's just not working Yeah, okay. Oh, I'm manually try uploading some of it, but I don't I don't know after stream Like again, I just like I've set it there. Maybe Yeah, yeah, I've reset it twice and I like rice During that period I still isn't pulling through which is annoying okay, I'll double check with uh with, uh With the program itself I'm using but Brides that I mean there's actually things so Greg is running its GTX 750. Yeah, I think he is If he's having GPU issues probably is So that is sort of your sort of ice for them You can see how they haven't closed that off on this side So they've been this side doesn't count if you look at this side and you see a rise So I'm potentially look for that fill in. Maybe we get that nine all the way there to pull up uh, see No, no, it's fine. Like I know it's it's it's annoying to me as well. That's an it's been an issue It should just be working Uh, so I mean so if we rise this there's a lot of room to move down There's a lot of room to move down and that's that's that's very evident Uh by the charts. I don't want to press the black button Even on four hours. It's an area we want to come to We've got we've got a lot of room. We've got a lot of space, which is nice That means more shorts to be played But once once this shorts has been played, we know This is the point where people start giving up especially on the weekend.  I've had I've had too much with this. Uh Yeah, it's awesome No, don't do that No, don't do it. Please don't do it Many things are moving again. It just looks like just looks very sort of It wants to make a decision It's decision should be and then and then it's perfectly, but it should be run out longs Reverse it after we get a lot nice little shorts here But that's not always the case is it? And I'm trying to look for a bottom so I can I can show you what that is It could have it happen in the same bar. So you see on the same day. They just run it out pull it up I'm looking for multiple days. There you go again run it out Run it out on the same day then final day that we go green green with envy And he ends here and I too ends on me. I'm very green I can Run this out. You can give you a nice little dosage of Cells and and these cells coming out of nowhere means the price was absorbed But you can obviously see some some cases where they just go. Oh, you know what? We'll get you But majority of it should be again It's pretty simple where we get sort of normal behavior and and not a typical behavior where we run it out We let these small biasing happen here. So this became a causation for this And then you can see these dots here these large heavy dots After this sort of super red as at some point in time. This was an entirely Red candle down so people got scared. So it's just this fear the sphere selling or the sphere shorting To the liquidation to then run it down and on the opposite side. It's also true And this just looking at the delta's and looking at the uh Oh, how far we are now and this is why I don't I don't really like these green bars here Where it's sort of I mean current weak sort of point of control Uh new months. So I can all that for now So the sort of the month sets up I want to share us. Uh, do you paper-handed it? Uh It's okay paper-handing paper-handing is very fun It's it's life That's the power of life Definitely you could see And then and waiting for these lower prices will be perfect if you can get them if you can that is what it is So just look out and just say What can we get? What can we get out of it? Uh, can we get this bounce go up higher take liquidity pull back down Trade this uh people start trading the short here and pull into there and trade into there a nice little whipsaw Uh ideal case would be great if we can do that But again, and the most things trade ideal if we get sort of slammed down take internal liquidity And a week for taking internal liquidity. We might rise up Keep trading down keep trading down until we reach this and then we slam it That could be another sort of uh priority sort of uh play But the way uh things are trading short, uh, we can look for that maybe take place And we have this sort of nice sort of pressure bar entered in here very sort of recently so could be well and truly a spoof Uh, just sort of keep price down Uh, so if this starts moving down with it, we know what to do. We know they're pushing price down with it Uh Same thing we asked looks like we asked yes Looks like they just hit our stops pulled back up Seeing some just unwillingness from buyers who try to step in here Uh, could see and spell trouble for us to try and pull this down. So If this doesn't start moving up in the pre-markets, we won't get this to move up We won't get this liquidity area taken. So we won't see that sort of fall afterwards Uh, but if we start pushing down again, it could just start easily pushing down Uh, hit out into this hit out into these start curbing into this Uh, here then pull up But again, we've got that low avoid to fill and once we sort of fill that we can maybe look for a gap closer So that that fills in around uh, 20, uh, 391 391.45 or 39 uh, 0 Uh, 64 so this area here so these sort of cluster of red bars that we look to trade to so it starts at And it's all in the beginning of this red bar and this open rather is at 393 