Good morning everyone. So this is the last day of EuroPython and in this session We are actually dealing with some of the very hot topics that I'm really interested in that's neural networks and deep learning and To begin with we have our first speaker T. Rashid He'll give a talk on a gentle introduction to neural networks. So please give a big round of applause Hello, hi, can you hear me? Does this work? Yeah, cool Right first of all, thank you very much for I'm coming to my talk For me, it's really great to be at an open community open source conference I always learn a lot actually I always have great conversations and there's always a very generous spirit So I want to thank everyone and also the organizers. So nice one and I'm gonna talk about Neural networks and I just want to be really clear my talk is very Intended to be very introductory. It's it's for people who perhaps don't know what neural networks are or how they work Or maybe you studied them a long time ago and forgotten So if you already know what they are if you're already an expert, you might be bored So I don't mind if you if you want to go to another talk cool My name is Tariq Rashid. I forgot to say that I'm one of the co-organizers of London Python And if you want to come and do something with us Please come along and have a chat with me We really want to do more sort of broad things everything from computer art to teaching people to code right cool So I'm as I said, this is an introductory talk and we'll talk a little bit about the background of what is artificial intelligence Why is there a lot of interest in neural networks at the moment? Then we'll get into the ideas and that's the the meat of this talk really. It's what are the concepts that are used In neural networks and what all you do is I'll I'll use some very simple kind of naughty examples Which which may not seem that interesting, but they illustrate the very key points that make neural networks work So I hope that you'll stick with them and that will help us understand what's going on inside a neural network We'll also apply them. I'll give you an example of applying neural networks to quite an interesting Challenge recognizing handwritten numbers. I'll give some pointers around how you might code your own and I might regret this I'll do a live demo at the end And if it goes wrong, that's going to be really embarrassing We'll give it a go and I'm not going to talk about Libraries and there's lots of cool stuff out there. There's the Arno. There's TensorFlow and there's there'll be lots of talks today Covering things like that. So mine is really about the concepts. What's really going on and how you might do your own? So just to get us into the kind of the right frame of mind and let's start with two questions So I have a seven-year-old daughter and I and she likes challenges So I set her this challenge. I said can you look at this picture and point out where the people are and As a seven-year-old child. She found that quite exciting and very easy They look like seven-year-old children and she counted the the people in the picture and that was that was fine She she can do numbers she can add she can subtract so I sort of said to her Can you add those numbers and and she found that very difficult? But you know that with coding with computers with Python Doing a calculation such as that one on the right is actually very easy But instructing a computer to find people in a photo is not so easy. So that's interesting That's easy for us and that's easy to code for computers But that's hard to code for computers and that's easy So there's something there and we would like to be able to solve these kinds of problems because they're interesting You know find me a picture of a cat Work out of what this sound sample what the words are in this sort of audio file You know those are really interesting problems And we want to be able to solve more of them and you know the terminology like artificial intelligence It means different things to different people. So for me it means being able to solve the kinds of problems that traditionally have not been That straightforward. So that's what that's what this is about And there's a lot of hype at the moment. There's a lot of bars There's lots of stuff going on that you you know, you won't have missed this autonomous cars There's health data being used to improve outcomes Google's been a very kind of active recently with You know being able to play go which which is amazing We thought that it would take another sort of 20 years and they use you know neural networks as part of their solution So that that's you know interest people And that's what we'll talk about today So let's go right back to the beginning, you know really really Assuming nothing. So we want to ask a computer a question and we want an answer and it's Do some kind of thinking well clearly you can't think it's a it's just you know metal and wires So it has to calculate, you know, that's it has to process and those are words I guess programmers like ourselves Understand we have input we have some kind of calculation and we have output and neural networks and artificial intelligence That's that's all it is. There's nothing mysterious about it. It's just calculations Just cleverly done So let's let's set ourselves a very very simple example just to get started Imagine that the conversion from kilometers to miles is a difficult problem. Just imagine I know it's not you know, it's not let's just imagine it is and imagine we didn't know how to do it So we invent a model in our mind We sort of say maybe one is the other one multiplied by a number That's a model. We've kind of come up with a model. We think it might be right. We might be wrong. We're gonna try it Let's start with a number. We don't know. It could be miles is kilometers times a hundred or miles is kilometers times Two let's start with a number. We'll start with point five and if we compare it with In a real examples of truth, we know it should be sixty two point one three seven, but our model Calculated fifty. It's not that bad There's an error of twelve. It's okay. It's not great Let's let's tweak that Point six that gives a better Answer still not exactly right, but the error is much smaller now Um Let's try again point seven. We've gone too far and it's much worse now Let's let's not be so enthusiastic about jumping and try point six one and that's actually getting quite close So this this idea of using a model and Tweaking a parameter inside it and then comparing the output with with what we know should be true Is how neural networks work and many other actually machine learning methods? We use the error That pops out the other end and use that to kind of tweak and guide the refinement of a parameter inside the model I hope that's clear and that's a super easy example But that's what a neural network is doing if you just replace that circle with a neural network That's really what's happening. You're training it. You're looking at the error and you're tweaking Parameters inside it to try and get a better answer at the other end You can go home now. That's it Okay, so there's the key points there, you know, if we don't know how something really works You know, we we we haven't got an exact mathematical model We can invent we can we can come up with a model that we think might be true And we can try it and we can have parameters that we can adjust And the important point there is the error is used to refine the the model So, let's let's take our daughter into the garden where she likes to pick up bugs and she's picked up some caterpillars and ladybirds and Imagine that we've plotted them on a graph with width and length so caterpillars are thin and long and Ladybirds are short and wide and if we plot them we can see there's two clusters two groups there Which is interesting and some of you will recognize this as clustering. That's that's that's that's cool What we did first with our first example was have a linear line a predictor The relationship between kilometers and miles we we thought was a straight line with and we changed the parameter We change the slope What we're gonna try and do here is see if we can apply that same simple model and see if we can come up with a way of predicting or classifying What a bug should be so that line instead of being a prediction line. It could be a separating line So things on one side of line are one type of object caterpillars things on the other side of the line might be The ladybirds that's not a good one because that doesn't separate the two kinds of bugs This one doesn't either And that one does So you can see that Learning to classify is not that different from the very first simple example. We looked at and You can also see through this kind of naive animation that we're changing the slope is a way of Learning to find a good separation line between those two clusters So once we've if we've learned that line if we've learned a good separating line if we then find an unknown bug You can say well, that's that falls in that half of the of the space. So it must be a caterpillar So classifying things is it's kind of like predicting things So we apply these methods when we don't really know what the model should be But what we do have is real data. So we learn from data We invent a model. We think it's a good one and we try to refine it To match the data that we've collected. It might be you know data from space You know the microwave background radiation which we heard about earlier in the week It might be voice data. It might be a sentiment We're gonna stick with a super super simple data set consisting of two items Just the widths and lengths of two bugs Here we are we're plotting them. So we start again with a randomly chosen Parameter for that line a randomly chosen gradient and we say, okay, that's not That's not good because it doesn't separate the two lines So let's look at the first example there and we need to shift the line up To that point. Okay, we've improved it but that kind of does a good job Now let's what could we learn from the second example? Well, we we look at the second example and we say all right the separator must keep that example On that side of the line and that kind of works and If you're interested in the maths, it's really simple. We've got straight lines It's very sort of simple linear algebra. You can rearrange the terms to work out what the change in grading should be if you want to Get the line to go through a certain point but actually We've made a kind of a mistake here because what we've done is we've looked at an example and ignored all the previous ones before it We don't want to do that We want to learn from all the data Not just the last one we looked at if we did this We would work through all the examples and we just Have an answer which you could have got by looking at the last example so one way of doing that is actually not to be so enthusiastic about your The amounts that you jump up by What you what you can do is you can say instead of jumping forward or changing the line By, you know, five we apply a factor like a learning rate So we only jump a little bit Safe if the first example wants me to go over there I just move in that direction a little bit if the next example wants me to go over there I move a