Hello, everyone. My name is Wasim. I am an entrepreneur in residence at Fast AI and I'm currently at the Fast AI headquarters at the moment in Australia, although I'm originally from South Africa, from Cape Town. And I'm joined here today by Tanishk. Tanishk works at Stability AI and we've been working together with a couple other people on diffusion models, generative kind of modeling, and that's been super fun. So Tanishk, do you want to maybe, you know, introduce yourself as well? Yeah, so my name is Tanishk. I am a PhD student at UC Davis, but I also work at Stability AI. And I've been exploring and playing around with diffusion models for the past several months. And so it's been great to also explore that with the Fast AI community as well in these last few weeks as well. Awesome. Cool. Cool. So this talk is us or me trying to understand the math behind diffusion. So, you know, if you've done the Fast AI courses before, you know that you don't need to understand the math to be effective with any of these models. In fact, you don't even need the math to do research, to do novel research and contribute to these. But for me, it was all about it came out of interest. And, you know, I thought it was kind of it's kind of beautiful how what, you know, diffusion models were discovered. And I think a large part of that was thanks to some some really clever math. And so I wanted to understand that I'm not I don't have a math background. And so I want to help kind of describe how I think about it and how I how you can kind of interpret, you know, all of these notations and things. Cool. Yeah, so so I can just dive into it, I think. So the first bit of math that we see in this paper is q of x superscript zero. And they call this the data distribution. Do you want to mention what the paper exactly which paper this is? Right. Good question. So this paper is the 2015 paper. Do you remember the authors of that paper, Tanish? I think it's Jasa Sol Dijkstein, who now works at Google, I think, and it's from Syria Ganguly's lab. So yeah, yeah. So this was the paper as far as I understand that introduced this idea of diffusion. Yeah, 2015 by those authors. They start out by, you know, defining this data distribution, and they use this notation and already, like a lot of people, you know, myself included find this quite confusing. But let's go through what's described here. So they have an x. And, you know, in math, x is often used as the input variable, much like y, which is then used often as the output variable. Yeah. And the fact that it has a superscript also implies something. So the fact that we have x superscript zero implies that there might be a sequence of x's. And, you know, I think it's useful to get comfortable with this idea of simple compact notations implying a lot more than, you know, might be obvious at first glance. So x implies that it means something about this quantity. It's an input variable. And the zero implies that there might be other things that you might have. You might have an x1, an x2, and so on. But we'll, we might see that. And then the third part is you have q. And q is a, what we call a probability density function. So the first part here is probability. And the question is, you know, what does, what does q have to do with probabilities? Well, it's because usually we use the letter p to describe probability density functions of interest. And then because q is right after that, it's another common one. So it's kind of like how you use x and y. We use p and q. And the fact that we use q here instead of p is because it suggests that there might be a p that we'll introduce. And maybe p is the thing that we're modeling. And q is kind of supplementary to that. Does that sound right, Tanishk? Yeah, yeah. And I think it's also helpful to kind of maybe think about like x zeros in a more practical, concrete way. Of course, if we work with images, then x zero would be, you know, that's, that's what's representing the images. So it's also useful to think about it from kind of that concrete practical approach as well. Right. So x zero might be, you know, an MNIST digit. And then we got q. So q, I'll just use this to mean q is some function. So we look at it as a box, and it takes in x zero. And it gives us the probability that this x zero, which is an image, looks like an MNIST digit. So in this case, you know, this would be 0.9, or maybe even, yeah, it's 0.98. So this is quite high probability that this is an MNIST digit. Hi, this is Jeremy. Can I jump in for a moment? Please do. Oh, thank you. I just wanted to double check. This looks a lot like the magic API that we had at the start of lesson one, that you feed in a digit, and it gives you back a probability. Is that basically what, what Q is doing here? Absolutely. It's a magic API. That's a good way to think of it. We don't know. We couldn't write down what Q is. But we imagine that somebody, somebody has it some way. Yeah, so this is a concrete example.  So another thing worth mentioning here is probability density functions. So these are these magic APIs that, you know, give us a number, tells us how likely the thing is. They, you don't often see them, they don't often make it all the way to your code. In fact, they very rarely will appear in your code. But it turns out that they are very useful ways or tools to work with random quantities. Because they allow you to represent random quantities as functions, just ordinary functions. And because they're functions, you have a whole, you know, centuries worth of math to analyze and understand it. So you'll often find probability density functions in papers. And eventually they work out to really simple equations or formulas that end up in your code. Do you want to add anything, Tonish? I think that sounds all correct. Of course, I think you probably will go over some examples of probability density functions, especially relevant to this one. But yeah, it's useful to think about the also the sorts of functions you may have in a simplified case. And that's what we probably are going to talk about next, right? Yeah, yeah, that's exactly what we're talking about. So we have this qx of zero. And then we introduce another one. And like you said, this is going to turn out to have a really nice simple form. But before that, the next thing we define is qxt given xt minus one. So we'll say what we define this to be, but to begin with, this is another probability density function. And this bar over here means it's a conditional probability density function, which you can think of as you are given the thing on the right to calculate probabilities over the thing on the left. In this case, you can think of it as something that takes images. So maybe another magic API and produces other images. But we don't know what these look like yet, because we haven't defined over here. And this, this we would call kind of, you know, xt minus one, which could be x zero. And this would be x t, which in the x zero case would be x one. Something worth noting is this notation can be a little bit confusing, because we said q is one thing earlier. Now we're saying q is another thing. So this year, I'm going to need your help on this one, Tanisha. I think people would usually, if in the strictest sense, define the first one, you know, like this maybe, and the second one with a subscript. And that this notation that we see here on the left is just a shortcut, where they, you know, they wanted to save the space of writing that and kind of included that implied it by what was in the practice. Is that true? Yeah, I mean, I think here they use the variables q and then of course later on vcp to kind of describe as we'll see different aspects of the diffusion model, the sort of different processes of the diffusion model, which we'll see. So I think that's what, you know, those, they use the same variables to kind of demonstrate this is corresponding to this process. The other variable corresponds to the other process of the diffusion model. So we'll obviously go over that. So I think that's where those variables or those letters are being used in that manner. But if you do want to make it more specific, more clear, yeah, I think that that that notation is fine as well. Right. Okay. Yeah, that makes sense. Okay, so so let's describe what this q does, you know, to the image on the left to produce the one on the right. So I'll start over here. So we have more space. I'll write it out first, and then we can go into the details. Okay. So kind of like the bar, you can think of this semicolon as, you know, grouping things together. And so you have the things on the left and the things on the right. My understanding is these two things on the right are the parameters of the model, sorry, of the probability. And the thing on the left is actually, Denise, could you help me understand what the thing on the left is? You know, right? Well, so this is again, like a probability distribution. And the thing on the left is saying this is a probability distribution for this particular variable. So that's just representing what it is a probability distribution for. And then the stuff on the right are the parameters for this probability distribution. So that's kind of what's going on here. So anytime you have like a normal distribution, and it's describing some variable, you'll have that sort of notation where it's the normal distribution of some variable. And then these are the parameters that describe that normal distribution. All right. So just to clarify the bit after the semicolon is the bit that we're kind of used to seeing to describe a normal distribution, which is the mean and variance of the normal distribution. So we're going to be sampling random numbers from that normal distribution according to that mean and that variance. Is that right? Yes, that's correct. Yeah. Yeah. So we need to describe a bit more there about normal distribution. We kind of, you know, skip past that. So we have this fancy N and fancy letters in math for distributions usually refer to well known distributions. And the N here stands for normal, which is also known as a Gaussian distribution. And it's probably the most well known probability distribution that you can find. And when I say well known, I mean that these things pop up everywhere. You know, you can do in all sorts of fields, measuring all sorts of things, turns out that they follow roughly something that looks like this distribution.  