So, hi all, I am Abhishek, I am CTO and co-founder at Infillect. First of all, thank you to ODSC for inviting me here to talk about some of the work that we are doing at Infillect. What I am going to talk to you today is about the core problem that we as engineers face. Having access to enough data which is labeled, right, you know, most often because we do not have enough data, we do not end up training models which work with very high F1 or precision or recall also, right. So, I am going to talk to you about a technique which can take you from having small set of labeled data to having, you know, a large set of labeled data, right. So, before I begin, if I can have a quick show of hands, how many of you are deep learning engineers, okay, perhaps around 30 percent. How many of you work on computer vision problems, okay, same set. So, all of us know that in deep learning stack, we have three main building blocks, data sets, algorithms and architectures, right. So, if I ask you this question that you are supposed to rank these three things from the most important to the least important, how many of you would have architecture at the top? None, okay. So, that, okay, one person. So, hopefully at the end of my talk, I will be able to convince you that, you know, a data set is the thing that can, you know, that has the most impact on the way we solve the problems, right. So, and this is, you know, not just me that I am telling you, there are pioneers in deep learning, you know, talk about not just more data, but having data which is better, right. So, we know that more data trumps algorithms and better data will trump more data. So, I am going to talk about a technique which can get you better data sets. So, if not anything at the end of my talk, I want you to have three takeaways. Okay, that real world problems are, you know, messy, they are chaotic, right. So, if you have worked on more than one computer vision problem, you would know that one problem differs from the other in terms of schema, in terms of the number of classes or the number of instances for each class. So, there is a lot of chaos out there, right. I am going to talk about a technique which given in any kind of data set, it will, you know, help you to transform it into a data set which is, you know, a near optimal. And hopefully, we will see that this technique gives us enough bump in, you know, accuracy, okay. So, taking a step back, the three things that I mentioned in the beginning, in terms of data set, in algorithms and architecture, right. So, no matter what press or media says about AGI and the robots taking over the world, we know that it's all about the game of taking a configuration of weights from one state to other, right. And all these three blocks help us to tune these weights. Because in the end, we want to arrive at a set of weights which help us to give very high precision recall on our test set, right. So, these three building blocks are the ones which you can use as parameters to tune our weights. I'm not going to talk about algorithm and architecture. I'm going to talk about the aspect of data sets, right. So, I'm going to assume that algorithm like backprop is fixed. I'm also going to assume that architecture to solve problem is fixed. And we'll see how to tune data sets so that we take weights from one state to other, which help us to get very good accuracy on different data sets, right. So, roughly there are three ways in which we can build our data sets. Either we can go and collect a more data, or we can synthesize more data, or we can augment data, right. And each of these have pros and cons. In terms of collecting data, if you take example of a self-driving cars, it's quite expensive to collect data, right. Because you have to ride the car all over, it's a bank load to collect data about the conditions of Indian roads. It's also an expensive to annotate this kind of data. And sometimes it is just not possible to get data, right. If you work with enterprise customers or inside big companies, if you work with different teams, you will understand that it is sometimes hard to ask other teams to give. Please give me 10,000 more photos so that I can train my models, right. It just becomes hard to get data. Synthesis, right. So all of us know about GANs, right. So GANs have been used to synthesize images. I think all of us know that it works pretty well on constrained settings. So if we take example of human faces, GANs are pretty good in generating fake human photos, right. But if you go beyond this realm, if you go into unconstrained settings like Indian roads, it's just hard to produce pictures which would look real, right. And also it is quite expensive, right. So GANs can take anywhere between two days to two weeks to get trained to be able to generate photos which can look realistic, right. And there are some recent studies which say that the pictures that the GANs generate don't really model the underlying distribution. So you may end up training your models on data, which is not really a true representation of the underlying problem. So as deep learning engineers, all of us apply augmentation techniques, yes. So those who raise their hands, who work on computer vision plus deep learning, how many of you do not apply augmentation when you have to solve problems for classification and for detection? None, right, so all of us apply. And we know that it's a fast technique, it is inexpensive. But most often the part where we struggle is how do we choose augmentations, right. So if I'm working on a problem of classifying different types of cars on Indian roads, how do I choose augmentations? Should I go for rotation or blur or noise? Or should I not use color transform, right? So we kind of make some intuitive guesses. We pick a few augmentations, and then we get some kind of lift in our accuracy. But it leaves us wondering as to are there optimal augmentations that we can apply to underlying data sets which can give us the best performance, right? And this is exactly what I'm going to talk to you about. Finding a good augmentation strategy that removes this approach of trial and error, right? So I'm going to take an example of a task of detecting different home decor objects. So here you see a few examples of objects like TV and bookshelves, tables, lamps, right? So for this particular example, there are around 15 classes. We are given 5000 photos. And there is a bit of imbalance in the data set, right? So objects like table or bed could appear frequently. But objects like bookshelves or lamp may not appear frequently, right? So we want to train a detector which helps us to get F1, which is perhaps more than 85% or so, right? So if I train a detector, let's take example of RetinaNet as a single-shot detector, right? So if I take that as my choice of architecture, and if I don't apply any kind of augmentations, this is what I will get. Overall, F1 of about 70%. And the classes which don't have enough number of instances, I will get lower F1. So here I'm getting around 51%. Now what do we as deep learning engineers do? We will choose a set of augmentation techniques like I mentioned earlier. So here I'm showing an example of the color augmentation where there is input image and I have applied this aspect of inverting the colors, right? So for the color inversion, we have a range of 0 to 1. I have chosen a parameter of 0.5, and this is the output, right? So my input image is transformed into this in output image. The other example of augmentation is a geometry augmentation where you can apply crops, you can apply rotations, you can apply a shear. So here is one example of crop where the chair is cropped, right? And last, an example is more of a bounding box augmentations where you are focusing on the object inside the box and then you're applying augmentations, right? So as you might choose color and resolution and shear, these as my choice of augmentations. I will get some bump in F1. So here that 69 is increased to 77. And the F1 for class which doesn't have any instances, it has increased from 51 to 53. So I'm still not satisfied with these scores. So then I change my strategy and I apply rotation, noise or shear. So these kinds of augmentations. So now if you see my overall F1 is decreased, but the F1 for the class which doesn't happen in instances has increased, right? But this has still kept me wondering are there any other augmentation strategies that I can apply by which I can take this number 69 to at least 85%, right? Which was my goal. And it may also talk to you about a few other quirks, right? So as compared to the problem of classification in case of detection, it is not easy to solve the problem of class imbalance, right? So imagine a data set where an object is appearing on all the photos, right? So that particular object will always dominate your distribution. If you have played with architectures which can detect objects, you would know that if an object is appearing in the same size and at the same location, only a few anchor boxes get trained. So at test time, if you get that object but which appears at a different location or in a different size, the architecture filters. And of course, how do we think about modeling the background class, right? Because we want to ensure that there are not many false positives, right? And then again, there are other challenges of the camera sensor being changed or the photo quality gets changed. Or is suddenly in your data set, you see that the compression technique is changed, right? And also one augmentation does not apply to all kinds of data sets, right? So if you have to detect a number plates and if you end up applying the augmentation of the flip, right? So it's going to change the whole story. Similarly, if you apply the color augmentation to the image on the right, it's going to be a recipe for disaster, right? So red to green will look at you into trouble. So coming back to the point that I'm making is that different problems demand us to think about different kinds of augmentation strategies. How do we think about building a technique, which is build once, but it can be applied in many times, and it removes the guesswork of which augmentation I should apply to work enough at the set, right? So hopefully I'm able to convince you that there is this problem, right? So how do we go about finding this technique, which irrespective data set helps us to do data augmentations? So I envision a future where this task of choosing the right augmentation strategies is automated, right? So this is what I have shown at the bottom. So you get a tag data. You first spend a lot of time in figuring out the policies that you need to apply on this data. Then you train a model and hopefully in just one shot, you will get the accuracy that you need. What happens today? We get that data, we apply some intuitions, we create augmented data set. We train a model, we are not satisfied with the evaluation. We go back, we again change a few things, and it takes time, right? So hopefully this future will take less time and it will also give us very high accuracy. So let me start talking about that technique, right? So in this technique, there are two parts. So in the first part, once I'm given a tag data set, I'm going to run an algorithm which may take time. But it is going to give me a set of policies, right? Once I have these policies at train time, so imagine you are doing a training.  