little bit So with lots of data you can see that you eventually get better and better but you're not overly influenced by each any individual data point and that's good because Data is noisy and there can be outliers There can be errors in the data and you don't want to you know Overgives too much importance to any one individual data points and that learning rate is is quite an important Idea in neural networks, and we'll understand why in a minute so let's Increase the dial up the complexity a little bit. So imagine we have data which is something to do with the real world and It's causal So maybe I'm measuring the amount of smiling in this room and the other factors I'm measuring are whether it's sunny and whether it's a weekend So you can see if it's sunny and it's the weekend There might be more smiles or if the sun shining and there's no cloud the temperature might go up You can see that in in the real physical world data can have causal links and we want to be able to model that I mean that that would be a great thing to do to model and Be able to predict or classify a data that comes from the real world. So here's some simple examples here You know, we have the Julian relations We have the and relation and the or relation if two conditions are true and the third one is The and is only true if the both inputs are true So some data can be like this Can we can remodel that with the very simple example the simple classifier that we've got? Just to say actually that that thing we did at the start with the picture There's nothing wrong with having two inputs into a calculation. That's okay So you can visualize them that data by saying, you know We we can plot these sort of you know the the two inputs as coordinates and we can say the output is colored So if we have an and relationship we color it green if it's if they're both on and yes The dividing line still works. We can still have a linear classifier to separate off data which has sort of an and Kind of causal link in there and same with all so that's cool We could use that very simple very kind of naive classifier to learn data which has the and Kind of cause in it or or logical or in it. So that's that's cool. That's that's that's looking hopeful But actually in history. I think it was probably in the 70s People sort of Became sad because somebody wrote a paper that said actually these simple classifiers are very limited And it's because those simple classifiers the linear classifiers Can't learn Data which has the XOR relationship. So if I have two variables, which are related To the answer with an XOR the only truth either of the inputs are true But not both can't do that and you can see why you can see visually no line Correctly separates those two classes. So so that led to a bit of I Guess a bit of a slowdown in research in neural networks But if you look at this you think well the answer is kind of there already We have two lines And this is an important point actually. I know this is a very simple example, but what this Suggest to us is that actually we need more than one of those classifiers to help us with data. That's more complex And that is actually why neural networks have many many nodes rather than just one node And that's that's that's I think that's an important point there So some problems can't be solved with just a simple linear classifier You kind of know that But it's the motivation for why we might want to explore using multiple nodes Let's take a shift a little bit and look at Nature again, we started right at the start with the example of my daughter's brain being able to Find people in a photograph, but me not being able to code that very easily So human brains are doing something and working in a way that's different from my kind of you know laptop here wants to work and it's you know throughout history people have tried to understand what is it about the way Biological brains work that makes them so good. What can we learn from that and replicate in new kinds of algorithms? And actually, you know just just have a look. I'm you know, this computer's got what is it 16 gig of RAM and How many mega kind of instructions per second? It's it's quite Quite chunky and yet a pigeon with which has a brain of point four grams It can fly it can learn to eat it can communicate It it can learn to do new tasks. That's really important Snails got 11,000 neurons. That's not a lot really, you know We can store sort of you know with big data technologies can a huge amounts of Data structures and we these things have just 11,000 this and this worm has 302 neurons, you know I'm sure I can fit that in a in a micro bit This is interesting there is a species of of of of whale Which has 37 billion neurons, but we humans have 20 and And it's using them because if it wasn't using them it would have evolved away because it's a cost makes you think that maybe we're not the most Superior things on the planet But anyway, the point here is that Nature's doing something with with brains that we can learn from and you know, which with apparently such small resources, they're able to Do tasks which we think are quite complicated So those neurons that biologists Know are inside our brains and our nervous systems if we look at them what they do is they kind of Transmit a signal along Onto another one Those are the just it kind of the names for the various elements, but what they don't do is they don't sort of pass a signal on Unin kind of you know without any Kind of resistance what they do is they only pass a signal on Once that signal is kind of past a threshold It's like turning up a dial and the light only goes on after I've reached a certain kind of number So maybe our computing neurons that we model maybe they should do the same and Some people think oh, I could use a step function to do that So if the input is past a certain point then it switches on Actually, you could do that But in nature we know that things aren't always sort of black and white and hard-edged things are softer So we we might try a softer Kind of function. That's a sigmoid function and there are others that you could use and We know in nature these things are connected like sort of a network and mesh and you can see the signals going along So maybe that's what we should try and model When we want to do some interesting tasks like recognizing pictures and again going back to that thing We saw right at the start There's nothing wrong with having more than one input coming into a computing kind of node And what we've just said here is that we're collecting the inputs Just as they do in in the in in nature And we're going to apply a threshold function so that we only have an output if that combination is big enough And that becomes our node in a neural network So after how many minutes of talking oh my god running out of time We've finally got a neural network. That's all it is a network a neural network an artificial neural network is our attempt to try and recreate what those biological brains are doing and Each of those circles is doing What we saw here? Collecting the signals applying the threshold function and passing on the output It is convention that we call these layers. We have a middle layer. We have an input layer. We have an output layer And there we have some connections so Let's pause a little bit and think With our very very first example where we wanted to convert miles to kilometers We had a straight line with an adjustable slope a parameter That's what did the learning the learning was the changing of that slope that that kind of multiplication factor What's learning in a neural network? What do we change? What do we need to tweak so that the outputs are better? There's probably lots of answers to that you might say that function that threshold function that curve Maybe we need to change the slope of that in each of those nodes That's that's probably you know, that's it's not a bad idea Actually what where history has taken us what people do is adjust the links the strength of the links between those nodes So if a link is strong a signal is amplified if a link is weak It is kind of reduced if a link is zero Effective if the weight if the strength is zero you effectively break break a link So that's that's one approach and that's the one that's become popular probably because it's easier as well So when we feed signals forward, let's just imagine we've got signal one at the top there And we have a link there go call between one and one and you can see it's got a weight a Strength of point nine what we would do is take that signal one multiply by point nine and that's what feeds into the next node Same here point five times point three is what would go up there. That's really easy. That's that's not complicated at all That's that's what is happening inside a neural network just multiple multiplying signals through Connections and feeding them on to the next node and collecting them That's just a reminder of what we're doing. We've got these signals coming in We add them up but this time you can see that we're weighting them We're using the weights of those links to kind of either boost or reduce those signals of color than there so you can see You can see the slides afterwards. You don't have to do the calculation now But you know you can if you want to you can verify that you know that times that plus that times that would give you That answer and that's that really is as simple as as it gets you and that's what a neural network is doing There's nothing nothing very complicated there at all so We had a very simple network here with just you know four nodes If we wrote out with a pen and paper what is happening at each node? So at that node number one in layer two if we wrote out what's actually happening We'd say it's the input one times that weight plus the input two times that weight and if you wrote it out for this one and we wrote it out again for all the nodes you start to see a pattern and That pattern is really helpful Because that allows us to write that calculation as a matrix multiplication So the weights matrix times input signals becomes the signals that go into the next Layer and that's really really Valuable to us because for two reasons it allows us to write that calculation in a much more Concise way so we don't have to write pages and pages for big networks We just write weight times input is the signal into the next layer The other reason that's really important is because computers numpy Can accelerate? Matrix multiplications and we want to take advantage of that whether it's numpy whether it's Fortran libraries We heard about earlier whether it's hardware accelerations. So using your graphics card to multiply matrices If we can formulate our calculations in terms of matrices then we can take advantage of the acceleration That's possible.  