And we understand them really well now. The reason that they use often in cases like this is because they turns out they have really useful properties and they're easy to work with. Some reasons are they're described by just two parameters. So the mean called the mean and the covariance. Another property is that they have kind of, you know, what people would call SunTales, which kind of means that they only, you only need to describe their behavior in a small region of space. You can kind of just ignore the rest. Yeah. Do you mind drawing a quick example of a normal distribution? That's a good point. So we have, let's say our random variable is just one kind of dimensional. So just a single number of floats. This is sort of what the normal distribution would look like. And in this case, that would be our mean. And the variance would sort of describe the width over here, which in this case, you'd use a small sigma because you're doing a single variable. In our case, we use a capital sigma, which is a symbol for multiple variables or multiple dimensions. And yeah, I also didn't say that this is the letter Greek, letter mu. So capital sigma, mu and lowercase sigma. I just wanted to note that typically the lowercase sigma represents the standard deviation, which is the square root of the variance. So for example, sometimes you may see in papers, sigma squared, and that's just the variance, but they will write it sometimes as sigma squared instead. So it depends on the notation. So sigma is the standard deviation often and sigma squared would then be the variance. Cool. Yeah, we can also show with our example what this would look like. So we start out with a M-ness digit, put it through this magic API. And what would we get out? Okay, so something we didn't describe is, you know, what does this I mean? Did you want me to talk about that, Wasim? Ah, yes, please. Okay, sure. So because I think this is something that actually, can I borrow your pen? It actually came up in the lesson we were doing kind of in an interesting way. So in that lesson, do you want to get in the video? Oh, no, they know what I look like. Oh, well, okay, I'm in the video now. Yeah, in the video. Hi, Tanish, nice to see you. Yeah, so in the lesson, like, we did this thing for clip. I don't know if you remember, Wasim, where we had the, you know, the various pictures down here. I'm so embarrassed, you're better at the graphics tablet than I am. And it's my graphics tablet. And we had the various sentences along here. Right. And we said, Oh, you know, it'd be kind of cool to like take the dot product of their embeddings. Because like if their dot products are high, that means they're similar to each other. And, you know, if we subtracted the means from those first, right, then you've got the and instead of having images down here, right, what if we had the exact same the exact same vectors on each side, then what you've got down here is basically x minus, you know, the average, right, if we just check that first, squared. And that is the variance, right. So that's like the variance for each one of these vectors. But what's interesting, as you pointed out, is that like normally, you know, at high school, when we look at a normal distribution, it looks like this, right, but you're not just doing one normal distribution, you've got a whole bunch of kind of normal distributions, right, for all of your different pixels. They're the pixels, right, Tanishk, normally distribution of every pixel. So there's a whole bunch of them. And so one of them might have a normal distribution that's there, and another one might have a normal distribution that's here, and another one might have a normal distribution that's like here. And it's more than that though, because like, it's possible that that, you know, one pixel tends to be higher when another pixel tends to be higher, or one pixel tends to be higher when another pixel's lower. So it actually kind of creates this like surface, you know, in n dimensional space, where there's a number of pixels. So if you now like look at like, okay, well, what happens if we multiply this by this, just like we did in clip, right, then if this number is high, then it's saying that when this variable is high, where this pixel's high, this pixel tends to be high, and vice versa. Or if it's low, it's saying when this pixel tends to be high, this one tends to be low. Or, interesting to us, what happened, oopsie, Daisy, sorry about that. What happens if this is zero? That says that if this is high, then this could be anything. Well, this is high, this could be anything. There's no relationship between them. So statistically, we would say that these two pixels are independent. And so now that basically means we could do that for all of these. We could say, oh, you know, these are all zeros. And what that says is that, oh, every pixel is independent of every other pixel. Now, of course, in real pictures, that's not how real pixels work. But that's the assumption we're making, because if we start with a very special matrix called i, which is one, one, one, one, zero, zero, zero. If we take this very special matrix, it's very special because I can multiply it by something, say beta. And if I multiply it by a matrix, I get back the original matrix. If I multiply it by a scalar, I'm going to get beta, beta, beta, and lots of zeros. And so if I multiply something by this matrix, right, then I'm just multiplying it by beta. But what's interesting about this is that this is what Wasim wrote. Wasim wrote i times beta, i times beta t. So what he's saying is, oh, we've now got a covariance matrix where for each individual pixel, it's like pixel number one, beta one, pixel number two, beta two, this is the variances of each one. And the covariance is, you know, the relationship between the pixels zero, they're expected to be independent. So that's where we're kind of going from like statistics you do in high school to statistics you do at university is like suddenly covariance is now a matrices, not individual numbers. Does that sound about right to you, Tanishk? Yeah, that's a great explanation of it. Yes. Awesome. Cool. So now let's try to describe, you know, what this would do with two M this digits. So, you know, we let's put back our mean equation and our covariance, whoops, our covariance, our mean and our covariance. And let's look at how this behaves, you know, at the edges, sort of. So it's really hard to, you know, understand this. I don't think anybody can kind of just look at this and know what it means. What we typically do is we try to describe it kind of at the edges. And so we'll start with like what what happens if that's zero. And we'll work with x zero as well instead of, you know, x t minus one, which would mean like an M this digit. So if beta zero, and we get our x zero, you know, square root one minus zero, which is one and square root of one is one. So that kind of falls away. So we just have a mean of our previous image. And this is just variance of zero. So we have a normal distribution with a mean of our previous image of variance of zero, which means we have the same image. Yeah, just to clarify, when you have variance of zero, that means that there's really no noise or anything. It's just at that mean and, you know, your distribution is just saying that's the only point that you can get from it. So, yeah, that's what it just becomes the same image because, yeah, there's no noise or variance because the variance is zero. Yeah, exactly. And then when our beta is one, we still have this and then we have, you know, square root one minus one. And that becomes zero. So this whole thing becomes zero.  And if it's just i, then as Jeremy described, it would, you know, imply a variance of one. And so our image through this function would just be pure noise. So let, you know, mean of zero standard deviation of one.  And kind of somewhere in between that, we have to say over here, you know, what would it produce? It would be some mixture. So, you know, like maybe a light, the lighter pixels of eight and some noise. Maybe a bit darker. And we can kind of draw this and you would have seen this in the previous lecture. You can draw the sequence of things that become progressively more noisy in very small steps all the way until it becomes pure noise. This is what we call the forward diffusion process. And we can now describe some of these things. So this would be a sample from our data distribution q x zero. This would be the function for the conditional probability density function that takes x. So of x one given x zero and so on. And the way that the terminology that we would use or that mathematicians used to describe this is they would call it a mark of process with Gaussian transitions. And you know, this this can sound quite scary, but we've just described exactly what this is. So when we say process, it usually means, you know, something where there's a sequence involved. When we say Markov, it means that the thing at time t depends only on the thing at t minus one. The transition is this function. How do you actually go from t minus one to t? And Gaussian is the fact that that transition is the normal distribution. Does that sound right? Yes. Just to also clarify a couple of things. When we say that, you know, we're sampling from the data distribution, what that is referring to is trying to find some random, you know, sample or some random data point that maximizes that likelihood or that has a high likelihood. So when we say that, you know, we're looking at that that API, that magic API, as we were talking about, and we're trying to get some, you know, some data points that have a high value with, you know, from that API. And, you know, for some, so for some distributions, when it's very simple, and we know how it works, like a Gaussian distribution, and we know the parameters of that Gaussian distribution, it's very easy to be able to do that sampling. And then of course, in other cases, it's not very easy. It's not, it's quite difficult to do that sampling. So then we have to figure out alternative ways of doing that sampling. But that's why in this case, with the forward distribution, we just have these simple Gaussian transitions. And we already know the parameters of those Gaussian transitions. So we can easily do that sampling. And going back also to that, I think it's a worthwhile to also kind of show and think about maybe how this is again done practically. Because one of the nice properties of Gaussian distributions as a whole is that you can, you know, simply take some normal noise with a mean of zero and variance of one. So that's, I think they usually typically call that a unit distribution. It's just like, yeah, normal of zero one. And then if you want to get to some other point with a mean of whatever value you specify and a variance of whatever value you specify, you can simply take that normal distribution, scale it by the, you multiply it by the variance, and then you add your mean. So then there's a simple equation that you can take to get the, you know, to get at any particular mean and variance. So that's how you would, you know, get the samples for these other distributions that we have defined throughout the forward distribution. So, you know, for example, when you're coding this up, of course, a lot of these softwares, they will have a way of getting a sample from this normal distribution of zero one. And then you just use that equation then to get it at the desired mean and variance. And so that's how it kind of happens under the hood when you're when you're kind of describe this with code. That's really helpful. Yeah. And this idea of we can't really sample from this thing. That's exactly, you know, the problem that generative kind of modeling is trying to solve. Like, how do you represent this in such a way that you can easily sample from it? And so it turns out that if you have one of these processes, you know, where you have many, many steps, so let's say a thousand steps, a thousand of these steps going to the right. And they're all very small steps that eventually go to noise. Somebody, you know, maybe in the 1950s, I think discovered that you can represent the process of going backwards in exactly the same functional form with just different parameters. So what that means is if we say P is the thing that goes backwards. So, you know, the previous one, given the current one, this P has the same functional form. So it's also the transitions are also normal. But the mean is, you know, some unknown. So we'll use the square and the variance is some unknown. We use a triangle. Is that correct? Yeah, that's correct. And just going back to our previous point about P versus Q, here we can see that the Q was describing the sort of forward process going, you know, yeah, the sort of steps that we're doing. And then the P is describing what we're going in the reverse way.  