point on 91 So that's something that you want to pay attention to as you can see we can We can just about miss these Gap closes, but when we gap close you can just see it's just closing the void to then bring it down And the short from here just running down This is really great. This way it's just say take partial profits. You can take profits here Wait for the rest to run down you can wait for it to try and run out down all the way here But if it runs down all the way here, you're gonna you're gonna see some level of uh, uh, pull back up because again this would just be This one this side goes up this guys goes down the side goes down the says goes up As you can see as you can see as you can see here and into an apex Apex point and the mountain starts here So this is where your mountain starts you can expect it to go all the way down to the mountain beginning Generally, but speaking on the intraday timeframe. You're sort of looking for Maybe sort of this sort of area here And if we visit the bottom of the mountain, we're probably gonna visit the bottom of the mountain with a bitcoin Just saying so it's always good to sort of look at both And and the bottom of the mountains here. This one's a very sort of this is more of a hill than a mountain Okay, and people just But in crypto people make a a a a molehill a mountain out of the molehill as the expression goes So that would probably be around here mountain star. So maybe see that go lower And once that goes lower than that low low enough I potentially see that rise but getting not really sort of a mountain more of a cliff that we fell off A hill that turned into a cliff we fell off the cliff twice I don't know how you did that but you guys managed to do that Uh do a nice range trade pretty wide on that trade But yeah, generally speaking Yeah, you're potentially looking for uh to see that short You can get that I mean We've got all the way until next uh next quarter next month for the quarter So once that quarter kicks in kicks in is is when you expect Uh price to ultimately sort of shift down. I mean you can come in a bit earlier Uh, why am I crossing everything out? I want to see the USBT Yeah, I mean it can again as you get there's your reaction upwards from this and it was a very weak reaction And it can slowly sort of be down to that sort of when you can come in early and say on from the 11th 10th middle that from uh, oh that's so weird. Oh, that's not a very piece Oh, yeah at 9 11 we can sort of maybe go down from there. Uh, things do tend to go down at I'm joking. Okay. Sorry. Sorry bad joke bad joke. Oh Oh, I'm so cancelled Uh, but yeah, there is a potential reason to sort of see this maybe uh through until uh, September 11th and from September 11th, maybe we have an early sort of uh, uh Offset and we sort of come in earlier and we start shifting down to the quarter And we start just constantly going down. I mean because this quarter started off great Halfway through sort of pulls it down Halfway through. I mean we're now a quarter way through We can maybe start seeing some some reaction I mean not even a quarter third of the way through the third so third two thirds in We put uh, we start having trouble lost a last third sort of sort of chugging sort of sort of uh Sort of just waiting for it. I kind of want to see some level reaction. Maybe Whoo Things go down, uh, maybe sort of see catch that on october The z for october, but generally october and and the winter times are usually worse off for trading December especially is when we always see traders, uh, sell off shares But this should be if anything will be a very sort of long sideways range that we play Uh starting from here. I mean it did try and escape at one point and failed miserably And now we can sort of try and say extend that and say, you know what now our range is is here And we treat as such from here to here that we've been playing 50% comes in Uh, if I'm not mistaken around here if my eyes are correct So we trade it to it rejected. Maybe we can maybe trade to it again. Maybe reject again As time goes on but slowly seeing some slower cells Maybe see that bounce off of this slam into here trade back down Why isn't the spot loaded? Maybe because I haven't loaded it I mean not much, uh, not much else to say, uh You guys have no more questions.  Uh, to then target the highlight range If you start sort of tending upwards and and start tending to this sort of stop on you potentially see that as a big trap To then pull up pull into sort of lower lower sort of regions uh But right now again, it's it's more leaning towards uh it being uh downwards Initially just because we are seeing that sort of trap out into here and this sort of uh wall just breaking its neck Nice little choo choo training is developing So if you sort of choo choo train along We'd potentially see that drop again We give choo choo trains a half an hour to come if it doesn't come in half an hour We leave the train station Yeah, that's that's all I have to say today trade safe uh and see you guys tomorrow and uh Tomorrow we're gonna uh do a code stream with bruce. So he's going to be asking me questions So there'll be some interactions because you guys Uh pretty boring you guys are like talking to a brick. Well, I'm joking Uh, do you have any specific strategies that they trade lower timeframes? It's exactly the same Whatever whatever I use higher timeframes. I use lower timeframes It's it's their market. It's very fractal in nature Whatever whatever they do in higher timeframes. They do they do to you in smaller timeframes That's the long and short of it Okay, make sure to subscribe to the channel, and I'll catch you guys tomorrow. Peace