Why would we have to do this again? But but this is the reason So that's cool. We're kind of feeding a signal forward through each layer of the network and we get an answer at the other end We know that actually we're likely to be wrong just like we were at the start. So we have an error and Going back to that very first example again. We use that error to refine and improve the Parameters inside the model. How do we do that here? So let's break it down lesson. Let's kind of have a simple network here simple picture just to see what might happen Well, we know that we need to change the weights that we've we've already agreed that we're going to change those The strength of those links in order to try and improve the answer. That's what we're playing with That's the parameter that we're tuning We what's the what's the error? We know what the error is right at the end of the network if the answer should be five and we get three the Errors to but what's the error inside the network as we need to know the error in order to change the weights So that's that's quite an interesting question and actually lots of lots of the guides and the books kind of gloss over that a little bit What we could do is well actually the first thing to say that there's probably no kind of Mathematically kind of perfect answer. So what we we do is we kind of think The word is heuristics. We think well What would be a an intuitive way of working at the area inside the network and one intuitive idea is to say Let's split it. So if the error is five, maybe I push two and a half this way in two and a half that way That's an idea another idea is to say That top link three with the weight three Contributed more to the error because it was a bigger stronger link. It magnified the signal Maybe I should put more error in that direction. So you split the error proportionate to the links So if if I've got weights of three and one the the links of strength three and three and one You can see three quarters the error would go to the top node and a quarter would go this way that kind of makes sense I'm sure there's more sophisticated things you can do, but we want to keep it simple, especially if it works So you can see there actually that the error from that node Is is being kind of split and pushed back And the same here and the internal nodes you would actually collect the several fractions of error that you that link to it Sounds complicated, but when you see it as a picture you can see the errors flowing backwards Back propagation of the error. That's where the term comes from error back propagation So feeding forward signals and back propagating errors Oh, it's too many calculations. You can look at it afterwards The point here is that you're summing up the error So if the errors point six from that top right and this one's contributing point one the errors then point seven So point six plus point one just collected the errors and again It's really nice really fortunate that if we did write out what what's really happening in in terms of in a variables We it becomes a matrix multiplication again Which is really nice because we can accelerate that and we can write it in a very concise way Without worrying about the actual size of the network. It's only slightly different because the weights matrix is then transposed. It's flipped diagonally Against super super simple Okay, so we've got the errors now at each node How do we how do we how do we change the weights? Okay So that's the output one of the output nodes those w's are the weights of those links inside I'm not gonna be able to untangle that if you can you know well done That's horrible So what we need to do here is to say we're not gonna be able to kind of untangle that in any kind of nice Mathematically clean way. Let's find other mathematical methods, which are perhaps approximate but good enough So let's go on a bit of a journey Imagine this landscape is a complicated function like that one we saw and If it's say an error function Because that what we had here is the output and the error function is simply that Minus what the what the actual target should be if this horrible complicated lumpy landscape is is a really complicated function Which we can't work out Analytically, you know with nice clean algebra Another way to kind of work with it and maybe work out the minimum where the minimum error is is to sort of say well if this was a landscape and I didn't have a map of everything And it was dark. I couldn't understand the whole function But I did have a torch what I could do is I could point the torch Down near my feet and say well its slope is going in this direction Take a few steps. It's going in that direction. Take a few steps and eventually You would work your way down to a minimum Some of you will put your hands up and say it might not be the best minimum, but we'll come to that So this this approach, which is not mathematically kind of clean It's an approximate method, but it works really well and you can see it working really well with You know, let's pretend the x-squared function is really difficult. Let's just pretend that You can say that you know We start at a point and we see where the gradient is locally and we kind of move in that direction And we keep doing that and you'll get to the minimum Which which works and that's that's kind of nice and you might even be more sophisticated and say as The slope gets smaller you might take smaller steps because you're getting close and closer to the real minimum And you don't want to overstep it That's an idea that's actually used in neural networks as well So if that if that error if that complicated function was the error function then we have a way of Finding You have a picture to show you there it is. So if we have the weights, which is what we want to Kind of improve and we have an error function, which is complicated. We want to use this gradient descent method to find the minimum of that error and We'll then know what the right weight should be So if we're over here with the wrong weights, we're gonna have high error and we're gonna try and say, okay I want to improve my position and move down the error function to somewhere where the error is smaller And then the weights there will tell me what the right weights in that network should be so this is called gradient descent and it's a way of Working with that horrible kind of Expression that we couldn't kind of do analytically before If you did write this out again with pen and paper And and worked out sort of the gradient locally. It's not that hard. I mean I won't do it here I have a blog post if you want to look it's very simple calculus It's the kind of calculus you do at school just using the chain rule nothing more complicated than that So if you are interested, you know have a look there's a there's a what I hope is a very clear kind of blog post on that So what we've what we've doing now is we've we've we've worked out a way of improving the weights based on the gradient of that error function and You know, you've seen this kind of a question many times where we iterate and keep improving Okay, I've only got a bit of time left. So I'm gonna zoom through Sort of how you might do it yourself. I'm not an expert Python coder There's people here who are but broadly speaking, you know, if you wanted to do this yourself You might think what would a Python kind of in a program or class look like well We know we've got to initialize this data structure this network And it's really simple, you know, all we're having to really do is set the size And initialize those weights to random initial values We know that we've got to have some way of training the network so we're doing the learning and then we've got to have a way a method of Queering the network.  NumPy is great for matrix multiplications As you've heard all this week SciPy's got some nice Functions in there for doing Like that that's curved equate that curve graph the threshold function. It's got that built in so you can you can use that yourself Plotting things is mapplotlib. You can use other things. I Started programming a long time ago, and then I stopped so I was coding Python in 1999 I think 98 Python 1.5 1.6 Numeric library rather than NumPy. Anyway, so notebooks didn't exist then so I came back to Python and notebooks are fantastic excellent So this this is an example of a function which initializes the network It looks complicated and don't be put off all it's really doing is setting the size in terms of the input nodes hidden nodes The the output nodes and you can see here. I'm using a NumPy function to randomize Do the weights which are a matrix? That's it. Nothing more complicated than that. You can see the SciPy function there as well X bit otherwise called X bit, but that's the logistic function the curve We'll do the training next but querying again really easy. We take the inputs I've turned them into an array It was a list here We can see that there's a matrix multiplication NumPy dot dot to do the calculation And that's it We apply the activation function to the outputs of that multiplication Simple as that you've then got the Signal at the next layer, and then you do it again. That's it. It's as simple as four lines Propagating the signal through a neural network as simple as that. I'm sure I could make it, you know even more concise, but I Just want to let you know really by doing this that that it's not mysterious or scary or complicated It really is as simple as just that And the training again it will look scary, but it isn't really The top half is exactly the same as what we've just had. We're feeding the signals forward and Exactly the same code as before and then what we're doing is we're saying the output errors is a target Which is from our training data minus what we've what we've worked out and then we use another set of matrix multiplications to work out the The errors internally to the network and then we change the weights Using that expression that we worked out with calculus. That's it. That's how you train a neural network I'm sure I can make it even more beautiful and clean But I just wanted you to kind of get the feel for what's really going on in the network and it's not that complicated you could do it yourself Okay with a few minutes left I'm just gonna sort of show you that with just those very simple ideas that we looked at I mean, you know people will have many more kind of you know sophisticated methods and optimizations and You can read quite a lot about neural networks, but just with the very very simple ideas that we've looked at You can do some powerful things So we can train a network to learn to recognize human handwritten numbers There's a famous challenge a data set called the MNIST data set It's got 60,000 Training examples It's all free open data. You can get it yourself if you want to look at my blog you can point you to it And there's a test set as well and you can compare your results with others If I looked at the data, you'll see the numbers there actually if you plot as an image Using what did I use that plotlib? You can see that's that's a five twenty eight by twenty eight pixels So if we feed those that data into a network and train it Actually, I missed something there. We have to choose what the output looks like What I've chosen is to say we have ten nodes at the output And if the answer should be say nine the ninth one has the biggest value So you can see here if it's five then it's the fifth node that should be of a high value and everything else should be low That's what I'm using to train the network. That last example is interesting because in this one the network thinks The answer is probably nine, but it might also be four And and you you know you you can get some really good results just with those simple