That's what they're kind of indicating, at least in the diffusion model literature. Mm-hmm. And P is kind of like X, you know, it's the one we want to figure out. So like Q is kind of like Y and P is kind of like X. That's how I like to think of that. And so, you know, we have this functional form. And the next question is how can we use this or, you know, we just don't know what these parameters are. How can we figure out what those are? And this goes back, you know, to early kind of statistics literature where you can fit this model by maximizing what's called the likelihood function. So we can try different parameters until we have one that maximizes the likelihood. It turns out that we can't quite do this exactly because you would need to calculate some integral. And that integral is over very high dimensional values, continuous values. So you can't actually calculate this. I think you can think of it because, you know, we're having these thousands of steps that we're trying to go in this reverse process. And so, you know, you have these thousands of steps that there are going to be many possible values for each step. So it's kind of hard to evaluate it over all these thousands of steps and all the possible values for all these different steps. So I think that's kind of where the challenges arise. And that's what it makes it difficult because you have to find you have to evaluate it over these multiple steps and try to find these functions for all these different steps. So that's what's kind of where the challenge is. And so you might see people talk not about the likelihood function, but about the log likelihood and correct me if I'm wrong here, Tanish, but I think the log here is a bit of a, you know, computational trick almost. So I think it has a few properties. The first is that it's always increasing and, you know, people would call this monotonic. You know, it looks always kind of increasing. And because it's always increasing, if it's the same, you get the same parameters if you optimize the log likelihood versus you optimize the likelihood. It also takes products to sums because and that's helpful because we have joint distributions, you know, which turn out to be products. So it turns out we have a lot of products here and they become sums, which is easy to work with. And the last thing is that, you know, this normal distribution has exponential or exponential functions and those disappear with the log. So this is a much friendlier thing to optimize. Yep, that's correct. Cool. And then there's one more step. You know, we still can't optimize the log likelihood of the thing that this eventually describes. But again, and this is kind of the beauty of math is that somebody figured out a long time ago that there's a way to optimize some other quantity called the elbow for short, which stands for evidence lower bound. And the evidence is just another name for the likelihood. And the lower bound means it's sort of, you know, the lower bound of the evidence. And if you optimize that, it's almost as good as optimizing the thing that we really want to. But this one we can calculate very, very easily. And so you can use this as a loss function to train two neural networks that predict our square from earlier, which was our mean and our triangle, which is our variance of this reverse process. And once you have that, you go all the way back here. So then you have these values. You can start with pure noise and keep calling these neural networks, sampling from those normal distributions, kind of applying that iteratively over many steps, and you recover the data distribution. One thing that's important to clarify here is that you can recover the whole distribution, but you can't necessarily take a single image, convert to pure noise, and then convert it back. So this operates sort of at the distribution level. So you can take this kind of magic API, you can reconstruct that whole API. And if you can do that, then you can generate images, image digits, or cats, or dogs, or whatever you want to. I want to just clarify one thing about this process of the kind of the loss function. So this sort of evidence lower bound loss function, the kind of approach that it's taking is that we have this forward process. We can go from the original images and figure out these sorts of intermediate distributions going all the way finally to noise. With this sort of evidence low bound loss function, what we're really kind of doing is trying to match our distribution that we're trying to optimize to those distributions that we saw in the forward process. So that's what we're trying to do. We're trying to match that sort of those distributions. And there's a specific type of function that is able to do that. It's called a KL divergence. That's the sort of function that can compare probability distributions. And again, because we're dealing with Gaussians, you can calculate that analytically, and a lot of the math becomes very simple. So that's again, with the whole Gaussians, we know them quite well and the math is very simple. So that allows us to do this sort of comparison between these distributions very easily and optimize that. And so we want to kind of minimize the difference between the distributions we see in the forward process and the distributions we're finally determined for the reverse process. Perfect. Then there's one more thing, I think one more kind of major step to get closer to the form that you would have seen in Jeremy's lesson. So there was a 2020 paper. The initials of that model is DDPM. Tanish, do you know what this stands for? Yeah, it stands for denoising diffusion probabilistic model. Okay, cool. And what they did was they said, let's assume that this variance is just a constant so we don't learn it. And we assume also that the step size from earlier, you know, the variance of the noise that we added each step is also a constant. We don't learn that. We're just predicting the mean and these are set to some really convenient values. Then the loss turns out to be that you predict the noise. So you can restructure this whole thing as you take in, you need to train a network that takes in images. So here's your network. And it tells you what of this image is noise. Thanks to these, you know, these simplifying assumptions. And even though the assumptions turns out you can train much more, you know, models that produce much better images. Now, I think this relates to something from the, you know, the lesson that Jeremy gave. Tanish, do you remember that there was something about the gradient or something like that? Yes, yes. So this idea of, you know, adding noise and learning to remove noise. The idea is that kind of by, you know, again, you have this sort of this image that you have noise, right? And by, sorry, let me think about the best way I say this. Oh, yeah, sorry. Okay, let me start over. So I'll just start. Yeah, so like Jeremy was saying in his lesson, what we want to do is we want to figure out the gradient of this likelihood function. So this is just kind of a different way about thinking about this. If we had some information about this gradient, then we could, for example, you know, use that information to produce, like we talked about, kind of this optimization, kind of produce images with high likelihood. So the idea is that we can add noise to the images that we have. So those are samples that we have. And that kind of takes us away from, you know, the regular images that we have. And, you know, that kind of decreases the likelihood, right? So we have those images and we're adding noise that decreases the likelihood. And we want to kind of learn how to get back to high likelihood images and kind of use that to provide some sort of estimate of our gradient. So this sort of denoising process actually allows us to do that. So there are actually theorems also, I think, from the 1950s that demonstrate that, especially in the case of this sort of Gaussian noise that we're working with, this denoising process is equivalent to learning what is known as the score function. And the score function is the gradient of the log of the likelihood. So again, they have this log here, which, again, makes the math nicer and easier to work with. But the general idea is the same because as we talked about, log is a monotonic function. So again, the general ideas are the same, but the score function specifically refers to the gradient of the log likelihood. So this sort of denoising process allows us to learn the score function. So that's what we're doing, this noise predicting that, you know, we had this whole probabilistic framework using that sort of likelihood framework, and it came back down to just predicting the noise. And that's what the DDPM paper showed in 2020. But it turns out that is equivalent to calculating out this sort of score function and using that information to be able to sample from our distribution. So that's kind of how these two approaches connect. So there's a lot of literature talking about maybe that sort of probabilistic likelihood perspectives of diffusion models. And there's also a lot of literature talking about this score based perspective. But, you know, this hopefully allows you to think about the similarities and how these two approaches connect with each other. Yeah. Awesome. Yeah. And that's kind of the, you know, the beauty, I think, of the math side of things here is that you find all of these relationships between different fields and also like between different centuries, basically, and that allows you to do really kind of powerful and unexpected things. Okay, so you can just do a quick recap of where we got to. So we started out with our data distribution, which we want to model. We said, you know, we'll define this forward diffusion process, which is a way of kind of adding noise to this model. And because we added in this specific way, thanks to, you know, some discovery in the 1950s, the reverse process has the same form. And then, you know, we already know how to train a neural network for this using the elbow. And then a couple years later came the discovery, you know, simplifying assumptions that in the end, all we do is predict the noise. And I just remembered we take actually the MSE of this noise prediction, the mean squared error, which is a nice, very simple framing of the model. And then Tanishk spoke about another way to derive all of this, which is the score function approach, the gradient of the log likelihood. Okay, cool. Yeah, I highly recommend checking out the course lesson as well if you haven't. You know, if you don't understand this, there's no need to be intimidated. You can still be very effective without ever using math. You can be very effective at deep learning as fast AI has shown us. And you can do novel research as well. For me, this is it's interesting. And, you know, it's even beautiful in a way. So I recommend checking it out. But don't feel intimidated. You can find the course lesson links in the past AI forum. We'll add those links as well in the description of this video. We'll also have a topic in the forum for this lesson. You can have discussions there, post any comments, add any, you know, relevant links to the math. And then we have another lesson, you know, video by Jono, which I really recommend checking out. He's a, you know, he's a great teacher. And he was, I think he was the first person to do a full course on stable diffusion. Yeah, Jono's video is kind of a deep dive into some of the code a little bit more and into some of the concepts a little bit more. So I feel like between these three videos, it's a good overview.  That's not to say you won't need to understand math. We'll be covering lots of math in these lessons. But we'll be covering just the math you need to understand and build on the code. And we'll be covering it over many, many more hours than this rather rapid overview. Perfect. Cool. And yeah, thank you so much, Daneesh. I had a lot of fun. And thank you so much, Westry. And that was awesome. Awesome. Cool. Bye-bye.