So you pick the first image, this is your input image. You apply the learn policy at train time when you're doing a training for your model, you're not doing any extra work, right? So you have policy already trained. On that input image, you're applying a set of augmentations, which is given by your policy, right? So here I'm saying that we apply n augmentations, n can be 1 or 2 or 3, right? And that transforms our image into an image which is given as input when we train the architecture, right? So what I'm going to talk about now is the first aspect, right? So if you're given a tagged data, how do you learn policies which are the best in augmentation policies, right? Any questions so far? Yeah, what? It could. So what I'm going to talk about is specific to computer vision, but there are certain clues which you can apply to even a text kind of data set. Yes, yes, yeah. So there are, so what I'm going to talk about is perhaps custom for computer vision, but the technique I think is generic enough, which you can apply to the kind of data that you are saying, yeah? Okay, so just to get the terms right, when I say policy, it's basically a set of augmentations that we apply on an image. And each augmentation is applied with a magnitude and certain chance, right? And if I have a set of policies, I'm going to call it a strategy, right? So as I explained, what happens at a trading time is you sample a photo, you apply a policy, and so you sample a policy from these set of policies. And once you apply that policy, you get a set of new photos, okay? So on the same picture across different batches, I may get different outputs, right? So here I have applied policy P1 and because each policy can be applied with certain chance, it's not like in all the time you will end up generating the same picture, right? So across different batches, on the same photo, if you apply different policies, you will get a different set of photos. You could, yes, yeah. Okay, so let me now talk to you about the core problem, right? So what is the core problem? We are given a number of augmentations, right? So you can think about augmentations like the ones that I mentioned, right, from color to cut-paste kind of augmentations, right? So this could be 30 or 40 augmentations that you have. Then in your input data set, you have a single number of classes. What we need in the end is given a class and given an augmentation. We want to arrive at a number which I call as magnitude, which says that I'm, so if you think about, as I mentioned, the augmentation of color contrast, right? There are parameters like alpha which can change from 0 to 1, right? So here, when I say in a magnitude, sorry. So it implies that I'm choosing a number between 0 and 1, right? And I'm going to divide this whole range into let's say 10 buckets. So if I'm going to choose a color contrast as an augmentation for a particular class, I'm going to say that apply that augmentation with a magnitude of 0.3, right? And similarly, I want to know if I should apply this augmentation to this class with this magnitude with some chance, right? So it's not like in all the time I'm going to apply the same augmentation, right? So given a picture, I want to apply n number of augmentations. As I said, this n could be 1, 2, 3, 4. But essentially, given a photo and given a policy, I'm going to get the in-output pictures, right? So if you think about the overall search space, right? So if you see, I mean, earlier, what we were doing, we would just choose intuitively a set of augmentations and we would just choose a certain magnitude. So if you want to do, let's say rotation, right? You would do it by maybe 20 degrees of rotation, right? If you want to do a translation, maybe you will choose something like, I want to do a translation by 10% or so, right? So all those things we are trying to model as a part of a discrete space, right? So this is the entire space from where we are going to sample a strategy. So there is not going to be any kind of guesswork. But this search space is very large, right? So you have a number of augmentations, c number of classes. And for each of these two combinations, you want to learn two numbers, what is the magnitude, and what is the chance, right? And since I want to do this n times on a given picture, this whole search space is huge, right? So if the earlier in bulleted list is not clear, essentially we want to learn this s set of policies. And for each policy, I want to choose a class. Once I choose a class, I can go and sample image from my data set. And then I'm going to apply in augmentation A with magnitude V and with chance P, right? And on input image, I want to apply two types of augmentations, right? So that is my overall problem formulation. So given it's a search in a discrete space, we are required to venture into the whole aspect of control theory, right? So here I have four main things to talk to you about. First thing is about running an experiment with an input, right? So input is going to be some strategy that I have sampled from this whole space, right? So once I get that input, I'm going to run an experiment and I will get some output, right? That output is used to tune controller, which is going to give me a set of better strategies, right? And as I spin this wheel, I can expect to generate the better and better strategies because the way I'm going to optimize this controller is to give me a better and better accuracy every time, right? So let me jump to the main slide of my talk, right? So how do we apply this kind of theory to solve the problem that we have in our hand? So as a part of the experiment or as a part of the enumeronment, we are going to have an architecture, let's say an SSD kind of architecture, right, where we are going to train an object detection model, right? So it's going to take a dataset as input, it's going to take a strategy as input, and it's going to produce accuracy as output, right? So this block should be very simple to understand. Now I have this block which is acting as my controller, right? So allow me to use this to explain what is happening here. So each of these cells is basically an LSTM. It's an LSTM of about 100 neurons inside. It's going to take a vector as input. It's going to produce a vector as output, right? That vector at the output of LSTM, it's going to have three types of softmax, okay? The first type of softmax is going to have output of C. So C is my number of classes. So it's going to have the output range of C, right? The other softmax is going to have output range of A, and the third softmax is going to have output range of B, right? So B is the number of buckets that I have, right? So if you see, we are going towards modeling, how do we sample C? How do we sample A, and how do we sample B, right? So the same vector is going to act as input to the next block. So as you unfold LSTM, right, you have a set of blocks, right? So these seven blocks are going to form one policy. Now, from the first block, we are going to first sample C. So C implies we are choosing a class, right? So once we choose a class, we are going to choose an image. Once we choose an image, the output of this from here, we are going to sample an augmentation A. From this block, we are going to sample what is the magnitude of that augmentation. And from the next block, we are going to sample what is the chance with which I want to apply this augmentation with this magnitude on this input image, right? And since I mentioned that n is equal to 2, on an input image, I want to apply two types of augmentations, right? So these seven blocks are going to constitute one policy. And since I want to have S number of policies, the whole block will have seven into S, these many blocks, right? So now imagine that the controller, the 100 neurons that we have, these are initialized with some random weights, right? So we will, in the first iteration of this whole loop, we will end up getting a strategy which is a bit of random, right? So it's going to just sample some class, some augmentation for that class, some magnitude, and some chance, right? Once you feed that as input to the experiment, right? We have our fixed dataset which has 5K photos. It is into train, val, test. We will train a detector. We get accuracy as output. That is now fed as input here, right? So here, what we are going to measure is change in the accuracy, right? So change in our reward, right? So if my strategy were good, and if I'm getting better accuracy, it implies that the output of the whole LSTM block is good, right? So I can reinforce the fact that, well, we are choosing the better strategies. So let me just show you this slide. So here, if you see, so the core aspect that I'm explaining is, how do you train this LSTM, right? So from where are you getting the ground-put information, right? Because as I mentioned that each block has three softmax, it is going to produce some kind of probability distribution, right? But how do you train this LSTM? Usually when we do image classification, and when we have softmax, we have the cross-entropy as the loss function, but we always know the output, right? When we do image classification. So here, that output is being generated based on the reward signal, right? So if reward is higher, I'm going to artificially inflate that in a probability distribution. This is called as a proximal policy optimization. So once I inflate the output distribution, it is basically saying that you were supposed to reach that kind of distribution, but you are only here. So now you should change your weight so that you end up producing policies which are better and better, right? And similarly, if I sample a strategy which is not good, then I'm going to reinforce a negative signal, right? Sorry, go ahead. State, so, okay. So in case of LSTM, so, okay. So this LSTM is going to unfold, right? So it is just a single LSTM block, right? As I mentioned at the output, I have three softmax. So when I say that there is a policy, I say that these four blocks basically form a policy. One policy, I'm not sure what you mean by state. Yeah, right, yeah, go ahead. Sorry, okay. So well, yeah, I think that's a good question. You could choose not to use it. But since I'm going to apply in augmentations or then image, let's say if n is equal to 2 and if I'm applying the first augmentation with some chance, right? It gives me four different combinations, right? So I may, in some cases, I may end up not applying any kind of augmentation at all. Correct, yes, yeah, right? So, okay, so let me talk a bit about the details of, yeah, yeah. So let me talk to you about a few details, right? So as I mentioned, this LSTM is of 100 units. I have three softmax. If you end up working with proximal policy optimization, you should be really scared because unless you figure out these numbers of learning rate and certain inequality numbers, it becomes very hard to make them in practice. So I'll just talk about one interesting phenomenon that we observed. So if you see here in this data set, I have my train valent test fixed, right? And I'm going to search for a set of policies here. If I have my val set fixed, then this block, because now it is learning on its own, this block is trying to overfit on the val set, so that you always end up producing good accuracy, right? Typically in what happens in, so okay, so overfitting happens when you have a large model and a small set of data, right? It will, right? But here, because I have small set of val data, and this block is aggressively acting to produce a very high accuracy result. It is just trying to overfit on that