ideas 96% accuracy is what I got in the first go That's not bad. You know sort of 20 lines of code and You know handwritten human You know numbers and you can get you know over 90% accuracy That's that's that's that's not bad at all. Is it I think if you have a play You can you can tune things like the learning rate or the number of hidden nodes And you can see that you can get improved performance and then it might not improve so much I've deliberately put in a Bit of a wonky graph there just to remind us that neural network training is a random process We're starting off with random initial weights and sometimes it can go wrong And that's for the scientists among us. It reminds us that We should do this many many times and take the best or or to make sure that we've not gotten a normalist kind of answer Remember that gradient descent before you might end up at the wrong minimum not the best minimum so it's worth doing this many times If you rotate your Original data set you can get 98% accuracy. This is actually really good because if you look at the academic papers They get sort of you know 99% 99.5% but they're using really advanced techniques And I think you know with just 20 lines of code. That's not bad I'll just skip this and what I'll do now is I'll just say you can actually do this with a raspberry pi zero everything I've done you can look at the blog as well. You can do with a raspberry pi zero which costs about four or five euros You don't need an Nvidia graphics card to do any of this So the last few minutes I'm gonna try and do a live demo and I might I Might regret it. So let's think of a number to classify. Let's let's um think of a number Seven okay. It has to be one digit. That's seven. Let's write it Here's one I did earlier actually from a newspaper. So that's the number two and it got that right This is a network of trains last night. So let's uh, oops Okay, all right. Let's three if it doesn't work. It's not my fault Okay, let's resize that to 28 by 28. Let's save that Png all this code is on get I'll be if you want to have an explore. All right, if I press go let's see Three yet. No work says three few I did a few last night and it didn't work every time Few I'll stop there. Um, I'd love to have a chat about this afterwards. I don't know how much time I've got left for questions Do I have any or did I use it all up? Okay, all right. Yeah, okay. Thanks for listening by the way. Thanks So any questions? Hi. Hello. So we have seen the output nodes of the numbers What are the input nodes are those the individual pixels? Those are yeah, those are the individual pixels So if we have an image of 28 by 28, that's 784 pixels, I think so you would have an input layer of 784 You can choose other ideas You can say I want to rescale everything or I might want to have different features as inputs You can have you can do things like that But this is a very simple example very naive example, which takes the raw pixels and it works but you People will do other things to perhaps if they know something about the data they're working with they might say I think another feature is more likely to be a Factor in the answer and they might use that to train the network instead. So they might use color They might use alpha values. They might use something else So any other question, okay, thanks a lot. It was great talk


Thank you all for joining tonight's meeting. This is the community meeting to discuss the design plans for the. So, feeling street, invest, rising your road intersection improvement projects. This is in council district 6. So, before I get started, are there any representatives from council district 6? Okay, so we'll go ahead and get started. So, my name is fancy and I'm the city of forward project manager. Our design project engineers are put in the engineering for tonight's agenda. We'll discuss the details on the status of this project. We'll talk about the design phase, the project details, the project schedule and then project contact information. So, we are currently in the design phase and just the overview of the design stages. We have the 30% submittal and for this submittal, we like to have the track. The consultants will do the traffic study. They'll come up with recommendations on the intersection improvements. Next is a 60% submittal. So, for this submittal, we like to have more in that details and more further engineering. Then we like to do a constructability walk through just to make sure that. Um, we are able to construct the improvements and then we like to start coordinating our utilities and right of late, if, if applicable for our 90% submittal. That's when we start finalizing the plans for construction for this project. We are currently in process for the 60% submittal so a little background on this project. So this project is part of the 2022 bond program. And our consultant did a traffic study to come up with how we can improve this intersection. There is a need to improve traffic flow during peak hours, reduce delays and improve overall safety for all road users. Improve pedestrian safety at the intersection by installing pedestrian actuated signals with push buttons. And also improving pedestrian access elements such as ADA compliant curb ramps. And again. This project is funded by the 2022 bond. So, we had a traffic operation study and the purpose was to evaluate improvements to increase capacity, improve mobility and improve operation based on the traffic study. There was conclusion that there is heavy westbound and southbound left turn movements. We have the North Crawley high school located south on healing street. And then I 35 and Crawley middle school located east on West rising your road. So, after further analysis, there is conclusion that the existing left turn Q exceeds available term name storage for all approaches, excluding the northbound. And then the level service analyzed for the years, 2026, 2031 and 2046. Based on the traffic operation studies, the recommendations were to reconstruct all left turn lanes reconstruct the westbound left turn lane to have an additional westbound left turn lane. So, we'll see the exhibits in a few slides. Installed northbound channelized right turn with the old operation. And then to extend the left turn lanes. So, again, this is our project location. This is South healing street and restores in your road. So, this what we're looking at is the exhibit. This is the eastbound approach. So, the west side of the intersection. So, we, we have proposed offsetting the left turn lane. And then this is the westbound approach or the east side of the intersection. This is the additional westbound left turn lane that we just discussed. This is the south of the intersection or a northbound. And then you see the offset left turns here as well. And then finally, this is the southbound approach or a north side of the intersection. And then again, you see the offset left turn lanes. And so that is to increase the safety and visibility for these left turn movements. Project schedule, we are currently in design phase currently we're not anticipating any right of way acquisition. We are in coordination with utilities to address any potential conflicts. And then we're estimating contract execution for construction to be summer of 2024 with construction starting fall of 2024 for a duration of 12 months. And the cost of about 2.2 million contact information. So, we have David Leslie with quality engineering, our design engineer, his number is 9, 7, 2, 2, 6, 5, 7, 1, 9, 2. My contact information, Fanta Cava with the city of Fort Worth my numbers, 8, 1, 7, 6, 8, 2, 3, 1, 8, 1. And then, whether it's for this project or any city wide, we have a few options to input service request. So, we have the mic forward that for Apple, we have the mic forward that for Google. Both links are on this slide. You can text hello to 817835 my Fort Worth or 6, 939. You can call the city cost sooner at<PHONE_NUMBER>. And then there's also a chat box on the city of Fort Worth website. So that concludes tonight's presentation and it's now open up for questions. Thank you all for joining us. Can you hear me? Yes, how are you? Hi, I'm well, thanks. Can you show the slide again for what the cost of the project is going to be? Sure, just a second. Thank you. Sure. Thank you. Will there be any changes to the northbound section of Huland at Risinger? I'll go back to the exhibit for the northbound approach. David, once I'm on that slide, do you want to give an overview of the northbound updates to the northbound approach? Sure, you bet. Yeah. So, so, Fanta's got, let me just introduce you. My name is David Leslie and she shared my contact information. I'm with Quiddity Engineering. And we've got offices scattered all over the state of Texas. And here in Fort Worth and in Plano. And so, so she's got the slide on the screen. North is looking to the right. And so the improvements here are going to be to shift that left turn lane over further into the median. And as Fanta mentioned, what that does is it creates a positive offset where you have clear visibility across the intersection to see when you can turn if you don't have that green arrow protecting your turn. So, so it provides a safer, safer turning movements there. We're also adding a channelized right turn lane that goes right there. She's pointing to it there, right? So. 2 of the other corners already have that channelized right turn lane. And so we're adding that in here on this corner. Can you go back to each 1 of those slides and speak to each 1 of them? It went so fast. We don't even know what we saw. Sure. Yeah. Yeah. Fanta, you want to start? That's good. Okay. So this is the, the east bound approach. So the, the west side of the intersection north is facing up. And so there's currently a single left turn lane. And it, it sits right about where you see some striping on the on the pavement there in this proposed slide. So we're going to take that and shift it further here again to into the median to where you create that positive offset. Have a cleaner line of sight for the turning movement. So that'll be, that'll be pretty much what we're, what we're capturing for the improvements here. Can I ask you a question on that? On that slide right there? So I see that turn you have on Walgreens and some work was recently done like utility work and recent months maybe here. So at CVS corner. Are you the city considering putting the same kind of a turn at that and that corner of the CVS because there seem to be lots of space seems to be there and make it a more smooth kind of turn on that corner. Oh, I see what you're saying. Yeah, that channelized right turn lane. Yeah, so opposite of Walgreens. Yes. Exactly. Exactly. So the traffic study did not show that that was needed at this time. And so. Certainly it could be considered in the future, but at this point, it did not include, excuse me, enhance the. It did not improve the level of service of the intersection. And so the cost of benefit of adding that at this point in time didn't prove out. Okay. Okay. Fair enough. Thank you. Sure. Okay, I see somebody's got their hand up there. Yes, that's me. Eric Janowski. Thanks. I was a little late joining your presentation. Could you remind me what's driving this. Project and by the way, the information