There are three ideas that I would like to share with you at the end of this 2050 year and the celebration of those 25 years. The first is about the past. The EMCDDA has become a trusted source of information for all its stakeholders. And this is mainly thanks to a unique network of expertise that has been built around the Retox Network and the early warning system and new psychoactive substance. The second is about the present and the COVID-19 pandemic that of course had an impact on the drug market, on the services to people who are using drugs, but also on EMCDDA. We have been extremely resilient and innovative. And the third is about the future. We are building a new business model and this business model will be based on digital transformation that has been boosted by the pandemic this year and also more co-production with our main partners and stakeholders. This is what allows me to tell you that we are ready for the next 25 years. Thank you.


Time to caffeinate. Got my coca-cola. Mmm, delicious. I don't drink coffee, so Coke will have to do. Well hello everybody and welcome back to a plan with me. Today we are working on Monday November 28th through December 4th. It's the first week of December and I can't believe 2016 is already over. Or almost close to over. I shouldn't say that too quickly. Um, if you see a lot of shadowing on this side, sorry. I have like a little lighting issue, so I had to set up some artificial lights. Just where I film this side for whatever reason is always super dark, so I'm trying to even it out, but the shadow is kind of driving me crazy, so sorry in advance. However, we are going to do something fun today. Since this is the first official week of December, we are going to use this bad boy. This eye hauled recently. These are from Erin Condor and these are these just sticker sheets. I think they're $3 if I remember right, but I really wanted to incorporate some of these in my spreads. So I know these fit the vertical better than the hourly, but you know what? We're gonna, we're gonna make it work today. We're gonna make it work. Plus, I really like that this is still festive, this falalalala stuff, and it's not like green or red. You know what I'm saying? So let's start the deconstruction of the week and putting all the things in. But first, I need washi tape. I didn't even grab washi tape yet. Oh my gosh, I'm such a slacker. All right, I'll be right back. All right, I found some suitable candidates. These are the washes I'm gonna go with. I like that this has some of the pinks and the greens and the blues and this is just kind of this nice green and pink. I don't know if I'm gonna use that. We'll see. We'll see guys, we'll see. All right, so we're gonna put everything down at the bottom because you know, it's what I do since I work so early in the morning now. All right, let's see. Will this fit? I think it might. All right, let's do this one. I don't know how this is gonna work, but we're gonna make it happen. We're gonna make it happen kids. Kind of messed up a little bit already. Well, maybe I can fix it. Maybe I can fix it. Okay, cool. Well, it just hangs over a little bit. That's fine. Do you guys see that? Whatever. But this one I have to cut a little bit, which is fine. But let me see. Maybe I can actually use, I don't know. Let's see. Let's see what I can figure out here. I wonder if I like not using a scissors if I can actually use this. Let's try it out. Well, it's kind of wonky, but whatever. That's all right. Put this kind of back on the sheet because you know, why the heck not? But yeah, I have no idea what I'm going to use the other ones for, but I'm sure we'll get into some stuff. Cool. Well, I kind of like the way that looks, you guys. All right, so let me put some stuff down here. Let's just do work because, you know, work has to happen, unfortunately. You know, life would be better without work. Actually, I shouldn't say that because I was actually talking to my mom recently and actually this Ben at 330 is my financial consultant. I don't know. Is that what he is? A financial advisor? Yeah, he's a financial advisor. That's what he is. I hit. Keep talking about future Emily and I don't know what I would do without working like some sort of work. Like I just don't know what I would do with my life. It just seems weird to not work. I don't know.  But you know what? It does. This takes time and effort and creativity and brainstorming and setting aside the time to do it.  So that's really all that matters. And I also like sharing. So that is good for the YouTube. If you don't like sharing, YouTube is not the place for you. But even with YouTube, I don't see it as like a career. I just see this more as fun. And I think it's cool to like, you know, get to know you guys and, you know, get to know what you're, you know, looking for for me and all that kind of stuff too, which is kind of neat. But yeah, but like without having like a regular 9 to 5, like I could go without a regular 9 to 5, but I would still like, I would still do YouTube. I would still, you know, need a purpose every day. Like, I don't know. So, but yeah, it would be nice to sleep in though. But I actually make my own schedule. So I don't even know what I'm complaining about. But I really enjoy getting off at three o'clock every day. So that's kind of what the driving force in my life. I really enjoy being done with work by 3pm every day because I feel like I have so much extra time in the evenings, even though getting, not getting out, but being at work at 7am is kind of a bummer, but you know, whatever. All right. So on Monday, like I said, I'm meeting my financial advisor at 3.30. I'm gonna grab something. I think I'm gonna use one of these guys here. And then I'll use some of this. Let's see. What color do I want to use? Let's see. Let's use blue because why the heck not? Then at 3.30 and he is in downtown Minneapolis. So I will be downtown for quite a while. I think, I think I won't be leaving until I want to say 5.30 or so probably. I don't know. We'll see. We'll see you guys. We'll see. Okay. Now let's put in my garbage. It's all in here. Garbage and recycling this week. So fancy Schmanz. Okay. Let's do a pink one. I like that this week is just kind of like a hodgepodge of rainbow colors. It's kind of fun. I need a camera. I need a camera. I have cameras in here somewhere. I know I do. Like, you know, when you know you have a camera icon in some grouping, but you don't actually have a camera, I can't find it. I think I'm like missing that page. I don't know where. All right. I'm also going to put a little calculator right there. All right. Now I need to find a camera. Like I need to get all of my stuff reorganized a little bit better. Like I thought I had it and then I don't. You know, that's the that's the eternal struggle of doing some of this sometimes. Alrighty. And then I'm going to try to incorporate some of these that I got. Let's see here. Just because I feel like the color palette will match pretty well. I don't even know if I've even posted the video that like I did for these. I did a haul, but I don't remember if I even posted it or not. But you know what?  Alrighty. Maybe I won't use it for that specific thing that I'm looking for right now. Okay. But I think I'm going to just put in all of my videos. I think I'm going to put in like all different colors because you know, why not? So we've got the plan with me on Monday and then Tuesday is November favorites, which is kind of fun. I'll do a purple one. November faves. And then I think I'm going to do an orange one. And on Friday is going to be really fun, you guys, because there's going to be a holiday giveaway. So that will be up at 9am central standard time on Friday, December 2nd. I haven't deciphered what all the rules are going to be of the giveaway just yet, but I do have the prizes and I'm going to be showing them. And it's going to be really fun, I hope. So I hope you guys enjoy the things that I have picked out this year. I actually did a holiday giveaway last year as well. I'm not sure which social media platform I'm going to post it on yet. Obviously, I'm going to do a video just to like show, you know, what I am actually, you know, giving away, but I usually don't do them on YouTube. And let me tell you why, because on YouTube, it's a little bit more difficult because there's a lot of spammers on YouTube, unfortunately. So it's a little bit easier for me to do giveaways on Instagram. So I probably will end up doing that. But I'm not 100% sure yet because it's actually Sunday and I haven't, you know, edited and loaded my video just yet. So as soon as I get to doing that, I'll kind of be able to like think about the rules of the contest and all of that. It will only be open to the United States. Sorry if you're international. I do apologize. The freight, like the actual shipping is just so atrocious that, you know, I can't swing it, unfortunately. But yeah, but it will be going up on Friday and I'm really excited. All right. And so Doyle needs his monthly medicine on Thursday because it's the first of the month. Where is all my Doyle stickers? I know I pulled them out. Everything gets like so compact in my little area over here. All right. I think I'm just going to use like a little gold foily guy over here. And every time I use these stickers of Doyle's head, people ask all the time where I got them. Unfortunately, this Etsy sticker shop is no longer in business. She actually custom made these for me. But yeah, unfortunately, River and Rose Design Studio no longer exists. I wish that they did because she would have lots and lots of good business because she made amazing stickers and they were all like, you know, custom animal heads, which was kind of cool. But unfortunately, she is no longer in the Etsy land. So that's kind of sad. All right. That's just a reminder for, you know, Doyle and his pills and whatnot. Okay. All right. So let me write in work. You guys ask, I don't know, I keep getting the same questions over and over. All the pens, all the things that I use will be linked below. There's always a link below to all the things that I use. So anyway, but I mean, some of them are like older videos that I keep getting the questions onto. But again, they're always linked below. I put an Amazon link just because I know that's easier for some people, no matter kind of where you are, because not everybody has like a Staples or an Office Depot or something like that in their area. You know, I know you can order online, but depending on where you're watching from, sometimes it's easier to just do Amazon. Okay. And are you guys ready for my exciting announcement that I talked about last week? Actually, let me show you my last week. I haven't even like filled it out yet. This is my last week. It's like exactly the same from what I showed you last time. I haven't filled any of this out yet, but anyway, I'll get to it. But I told you last week that there was going to be an exciting announcement this week and here you're ready for the drum roll. That's like the weakest drum roll ever. I am going to be vlogging the month of December, which I think is called Vlogmas. I don't really know. That is my exciting news. I know some people have already started Vlogmas. I am not one of those people. I wanted to do vlogging from December 1 to December 31. I didn't want to start it any earlier than that. That's just, you know, whatever, what I wanted to do.   