val set, right? So what we had to do, we had to do sampling from the val set every time we're in an experiment. So if you see, one round of this experiment is that every time you have to run this loop, you want to initialize your architecture with some initial weights, right? So because you want to figure out a new strategy, it is always a new experiment that you have to run, right? So there we had to sample the val set so that the controller doesn't overfit. So a key thing here is the cost involved, okay? So one run of training detector involves around 150 epochs. And to find a strategy, the best strategy, it took us one key iterations of that whole loop, and it roughly amounts to eight days, eight days with certain GPU compute. So is this cost worth it? Well, in the end, we could achieve that elusive number of 85%, and if you see for the class which didn't really have a lot of examples, even there we are seeing a jump of around 17%. So these are some of the policies learned, right? So here if you see there are a lot of, I'm not sure that it is visible. But there are a lot of edges, and there are a certain set of cover contrast values that it has learned. Yeah, so that's because I guess that's how the network wants to learn. So nobody has really fixed these magnitudes or probabilities. It just starts from the data set, or the controller has learned to produce these kinds of augmentations, okay? So I'm showing you the same image input, but across different batches and across different policies. That's the output, okay? So that perhaps you can talk offline. So here, for example, we learned a lot of edge detectors. That is a lot of texture. So if you see, it is trying to bring out a lot of texture, right? So there are a set of augmentations which we can discuss offline, yeah. Go ahead. Sorry. Okay, right, yeah. So that's a good question. So some of these augmentations we also applied to adjacent problems, and it worked quite well. So that was coming at that, hold on one minute, yeah. So, yeah, so if you see, so I as a deep learning engineer, I would almost always choose rotation blur or shear. And in some of you may also do that. But the controller didn't learn that. It basically instead focused on contrast, edges, or image crop, or flip inside the box. So these are like in out of box, unusual kind of augmentations that the controller learned. So keeping us at the time it took to learn all these policies, it was also a good learning for us as a team that perhaps we should have a better approach to augment our data sets. Because of course it takes eight days, and if it is going to take eight days for every damn problem, it's not going to be scalable. But so this was a very good learning. So this was more like aha, a moment for me that, well, what we were doing to solve problems was perhaps not right. And this kind of judgment where you end up choosing a magnitude and a chance to do augmentation, this kind of judgment, we as humans, we cannot make all the time. So I would say it was worth it spending eight days of GPU compute. So then later we converged upon better technique where you can replace the controller instead of having RNN there. You can also think about having basing optimization. So I hope this future we can reach sometime soon where this block of auto augmentation can be done in 10 hours or less than 10 hours or so. So my advice to you would be that instead of being AI research scientist, if you become a data set hacker, that will give a lot of rewards in your work. I'll just stop here and if you have any questions, I'll take them now. Yeah. The augmentation parameters. So how do you know that the same optimized set of parameters would also be optimal on the full model? And the second question is that why did you choose LSTMs? I mean, it's such a complex controller instead of that because the parameters for each policy are only seven and there may be multiple policies, but still the number of parameters there since it's small. Can't you choose a more simpler controller to just search over that space? So answering your first question, right? So we can't expect the same amount of performance if we apply the same policies on a larger architecture, right? But when we applied these policies on larger architecture, we still got a considerable pump as compared to what we were doing blindly. So that's first. Second thing, if you see in LSTM, I only have 100 units. So it's a lot, a simpler unit. It doesn't take time to, it is not the most time consuming or resource consuming aspect. The core aspect is how do you run those experiments fast? And which is where we had to strip down the version of the architecture? The reason I was asking is LSTMs are especially good for sequence type data. And in this case, the order of policies, parameters. So it matters, it's what I'm saying. So why that specific order? So here, if you see, we are first sampling a class. And then with respect to a class, we want to choose an augmentation. And with respect to that class and augmentation pair, now we want to choose a magnitude and a chance. So there is a sequence involved. What? Thanks. So go ahead. Actually, I'm coming from oil and gas industry. And just translating your idea into that domain. We have oil fields producing for 30 years. And every 10 years, you have newer kind of sensors, newer kind of distal oil. So your legacy data is different from, and how do you augment that if that's what? And then nowadays, you are having lots of wells and you are collecting data from lots of sensors. So then how do you, across space and across time? How do you? I don't mean that I can answer that question quickly in one minute, but as you can talk offline. But I think Bayesian optimization is something that has been applied to the kind of data that you are saying, where you essentially try to learn the data augmentations. So you don't really have to go for such a complex technique all the time. So what I'm saying is that there are certain algorithms that exist which you can apply to the kind of data that you are saying. Yeah, so time axis is something that we have to worry about, that I'm not sure. But we can talk offline on that, yeah. Yeah, so yeah, I think my answer would be it is possible, but it will take perhaps 10 times more time and cost. So if you have it, feel free. Yeah. Yeah. So if you have questions, please come. Sure, thank you.


Welcome back through the videos on multiple linear regression. In this video, we're going to talk about how we can integrate interaction effects into our multiple linear regression model. And so I listed here that we can use a star or colon, and that depends on how you want to actually use the interaction effects. So a star tells Python to consider the term separately and as an interaction where colon says, just consider the interaction. And so we're going to demonstrate both of those ways here. And so we're going to, once again, go back to our SMF OLS model. And we're still working with that NOx and RGGI dataset. So that is our Y variable. And in the previous analysis, we included all of these different variables. For simplicity sake, I'm only going to include NOx base and the state variable. And so our first model is going to look at both the interaction between, or is going to just look at, I'll do the colon first. It's just going to look at the interactions between NOx base and state and not consider them individual variables. And so the data is still DF merged. And we still need to do our has cons equals true. So even though, as we've done throughout the whole thing, we're removing that we still need to set that in order for it to run correctly. And I'm actually going to come up here and change this to results three so that I don't override what I did before. So let's turn to results three summary. So here we're at 9.81. So even our best model with all of these variables only got 786 last time. And so here we are at 9.981. So this is a really good model. This is, you know, where the probably one of the best models we can get. Because we have this interaction. So having the two separate variables didn't help too much. But having them interact needs to all of them being significant. And so this is, you know, looking at these interactions becomes really important for developing these really good models. And then I'm going to demonstrate what it looks like when you do that. Asterix. So I've changed the name and then all I've done is switch that here. So we can run this. And we do see a little bit of an improvement point 982. So still an improvement, but not anything massive. But where it really changes is now in our results. We now also have all of these variables as individuals in addition to the interaction terms over here.  So if you don't think you need all of the individual variables, you can always just do the colon instead. But nonetheless, this is probably one of the best models that we can get. And so we can go back to what we did up above. We first plotted this data. And then we did a residual analysis. So I'm going to just copy this visualization code and come down here. So this stays the same. We still need to get results, but now we're going to be using results for. So we want to make sure we actually pull out the correct data for our results. So we do results for we have nox base nox RGGI color by state stats move. And then we've got our special color scale. And so if we run this, we can see that now we've gotten the lines actually match what we would expect. So once we put in that interaction term between nox base and nox art or nox base and the state. We end up getting these individual best fit lines that actually look really good, especially when we compare them to what we got above. Where we can start to see some differentiation, but they're all basically still following the same thing. But essentially what we have shown is this nonlinear interaction between nox base and state is what the critical predictor is not necessarily the both variables being included. And so this figure looks like we are doing a good job at plotting data. But of course we need to check the residuals so even though it looks are adjusted our squared is really high. Our figure looks like it's very good. There's still we still want to see is our model statistically valid. So for that we need our residuals analysis. And so we can override that residuals column and say DF merged nox RGG I minus Dia merged why hat, which we just recalculated up here. And then we can do our GG pot. So our X data is why hat, and our Y data is residuals. And so we can see here that it actually looks okay, it looks much better so we're not seeing any real distinct fanning or wedge shape. The variance does seem to be, you know, fairly evenly distributed. There maybe are some outliers that we might want to look into but by and large, this is a pretty good example of a residual plot that is meeting all the conditions. We might have some worries about this being so large negative 500,000 to 500,000. But given that our why hat is going up to 5 million. That's maybe not as worrisome so when thinking about the magnitude of error. In terms we want to think in relative terms like 500,000 could be a large difference if your data is, you know, on the 100 to 200 range, but in the scale of 5 million, perhaps 500,000 is not terrible. Still not ideal, but not terrible. Using these multiple linear regression models and using these interaction term, we can actually arrive at not only a better fit, but also a more statistically valid model.