I got on this project initially was that it was supposed to be around about, which I'm glad it's not. Um, I don't know where this information was coming from, but obviously it was an error. But what is driving this improvement? It's just the number of accidents at this intersection. It is, it's, it's the number of accidents. It's the, it's, it's the level of service that you have at the intersection. It's the, it's the number of turning movements. The schools are there, right? And so the crash data was, was high that we were seeing, so taking advantage of, you know, when we have a wide median like we have at all of these intersections, all of these legs, it allows for this offset left turn lane that will here again improve that. Uh, the same, sure. Well, I, thank you. I certainly hope it does the trick because there was just an accident yesterday morning there. Right, just to prove your points. Right. All right, thank you. Sure question. Sure. Go ahead. Thank you. Um, will there be another meeting to look at the 90% plans? So, we will have a pre construction community meeting as well. And then we'll give an update on the design plans, but we'll also give updates on what conditions you can expect very construction. Okay, thank you. Sure question in the chat. And they just make the lights turn on at each section to prevent the accidents. Um, are you mentioning like each corner of the intersection or like. The section throughout fuel and or rising there. I'm sorry, ask that 1 more time. So, are you trying to refer to like each corner of the intersection or to the. Like the roadway section of fuel and or rising there. But the street lights. Yeah, I mean, the, the, the. Without doing any pavement improvements. Trying to read time the intersection the signals. Doesn't solve the problem and so. So, so adding the here again, the additional left turn lane. And and providing the shift over in the other left turn lanes. And then here again, adding that channelized right turn. There will be new signals that are put up, but. But just doing signal work alone. Does not improve the safety of the of the intersection. Thank you, David, and then I have the westbound exhibit up if you want to give a rundown on sure, sure. Let's let's do that. Right. So this 1, this is the 1 that maybe we're seeing the most improvement on. And so, yeah, as, as phantom mentioned, this is the westbound approach or the east side of the intersection. And so, so we'll be coming into the median there and adding an additional left turn lane. And so, so our traffic study showed here that, that there was, there was sufficient queuing is what we call it when you have a lot of cars that are trying to turn and all you can't all get in the turn lane at once. And so, we're having this dual after we'll we'll conservatively improve that situation. So, so we'll be that that's the that's the extent of the improvement there. Of course, on all 4 corners, we're we're bringing up. The handicap ramps the pedestrian access as phantom mentioned will be will be improved on all 4 corners. So. This this lag of the intersection already has the channelized right turn lane there at the gas station. That that that heads northbound and so. So, here it's just just simply adding that additional left turn right. And so this is the, the, the northbound approach, the south side of the intersection we really talked through this 1, a minute ago, but just kind of repeat. So, it's. Shoving the existing left turn lane further into the median to create that positive offset and create a cleaner line of sight across the intersection. And then also adding this channelized right turn lane here to head to the east. And then this is your southbound approach or or the north side of the intersection and so. Here we're we're adding we're shifting the left turn lane over into the median. The slide, if you see it has a little. Parallax to it, and so it's the image is shifted slightly, but. But, but nonetheless, it's it's just shifting that that. Proposed left turn lane further into the median to create that that offset. We're also shifting it enough to where we can add a left turn lane, an additional left turn lane in the future. Here again, our traffic study showed that the movements are almost there to do it, but not quite. And so. So, working with the city, we've decided to go ahead and. Here again, create that now to where all they have to do in the future is just restriped that 1 area to add that additional turn lane. And then do some signal improvements just to just to be able to signal that. That other line, I believe that was it we get. I think we got all the way around there. Yeah. Yes, thank you. Are there any other questions? I have 1 another 1. Sure, so will the construction how will that take place? Like in faces, for example, the north. North found those 1st and the West, East and so on, or how was the construction going to go? Those are those are very good questions for sure. And, you know, as Fanta mentioned, we we've just completed 30%. And moving into 60% design now. So, we'll start looking at sequencing of construction. And so, you know, we certainly want to to try to get this work done as quick as we can to minimize impact. It's unfortunate that we can't just snap our fingers and boom, the improvements are there. Right. But so. So, we'll certainly be taking into account, you know, the traffic and the best way to sequence and phase the construction to to minimize the disruption during construction. Understood, thank you. It'll be hard to happen at the same time. I imagine. And so, follow up on what David mentioned, those are all great points. We will have more information on the construction phase and what conditions to expect during our pre construction community meeting. So, it's good. Thank you. I do have another question on the northbound. Direction is there is there a concrete median there that delineates that. Left turn lane is that with the gray? I mean, the green dotted spots are that that's actually going to be a landscape island. So we're trying to just incorporate as much green as we can to go back. And so. So, it pretty well that that edge pretty well lines up with what's there today. And so we'll just be kind of cutting through. The grass median, if you will, and so, so, yeah, what, what, what ends up is a is a little grass. Medium and the concrete, the new turn lane will be between between 2 pieces of grass, if you will. Okay, well, wouldn't there be like just a double turn, like you have on the other intersections on the other lanes? Well, here again, the, you know, the traffic patterns didn't didn't show that. And so didn't didn't show an improvement in the intersection for that. And so. Certainly, if it ever comes, then, you know, the ability to improve this, this, this leg of the intersection is there just simply by taking out that that that grass median there. So. So, here again, it becomes kind of a cost benefit for everything we do sometimes, you know, and so, so they were just at this point in time, there were no significant improvements for the, for the cost. Hi, this is Karina, rent area. Can y'all hear me? Yes, ma'am. Okay, so you're speaking of that air, what's view on view right now on the computer. And you just say that it's going to be a grass little. Grass and lay there and you're moving that lane to further to the left that way. I guess when we're driving, I guess we're facing east going to turn north so we can see both lanes that are turning south from the east side. Is that the the idea of that also in this in this picture that's on the screen. So, this is the, this is the north bound side. Right. And so this left turn lane here would be, you know, set up just as it is today, you know, here again, turning, you know, for turning left and heading west. And so, so the offset on the left turn lanes so for, for the southbound traffic or the opposite side of the intersection. You know, right now those those left turn lanes are far apart and you if you're if you're sitting in the left turn lane today. You kind of have to look through or over the opposing left turn. And so when you shift them in this manner that proposing. Here again, you clean up that line of sight to where I can see the traffic the through traffic heading southbound. I can I can see it much, much better, much better. Okay. Well, when you know, I live directly behind CBS or we do and I mean, we hear those accidents all the time and that seems like that's the area that they're constantly having. Um, the wrecks is because somebody is going, um, they're turning opposite, so did is that not what y'all are looking at? You're looking more that they're going east and west turning. And that's where the accidents happen. Well, I would see it turning as you're as it's visualized on the screen right now as they're turning to the west and somebody's coming and they're racing light, I guess, and they're going east. And that's where they, you know, the accidents happen. So why wouldn't a two lane two turn lane be issue or input there if that's where the impact seems to be more. Right, right. Yeah, so, so the, yeah, the, the dual left is is really so that more cars can move through the intersection. And so, but it, but it doesn't really improve necessarily the, the line of sight or, or like you say that eastbound car trying to. Trying to beat the beat the light, you know, and so, so yeah, I hear what you're saying there, you know, there's, there's, there's certainly limits. I mean, if people are trying to beat the light, you know, there's, there's limits on what engineering can do to improve that and so, you know, we're, we're, I mean, we're certainly making the intersection safer. We've, we've, we've been able to demonstrate that and working with the city's traffic department. They're, they're certainly on board. So, yeah, this is, this is kind of where we are at the 30% stage. Okay. I just, like I said, we live directly across in my backyard. Anytime there's a crash, I can see that they're going from north. The northbound traffic is going is turning west and then you've got the southbound traffic turning east and that's where the accidents happen. So, I, I mean, that's most of the accidents that I see. That's what I've seen. Now, it seems like this is going to improve more, you know, the double lane going to North Crowley, the school, which I understand that too. But I don't know. I just, I'm just trying to get clarification on that. Sure. Well, you, you brought up a really good point and, and Fanta, if, if you can back up a couple of slides to the, to the overall view. There you go. You're back one there. You're perfect. So, so, so you mentioned that the, the northbound traffic that's, that's turning left or turning, you know, heading west, right? And there, there, you know, there's collisions with the southbound traffic that's turning left and heading to the east, right? And so, so when we, when we shift these over and I don't have a very good picture of that. But when, when we, here again, so, you know, when you, when you're in these left turn lanes and you don't have a green arrow that, that's protecting your turn. You're having to look through the, the, the opposing left turn that, that people that are in that left turn lane coming south, if I'm sitting in the northbound, they're kind of blocking my view of, of any southbound traffic that's coming, right? And so, so that's what, that's what we're seeing is the, the benefit of being able to offset these left turn lanes and create a cleaner line of sight across the intersection. Okay, so that on the light, we're not going to have a protected view at all times. The light is on is it still going to be a flashing yellow light so they can turn. I mean, I think that's what it's down to is that people just don't know how to view those and they try to be whoever's coming through and they don't float it. It don't yield at the flashing yellow. And they just tried to rest through there and, you know, the result is going to be a, an accident and a lot of it does have to do. I mean, it does have to do with the students from the school because I've seen plenty of students that, you know, they just, they're new drivers and it's, it's, they, they cause that accident. Exactly. Well, I mean, there will be, there will be protected turns.  