Can you hear me fine? Yes, sounds like it. Okay Well, it's been a long day for you guys. It's been a long day for me Flying very early this morning from from Paris, but it's good to be here. I hope you have some energy left It's a it's a longer session And so we will have plenty of time and I suggest if you have any questions during the session Please ask your questions. Please raise your hand and we have a microphone somewhere That you can grab and ask your question, okay? I'd rather have a more interactive session than just wait until the end and answer questions on stuff I was talking about 34 minutes ago. Okay, so please raise your hand and ask all your questions so my name is Julian or Julian or Julian or whatever you want to to use that's fine. I'm a tech evangelist For a ws I'm based in the Paris office some sometimes because most of the time I'm really traveling and And talking to developers and like today and that's that's fine I've been with a ws for about a year and a half and before that I was I've spent the last 10 years or so as a CTO and VP engineering in web startups in Paris. Okay So indeed today we're going to talk about artificial intelligence and I'm going to take you on that long journey that started in in the 50s and If everything goes well, we'll end up running some stuff on my Raspberry Pi robot, which is Waiting in the shadow To exterminate us all maybe who knows and and we'll see what we can do with this. Okay? So I'll start with a very quick introduction of AI and and why it's been mostly frustrating so far and then we're going to talk about Amazon AI Why what do we do at Amazon and AWS and Recently fairly recently we've had some some new stuff coming out And then I'm going to talk about and that the most of the presentation actually will focus on An Apache project called MX net Who has already heard of MX net? All right. Well, okay I'm progressing usually it's zero people which is why I'm talking about it So MX net is is a deep learning library, which is extremely developer friendly It's really designed for quick experimentation By developers and by non experts right so I always say you don't have to have a PhD to use MX net and that's fine. That's exactly what we want to do So we'll talk about MX net for a minute, you know the high-level features and so on and then of course we'll go into some demos Using Python code code. What a surprise, right? I can show you some other code if you want, but you're gonna throw stuff at me, right? So it has to be Python today And then of course, I'll point you to some more tools and some more resources to get you started So the story so far. Well, that's it, right?  All right Okay, so you make thank you for make me making me feel very old Yeah, I keep thinking some people in the room are older than me But as time goes by this becomes false and you know all the time So this obviously is from the Stanley Kubrick movie 2001 a space Odyssey if you haven't seen it you have to see this it's a masterpiece and it's the first visual representation of artificial intelligence it came out in 1968 or 1969 and This computer is inside a spaceship and it's really running the ship and the astronauts You know, you even wonder why you have astronauts in there Which is probably why the computer decides to kill them eventually So, you know the end, but you should still see the movie and I guess a lot of geeks like me and computer scientists and researchers have been obsessed with with this You know when when we first saw the movie. This is what we're trying to build right? This is Ultimate artificial intelligence that can understand natural language that can speak That can handle very different and very complex tasks, you know imagine driving a spaceship, you know, how complicated is that? so this is basically what people have been trying to conceive and build over the years and well, have they succeeded and Actually in 2001 The real year 2001 A famous computer scientist called Marvin Minsky is one of the fathers of artificial intelligence. He founded the the AI lab at the MIT and did plenty of other things and Actually was an advisor to Kubrick on the movie, right? So he worked with Kubrick in 68 to design what hell would look like, right? So pretty funny and so in 2001 Minsky wrote a paper said it's 2001. Where is hell? and well, obviously nowhere and It gave a number of reasons why artificial intelligence had not made a lot of progress in 40 or 50 years, right? And he thought we were very still very very far away from having how and I think it's a paradox because in the mid 2000s Machine learning started to explode right and now today everybody who's doing machine learning in the room? See all right, okay. Thank you Everybody right all right at least everybody has machine learning on their resume and that's fair enough, right? I have it So It's a commodity. It's easy to do machine learning You can do you can get some open source libraries. You can grab in, you know, the psychic tools in Python and build Machine learning models in just a few lines of code. You can use cloud-based services. You can use, you know, you have a wide choice. So You know, why did machine learning become so successful? starting in, you know, 2000 Until you know 2010 and now even more so why did this make a lot of progress? And why did AI not make a lot of progress in the same years, right? Well, it's because as you know in machine learning It's all about the features, right? So you it's fairly easy to build a prediction model Provided that you have clear features and actually most of the work of data science is to find what features are useful in the data set How to engineer them how to prepare them so that they can deliver a nice efficient working model Okay, so let's take an example if you have a web log Apache log or something similar and you want