After the show, Mike, in his dressing room, Mike, like, there's like, Mike, I'm gonna introduce you to somebody. I walked through the door. You ain't gotta introduce me to him.  I know Boba Dub. That's my nigga. Yeah, we on Boss Talk 101. It's up. Listen, man, when Roger getting out, man? Man, let me tell you something, man. Every time Roger come up for parole, I go right back up there. Nigga ain't learned it. Listen, look at them goddamn luggers. He writing.  Look, it's funny you say that because I got a movie that's wrote called Roger's Coming Home. Wow. So I got to my boy Anthony Corbin. So I got to my boy Anthony Corbin. We collaborating. He's a dope rigger. Anybody out there right now, listen to me. Anthony Corbin is a dope rigger. Movie? God, in two months, he had your best movie wrote. He can wrote me two movies so far.  But we got a movie wrote called Roger's Coming Home. Wow, that's gonna be dope. That's gonna be dope. Your people, see, you got a fan base and it's kind of, people don't understand what this social media does is it gives you an independent fan base, I can say, to where they, you got them where, that's how I seen when I went to Country Wayne's show. Yeah, you got them. Man, it was 3700 seats full. He got them. And I was invited and they brought me down and then we went to the back and talked to them and I was like, damn. Like, I understand what you're saying about Faizong and him, Faizong need to see this. You know what I'm saying? Country Wayne is one of these guys. That never filled them seats up, bro. He special, man. Country Wayne is special. Crazy. Country Wayne is motivation. I look at Country Wayne as motivation. He very much motivation. Love that dude, like the way he coming with it. I look at y'all, I be looking at all y'all, man. Diz, what's the other one that you call me? We talked about to do, I forgot his name. Funny Marko. Funny Marko. He hit me back. He hit me back and said he coming on the show as soon as you get in this area. Funny Marko, no, we did that movie. Yeah, yeah, how was that? Dope. What, just, can you tell us about the name of it? He never gave me the name of it. He never gave y'all, just went out and, how did y'all get y'all parts? Oh, you know, I got e-mailed that. They e-mailed it.  You know, that's my boy. You know, I can just read the part cool. I'm snitching in the motherfuckers. That's easy. You know what I'm saying? That's me. That's the character.  And you know, he flew me out, just keeping the 100. Good dude, good dude, solid dude.  Yeah. You know what I'm saying? Like, man, I gotta get paid. You know, I got all that all the time, my budget. It's about building a relationship with these people. You know what I'm saying?   But you ain't all the way gotta take money. You can take the back end. Yeah, real talk, the back end, seriously. Man, let me get 8% of this movie on the back end, brother. Dope. Cause I know what it's gonna do. It's gonna get right. How did you and, how did you, Michael Elps? Michael Elps. Michael Elps. Did you hear me say Michael Elps? Yeah, I heard you. Sorry, Mike. How we linked? Yeah, how did y'all link? Cause I seen y'all. We had been talking in the inbox.  Dezzie Banks had a so and they signed. Dub, you need to pull up. I'm gonna introduce you to Mike. Cool. Get there after the show. Go meet Tommy Davidson. Oh, Tommy Davidson, that's heavy. That's heavy, heavy. Oh, I can't think of anything.  I just did that while I was going some.  Oh, the one, the one. Elroy, I can't think of anything real right now.  Oh, I don't even know his name, but I know who Elroy is cause he be cutting up on that movie. You can't not know who he is.  But no one's story is short. After the show, Mike in his dressing room, Mike, like Dezzie, like Mike, I'm gonna introduce you to somebody. I walk through the door. You ain't gotta introduce me to him.  I know Boba Dub. That's my nigga. That's it right there, boy.  As soon as I come in, I've been telling you about this motherfucker for the longest, Mike. Put him on the motherfucking stage. Just like that.   Boba Dub, he's like, man, how you doing? Good, bro. And cause you know, I have my wife there.  She loves me.  I'm trying to get on the big stage. Yeah, yeah, yeah. And he was gonna put me on the show in Dallas. He came up here to the, what was it? Okay, thank you. Was it Nokia? No, the trust. The trust, okay. Yeah, he was gonna put me on that show and just keep it in 100. He was gonna put me on the show. And you know, I was just gonna do it on GP. But then I got one of them calls. And you had to go. I need you on this date. You had to go. I looked at the numbers.  I'm just real tired.  I looked at the number. I had to call Shaq. Oh, damn. Mm-mm. I said Shaq, man. Look, great opportunity to me to go over and do this show with Mike. I said, what Mike paying you? I said, I'm doing it on GP. Tell Mike you get what I'm like, go handle your business. Exactly. That's love, man.  I'm just, I'm saying PG. I'm saying in PG how Shaq said, but Shaq didn't say PG. I'm just saying PG. My boy got the commercials everywhere I want. Said what he really said. He was out of love when he was telling me. But he was out of way telling me in your position, bro, you're trying to get there. Don't you miss no mother? Man, that means a lot, man. When you had those conversations. That makes a lot of sense. You know, you gotta, and then to have that type of energy or that type of individual to be able to affirm the fact that you're making the right decision. And they've been around so long in this entertainment industry type field. And man, that's dope. I mean, he was, we like, Mike on the stage. No, that's what, and that's what I know he knew. But you being one that's on the, hey, man, I don't want to miss this relationship or how he might take it to have that reassurance is dope, bro. Oh man, that was love when he...  Okay. I was in love, I was in love. You know, that's one thing I like about this now in the position I'm in, I may be able to call those type of people.  That idea is brand, man. I could call and, and just run, you know, just ask them simple things, simple questions. Yeah, we on Boss Talk 101.


Hi, I'm Rusty Komori, and this is Beyond the Lines. I was the head coach for the Punahou Boys Varsity Tennis Team for 22 years, and we're fortunate to win 22 consecutive state championships. I retired as head coach in 2015 to write a book titled Beyond the Lines, which is about leadership, creating a superior culture of excellence, and finding greatness. This show is based on my book, and we are broadcasting live from the beautiful ThinkTek Hawaii TV studio in the Pioneer Plaza in downtown Honolulu. My special guest today is the head coach of the Kaimuki Varsity Football Team, who is a great leader and a fantastic person. He is David Taltofi, and today we are going beyond football. Coach David, great having you here. Great to be here, Rusty. Thank you for having me. You know, I know how busy you are, and I wanted to congratulate you on winning the OIA Division II Championships this year. Yeah, it's definitely a great experience, you know, and one that will live on with these boys for the rest of their lives, and for the coaches as well. For sure. Yeah. Now, you grew up in Palolo. How was it growing up in Palolo? You know, getting to look back at my life growing up in Palolo, it's humbling. You know, we definitely went through the struggles that most kids coming out of Palolo go through, from the emotional stress to the mental stress, into just the reality of having to get by day by day, not knowing when your meals are going to come. You know, wearing the same clothes from middle school, when you're in high school, and all that stuff, but definitely something I wouldn't change. You know, it definitely helped me to be the man that I am today. Coach David, when did you start playing football? I started playing football in the 10th grade. I was just inspired by a cousin of mine that was playing for the Chargers at the time, Junior Sale. Oh, yeah. And so the families always talked about it. Our families were all USC fans and now Charger fans. And so my dad grew up in Oceanside with Junior's dad. And when Junior was in, was middle school age, he grew up, he was raised by my dad. So the influence on that really played a big part of, you know, getting a shot at football. What inspired you about Junior Sale? Just the greatness that he represented.  It made me proud to know that, hey, that's my cousin. And so, you know, that set a good example for us. And knowing that he knew us personally, and he'd always come to visit us, really inspired us to want to strive for the best for us. Well, you know, the irony now, Coach David, is you're inspiring a lot of people. And I know that you played college football for UCLA. How was your experience playing for UCLA? You know, you can only, you can dream of it, but even your dreams, dreaming of it, doesn't really add up to, or doesn't really make out what it actually felt like when you actually stepped on the field. You know, I still remember the first day we had Game Day at Tuscaloosa. We played against Alabama at the time, and Alabama was second the nation. When I first stepped on the field, I almost froze, you know, because I'd never seen anything like it before. And just the intimidation of just the crowd and the energy that it was, just getting off of the airplane to the escorts with the police going into Tuscaloosa. And then just seeing just the, just the camaraderie that the Alabama fans had and just the excitement that that energy puts out for teams and for games like that, you know, coming out of Palo Alto, that's just something you don't come across every day. So those guys must have been like really huge too, huh? They were humongous. You know, I thought to myself like, do I really want to play football? When I got my first snap in, you know, during the second quarter of that game, and I still remember to this day, I still remember standing there as a defensive end, and I'm looking at this guy who's like six, eight, 320 pounds, and I'm like, maybe I'm in the wrong sport. But it's just, you know, it's just one of those memories that will always, you know, will always be in the back of my mind when I think about it.  Why are they so dominant? You know, Nick has been able to really get his team and everybody in the program that supports that football program to buy in to his philosophy. And, you know, he's been around some of the best coaches like Bill Belichick. Yeah. And he's had a share of time in the NFL to really sharpen up where he's at today.  So after graduating college, what jobs have you had since then? So, you know, right from college, I went out to, I started with law with the National Oceanic and Atmospheric Association and was a marine biologist actually here in Ireland. So I was out about two years as a marine observer, you know, working with the Long Line Fishery Services here. And I did that for a couple years and then I went back to Vegas on a vacation and never left a vacation there. And so I started doing stagehand things, you know, as I started getting my life ready for the mainland. And from there, I went on to work with Cirque du Soleil. So I did Cirque du Soleil for five years. I did everything from being a high-tech, safety technician, a rigger, to an artist and also to a musician at the show. Oh, you do it all? I did it. But it was just an experience that was like no other, you know. And that's when, you know, my life kind of went into a transition from that to teaching and coaching. When I brought my brother Desmond, who was at the time, you know, one of the, considered a high recruit from Hawaii, who was in Palo Alto, growing up in Palo Alto, I brought him up there to help him to get his life together so that he could have a chance. And, you know, and so that's, I thank my brother for me being where I'm at today, because I remember leaving UCLA. I never wanted to have anything to do with football, you know. I didn't want to have anything to do with kids. I just didn't think I had the patience to.  