During the intersection, during the timing that, that it might be a flashing yellow, right?  So, why doesn't the city put a protected left turn like throughout the entire time? I'm just wondering. I mean, I just feel like that's a dangerous intersection. I mean, when you're, while you're working on it and you say it's going to improve and we see an improvement, maybe then put the yellow for, you know, the yellow flashing light to yield. But meanwhile, if y'all are working on everything, why can't we have the protected green arrow? Just for turning, is that backing up traffic too much? Is that what it is? Right. And, and I honestly, I don't have that information in front of me. To know what the signal timing is today, to know what that, what that looks like, you know, for the protected left. But I mean, you live right there, you drive through it a lot more than I, than I do, I'm sure. But, but I would, I would certainly think that there is at least some portion of that left turn that is, you know, is a protected left. It's a, it's a right.  And so, right, right. Then it turns, then it turns yellow and then, and then it's back to red, right. And so, so I think the signal timing, that'd be 1 thing that that certainly will be, you know, improved as you, as you go through this, this process. You know, phantom mentioned that we're also able to add some additional length to the turn lanes and the, and the cues so we can. Store more cars there, but I think here again, the natural progression is you, you know, you step through from, from a green to a, to a yellow to a red. Okay. Well, you answered my question. I was just, I mean, I can see where the backup would happen. And that's why, you know, they leave it as yellow longer because I see a lot of cars turning those 2 directions. But it just, I just wish people would slow down at the intersection, especially the youth that they just fly through their life. You know, they're at a, I don't know, at the race, race track or something. Right, right? Well, these were very good points and I appreciate you talking and, and, and asking these questions, particularly since you're.  So it's pretty bad. It's pretty bad. Well, to the city to, to forget in the project going for sure. Right, so with these improvements, I don't know if anybody asked the question about the, how much it's going to cost that comes out of our taxes, right? So we're going to have our taxes go. Correct. This is actually a bond project, right? Phantom. So, so this is, this is already something that's. I hate to say built in, but obligated, right? And so, yeah. And so our current construction cost is. It's just a little over 2.2 million dollars and so. And it's scheduled to, to, to go out for bids, you know, in summer of 24. So we're just. About a year away, a little less than a year away. And so we hope construction cost will. We'll flatten out at least, right? And, and let us, let us get this thing built for about that price. Okay, and this bond package is that is it 10 years? Is that what it is?  I'm going to have to have you help on that when I don't know what I'm going to. Uh, I hear it. Okay, sorry about that. The, the, the bond program is 4 years. I was trying to mute some people to get rid of that echo. I may have accidentally muted the person who asked the question. And I apologize if I did that. I didn't mean to, but. If you're still there, you may have to unmute and thank you for answering that question. How would we get the recording for this webinar? If you can please put your email in the chat and then. We will make sure to send you the recording, the presentation, as well as the city webpage for this project where you can get updates. And then also, I'll go back to the slide with my contact information. You can give me a call or send me an email requesting that information as well. And then I can make sure that you receive it. And go go back to the slide with the dates and the amount. I was just going to screenshot it. Well, thank you guys very much. Very important information. Sure, thank you for joining us for tonight's meeting. Are there any other questions, comments or concerns? Thank you, Fanta. This was great. I don't see any other questions in the chat. There was there is 1 email address in the chat. And we made sure to get that. I don't see any other questions though. Okay, thank you all for joining us for tonight's meeting in regards to the healing and rising your intersection improvement projects. All the everyone who's putting your information in the chat, we will make sure to get your recording of tonight's meeting as well as the presentation slides and the. The city website for this project and again, we will have another meeting for the pre construction. Once we are further with the design and we'll have updates with the expectations for construction phase services.


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YouTube CFM week 5 went for our biggest challenge yet Now I will tell you guys I've been hearing your comments the chat is here You guys want to watch these games live pretty much every other day check out twitch the link is below follow You'll find my twitch you guys can watch these games live all the time check them out every other night We play we play every night, but CFM is probably every other night I am probably looking to get into another one, but I have been hearing your comments It's good when pros don't have the abilities it's good when pros can't rely on their cheesy mutt abilities I've heard these comments and I agree that would be awesome. I'd love to play that mode I would love to play a mode where nobody has abilities that would be awesome The thing with this mode is everybody has abilities except me, but that's the way I chose it This is pretty much no money spent CFM style. All right boys You know, this is my first year in a league. We are one year behind everybody. We did not draft this team We did not pick this team.  We're still grinding. We're still getting it. We're still getting it done We are three in one right now all we got to do is get this team ready for the playoffs the long haul and You know how it goes with CFM is the reason I don't really join it because by the time my team catches up All these guys will quit and want to start a new one because the big bully guy is just dominating You know, that's pretty much how it goes. That's how it happened before That's how I was gonna happen to get in this league. They're all gonna quit But here we go. This is who we're playing this week. This is the four-time champ In fact, he put four-time champ in his bio, you know So this guy and apparently he has spent the most money in the league. I in fact I in fact once saw him type in the league chat if you're not buying abilities You're simply not trying so let's talk about it now this claimer one I think paying for things in CFM is trash It does help with the funds of the league and the overall prize pool of this league Which is $1500 or something and we don't pay an entry fee for me I'd rather pay an entry fee 100% buying things just trash You know, we already pay a mode that's super pay-to-play and now you're gonna throw it I can pay in but if I had the choice.  Why would I why would I be able to make somebody younger? I that doesn't help that doesn't help anything So essentially nobody if you get one person five years younger every single year nobody will ever retire in this league You'll never have to deal with age as long as you pay the $10 that so far as me I'm concerned, but that's the rules of the league and you know it's their league So it's time to embrace them But here we go. This is what we're playing against this week boys The Buccaneers the defending champs who I believe is three in one right now like myself NFC team. Let's check it out He's rocking Marcus Mariota. The one thing about this league is every quarterback was granted every ability or whatever abilities You wanted for a quarterback, which is kind of cool quarterbacks without ability suck Also quarterbacks with abilities are super OP as you've noticed like when your quarterback is this good It's almost impossible imagine rags with mutt quarterbacks. That's pretty much what we got going on here So yeah, Marcus Mariota up to a 97 overall We got Alvin Kamara. Why receivers are Lazard who's pretty good a fast guy All these guys have like short in elite and route tech and stuff like that But his defense is really what's what he's built is the Neil Hunter with edge-threatened double or nothing And he has jok who was crazy I did not take as a pass rusher as you can see he's boosted the finesse move I guess to turn him into a pass rusher This guy was more of a off-the-ball linebacker J. Okay, but he's turned into a pass rusher and that's a 90 speed defense event With edge-threat, so that's gonna be tough and then the corners He has four corners right here Chris Westry Chevarius Ward Buddha Baker playing corner and Jalen Ramsey all these guys have not only inside shade But they have one step ahead which is inside you guys and no one even has one step ahead and mutt and if they do It's like eight AP so Jalen Ramsey and Buddha Baker probably will not get beat by my rice my wide receivers with no abilities Maybe I can pick on Chevarius Ward and Chris Westry, but these guys have inside shade So that will also be tough and not only do they have these abilities these guys also have deep route knockout So if I do beat them, I do I do get open they're gonna knock the ball out So we're gonna have double edge threats against my boy Jedrick Willis and the boys and we're gonna have a bunch of one Steps ahead playing man coverage on me, but we can win this game This is why we're managing all right gotta get it done gotta make it do if you guys are like in a CFM videos I'll be honest. They're my worst videos. I don't know what I got to do You know you guys say everybody tells me they love CFM. They're not performing like the mutt videos So keep commenting. I've been thinking about not streaming the videos Maybe just put them on YouTube, whatever it may be but twitch keeps telling me. No, you can't you got a kid You'll be one of my slide we want to watch that We need some likes when we need some comments What should I do with the team who is my most important player going forward in the mid-season run that we're about to make? Boys check this game out. I'm gonna do all we got to all we can I'll come back with a postgame commentary for whatever happens I don't know what's gonna happen. We'll see how OP this is and if we can get some things done and make some things shake It's one of the first times I'll probably be like a super underdog or a I have to play smart and you know, you know Use the field use the clock and use my mind cuz I've this guy is not good But team is OP so we shall see let's go. Let's get it Okay, why why we're washing your hands after a piss is kind of soft Can't lie to the boys.  