to use those logs to predict user activity You know, it could be predict what link they're going to click on but add they're going to click on etc etc typical activities then All the features are pretty much available, right? You just look at what the log has and you know the time and date and and URL and user agent and and Blah blah blah blah and probably 50 more and You just have to figure out which ones a user ID obviously and you have to figure out which ones are the ones that are the most relevant for your machine learning model, okay, and you go and tweak them and combine them and Twist them into form until you have a working model now. Let's take a different problem. Okay Suppose I take a picture of this room, you know, a thousand pixels by a thousand pixels And I want to know who's in the room or what's in the room or is that even a room? I Want to know what the picture is? Okay, so it's a million pixels and if it's a color picture, it's likely that it's actually three million pixels, right? red green and blue So does this mean I have three million features? should I Take those three million features, you know flatten them and and and send them into a prediction model would that work Probably not right. I'm sure some some people have tried but you know, that's not really working And if you think about it, does it even make sense, right? Is every single pixel in this picture? a Useful information right look at your this the seat you're sitting on I mean, it's all gray and it's all the same color So do I need all those individual pixels to figure out? This is a seat and it's gray Again, probably not right common sense tells me no I don't okay But that's the difficulty in in building those smart applications right common sense Human common sense tells us the answer immediately if we brought a five-year-old kid a five-year-old kid in this room and And asked okay, what do you see? He said well I see people sitting in the room So it looks like a classroom maybe right and If you show animal pictures to that kid and say okay, is that is that a cat? Is that a tiger? Is this a dog? He would know instantly Okay, but if you ask that kid, okay How do you know it's a how do you know it's a lion and how do you know this is a cat, right? Then it becomes more complicated Okay, and he would give you some answers right? but how do you fit that into Data that a computer can understand and that's the number one problem with deep learning right and this is the problem that Deep learning is actually trying to solve It's trying to solve It's trying to teach computers to understand Informal things right things that you and me know Pretty much, you know from four-year-olds From being four-year-olds and older, but it's impossible to teach a computer to do this Okay, so machine if you try to do the machine learning way it doesn't work Okay, because there are too many features There's just too much information and you cannot feed all this information to a machine learning model and get a decent result So of course the answers to this is neural networks, and they're not new at all. They've been around for Decades the early work even goes back to the late 40s but the The first major Applications came in the 50s right so it's literally 60 years old technology right 60 years old and And What is the neural network actually well? People have Written books about this I've spent their life Explaining this so I'll keep it shorter and simple Basically a neural network is a universal approximation machine It's the name of a theorem that says that if a network is large enough, and if you give it enough data It's gonna learn anything perfectly, right That's it so it's a learning machine It's a learning machine You design it you show some data over and over and over again, and it learns perfectly how to predict a given output from a set of given inputs it can predict absolutely anything and You know magically you don't have to understand exactly what happens in there, which is nice But okay Thematically the gray they're great theoretically they're great for limited applications. They were great, but You know until very recently they didn't really work, right? They didn't really work and if some of you the older ones like me Where in the University? Let's say in the 90s and you studied probably you either you know a few hours on neural networks and AI I'm sure your teacher told you something like well Yes, okay artificial intelligence is really cool neural networks are really cool You can do all kinds of crazy stuff on paper but you know Outside of the lab they're pretty much useless because we cannot solve bigger problems with those so it's all about scale scale scale so That's it. That's the reason why they didn't work That's the reason why in the 60s and the 70s and beyond they just stayed in the lab And they were cool stuff to play with but there were no industry applications. No real-life applications of these because Data was just not available Okay, and remember I said you need lots of data to train and Computing power was not available either right so tough luck But it changed right It's changed for three reasons the first one is Data sets are everywhere right digital data is literally everywhere. Okay? audio text Pictures everything Some public data sets are available on the internet large ones. You can grab them. You can mind them You can go on Kaggle. You can do machine learning competitions deep learning competition. So data is just everywhere So everybody now can grab data and start training and start building apps the second thing is computing power is Well, it's always a problem, isn't it? It's less of a problem than it used to be right because now we have GPUs and in the mid 2000s and later on research teams found that Actually GPUs could be used for something smarter than playing 3d