Yeah, for sure. So what led you back to Hawaii? Five years ago, I came back for Christmas. And so in new years, just spent time with the family. And I heard things that were going on with the school, you know, the enrollment dropping toward it was. And then just things that were growing up in the housing, because that's where I grew up. And so when I went up to the housing or visit my brother, I saw things that just blew my mind. Because back in our time, that would never happen.  Nowadays, it doesn't happen that way. You know what I'm saying? In fact, you can live next to your neighbor for years and don't even have any conversations with the neighbors. And so I saw things that were happening. I saw just how much it affected me, you know, with the kids smoking and drinking. And to a certain extent, even adults being okay with it. And that really hit me. And I didn't realize how much it was going to hit me until I went on the plane to go back home to Vegas. And I thought, you know what, let me just get back into the swing of things that work at the school. It'll drop. And it didn't. So it was heavy on my heart. I prayed about it and really considered it for a long time.  Well, I'm glad you're here. Now, Coach David, let's talk about you, your team winning the OIA Division II football championship this year. And you must be so proud of your players and coaches. Absolutely. You know, and it's like yourself. You're the only one that really knows the trials and the tribulations behind the scenes, you know, that these kids have to deal with.  So they've gone through a lot, especially over the last years, you know, and a lot of these kids that have been a part of the program since they started school. And just seeing the accomplishment that most of the times these kids, they don't have the confidence in themselves to think that they're worthy or they're capable of doing it. And now for them to actually do it, you know, I know it's not only going to be a life changer for them now, I'm sure that it changed their lives as well as the coaches. Now, Coach David, how many players do most football teams have? And how many players do you have on your team? Most teams, you know, on average is about 35 plus. The bigger programs, you see 50 roster teams. And in the Division II, there's quite a few teams that have about 40 to 50 plus kids. But we've suited up as little as 18 kids a couple of times this season. And right now we have about 27 on the roster, but we don't get to suit up all of them because, you know, they're either recovering from injuries or other things that they have to deal with academically. But right now, technically about 20, 21 kids that we suit up. Geez, well, you know, the good thing, Coach David, is at least they get to play a lot. You're absolutely right. You know, and sometimes it's a joke amongst me and the coaches like, hey, be careful what you wish for. So the kids, I mean, they're playing offense, defense and special teams often. Absolutely. And, you know, it hasn't been easy in the first few years that I was here, because this perception that a lot of these kids get that, you know, especially with special teams, and you talk about the phase of the game, a lot of these kids are very influenced by the culture and what they hear and see outside of the reality in which they live in, where special teams, you know, is really a time for them to take off. And, you know, it's really, it took a little bit for me to get that through the heads of the players that it's not a time off, you know, that it actually is a big part of our success.  And, you know, in the last three games, our special teams has become, has come up big time for us. So definitely big too, because our star players are on special teams. And it's hard to see that. Most programs that have a lot of kids, they put in their backups and all the other kids that don't get much time playing. At Akami Key, you know, we treat special teams the same way we treat offense and defense. And I mean, we work on special teams every single day. And it's just, it's cute. And I run it to see how it all ended up, you know, winning by a field goal. We practice those situations every day since beginning of season, you know, since the summer. Never had to come down to that, you know, and sometimes it never will come down to that, but it came down to that on the biggest stage.  And it just showed a lot of the maturity in it. So really, it's a, you know, it's been a blessing.  Now I want to ask you, what is your, what is your coaching philosophy? You know, my, when I think about that, it's, it's really not, I mean, it just feels like a broad kind of answer that I would give. Yeah. But my philosophy isn't so much about the X's and O's of the game, but really paying attention to the heart of what our duties are as a coach, especially at the high school level to, to, to become and be for these kids. And, and, and that's being a mentor. And that's the definition of a coach being a mentor.  And a lot of these kids are, are, are not being raised to be the men that, that we kind of grew up being. And it's not often that you see these kids you know, wanting to be like their father, you know, where I remember growing up, I always wanted to be my father. My father was always that. And so that gave me a lot of experience and understanding that part. But my philosophy really just, it starts with the heart of these kids and really trying to understand where they come from and understanding that you're not going to teach these kids as much as, you know, any more than what they're going to teach you. Yeah.  Well, Coach David, you know, talking with you before the show, I mean, we have so many similarities between us. The way I, I say it is I say that I, and my first priority is coaching champion athletes of character first, and then great tennis players second. And that's exactly what you do. I mean, you're, you're helping them develop champion athletes of character as your first priority, and then building great football players second. I mean, that's exactly what you're doing. I love it. Now, who are some coaches that you admire greatly? Man, there's a lot of coaches that admire greatly. One of the coaches that really stood out to me, Coach Cliff Frazier, he was a coach that really had influence on my life when I was in Las Vegas. He's one of the, he's in the coaching hall of fame out in Las Vegas, an archer in the state of Nevada.   And you think that if I've been to this program, then there's not much that these other schools can like tell me. But he really brought me into understanding the mentorship of coaching and the purpose in our duty as coaches. He's been a big influence on my character as a coach. Doug Lee, former NBA player, also out in Las Vegas, basketball coach, also had a big influence on my life. And then, you know, right now Bill Belichick, Bill Belichick, you just, you know, how he's been able to do what he's done for so long. You know, he's, he's done things with the least amount of resources, you know. And I mean, every single year, it just seems like he's just creating players from nothing.  And I really do believe you can do that at any level, you know, as long as you understand that, you know, it starts with the heart and the character of these kids. I love it. Coach David, we're going to take a quick break. And when we come back, we're going to continue going beyond football. Sounds great. You're watching Beyond the Lines on Think Tech Hawaii with my special guest, Coach David Taltofi. We will be back in a quick minute. Aloha. My name is Duretian. You are watching Think Tech Hawaii. I will be hosting a show here every other Wednesday at 1 p.m. And we will be talking to a lot of experts and guests around sustainability, social justice, the future here in Hawaii, progressive politics, and a whole lot more. So please tune in and thank you for watching Think Tech Hawaii. Hey, aloha everyone and welcome to the Think Tech Hawaii studio. My name is Andrew Lanning. I'm the host of Pretty Matters Hawaii. We air here every Tuesday at 10 a.m. Hawaii time trying to bring you issues about security that you may not know, issues that can protect your family, protect yourself, protect our community, protect our companies, the folks we work with. Please join us and I hope you can maybe get a little different perspective on how to live a little safer. Aloha. Welcome back to Beyond the Lines on Think Tech Hawaii. My special guest today is the head coach of the OIA Division II Championship Kaimuki Varsity Football team, David Taltofi. And today we are going beyond football. Coach David, you know, coaching teenagers, I've done that for many years and you're doing it now. How do you get them to buy in? You know, when it comes to getting these seniors to buy in, it has to be bigger than the sport. It has to be beyond the sport, which is just ironic how the title of this game is beyond the lines. Because there's a bigger message in this sport of football as there is with tennis and you know that. I mean, you have probably one of the more decorated coaching careers in the state of Hawaii. And getting these kids to buy in really comes from the time that you actually take to get to know these kids, getting your hands dirty in what they really are going through in life. Empathy. Empathy. Absolutely. Empathy and being able to relate, but at the same time make them feel like, you know, you don't feel like you're better than them, which oftentimes, you know, these kids already put you on a platform to where like, wow, you're the coach, you know.  It changes the way that they think and it gives them a lot more strength to be able to battle the things that every single day they face. And sometimes they don't realize they're facing those things every single day. And just from natural instinct, they instantly go through a negative mentality about things or perspectives as we spoke about earlier. It really comes down to your personal relationship and how much of it are you, how much time you're willing to invest in the personal relationships. And you invest, you're investing a ton because I know that. I want to ask you, Coach David, why do you love coaching so much? You know what, there's so many reasons that I love coaching, but the biggest reason has never been about championships, but to build championship character, you know, championship leaders. And it just, I mean, there's just a joy and a fulfillment, as you know, that you get from coaching that you don't get from anywhere else, you know, because you really are building the leaders of tomorrow. And whether people want to admit it or not, sports has such a big influence on every school regardless. And we've heard about the battles with education over sports and sports, you know, all that sensitivity about that. But the reality is, you know, you have just as much or probably more influence on these kids than regular teachers do. And it's not a battle on anybody's part. That's just where it's at with sports. And so, you know, it's just being able to plant those seeds and hoping and praying that those seeds are going to come back in return. I mean, with all the seeds that were planted in me from when I was coming out of the housing, not knowing that I'd be doing what I'd be doing today, and that people would be influenced by what you do, how much more greater now would these kids possibly be?  And I'm not saying I'm anything, you know, I just wish somebody woke me up. I wish somebody was hard on me, but in a loving way, you know what I'm saying?  