He just buys abilities, you know What am I gonna do on defense boys? I have no idea But we're going to fight base first play cuz you know, I'm that guy. Yep. Oh, yeah we get 15 yards nice, dude. I really feel like I just can't blitz. I feel like the blitz is just so obnoxiously slow in this mode Bro, you can't stop to run it off unless you pinch your line. You can't like it. Just too much Command God my team is so God slow, man. Oh my god I just should have never did that I just put I put the line back in a deep blue just to cover But like it just didn't it wasn't it. It's just I should have kept them on a running back But it was fourth. It was dirt and gold from like the 15. I didn't want to slot streak up the middle Just we just need to get there and tackle like it's he dumped it off and let's just Come on man on man So you got do against two men under you got to make them put a spy dude. It's just a numbers game, man It's really just a number. It's really just a numbers game, you know, you just want to play two men under He needs two safeties. He needs everybody manned up and he needs a lurk. You got to give up something, you know I mean, dude, how can we not get to the linebacker, bro? God the run is just actual dog This guy's just a mastermind on defense.  So glad I did That's great. Once again great blocking from the boys in the check great blocking Let's go Mixing man god fighting through everything worse blocking ever somehow we still get an end zone Come on. This the guy Richie Richie lost to by 80 Our 301 he gets the ball a half I don't I can't move the ball quickly on this this defense, you know I got my one free touchdown on the first drive But I'm gonna be hard for me to move the ball quickly so to thinking I can score again This half is just not realistic. I mean you could get over there for me kid Like it would be awesome if you did that I mean our inability to stop the run dude. It's just like hurting us right now. It really is Dog like I need alignment or something here like come on man dog Miserable d-line right now just absolutely miserable like I just never like this is inexcusable right now What's going on? Enough people over there for your boys or what like I mean how many people do I got to put over there to guard that you know Just moron to look at the time on a clock like Absolutely perfect timing right like actually absolutely perfect timing right like perfect timing Oh, I only have one more second to throw before the clock runs out. Oh, here you go get wide open We win this game.  Holy I Kind of played that goal line possession to where like the time was gonna be against them You know, he was only gonna have eight seconds or so to throw So I kind of man. I almost dropped everybody, you know, what does Wendy's do better McDonald's chicken? I rest my case Don't why the f*** is this Stevens was that something that terrible?  I really can't greenlaw. You got to play at the end. Sorry You're growing up against a dude. That's a star, but there we go good old zone man. Just amazing I'm cool with that pass. Good work boys. Good work. This is bad. This is going around every day My god, I was bad defense man. I don't know like just my flat zone. Maybe there were still hard flats I really don't know it feels so helpless. We're not getting a lick of pressure. We're not covering anybody This is just feels helpless man. It really does It's just I got a when he motions out. I got a flip my user. I got a just I got a pop different defense right there I was bad Now he's gonna blitz me is that what we're going to we're going to blitz games No, he would never do that you can't scream dude.  I mean this is just Fumble I we're just getting shetted so fast. It's like just AIDS man.   It's just it's not much so these dudes aren't 99 speed and it's just my red zone defense. This isn't I like my red zone defense is not good enough So far in this mode God try to throw.    My red zone defense has just not been good, dude. It just has not been good Though there's just no way McKinney. There's no way you like come on man.   Absolutely. Just like just I just can't cover in anybody. There's everybody's just wide the open dude absolute AIDS We haven't covered a soul. We haven't pressed anybody.    Awesome.  We won because of quarterback fumble on instant sheds with our five hundred dollar CFM team, dude So cool, man.  I need defense Defense in this mode is actual impossible This sucks, dude. I mean fire move got pressed like dude Yeah, now now we're passing the ball look out. Oh my god. I swear to God We're up ten now. It's time to air it out. Yo, get me the dog.  Oh my god.  Now it's time. I'm up ten.  I'm playing Richie Rich now big man is here a minute ago.  Yeah, he needs them. Definitely.  I can't even play zone like you just chucked the my team's gonna get better Yeah, I should just pay five hundred dollars to get my team better, bro. That'd be awesome Let's just pay all my money. I can play two man under the entire game not a single adjustment not a spy Not a yellow zone not but two man under Not a yellow zone not a purple zone not a spy who man under welcome to the show boys.  I swear to God, dude get these kids and he's so happy Oh, he's just in my chat happy and making me money. Oh, he's so happy I mean the instant shed is just just aids man. It really is I just this is just just giving me I like bro like This is just pissing me off, dude Good pass rush boys. I appreciate you guys getting after the quarterback there Good pass rush boys. I appreciate the pass rush. We're getting right now. I really appreciate it man. I Really love the pass rush we're getting right now. This is just miserable man.  What is this? How are we just getting Like look at my d-line man fourth and ten. Let me tell you something man our boy fire move Fourth and ten I drew it up for fire move here boys. So our boy right here. You guys ready? I had a chance to do a corner out there, huh boys Great double team EA. Just love 68.  I can try to score. It's CFM. I need my points No, dude, you don't do that. Definitely seemed like a 30-point game definitely definitely did you know? Meanwhile, I have nobody that gets open. They have everybody that gets open I have nobody to blocks like what are we doing? I did y'all what do you guys watch this?


here from Dragach. Garonni, you know how long playoff series can be. This is just game one, but how much does this one stay? A lot. We've been up three points, what? 35, 40 seconds left of the game. We said no threes. Brown get a easy layup, and then we went down the floor. And they play really good defense and that possession. We still had a chance to get a stop, but unfortunately we didn't.  Let me just be aware and do a spin and lay it up with Oster. What's your big takeaway from this one and how you guys perform overall?  Definitely the first half we had about, I think so, 13 turnovers. On the second half we did much better job, only four. But, you know, we need to stop fouling. I feel like we're in bonus too quick and it's tough to play defense like that. You put him on the line. But, you know, at the end of the day, we had our chances.  That, you know, the first half we still had our chances. And unfortunately we didn't convert so. In your mind, I mean, despite, do you look at it as, you know, when you spot a team that's good, a 15-point lead and you turn the ball over, you get beat on the boards, that we've kind of given this away in the big picture? Or do you look at this, hey, this came down to, you know, one brilliant play by Tatum or intelligent play in the end? Ma'am, I look at it as a boat. First of all, you know, we just make silly mistakes. You know, the one that is, we had a ball under our basket and nobody was there too. You know, those are easy mistakes. There's like, you know, when kids when they're playing. So we just have to take care of those easy ones. But then, you know, like I said before, we had our chances.  And unfortunately we didn't.  Did you get the sense watching Kyrie, that all the hostility that he was dealing from the crowd will focus him in, especially in the second half? Yeah, you know, Kyrie has been amazing. He was carrying us this game. You know, he did his job, but we have to help him. You know, it's not individual sport. We have to play as a group, you know, especially defensively help each other.   Corey, you talked about the fouling. I mean, how do you correct that in such a short period of time? I mean, no reaching, you know, be more solid in defense and try to be between the guy in the basket. I know it sounds easy when you say it, but, you know, on the floor, you just have to be more focused, you know. Like I said, you know, when the guys are driving, we have to be there. The weak side needs to help and it's a team effort. It's not only one-on-one. You cannot leave the guys on the island, especially in this league. So, so good at, you know, creating in their own shot, so it has to be a team defense. And with that run at the beginning of the second half that they made, I think they went on a 15-2 run, just what do you think led to them being able to do that? Was that in a third quarter or the, yeah, I mean, like I said, we were just not focused. You know, we came out with, you know, our energy was not right.  But I feel like, you know, we did respond to, we came back, you know, even took a lead in the fourth quarter, but unfortunately that was not enough. Goran Dragich there was still waiting on Kevin.


Here we go. Here we go. Here we go. Here we go. Here we go. Initializing search protocol. Find the target as soon as possible. Did it. Did it. Did it. Sheesh! Oh, this game is so good. Where did they go? I hate it. I hate it. I hate it. Fuckers! Tick! Spider-Man, innit? No, that's Batman. Ah, sick! What if I'm gonna fall on my head? New action.  Fuck! Like a space station. I wanna go up there.  What is this place? Did I just hit a tree? I don't know why I didn't see that. I got a blodish place up. That's what I'm gonna do. I got a blodish place up. Let's burn! Gotta get out of here. Gotta get out!  And not fall off. Not fall off.  I'm gonna cross between. GTA. My eyes.  See what we can do with this bad boy. See where we can go. We can torture some animals. Oh, there's plenty of cows. Look at that. That's your crazy thingies as well. I can unlock more taverns. So I believe I can have up to like three or four.  How sick is that? I can release the taverns as well. I'm first in a van. Let's see how far it can fall. See if I can fall at the same speed with it. It's nice. I'm just trying to get my dinner.  It's pulling me in a bit. There we go. I mean, you never thought it was just sick. Sick in the end.