Ethan Get on your knees Why I'm ten feet away. You have safety glasses, and we're outside Mark today's the day Mark today's the day Will you back up a little bit further no ten feet? That was the exact? And I just measured from where you are to where I am is ten feet. Okay, but hold on A-heart is this really the best path forward Even for us, this is cruel unusual I think somebody's gonna love that Dr. Sherman here to wish all site 42 staff a happy Thanksgiving in cafeteria a for all staff who got a B or higher on the performance review We'll be having a dinner prepared by SCP 5031 the invisible four armed murder monster We taught to speak play classical music and cook Michelin star rated meals in cafeteria B for staff who didn't reach that approval rating Your dinner will be prepared by SCP 3307 whose experience with alien abduction led him to being able to produce any of your favorite foods From his vomit snot and or other bodily orifices Do better next year Oh You're here good right on time agents welcome to your briefing you've been enlisted in our newest mobile task force MTF Delta 7 codename doubloon bandits as you may be well aware an anomalous currency known as doubloons has spread quickly across the Tiktok app as per our mission statement It is the job of the foundation to contain the anomaly your mission agent is to contain every doubloon You come across on the app by commenting all of your doubloons have been contained by the scp foundation at site 42 Good luck in Godspeed agent secure contain protect What the hell is a doubloon you may be asking I'm dr. Sherman tiktok's resident scp researcher and also explainer of tiktok memes sometimes in 2021 there was a popular Instagram meme of several pictures like this of cats holding up their little toe beans with the caption for doubloons a Year later the trend has made its way to tiktok where different videos of cats people or cosplay personalities will make videos where? This video gives you x amount of doubloons this one cost you x amount of doubloons this one steals all your doubloons This one's a shop where you can purchase things with your doubloons Etc etc and just like who's on this in anyways the rules are made up and the doubloons don't matter Similar to early 2022's council war between the van to lord and the rogue There's an interesting intersection between role-playing yes, anding and meme tomfoolery They can only really take place because of tiktok's unique digital culture Twitter means have limited characters YouTube videos take too long to produce and you can do this kind of lane work at Renfairs, but that's not online. So it's actually a really interesting cultural artifact.  Do you think it has the pestilence that scp 049? Well, we haven't tested every page because you know it gives you the disease you read about But I'll let you know if we find out and put it in front of scp 173 Would he be? Oh, no, that's no better than a camera, which we know doesn't work in a mannequin I'm sorry scp 343 misplaced his what?  I said make the tree scream not make the tree score two scp 173 staring at each other Basically do nothing forever or do they go listen to scp 3220 on the site 42 YouTube channel There's a fascinating scp about just that happening.  I said make the tree scream not make the tree begin the next test interesting next level Fascinating what happens if you put scp 173 underwater and look away can you swim up and snap your neck? Or does he just sink to the floor like a sad conch it will get out We put it in a room with doors for a reason looks at scp 096 and then gets roblox Oofed and is brought back to life as shy guys still attack them depends on if they have their memory or not That bounces faster and faster forever after you throw it Does that mean you can tie it to a generator of some kind to make infinite energy if it's too chaotic it would break out Larry looked at shy guy and shy guy was able to get 106. How would shy guy eat? We have an on wiki story about 106 losing to the statue, so I think it would lose to 096 to add stomach burn after you subscribe Tell me Every time I mentioned my age on this app I get multiple comments like this and if you too want to remain ages like a beautiful vampire All you need to do is moisturize work out and commission a portrait that bears the weight of your age And or sins and hide that in a closet where no one will find it What happens if you keep scp 173 in an enclosed glass hamster wheel and then look away from him? Is it free energy? It breaks the glass our thoughts what happens when scp 012 is completed like it runs out of pages Are people still incompletable? There is no run out of pages. We wasted a lot of paper testing that become neutralized When you think about it the chaos insurgency and every other group of interests are really just proof that the scp foundation is horrible at keeping Half of them make anomalies that's not the same