And so, you know, to see what these leaders will become and could become, the potential that they have is exciting, you know, and I'm sure you miss coaching, you know, I'm sure you miss the heck out of coaching, but I know also that you probably run into former athletes and get calls and so forth from former athletes, and they continuously thank you for a lot of things that now they are able to do now as a leader in their families, as a leader in their businesses, as a leader in the community.  We're all leaders at some point in our lives. And, and, you know, if these boys can understand that they actually are the authors of their own life. Exactly. That they have the pen in their own hands and nobody can tell them, you know, otherwise, you know, it's going to change, it'll change the future for tomorrow. And we know the challenges and the issues now that our youth face.  Absolutely. Absolutely. You know, so in there, I'm talking a lot about, you know, leadership and how great leaders build other great leaders. And that's what you're doing. What do you feel the best leaders do, Coach David? I feel like the best, what the best leaders do is they don't, they don't tell you once you want to hear. They tell you what you need to hear and what you need to understand. But they uplift you.  And, and also inspire you. You know, leaders inspire you to be like a leader or better, you know, and, and so there's, there's just so much about what leaders can do. It's a loaded question. Absolutely. Absolutely. Now, Coach David, you know, you're very authentic. You're very genuine. I want to tell you something right now.  For you. That's powerful. That's powerful. I know the influence that it already had on a lot of big people. And I'm sure we're not going to go over those people on the show. But I mean, to have that kind of influence on people across the, across the nation, you know, it's going to do some big things for these boys. No, but I love what you're doing to inspire them. And I want to do my part to help in any way I can. So you send me their names and I'll sign each book to them specifically. That is awesome. Thank you so much for that. I mean, we talked about it earlier, reading books has almost become a lost art, you know, because phones and all these other things take in place of it. But, you know, this is a blessing. This is a blessing that these boys can not only read, but just what they will hand off to other people because of the inspiration that this book has made in their lives. Just like myself, I've read so many books that inspired me, inspires me to give it to certain people like, Hey, your book is one of those books. So definitely excited about that. Thank you so much for that. You're welcome.   As we hear about star athletes throughout the nation that are now big time players in the NFL, NBA or whatever professional sports, a lot of them have inspiring stories because they started from nothing. But, you know, those stories are very powerful and inspiring. What a lot of them also probably touch up on is really the mental and the emotional well-being of kids growing up from when they're young to now. I mean, there's still moments in my life that I remember when I was one years old, you know, that took place and I'm like, how do you remember that?  And so a lot of the challenges were just the confidence that I know these boys are challenged with today, confidence in who they are, the confidence in knowing who they are, the challenge of being positive and the challenges of believing that, you know, you're worth something, you're worth it, you know, that you can and that you're capable of doing it.  And these boys are going up, especially in today's society. But it's still been a challenge today. But at the same time, you know, I've been able to surround myself with the right people and God's blessed me with good people.  It's been that. It's been that.  And you know what, and I feel like society dummies down these kids too much, you know. And so because of that, we don't feel like these kids are capable of understanding that level of what we speak of, you know, but they they're more than what they're more than capable of doing that. I mean, these kids are three years old, they know how to turn an iPad and they work everything on the phone that I'm so like, how did you learn that?  But it definitely is. Yeah, it's just too much to talk about. I hear you. I hear you. Coach David, what's what's the best advice you've ever received? The best advice that I've ever received, man, there's a million different advice pieces of advice that I've gotten. And I thought about the best advice that I received is just being true, being true to yourself and being real, being real. And that's what that's really what it comes down to. And it almost seems like something that is commonly shot out in text or post or whatever you see on social media or on the internet. But there is truth to that, you know, because a lot of us, a lot of these kids find their their self worth in what they put on social media. And when they show these things of money that they really don't have on social media or they start to identify their character with something that they really aren't, you know, it sets you it sets you up for failure and disappointment, depression. And because of that is such a greater risk for the kids that I deal with.  Because there's no other people, you know, no one else is really showing them how to deal with those things. And a lot of these kids, what they miss is the emotional and the mental investment, you know, that starts at home and starts with us as coaches, they get praised for all the things that they do, they get praised for for how good they look.  And sometimes these kids just want to cry, but they don't know how to, they don't know how to show that they're hurting. Society today tells you that, hey, you're not tough if you cry, you know, you're not tough if you're dealing with these things. And so, you know, it goes back to all of that and understanding that we can we can cry. Absolutely, man. It's crazy, you know, I don't I don't cry much, you know, and I think with the, you know, the boys are, I'm sure they'll see this interview and they'll be like, well, coach talks a lot, you know, I might talk a lot to them, but they don't see me smile too much, you know, and there's, it's not because like I'm hard on them or anything. I just, you know, that's just how it was. As I was growing up, always looking serious. Coach David, I got to say, you are so authentic.  And I really want to thank you for being on my show and sharing your insights. It's an honor. It's an honor, Rusty. Thank you so much. Awesome. And thank you for watching Beyond the Lines on Think Tech Hawaii. For more information, please visit RustyKamori.com and my book is available on Amazon and Barnes & Noble. I hope that Coach David and I will inspire you to create your own superior culture of excellence and to find your greatness and help others find theirs. Aloha.


This is stock number 7T145B, a 2010 Hyundai Santa Fe. This vehicle has the 3.5 liter V6 motor. From this HD video, you'll be able to tell that this SUV is extremely clean, considering the miles and age on it. No dense dings or cracks in the front bumper. It has factory fog lights, projector lamp headlamps, front fenders in really nice condition. It has factory alloy rims and Goodyear 235-60 R18 tires. These are Viva All-Season Goodyears. These tires look like they have probably about, I'd say, 60%, 70% of the tread left. It's kind of a bluish gray metallic color. The hood is very clean. Didn't see any major dents on it. The passenger rim is an excellent conditional scuffs or scrapes on there. As you go down this side of the vehicle, you can see just how clean the body is, how reflective the paint is. We take these HD videos so if you are far away, or even if you're close by and you're just on the fence of making the trip down, hopefully this video pushes you over the edge to make the trip, because it is a high quality vehicle at a high quality dealership. I just want to make sure you get the most accurate representation possible so you can make a smart buying decision before you even get here. Back rim is an excellent condition as well. No scuffs or scrapes. And the back tires have just as much tread as the front tires. Down this side, you can see the lower valance and everything looks really good. All the lower rockers on the doors look really good. No corrosion or anything, which 100,000 mile vehicle in Wisconsin. Last time I checked, pretty rare not to have any corrosion. Rear gate is an excellent condition. Rear bumper is in really nice condition. Not perfect, but it's a 100,000 mile 7 year old vehicle. You can see that metal flake in there. Like I said, it's kind of like a blue gun metal, grey type deal. This gate lifts up like that, has a nice all season floor mat back here. You can see it's very clean. And then underneath here we have storage area for your jack tools and everything like that. Is a manual closed gate, so you just grab the handle and put it down like that. Down this side of the vehicle, just as clean as the passenger side. Paints very glass like, very reflective. Very nice condition all the way around on this car. Inside, you can see you have the leather. And it's kind of a cloth mesh. Power driver seat, both of these seats are heated. Seats in pretty nice condition. Just a little bit of wear on the edge there, which is pretty common on a vehicle with this miles. Carpeting is very clean. We have factory floor mats throughout. Second here, technical difficulties with my mic. Alright, we're back. But very clean inside. You can see this vehicle does have, in fact, 107,093 miles. Has the Bluetooth audio controls. Radio controls on that side. And then you have cruise controls up on top here. Leather wrap steering wheel. Very nice condition. Has AM, FM, and Serious Satellite Radio XM capabilities. Got your heated seats. You have an Augs and a USB down there. Vehicle's never been smoked in. Cigarette lighter is absolutely perfect. Passenger seat is very clean as well. No rips or tears on that. Smells very clean in this vehicle. Has the compass in the mirror. Passenger side floor mat. Very nice condition. Just a very nice vehicle inside and out. Down here we have fog lights, stability track. And then you can lock it into four-wheel drive or all-wheel drive. We'll take a quick look at the back seats. Power windows, power locks, and power mirrors. Second row seats are in excellent condition. No rips or tears back here. Does have latch child safety system. These seats do fold down for extra storage. And you can see that they almost go completely flat, which is really nice if you are carrying extra stuff. And they also do recline. So if you want a little bit of a snooze, you could put the back back. Then you have storage right here. And it's got little cup holders. Got floor mats back here. And the carpeting is in excellent condition. We'll take a quick look under the hood. 3.5-liter V6 motor. Engine bay is very clean, runs very smooth. This vehicle has been fully safety and inspected by our service shop. Has a fresh oil and filter change. All the fluids have been checked and topped off. And this one is 100% ready to go. And to see more pictures of this SUV or one of our other 400 new and used cars, trucks, SUVs, minivans, or anglers, you name it, we got it.  Videos of every single used vehicle that we have. All at SummitAuto.com. Thank you so much for checking out my video. If you want to make this one yours, give us a call right now,<PHONE_NUMBER>. Ask for one of our sales associates to make this one yours today. That's<PHONE_NUMBER>. Thanks again, we look forward to helping you with this super clean 2010 Hyundai Santa Fe. And if you want to see more videos like it, go to our YouTube channel, which is youtube.com. Like, subscribe, and share. In fact, in a second, you'll see a link to subscribe to our YouTube channel on your left. A link to more used inventory videos like this one on your right. And if you haven't been to our website, that's www.SummitAuto.com. Thanks again.