Hey, everybody, welcome back to another episode of Movie Feuds. Apparently, there's another Adam Sandler movie coming out. Of course there is. It's been a week. Which makes us think to ourselves...   That's to beg the question, how has Adam Sandler really ruined our lives? You know, to answer that question, we're going to give you the top worst things Adam Sandler's ever done. There's five of them. Oh, or six. We'll see where it goes. You know, I consider myself quite the carnivore of 80s rock music. I think you mean kind of sewer, but whatever. Let's get through this. I think it's the same. But unfortunately, anytime I hear sticks or R.E.O., there's a certain stink to it. Because it's been in the shitty Adam Sandler movie. Which brings us to the five spot. Adam Sandler ruins good music by making shitty movies and putting them in it. Spot. Clips? The next thing Adam Sandler ruined for us was our friendships, really. I agree with that. Because after the waterboy came out, everybody and their dog were saying things like, You can do it. You can't do it. Stop doing it. You should have never done it. Don't do it. It's done. When I sit down with a Nest tea and a popcorn on a Sunday afternoon and pop on a flick, and I see that Happy Madison logo come up, I immediately try to choke myself out. Because that's the most logical thing in my mind, rather than turning off the TV or changing the channel. He doesn't even have to beat in the movies to make them horrible.  He produced Paul Blart Mulkop. He's done Zookeeper.   To piggyback off those crappy movies, we get the number two spot. And that's careers Adam Sandler has ruined. We're talking Rob Schneider, who at one point was good as a supporting actor. Now it's just, now it just gets movies like The Animal. And we have Kevin James, who's a zookeeper. We get Chris Rock, who used to be funny.  Well, that brings us to our number one worst thing Adam Sandler's ever done to us. And really, it's lied to us. Slide right to our face. In the mid-90s, when, you know, a decade downed by Jim Carrey comedies, we saw what Billy Madison first, and then immediately followed up with Happy Gilmore. Awesome movies. Very promising. We see this budding star from Saturday Night Live, but then a downward spiral. We see this too. That's true. The guy that gave us Gay Robot and the Hanukkah song now gives us Jack and Jill. And Crazy Nights. Freakin' Don't Mess with a Zohan. That's just the tip of the Sandler Berg. This is almost a public service announcement at this point. A plea to Adam Sandler, if you will. Just stop. You know what I think it is? I think it's you, America. It is. You need to stop seeing his movies. I don't see Adam Sandler movies. I refuse. Do yourself a favor. What is it? Stop seeing him. I think Jack and Jill made $74 million. That's what it costs to make the movie. We're kind of heading in the right direction, because at least it wasn't hugely profitable right away, but it's going to make money. That's a problem. And now he's going to drink down Andy Sandberg, which is Saturday Night Live. Misty, buddy. Let's start off fun. Now I'm just kind of depressed because I had to relive some Sandler things. Maybe you have a moment that you'd like to share, a way that Sandler has ruined your life, or loved ones. Leave us a comment on Facebook or on YouTube. Tell us how Adam Sandler has ruined your life.  Or maybe you like Sandler and you want to defend him. By all means, go ahead. And we'll ridicule you for it. That's right. More than just reviews. This is movie feuds. I don't remember completing each other's sentences. That's cute. Smell it, smell it. Now take it. That's for you. You can do it. You can't do it. Don't do it. You should stop doing it. You should have never done it to begin with. It's done. It's done. Yeah. Worse things Adam Sandler's ever done. There's five of them. Or six. I don't know. We'll see where it goes. Number five.


Well We were wrong about merch by Amazon No, we weren't You're right.  It was Christmas time and all that So it's just funny that it blew out like that and I want to look at the numbers and discuss but I Still merch by Amazon is still honestly.  Thank you I'm done Playing with these business models trying to make the $300 months extra.  What do you mean? It's funny to even say the fact that I even have to say that out loud It can't it's just makes me feel silly. What? Yeah, let's go to the computer.  It was pretty unbelievable Doesn't change the fact about how I really feel about it because you'll see what January is like so far It's just because of Christmas, but yeah, let's go into the computer and look at it. Yeah We're now inside my computer and firstly I want to look at your tricks to get into the computer Yeah, that works. All right. First thing I want to look at is Last month December 2018.  Do you have any $1,440? Nice Like I'm really I can't believe that happened I mean that more than pays for your rent of this villa mansion in Bali. Bro. How ridiculous is that? Bro, even after taxes it's still pays for it. That's so that's so crazy.  Why would anyone have a job when I could do this? Yeah, and But now it sounds like you're promoting first man. I know but yeah That's just online business in general And then why would you live in like the US or wherever the US is nice and cool and awesome and everything But in Bali one-tenth the price of everything for this. Yeah Showing pictures of the place we live in this. Yeah, like not one-tenth the price People are gonna call me out like one-fourth literally one-fourth. Okay, maybe one-fourth, but look $1,400. I Literally did not do anything. I didn't do anything But can I say it just happened on its own that last month the month before I need to recast a song It's probably in the $500 a $600 range about that 1,400 passive you didn't do anything to earn it, which is awesome. It's amazing But we're jaded completely jaded by that by which part by audiobooks Yeah, so like 1,400 passive like in audiobooks like all right. It's cute. I mean that's good. No, that's great So because of audiobooks and like I've been like 10k passive, but yeah, I know I know it's a penny I'm not penny. It's a fraction. Yeah compared to 1,400 passive Yeah, it does not take anything away that that's really awesome But when we get 3.7 or a third month with audiobooks, yeah, I'll link the description to oh wait now the mini course The mini course still didn't any course for now. No, I'm not watching this in the future It's gonna be a webinar Can people know that they can know that we're making a webinar and I made a webinar. Yeah, so it's gonna be epic Yeah, okay.   That's true. That's true. That's true. That's true. That's true I agree.  I feel you so let's talk quick about Merchbams. I feel you Let's see what you guys think is it worth it for me to show you guys that yeah But the thing with Merchbams on is I mean First of all we want to be millionaires multi-millionaires. Yeah, whatever, okay? I understand that not everyone wants to be that okay Not everyone wants to be that and that's obviously perfectly. Okay 90 percent. Can I say 90 I think Most people don't want to be that They don't want to do it. They don't want to do it. Okay fair. They want it by manifesting it But they don't want to put in the work. We want to put in the work so but Like which online business you choose to do what you choose to do every day all depends on What you want in life like your goals from online business and everything so like if you want to build Five eight ten K and passive income a month like actually I've done it. We're doing it again You'd go into something like publishing specifically audio book publishing but publishing in general amazing Passive income source like that or if you want to go become a millionaire you do this other shit the stuff that we're kind of things That we're working on now but if you want to Make a thousand bucks a month and if you want to really struggle doing it if you want to if you want to Really struggle to get to a thousand bucks a month. Merch by Amazon is for you Merch by Amazon is for you For you So yeah, so I mean that that's the thing if you want to struggle and like create cute designs and like always be Frustrated about your income and not understand why it isn't working Merch by Amazon is for you Hey, so who are we to say you shouldn't do Merch by Amazon. You're right And all about your goals if you want to have a tough time and you enjoy being frustrated, but it also has to Do you enjoy it? So first of all, what's the point of what you're doing? What's the point of Merch by Amazon is it to make money then there's other better ways You just don't know about them. So that's all different topic. Of course publishing is our preferred way I think this is on the totem pole of like ways to make money online Merch by Amazon is like It's in hell I get maybe ten ways better But no, but the passiveness is real right passiveness is that's as good as any if they're actually making sales So we'll give it that but $2 a month passively Not making passive income doesn't mean anything you can't put in your Twitter bio passive income Money making machine. Yeah, well, so about that now. I want to look at Passive December sales. That's why I did so well By the way, my previous house $930. Mm-hmm So you kept out working on but you're always like the eight seven to nine hundred dollar range Yeah, yeah, and then I stopped working on and then this happened, but we know why So let's look at June so far. I want to call out some viewers that are like they're literally sitting in their chairs around So we should acknowledge them afterwards, yeah, we should but I was looking at the month so far We are five days into the month and it's January 5th right now Fifty three dollars. That's ten bucks today that comes out to three hundred bucks a month So we're gonna go back down to the three hundred Obviously, that's nice.  I treat myself to a really nice dinner for that Yeah, one really nice dinner. That's a really nice there.  Yeah, but it's just real I know it's real. It's just real like I really want to help people don't waste your time with my Amazon So was it something else you want to say? Just the people who are watching this video. They're But I make seven hundred dollars a month I haven't touched it in two weeks And I only spend 40 hours a day designing t-shirts I Don't even have to pay for software to design my t-shirts It's all free zero expenses Okay, I feel like people get the fucking point Yeah Let's but do let me know if I should make another video just releasing all on signs because I don't care bottom line Merceway Amazon sucks I cannot be convinced otherwise and again, there's different mindset for us We have we're just our mindsets are different places compared to other people who are making who want to just design It's a my t-shirt. It's a nice thing. So it's okay. We're P. O.  We have to blow this out red. We have to blur it out