That's gross, Emily. You're nasty. Okay. So then there's my little thing. I'm a jag, put my little camera, which is like super cute, which I thought I pulled these out already, but I hadn't obviously. And then I'm going to write vlog. Oh my gosh, you guys, it's going to be so boring and I apologize in advance. Maybe it won't be. Maybe I'll make something like interesting to talk about. I believe that the YouTubers call it the Vlogmas, but I've already seen videos up recently that say Vlogmas, but it's like already started. So I don't really even know what I'm doing. But anyway, you're going to see a vlog from me. I'm going to vlog every day. The Monday, Wednesday, Friday videos, like I said before, will not be affected. They're still going to go up. The vlog videos will go up at a different time than the Monday, Wednesday, Friday videos. I do not know if I'm going to post like every single day or if I'm going to like take three days and then post and the next, you know, I don't know what I'm going to do yet, but I'm going to figure that out once I do my first day of Vlogmas. See how much footage I have and then go from there. I am not a professional vlogger by any means, but it might be fun to show you along on my journey. I have a couple of fun things happening with family. I've got some really fun things. I'm going to be traveling and I don't know. It might be kind of fun. You never know. But if you don't want to watch the vlogs, that's okay too. But I'm just letting you know Vlogmas is happening.  Just some weird stuff. I'm just going to prepare you now. I'm going to use these for the laundry. Let's be more realistic. I really do the laundry at like nine in the morning. I don't wake up at seven a.m and do laundry. I mean, I could, but I don't. I wake up at seven and then I take the dog outside and then I just kind of chill because I can. You know what I'm saying? You know what I'm saying? And then every Friday is payday for me now. So I have to put in all these little like cute little payday things. Just kind of nice. You know what I should start doing is actually like putting it like into my work like this because that'll signify that it's like a work payday. Does that make sense? I don't know. Do I like the way that looks? Maybe. I have no idea. I think it's kind of cute though. Why not? So as for this week, I don't think I have anything else going on. I mean, I'm pretty boring. There's going to be some vlog-age happening starting on the first, but that video will not be up on the first. The first day it would be up would be on the second because the idea is that I vlog all day and then I edit this day or at the very tail end of this day and then it's up on this day. It's probably going to be sometime in the evening, central standard timish. So I don't know when that's going to be, but I will like essentially make myself a little schedule because I like a schedule as you guys know. But yeah, I'm not like 100% sure yet of that schedule. But once I know it, I'll probably say something on my next week's video. Here's a look forward to next week a little bit. I got some stuff going on, which is going to be kind of fun. Some fun stuff for the holidays. I will also be doing some holiday prepping and packing and stuff like that in the vlogs, which will be kind of cool. So yeah. So anyway, I think that's going to be it for me right now. I hope you guys stay tuned for the Friday video, which is going to be the Masterful giveaway. I am super excited. I do a giveaway every single year in December just to say thank you to all of my lovely subscribers.  So make sure to subscribe if you haven't already, so you don't miss out on that one. Also, if you liked this video, make sure to give it a thumbs up. Let's me know that you like to see the plan with me's and that you like to hear me ramble every Monday. So as always, I hope you guys are having a fantastic day and I will see you all in my next video. Bye guys.


Hey, old blue How's it going? Got some stuff. We gotta do today. We are gonna get you baby. Don't worry. Don't worry We're taking you to the blue beacon in Fargo next week. We're getting the most expensive package they got We're gonna treat you good.  You're still shiny under there. You see that? It's still shines. He just You know what? You just need just need a little bit of tension. Okay. It's coming Patience. Our trailer is still out on a rental. They're having a good time It's good to hear. I'm gonna bring my pickup truck in here now and unload a few things Kind of a big exciting day for me today My new cooler came in but wait, it's not it's not a cooler It's a fridge that looks like a cooler in the back of the pickup right now. Oh One more thing. It's been an exciting day of purchasing things. I got a new printer. I Have a printer on my desk over there. It's an office jet 4,500 G. It takes 901 ink cartridges Now, I don't know if this information I got was bad, but I went into Staples here in town Which is like an electronics store sort of like Best Buy so I went into Staples and I said hey I need 901 printer ink for my HP printer. Do you have it? No, we don't Are you gonna get it? Now.  Did they discontinue it? Yeah, I went on Amazon. Okay, I'll just buy it on Amazon. Fine. You have it in store I could I know I'll find her on Amazon. So I went on to Amazon and I looked it up a $150 for one black cartridge So I guess they really did discontinue them and now like the remaining ones are being sold for astronomical prices So like no, no, no, no, no, I'm not paying $150 for one cartridge. I would need to I was by two So that would be like $300 for two cartridges that last me a couple of months I'm not paying $300. I was buying a printer So I brought him I bought a new printer for 150 bucks with ink that they had in stock So we got a new printer to unbox to if all blue is not out there and I'm not making money I guess I may as well be spending money, right? That's how it works. If I'm not working. I'm spending money That's why sometimes I just like to work steady for a set amount of time because I know in that time I'm making money as soon as I stop working and I come home Now I'm spending money But eventually I got to go and make money so that I can come back home again and spend more money We're in one of those times right now where we're spending money But at least we have the rental business actually, you know what some of you've been asking the rental business has actually been going pretty good I've been quite Pleasantly surprised. We'll talk more about it in a bit, but we're fully pretty much fully booked through August already So it's just bang bang bang bookings have been coming in everything's been going great to the renters have been enjoying it Everything has been coming back in one piece the way it should they've been taking care of it gone wood So far it's been going really good and hopefully in the future we can expand it to have more models to rent out And more of those too you guys want to drive for me. I'm gonna be looking for drivers in the future. I'm gonna buy another one Not right away first a house before any of this we have to buy a house once we buy the house I want to buy another one of those and I need one of you to drive for me I want to take you want to drive one of my trucks. Are you gonna take care of it? You keep it clean.  Okay, we're gonna have a reward system for taking care of my truck If you keep it clean on the inside and the outside, there'll be like random inspections once every three months Walk in there. Let's do a zero to five star review on how clean you keep my truck And if you get a five star review you get a bonus. Okay, we'll talk about that in like a year or two Let's go get the pick up in here and We'll talk more about these things in the unbox. I'm excited. It's gonna be a good day All right I'm gonna have to sweep this out first because if I walk too much on this because this dirt gets tracked in when I back in right and If I walk too much on that it sticks to my shoes and then it gets everywhere and then it's much Then I got a sweep the whole shop instead of, you know, just where the dirt is crickets We got a cricket I wouldn't call it a problem because it's not bad. It's just whenever I open the doors They like to come inside because it's dry and warm in here. I guess they but Made in China You think about are we not able to make barbecues here in North America? Is it really better? Well, you know what? I'm gonna leave this in the box actually until the RV gets no I gotta get it on the plastic I've got to get it ready They're gonna have to test it once we get the trailer back and I can't test it until then because it connects up to the trailer I don't have the adapter that'll connect it to a regular propane tank directly It's gotta go through the trailer, right? Or maybe it comes with the adapter. Does it come with the adapter? That would be cool But I don't have a propane tank here. That's full. They're both on the trailer Have a propane tank at home. No Come on. We love spending money All of us do. Don't lie to me This is investment money though. This money will get back Exactly like our old one. Good. Good. Good. And then this is the mount See the mountain we have now is a little bit rusted So this is the new mount. It's gonna look brand new So when you rent something like a trailer you want everything to look nice, right? Doing my best The beginning of August here has been kind of it's been slow and Unscheduled vacation time. I wasn't planning on taking all this time off. But while we're here we're getting all this stuff done We're getting back on the road soon though a couple of days So you guys who like the truck and stuff? Don't worry.  