hi guys you are watching oxy vlogs and if this is your first time here and you would like to know more information about life in dubai don't forget to hit subscribe button in the bell icon so you will get a notification when i post my next video if you're planning to move to dubai to look for a job or if you've got a job and you want to know how much money you need to have to live well during the first month keep watching this video because today i gonna give you some idea about the monthly basic monthly expenses in dubai dubai is an expensive city to live and it's so easy to spend money so i advise you to put some part of your salary around 20 percent on a savings account so in case something happens you lose your job you will have some money to live here while you will be looking for another job and now let's review the list of expenses the biggest part of your salary will go towards accommodation there are so many accommodation options on the market starting from a bad space and the prices vary depends on the location if you're moving to dubai and you are on a budget i advise you to rent a room during the first month in this case internet and utilities are included in the price because in dubai if you want to rent your own apartment you need to sign a yearly contract and pay for three months up front i don't recommend you to do that because it's quite risky to sign any contract at the beginning you don't know the city yet you don't know if you're gonna like your job if you feel comfortable in this country because if after one month you realize that the city is not for you you don't like your job and you want to go back to your home country you will be stuck with the contract but if you feel confident about your job and you have a budget you can rent an apartment on a short term basis in this case you will pay on a monthly basis to your agent or landlord and you will not sign any contracts but the price will be 15 20 higher so now i will show you the starting prices for different options in different parts of the city let's move to the transportation i advise you to rent your accommodation close to the metro station or your office in this case you will reduce your expenses on transportation public transport is not the developed in dubai not all areas have access to the metro and there is a video on my channel about the public transport in dubai so check it out when you start looking for an accommodation make sure that you will not have any issues with getting to your office and one way bus or metro ticket will cost you five dirhams so let's say you have 22 working days per month you use metro twice a day so it means 10 dirhams per day and if there are 22 working days you will spend at least 220 dirhams per month only on the metro there will be situations when you want to use taxi especially during the weekends the taxi starting price is 12 dirhams and they charge two dirhams per each kilometer so i think you should have around 180 dirhams as a budget for every month the next biggest expense is food guys i highly recommend you to cook at home as much as you can firstly you will stay healthier and secondly you will save quite a lot of money groceries will cost you approximately 150 dirhams per week or 600 dirhams per month i'm not talking about the organic products or some imported products which are quite expensive in dubai if you eat outside in an expensive restaurant one meal will cost you 30 dirhams and i assume you will go out with your friends during weekends and you will eat outside so let's add 200 dirhams to your food budget and in total the average cost of food will be 800 dirhams per month if you want to know the prices on some particular products visit numbeo website where you will find more information and now let me give you an overview of the basic monthly expenses so guys that's it for now thank you for watching if you have any questions please leave them in the comments below if you found this video useful don't forget to hit the like button under my video and i will see you in the next one bye bye


Hey there friends, thanks for checking in. Today we're looking at a Springfield Armory XDM 5.25. It's a full size handgun known as a race gun with a 5.25 inch barrel. I love full size handguns. The accuracy it delivers and the confidence that it builds is excellent and it's just so much fun at the range. If you can't shoot well with a full size handgun then probably a need of a training class. But this one here is a 9mm, does come with three magazines that carry 19 rounds. I already mentioned five and a quarter inch barrel and a trigger pull right around five and a quarter maybe five and a half somewhere between those. It does have an ambidextrous magazine release, adjustable rear sight, black serrated with a fiber optic front sight and lightning cut in the slide. That offsets some of the fore end weight with the longer five and a quarter inch barrel. It does have a pick rail there, a couple additional back straps to customize the shooter's hand size and the loaded chamber indicator bar and the striker fire indicator right there. But I have to tell you this gun has been around a long time and now it's only available with their Elite line and this is part of their Elite line. Now this is a 4.5 XTM Elite 4.5 and some of the changes they made I think you will find advantageous. Let's start with the slide serrations. A little bit wider cut in both front and rear serrations with the Elite model. If we look at the grip safety we'll see that's a little more 1911-ish. Comes out a little more as where this is a little more flat. We will notice that it has a flared out magwell all right. So this will come with that, mags already out, a little more traditional right here. This will come with the flared out magwell and I mentioned ambidextrous magazine release but this also has a slide stop that's ambidextrous all right as where this one does not but the greatest advantage that I see is the trigger they shortened up the reset it's a flat trigger also has a trigger bar safety in there and then the reset is right there opposed to remember I said it was about halfway to full position that's not that's a lot shorter reset so this is the 4.5 model all right the 5.25 model will have those additional features if you go with this and it will be called the Elite. Now it will also have magazines that carry 22 rounds the base plate goes down probably about that much further but let's compare this with a couple other handguns here we have the walther q5 match all right running out of camera space these large guns here and we'll take a look at these two fine hand guns that's not lined up there it is all right a little bit longer with the xdm and then the grip length a little bit longer with the xdm as well this is carrying 15 rounds this is carrying 19 in its current form here's a ruger american competition a little bit a little more uh heavy here all right and over here we could see i'll go this way this time about the same slide length and the grip length a little bit longer with the xdm this is carrying 19 this is carrying 17 so it aligns well with other hand guns okay a little better capacity but we'll go ahead and show the internals we'll lock the slide back move that lever up drop the slide off of the frame polymer frame all right we knew that and then an uncaptured recoil spring all steel guide rod and over here is five and a quarter inch match grade barrel look at that i love full-size handguns i know i've said it already but i continue to say that whether it's a 1911 or something like this it is really nice because sometimes i do i get this a little better working working with my thumb there it is all right and so it's uh extremely accurate and a pleasure to shoot let's go ahead and get it on the scale then we'll wrap this thing up the unloaded weight is right here one pound 12 and three eighths ounces we move that to just ounces 28 and a quarter all right that's the same and then over here we've got 19 rounds of target loads the loaded weight 36 and three eighths ounces so about 36 and a half ounces now a lot of full-size handgun owners they don't worry too much about the weight but if you're a competitor you might you might worry not worry but you may be concerned with the weight because you're you're running around you're you're firing you're reloading you're doing all that stuff and the elite model would be a nice choice with this especially with those quick mag loads with the flared out magwell but there it is all right it does come with additional posts i guess i'll show that the case three three magazines additional uh fiber optic posts there you can go with the yellow you can go with the orange i love fiber optic front sights really aligns well with that black serrated rear okay pick up that picture nice and bright at the range but there it is xdm 5.25 it's available in their elite line and i really like the elite line i think they made some great changes if you like videos like this please subscribe and share i always appreciate thumbs up button thanks for watching and you guys be safe


Hi everyone, Joe for JaspeysCaseBreaks.com. We got a filler for that three case break, but it's gonna involve a one box hobby break of Bowman Baseball, the brand new release today, hump day, 2023 edition. No paper ships in here, and at the end we'll re-randomize everybody's name. Top name gets a spot in that triple case break, which is a $1,000 value. That's pretty nice. Big thanks to, big thanks to nobody. I didn't fill in this list here. No, big thanks to this group right here, of course. Thanks for getting in on it, appreciate it. Probably means I didn't put in teams. I did not, I was probably very eager to do this break here. All right, now let's roll it. Let's randomize names and teams two and a one three times. Different dice roll for the giveaway apartment. One, two, and three. And I will actually, just because it's a new release, it's a whole box break, we'll have a little trade window too for a filler break. All right, two and a one, three times for the teams. One, two, and three.  Let's alphabetize by team. And we're gonna pause the video just for a bit. When we come back, we're gonna see if there's any trades and then we'll have the break, the key cards in this 2023, the moment, key players that we're looking for, Drew Jones, obviously, for the Diamondbacks, Justin Crawford for the Phillies, Edward Julian for the Twins, Cam Collier for Cincinnati, Spencer Jones, New York Yankees. Now, obviously, those are the key players now, but who knows, in the future, any one of these players, that's the beauty of the moment, any one of these players could develop into one of the more highly sought after group. All right, so we're gonna pause the video, we'll be right back. All right, welcome back, ladies and gentlemen. No deals were done. So the list right here remains the same. Big thanks, everybody, for getting in on the action. Appreciate it. At the end, we'll re-randomize everybody's names and we'll see who wins that triple K spot. Here's the single hobby box. There's Drew Jones, there's Gunnar Henderson. Let's see what we got in this hobby box. Hobby box, we're looking at one autograph per box plus parallels. Good luck, everybody, we've got Dodger's Pirates on in the background. Kebyn Hayes, Tony Gonsola in the back in the action after a weird ankle injury, I think in spring training. He was just, I don't know, walking off the field. It wasn't even during the game. I think he was walking off the field and his ankle just turned underneath him and he springed it really bad.  I don't know, scoreless in this Dodger's Pirates game, but I'll just have a run around first and second, one out, Brian Reynolds up to bad.  I think in the spring, Brian Reynolds was like, contract wasn't working out and they were like, hey, I want a trade, which didn't happen. Season started, see it went underway and Pirates came out of the gates hot.  Nice Adley Rushman. So in this filler break, all paper won't ship but all chrome will obviously ship. All right, is this a special parallel? I like that, I like that look. Maybe it's not a special insert but Rushman's been playing well. That's pretty cool, four out of five. It's a real sharp look, I think. That's Jackson Holiday. Another Oriole for Ryan. Got Edwin Arroyo insert. We got a Jorge Ruiz, sort of a different aqua shimmer type pattern in the background. Looks cool, not numbered though. Sights on September, nice. And we've got a Fraley Encarnacion 102 out of 125 blue paper for the Red Sox, that'll be for Tristan. And there's our auto. We got a Glider Figuero. Figuero, a short stop for the Rangers going to Brandon in Texas. Speculatograph 227 out of 299. Josh Young. And Ricardo Perez, 15 out of 150. Blue Raywave, I believe. For the Phillies, that'll be for Tristan. Marcelo Meyer, 53 out of 499 for the Red Sox, that'll be for Tristan. And there's Xavier Isaac, Atomic. And I know paper don't ship, but as a Dodger fan, Jay, I'm gonna send you this James Outman rookie card, playing pretty well thus far. Those are one of our first Drew Jones sightings here, Ryan with the Diamondbacks. Just a regular base bowman first. And that is that. All right, not bad at all. Now, the whole point of this is who's gonna win that spot, that triple K spot for a fraction of the price. Let's gather everybody's names, new dice, new list. Just one winner. So 29 sad spots, one really happy spot after six and a four, 10 times. Good luck. One, two, three, four, five, six, seven, eight, nine and 10th and final time. After 10, a lot of sad faces here, none more sadder than Zach. Sorry, man, very close, but not quite. I appreciate you trying. Person on top though, after 10, real happy times for Jerry. Congrats, Jerry. You're gonna be in that triple case break. I don't know when that's gonna happen, but let's just keep chipping away at it and we'll eventually get to it. To ask these casebreaks.com, bye-bye.