Uh-huh, huh? Huh? You getting hungry yet? Ah and instructions, huh? Don't need that. Oh, it's not exactly like the old one. The old one didn't have this little Second level here See that the other one didn't have that top grill up there. This one does It's not detachable though. So it's a It's a camping grill, right?  Okay. I've got my gopro yet speaking of which I got to see if it's been delivered yet You want to see my home screen every time I open my phone? look Doesn't that just make you happy every time I open my phone makes me so happy so we got to check to see if uh As the GoPro arrived, it arrived, it's been delivered. It has been delivered Okay, so we're gonna open this cooler up in a bit. I'm gonna quickly run back over there grab my gopro Before that they close up there. It's here Hero eight. I know it's not a new gopro. The new gopro is like I wanted to get the gopro max That's the best one. It's got like the 360 views and everything but Later we're still trying to save up for a house It's the whole bowl of everything. Hey, let's do that So this is just fine. That's all I need This is what I have on my head. It's an action camera for road shots and scenery I need a gopro part of my essential arsenal For vlogging on the road It's also very easy if you're walking into public places and stuff just to like Slip it in your pocket and pull it out. Wham! Now you're vlogging right in your face And I got a case that comes along with this Oh And look at this. I got another thing. That's right. I forgot I ordered that too. It comes with it Couple of mounts. Well And here's the sucker himself Eight seconds So we're safe This is what I've always filmed my vlogs with Just a simple gopro And I like the hero eights because they have better low light capabilities in my opinion than uh the hero tens and nines But eventually like once we buy a house and we're able to start spending more money on fun stuff Uh, and uh things like that. I want to get the gopro max But who knows what they'll have by that time.  I did order two batteries with it. So where'd they go? We got to be in here somewhere I have to charge the two batteries that came with it. They were in there, but I have charged batteries already That's what another reason I bought the eight is because I have all the accessories for the eight already I already had a battery that was charged ready to go. I've got the chargers Just work so better this way Plop that in there Put it in there Turn her on Turn her on Turn her on What days do what's going on here? Come on Tommy. This is the faulty one. I'd just be my luck right? Oh, it's like You guys aren't hanging here very well. Here. Let me put that under you.  The light on the front is staying on. Usually it goes on and off Interesting interesting One second. I'm gonna go get one of the batteries that came with it and see if it's just being picky All right, the batteries that come came with it is obviously uh a gopro One second here. There you go a gopro Brand the other one is an off brand that I bought just for extra batteries But those were charged. These aren't quite charged yet, but maybe Maybe this gopro only wants to run off the gopro batteries That'd be smart if I was the CEO of gopro. I would do that too. I wouldn't want people buying cheap knockoffs To operate my stuff. I would want them to wear by My up priced stuff so that my stuff will work Come on. I know how to use batteries or I know I know how to use gopros What's going on here? For a second there. I thought it turned on. Nope Holding it holding it holding it If I got a faulty one I'm gonna be kind of Come on Product doesn't work It's faulty won't turn on I've tried everything so uh Got to send it back Get a replacement This is still like a four hundred and seventy five dollar camera You'd expect it to work when it gets here. I guess every now and then That's really disappointing. I gotta figure out how to do this return process Gotta put these return labels on it and send it back. It's just my luck, right? I'll just buy one in person if they have an eight. If not, I'll just have to order another one, but I'm gonna find a different supplier Then again, maybe you know what happens. It happens You know, there's faulty stuff out there of faulty electronics. I just happen to be the unlucky guy who Has to deal with it now I guess it's my turn. We all have to go through this sometime or other Back to the post office. I go Hi ho hi ho off to the post office. I go with my faulty gopro To return it and get my money back Hopefully the post office is still open. I don't even know if it's still open It's uh, it's almost five o'clock three minutes two minutes to five There's no way a government service is still open We could have made that light if you were doing the speed limit my friend My friend the speed limit was 50 not 25 Now I got to sit at the red light There's the post office right there to the right.  We made it We're a little wet, but we made it.  Apparently I gotta wait like two weeks or something now I don't know. I'm gonna go to staples Uh staples a canadian company. I don't know. We have a staples in town. We don't have a best buy I don't know. See if they got any go pros there I want an eight like I said. I want a hero eight I'll see what they have It's not getting any closer. They're open till six. They're open for a whole another hour yet Wow steinback six o'clock Staying up late now getting rowdy, aren't we steinback is known for everything closing way too early Got a little bit of moisture outside today I'll get myself back to the other side of town This is where a gopro would come in very handy, you know This camera doesn't always want to focus on what I wanted to focus on outside Especially when it's raining Okay, this cross street does not have a stop sign. Let's not get t-boned here. Give her We're good. We're good staples kind of Kind of like one second Say it's kind of like Best buy it's just really small This is the old canadian tire building this building used to be canadian tire And then canadian tire got a real building over there and they they packed it full Believe me those of you from steinback who go to canadian tire know exactly what i'm talking about Canadian tire and steinback is way over stocked I mean, it's good to have a lot of stock, right? But I guess they don't have very much room in their back So they pack everything out onto the floor there and you can barely walk around in the store It's a massive store And you can barely walk around and they got bikes hanging from the ceiling everywhere and all kinds of other stuff Hanging from the ceiling because they don't have any room for it on the floor It's chaos in there, but uh, they most likely have what you need in stock Which is a lot more than we can say for steinback walmart That never has anything in stock Scratch that idea. They do not have Go pros at staples in steinback Not at all not even he said he could had he seen them at sport check before in the mall Does walmart carry go pros if I can't find one in steinback I'll just have to buy again off the internet which is fine. But then I gotta wait for it So I'll go find one We'll keep looking around but I did get something from staples. I was running short on folders Very important very exciting We got one It's a hero 10. So it's two newer Two versions newer or generations newer and I've already had one of these before And that one broke. So I do have batteries and accessories for this one as well So that'll work and you know what's interesting this 10 was cheaper than the eight that I just returned How does that work? So the gopro with tax that I ordered off the internet it had some accessories though I had that right it had that case and an extra battery. So there was some accessories with it That whole thing cost $492 I returned that they're gonna refund that This 10 it only comes with one battery and it doesn't have that special fancy case that the other one did But it's two generations newer this was $470 but I also got $60 off because it was on sale at sport check. So I just saved money by having a faulty camera sent to me on Insta on Amazon saved money cheaper and I got a newer camera all bad So I met brit here for Supper at prairie donair delicious She had theo with her obviously so I got to sit with theo and brit and have supper now. It's time to go to the shop and Set up the camera set up our new printer and set up that cooler I still got a few things to do and I don't want to be at the shop all night I want to go home and have time to watch a movie and relax and hold theo So I only got a couple of hours now. So let's get back there and Get her done. So we got that charging now Charge that up and we'll have that this is a ink for my new printer Which I got to set up yet, but first we're gonna set up this cooler. We have a vlog Way back there in my office That's getting ready to be uploaded. So we'll be done there right away Oh boy We're on now. Don't look at it. Don't look at it. It looks awful. So let's uh unbox this and see what this is all about And we'll see how fast this cools down while I edit another video I'm still gonna have to find where those batteries are. I have extra batteries at a charger for my tent I don't know if they're in here since we moved everything has still sort of just been placed wherever so I need to go through everything Who knows when I'll get to that probably winter sometime when other things don't need to do it But uh could be it helps too GoPro is charging so now for the big one The cooler fridge I am excited about this. Okay, so this is the cooler compressors down here I gotta figure out how this works. You see this is the freezer side here And the fridge side But from what I've seen on the reviews you can take this thing I ask you can take this thing right out and there's a sensor in here That when you take this thing out it automatically knows this is all one compartment And then you can either make the whole thing a fridge or the whole thing a freezer like I was telling you Now how cool is that that's got quite a bit of space in here aside from that little block there. That's the compressor So this is going to be great. I'm going to be able to actually have food with me real food And I can take the food from home instead of you know Buying food every single meal it's There's only so many things I can take along That don't need to be refrigerated from home And like I said earlier in the vlog even sandwich meat, I put sandwich meat in that other cooler It would last two days then it would be bad like you don't want to eat it bad We'll plug her in and see what happens I'm reading the instructions here trucker Josh reads instructions sometimes I just want to make sure I get this right because I'm very excited about it. I don't want to break it And then have to return it Probably wouldn't give me a refund Okay, so this says that when the ambient air temperature outside is 25 celsius or 77 fahrenheit this thing can keep As cool as minus 20 celsius That's nice. That's nice Wow, that's amazing. That's going to be so awesome. Okay Let's test it out. Shall we I'm going to plug it in and then I'm going to go edit a vlog And then we're going to come back and see how cold it is Oh, there's an app for it too. I have to figure out where's the app Using my app Oh app connection is a QR code here. Nice. Cool. Cool. Cool. Cool. Cool. I will touch base with you When we've uh had this thing on for a little while we'll see how good it works I'm just trying to figure out the app here now But uh, here we go. So this is the temperature of the cooler of what it's at right now I have it set at minus four fahrenheit. So it's going to go all the way down to there This is the fridge. I have it set to 14 fahrenheit And it's currently at 47 so See what happens That's so cool. So cool I should probably have a product code or something that I could give you to buy one of these because you're probably gonna want one too But I don't have one So if you you want to look at one of these on amazon Go on amazon and look up the name bodega cooler. They have all kinds of different sizes and shapes and everything Check them out. Uh We'll see how it does for me here if you want to see how it goes for me first Speaking of trial runs those tires these black hawks are still doing great Fantastic so far. I've been very happy with my purchase. Well, unfortunately I couldn't get it to connect to the app here. I'm having some trouble with it It seems to need like a wi-fi connection and I don't have a wi-fi connection here I just have my hotspot from my cell phone So I'm gonna take it home and see if I can figure out the app there, but the cooler is working great That is awesome. It was almost all the way cool down to zero By the time I unplugged it here. It's only been 15 minutes or so We'll test it out more at home So we got everything figured out now and got it connected to my phone It did need to use the home wi-fi for some reason There's cooling it down now. I changed it to Celsius So the freezer parts at 17 negative 17 Celsius And the fridge is at minus one but that's going upwards. I set it for plus three so just above just above freezing and Test it out. See how it is. It didn't take too long to get there either Can't even hear it. It's running right now Now listen, it's pretty quiet Pretty good, eh? Where is the rest of the family? What's going on back here? What's going on back here? We got the Bouncer right here ready to bounce anybody out of here. Who's not supposed to be in here? Dad, I'm gonna need to check your ID I'm gonna need to check your ID.  It's like a whole new world It's quite a bit to take in There's a lot of colors in this room Sensory overload. It really is Likes that seahorse though. He really does. Yeah I think he likes the puffer fish with the mirror over there too, but he can't see it Turn around, buddy.  Where did my seahorse go? Oh Not the turtle Does the turtle make sounds?  Can't all be the same. That'd be weird Yeah, but very close. Very close. You're pretty perfect, buddy Can't imagine a baby getting much more perfect than this one Okay Britt and I both have sore backs I think I pinched I got a pinched sciatica there in my back. So Like there's times where I feel like I can barely walk So I'm fighting with that's trying to there's good days and bad days Britt's got similar I'm just broken all over. We're getting old We want more kids though. So we can't get old too fast But anyways, it's been a long day today. Got a lot done did a lot of running around and We're gonna leave it right here I'm home for another couple of days for about another day and a half So for tomorrow and then the day after that there will be uh, we're headed to canora. So say goodbye to you


Hi, I'm Jimmy Glass and this is my match and my catch challenge until you drop one You can hold on to your legs. What's the record? 10 I guess I'm a 10 So all these people is it octopus? Well, I'm wearing and hanging out just slow down sir Until you drop one. Yeah. Oh trying to move it over there.  Hang on they're all going


All right, cyber traders welcome on back here to wrap up our week putting a bow on this week this Friday December 9th Shortly after 2 30 p.m Eastern time great to see Norman and Steve still with us here and Brad as well and skip and all of us just joining us Otherwise right now great to see Jeff and Andre still with us here loud and clear from them Ray as well with us and great to have all of us. Otherwise back on social media Facebook live or YouTube Another day another stock making a historic run-up, right? So no yesterday was the pH vs stock today. It's the AM AM if you if you think I sound tired It's because I am I mean, I'm sure you are too if you've been with us all day today watching this stock pump up It's gotten halted probably 30 separate times today this one the AM AM It's still up over 700 plus percent right now making another squeeze back up here You know taking us into this afternoon meeting. It's currently halted again Should reopen up exactly in about three or three minutes at 2 30 3 14 It should reopen up five minutes exactly from the tape But for right now, we'll at least just put this on our watch list folks to begin the AM AM I mean just the fact it's still giving us another run That's the only reason why I'm watching this right now because otherwise for a stock that makes this type of pump Up you got to expect for this thing to pull back down at some point in time So it's easy to leave money on the table for the stock. I did not take part in this thing one bit today I mean hey after a certain point. I thought it was just gonna crash back down So you bet you I was checking after a certain point I did miss that miss out on it though initially just from pre-market and from after the initial market pop here or Initial pop after the market opened up. I could have focused on this I was more focused on Carvana and Neo myself Hey, Carvana at least for the meantime Now is a hell of a pop-up to be caught as well finishing up the audio commentary that we had from the morning So, you know, this was another big winner a couple of huge Iceberg orders showed up on CVNA earlier on the bid at 470 and 472 It led to a nice pop right from that point So we were in the trade right from there a little bit above it and led to the pop the halt got halted a couple of times On the way up. I think even here. I even forgot where I got out I think it was like over 550 just around this area I reckon but it was after it was before it ended up pumping up towards six and then pulling back So this ended up making a nice push, but look where it's at now It was actually as good of a short as it was along right a full round trip back down Why I'm bringing this up is because if Carvana name brand company that's like, you know Very heavily traded day to day makes that pump and dump What is gonna stop AM AM from doing the same thing? Coming up here Be careful. That's what I'm saying. All right, so AM AM definitely for right now We'll keep on our list. I mean a beautiful trend But let's see what else we got for the meantime though spy has gone utterly nowhere this whole day really I mean like a little up movement up here, but just completely flat otherwise So, you know, we'll see if there's anything anything else out there that looks good and Recap might I say a couple others that ended up pumping up for us outside of the AM AM and Carvana Of course CLR CLRO being one of them So CLRO ended up having a big pump early in pre market It was below a dollar yesterday gapped up pumped up and Exploded ended up making a very similar move pumping all the way up towards to ended up making like a second squeeze up But obviously led to nowhere ended up coming back down pretty quickly So more from the morning, but the CLRO is a nice trade at least for us CJ JD, I don't think that we had called this out I saw a pop-up on Benzinga a couple times just before this meeting, but it's got a really thin order book So no need to follow this for end of the week Friday The Acer a CER this one we're actually pretty familiar with Acer therapeutic company not Acer Computers, but nonetheless Acer here ended up making a big squeeze up from this morning You know, it's trying to get back up there right now at least right You can see it's trying to make a bounce subtly