Welcome to the screencast, which is the first in a couple of screencasts where we're going to think about comparing two functions. In this screencast, we're working towards a rigorous understanding of what it means for two functions to be equal to each other. So to do that, let's first remind ourselves what five basic ingredients everything that we consider to be a function needs to have. First of all, everything that we want to be a function needs to have a well-defined set of objects that we wish to change. We now know that by the name domain. Every time I give a function I just specify explicitly what the domain is. Every function also needs to have a well-defined process that carries out or describes how to carry out the changes I wish to bring into effect on the domain. That could be a formula, it could be a set of verbal directions or a picture, something that tells me how to change stuff in the domain to something else. And then we have to have a set that holds the outcomes of the process that we just described. And we now call that the co-domain of the function. We also have these other two properties down here that every valid input needs to produce some kind of outcome. There is no point in the domain that simply doesn't compute to anything. And then any valid input needs to produce only one output, so we don't have a splitting of one input into two outputs. What we're going to do here to compare two functions is really focus on these three ideas up here of the domain, co-domain, and process. And when we compare two functions, we're going to compare those three things. Now let's take a look at a couple of examples, each of which consist of two similar but possibly different functions here. So here's the first pair. F is a function that goes from the real numbers to the real numbers. So notice I've already specified the domain, which is the real numbers, and also the co-domain is the real numbers. And so now I can tell you what the process is. The process of change here is when I'm given a point in the domain, I'm simply adding two to it and that's my output. So this is a very easy function to understand. In fact, I'm going to start setting up a table over here for this function just to kind of record some outputs here. So let's go 0, 1, 2, 3, 4, for example. And this is really simple, f of 0 is 2, f of 1 is 3, 4, 5, and 6. A very simple linear function. Now look at the second function, g. It has a slightly different domain here. This is the set of all real numbers except 2. So on a real number line, it would look like this. Not the whole real number line, but find 2 and just subtract it out. So everything, every real number except 2 is in the domain of this function, r minus the set containing 2. The co-domain is also the real numbers, just like it was for f. And here's the formula for this. Now let's just use that formula straight and make a little table for it. So if I put in 0 for this formula, I would have 0 squared minus 4 divided by 0 minus 2. That would be 2. If I put in 1 for x here, I'd have 1 minus 4, 1 squared minus 4, which is 1 minus 4, over 1 minus 2. That's a negative 3 divided by negative 1, that's 3. If I chose to put in 2, I would be mistaken because I cannot choose to put in 2. 2 is out of the domain, so I'm going to put dne for it does not exist. 2 is not an eligible candidate to be put into this function. I've specifically removed it from the domain here. That's important, so hold on to that thought. If I put in x equals 3, let's see, I would have 9 minus 4, which is 5, over 3 minus 2, which is 1, so I'd have a 5 right here. And if you put in x equals 4, there would be 16 minus 4 up here, divided by 4 minus 2, and that is 6. So what I notice here about these two functions is that a lot of the time, they have the very same output, here, here, not here, but 3 and 4. And if you know, if you're doing a little algebra in your head, you're seeing something here. If I just look at the formula for g, x squared minus 4 over x minus 2, you know, the top is a difference of 2 perfect squares, so I can factor. And I can divide out the x minus 2s if, this is a big, big if, if x is not equal to 2. And that division does not always work. I can't divide out x minus 2 if that x minus 2 equals 0. So that division, that cancellation process is only valid if x is not equal to 2. And that is actually specified here in my domain, but just realize algebraically, this fraction is not totally, totally the same as x plus 2. It's only equal to x plus 2 when x itself is not equal to 2. And we can't just always divide off variables like that. So are these two functions the same or are they different? That's the main question we're dealing with here. And a lot of points there are the same. Every single point except for 2, we found they have the same output. And in fact, algebraically, I can prove that as long as x is not equal to 2, these two functions really do have the same formula. Okay, but they're not totally equal to each other either. And there's one reason why. The number 2 can be put into one of these functions, but not the other. Okay, in other words, this function has a certain domain, and this function has a different domain. Okay, so the fact that domains are different, means that the functions themselves are different in some fundamental way. We would not consider these two functions to be equal, because one function, you can plug 2 into it, and the other one, you can't, very simply. So let's look at another example here of two functions, also called f and g, but defined differently. These two functions both go from the integers to the integers. So they both have the same domain and the same co-domain. Now take a look at the formulas here. The first function's formula is really simple. Anything you put into it gets sent to zero. So let's again make a little table down here just to kind of hold some of the outputs, an n and f of n. This is going to be the easiest table you ever made here. I'm just going to put in five values, negative 2 through positive 2, and everything you put in for f is zero. So you don't have to do any math whatsoever on this. Now looking at g, though, we do have a little bit of a difference here. So g takes n, and first of all, computes n cubed minus n, and then finds the least non-negative residue, mod 3. So let's try to play with that and make our little table here. So let's start in the middle of the table because that seems easiest. So when n is zero, this is going to be zero minus zero, mod 3, that's zero. When n is equal to one, that's going to be one cubed minus one, mod 3, that is also equal to zero. When n is equal to minus one, let's do a little math on that. When n is cubed, n is minus one, or negative one. I have negative one cubed minus negative one. This is negative one plus one. That's identically zero. So if I reduce mod 3, it's still zero. Let's try one of the more interesting ones, like 2. If I put in 2, I get 2 cubed minus 2. That is equal to 8 minus 2, which is 6. Now 6, of course, is not equal to zero, but it is congruent to zero mod 3. And you'll find the same thing happens if you put in negative 2. So now these two functions also seem to have the same output everywhere. It seems to be identically zero no matter what I put in. Even though the formulas are different, I'm noticing they have the same domain, the same co-domain, and what seems to be the same output at every single point. And in fact, I can prove it's not just, this isn't going to happen just for these five points, that you can prove as a lemma, and I will encourage you to prove this as a lemma, that n cubed minus n is congruent to zero mod 3 for every integer n. This is a true fact, and so you should try to prove that. It basically boils down to, if you factored this cubic polynomial over here, you would have n times n plus 1 times n minus 1. That's three consecutive integers there, 1, 2, 3, and one of those has to be divisible by 3. So this lemma is actually true, and n cubed minus n is congruent to zero mod 3 for every single n. So that would prove that these two functions have three things that line up with each other. They have the same domain, they have the same co-domain, and although the formulas look different, they always produce the same output no matter what the input is. So those two functions, although they're superficially different, we might want to consider to be the same. So that leads us up to our big definition here. The functions f and g are equal if three things happen. First of all, the domain of f has to equal the domain of g. The co-domain of f has to equal the co-domain of g. And then for every x in the domain of f, f of x has to equal g of x. So this is what happened with our last example. The domains were equal for those two functions, going from the integers to the integers. The co-domains were equal, and at every point in the domain of f, the functions agreed with each other. So here's a concept check to see how well you're acquiring these ideas. And I want you to really think about this. So let's consider the function f that goes from the real numbers to the real numbers, given by f of x equals the square root of x squared. And this x squared is entirely underneath the radical sign here. So to which of the following functions, as you say is, type over there, is f equal? To which of the following functions is f equal? I'll tell you that this f is equal to at least one of these guys here. So take a look at these four possibilities here. Play with them for a few minutes as you pause the video, and then come back and make your selection. And the correct selection here is going to be only one thing, and that is b, all right? So let's see why b is true, but why a and c and d are not equal to f.  And we have this property that we know from algebra that the square root of x squared is equal to the absolute value of x for all x in the real numbers. This is a basic notion of absolute value. So we know that the absolute value function is going to produce the same thing as square root of x squared for all real numbers x, okay? So those two functions b and f have the same domain, have the same co-domain, and they produce the same outputs everywhere. Now, why aren't all the other ones true? Well, it's really easy to get rid of c and d. And d and f are not the same because the domains are different. This one has a domain of the natural numbers, and f doesn't. Okay, there are things that I can plug into f that I can't plug into c. d is not going to be the right one because the co-domain is different. This is the interval 0 to infinity. That's the set of all x's that are bigger than or equal to 0. And that's not the same thing as the co-domain of f, which includes all real numbers. So those two functions are not equal to each other. Finally, function a is not equal to function f. Even though they have the same domain and the same co-domain, they don't always produce the same outputs. For example, a of negative 2 is negative 2. But f of negative 2 would be equal to positive 2, and you can check that out. So they don't always produce the same output on every single input in their domains. So to recap, two functions are equal if and only if their domains are equal, like b and f, their co-domains are equal and they produce the same output at every point. And that's what it means for two functions to be equal. Thanks for watching.