from from 207 208 here So I'll come back to this just at the tail end. I'll put this on our side list CER uh-oh I didn't mean to give this the kiss of death folks. I swear, but you knew this was gonna come The AM AM trade just got halted on the way down Steve took it took his profits wisely Very nice Steve Steve one of our longtime students He's in the she was in this trade from 268 to clocked out right now. I was saying it was a good time to get out Steve I don't I don't just say that to say that right. I mean hey if there is a continuation that I'd say look for more but It already pumped up so much from earlier. We barely trade the stock or I don't think we ever did The fact that this just got halted on the way down again. I Just kind of you know what we'll keep it on our list But let's see if there's anything looking a lot better a lot better of an opportunity to continue to pop Momo being possibly one of them, you know ended up having a huge squeeze from this morning in the market open Continuing the push that it had yesterday But this one at least is a lot more liquid this one typically does trade a lot of volume day to day You know maybe outside of today and yesterday Yes, even outside of these past two days it still trades over 1.7 million on average So no, it's not one of those low-flow pump-and-dump type of schemes folks This stock though had made a nice pop though today, and it's still holding strong. So for right now I'll put this back on our watch list the Momo Breaking back over nine right now to Chuck had called out Armed and he was in this just before Armed in from 566 out at 595 Still climbing Chuck posted that about a minute ago two minutes ago and still go and as he said yeah I mean RMED how could we not? All right, I don't have the level four for this one open right now and honestly the way this is pushing It's not gonna shock me that this gets halted soon Maybe one more little pop up and it should get halted Who yeah, H U Y a Chinese stock ended up making a nice big push early this morning I'll tell you what Billy also had a nice pop today to BILI big move up from the morning It just had a tough time trying to break over the 26th level Checking out a couple of the other Chenyu stocks we've had our eyes on this past week Nio's going nowhere IQ looked all right actually just a moment ago there trying to squeeze back up and above This looks like more of a level one stock if anything the H U Y a though I mean this one ended up making the bigger of the two moves That's trying to hold strong for right now. I'll come back to this if we need to I'll put this on the side All right, so let's just really squim skim quickly Maybe not that fast, but skim quickly at least across the gainer side. See what else we got It was intending on making this meeting relatively quick So pardon me if I'm going a little faster than usual But ended up having a nice pop this doc you saw in trade ended up You know blasting upward from the morning making higher lows across the early afternoon I'm pushing back up for right now. If you'd like the more expensive stocks at least this is pretty liquid for a more expensive trade Really? So, you know, if you tend to follow the more expensive stocks put this personally on your list Boil natural gas ETF we were following this of course going into the market open today You know this UNG cold so it looks like those are still pushing back up at least UNG and boil cold We'll trade inversely to that So I had both on the on the watch list just in case if it was a reversal But either way for right now will definitely focus more on the boil trade and the UNG All right to wrap up the gainer side here. I don't really see much else. That's a familiar name. We checked out Billy already here Bird had BIR Dean is this like bird like the scooter company I went to California to visit a friend recently They never had him in New York and I didn't see him in Florida But the bird scooter company is this it bird Ended up having a nice morning here little push-up not a whole lot happening at least of recent All right, let's hop over to the loser side right now again markets got nowhere So, you know, I'm not expecting much on either side. We've built up a good list so far at least You know went through the bottom of the barrel got through a couple of different things and you know I'm not a bad watch list for right now, but I'll tell you I'm not really expecting to find a whole lot on the loser side Eli could be a nice opportunity This had dropped off pretty nicely from the morning and steady downtrend at least taking us into the afternoon I just I feel like this could be more of a slow poke, but it is near the lows So I do like it as a short Lee Eli That GameStop are not gonna really trade that though. Even if it is easy to borrow my platform today Tgtx we've actually had our eye on this stock of late Ended up actually providing a pretty nice short trade for us last week It was I believe but dropping back down again breaking below the support You could at least see from the daily chart had a big jump out mid-november and I fell flat since but now breaking lower at That point so you know what actually I like this trade again tgtx. We did ball on it last week as a short Why not again? All right, I'll say actually plucking to from from the loser side right now is a pretty good win for us So, you know if there's anything else that you see type it in the chat board Let me know probably more on the upside I'd say but if there's anything else either up or down Let me know here folks I'll get this out the way now for all of us on social media Facebook Lover YouTube Of course, enjoy the weekend first and foremost hope it's nice out where you reside Any questions that you have feel more than free to send over through email Josh at c2 trading comm is my handle I always like to post that at the end the streams here Then otherwise, I don't have my banner up like normal But now we do if you wanted to join us inside our live trading room Just go right down to the link on the yellow banner below the stream right there to get yourself started But with that though folks last call, let me know if there's anything else up down left right more than 5% Another halt on AM AM you got to stop even saying it in the chat board like every five seconds It's gonna get halted so you know, it's just not worth your energy I mean the time it takes for you to write that again You're gonna miss out on a stock making a better move So, you know for right now, we'll keep this on our watch list But it's gonna get halted over and over up and down up and down probably more down than up going forward Did we look at our meta we did later, but still a great call We have this on our on our list rightfully so so this looks really good still You know, perhaps at this point We'll see it pull back maybe build a higher low in the making off a 6 or 590 and then from there for the Wholeful pop higher. It looks good though. We'll focus on this Actually, hey pardon me later. Give me a quick second. Hey Check my calendar. All right, you better be in the trading room on Monday later. That's all I'm gonna say All right with that in mind folks We're gonna wrap up and I say that of course extremely friendly I looked forward to Monday later with that in mind folks just again If you have any questions feel more than free to private chat me inside the inside the live trading room Or you can email me on top if you're dipping out from right now Josh at c2 trading calm But otherwise folks you got rich on the mic coming up at 3 p.m Eastern so in about 15 20 minutes he'll be on wrapping you folks up going into the close All right, otherwise, hey, I'll be in the chat board up until 3 30 So if you see something say something as well, I talk to you soon Come a cyber group member today Just click the link below and receive all these amazing products and a world of knowledge For just nine dollars do it today


So, in this video, I'm going to show you how to report a problem to Instagram directly through the app. So, you might want to do this if you've encountered some sort of technical glitch or issue. If, however, you want to learn how to do something, then you can read the Instagram help also available within the app and I'll point that out to you. Of course, you can contact NILC and we will train you how to do it. So, we are looking at my Android phone and we are on my profile. The process for contacting Instagram is very similar on Apple devices like iPhones and iPads. This is how you do it. So, we go to the hamburger menu, the three lines at the top here on my profile.  So, within help you've got the help center. So, as I mentioned, if you're looking for advice on how to do something, you can find that here. If you want to report a problem, you can do that here and we'll come back to that. And once you have reported a problem, you should start to generate a dialogue with Instagram and that will appear here. So, what you do is click on report a problem and then you've got three options. If you want to report spam or abuse, that is a separate process there. If you want to send feedback, perhaps on an existing feature or you might even want to suggest a new feature, you can do that here. But to report a problem, some sort of technical glitch or issue, this is the option that you need. So, we'll click on this and what you want to do is explain very clearly and concisely what your issue is. So, that the support engineer on receiving that can get fully to grips with what the problem is and help you to resolve it. Also, I would strongly advise that you add a screenshot to aid that process. So, you can take a screenshot using this button here or you can pick up an existing screenshot from the gallery. Then, when you're ready to submit your problem, you can just click the submit button here. So, that's how to report a problem to Instagram. Thank you for watching and if you'd like to learn more skills by watching short videos, please subscribe to our channel here on YouTube. And if you'd like to continue your learning journey with NILC, please visit us at nilc.co.uk. See you next time.