games, right? So instead of using all that fantastic power to build 3d games and shoot each other Well, we could you know do some actual scientific work and you know, I guess that's the good thing And so now GPUs are everywhere They're fairly cheap and and they deliver massive amounts of computing power and the third thing that helped deep learning explode is The elasticity and the skill of the skill ability provided by clouds okay, because Just like everything else Why would you buy 50? fancy GPUs to train for you know, a couple of hours a week and Have them do nothing the rest of the time when you can just go to the cloud and grab a few GPUs for a few hours Train your model and pay for those few hours and release them, right? So the elasticity the pay as you go, you know everything that you know for cloud Also applies there right compute storage, etc. It's all there grab it use it Release it and pay exactly what you have to pay and nothing else so deep learning exploded right but let's look at a concrete application every year there's a competition called Is ILS VRC Among you know research teams across the world and what they do is they take that in image net data set Which is a very large data set Hold which is composed of images With a single thing in them so it's either animals or objects or plants no humans and They have thousands of categories and they have to predict the right category for each image, right? That's the game So actually they can predict five categories for each image And if the correct category is in the top five then it's considered a win, okay? And so they've done this for years and years and years Here's an example These are real images from the data set so who thinks These dogs are so it's not the same dog, but are they From the same breed who thinks they're not the same breed Do we have any Eskimos or Norwegians in the room right Norwegians are usually pretty good at this Yeah, and who has no idea Well, personally, I have no idea and I think if you gave me 15 minutes. I would still have no idea Some some things tells me some things tell me it's the same some things tell me it's not the same, right? So for the record, it's not the same breed Okay, but How would you know right? How would you know unless you're real your real dog expert? And you could actually explain to me that oh see the difference here and here on here Okay, and then I show you different breed and you're not an expert of those dogs and you don't know, right? So that's that informal knowledge. We need to fit into the computers So they've been playing this game with dogs and plenty of other categories for years and these are the results So they started in 2010 and the blue line is The blue line is the error rate. Okay, so it goes from 28 to 25 to 16 to 11 down to 3 percent last year Okay, only 3% error and The red bar is how deep how many layers? The neural network that one was okay So in the first couple of years, it was just one layer and then it went up as you can see 8 and 19 and 22 up to that Crazy number of 269 layers Okay, so now the question is what do you think this the score would be for humans? Right a normal humans If I gave you the image net data set all right and lots of coffee and as I asked you to score It's it's millions of images. So it would take a while, but okay theoretically you could do it with lots of coffee What would be your average error? I have no idea. Well, I guess our brains are much more than 269 layers Okay, so but still our brain is different So what would be the what would be the score? Who says Less than five or less than three Who thinks humans would actually beat the machine? Who says more than 10 percent? Okay And between five and ten Okay, so so the answer is actually five 5.1 if you want to be exact Okay But again, it's theoretical because if I give you maybe 50 images You would do this if I gave you a thousand images Maybe not so much if I gave you a million images you would never get to the end, right So the computer can do it faster longer Is never tired And you know, it will give the same answer all the time So what this means is actually Deep running models and computers are now better at recognizing stuff than us right with the With I would say Given the condition that they actually have been trained on it, of course If you show them something they've never seen, right, they won't know and maybe we will Because we're smarter, right But still, um, it's an impressive number and I'm sure it will keep going down So This is just one example. There are many more applications of deep learning and we'll see a few more as we go Um, but now let's try to talk about, um, how we how you can actually oh, yeah, please you have a question What does it mean a layer? Means this And the top in the top right corner. So a layer is A set of neurons That are connecting to the connected to the previous layer and to the next layer And and they all work in parallel to do some computation, right So at the minimum you will have an input layer Okay, which will be your input data. So let's say my pixels, right? So let's go back to my million pixel example. So I would have one million input neurons Okay, each of them with the input with the picks with one pixel value And the output layer will be probably The number of categories I have so let's have a thousand different categories Okay, so I would have a thousand neurons in the output layer And I would just want one to be activated for a given image, right and in the middle I've got What we call hidden layers, which are just additional layer of neurons that Do their magic, right that just extract features. We'll see some examples extract features from the input layer and gradually Learn how to Activate the correct output neuron for a given input Right So there are different structures in this example. Here's is what we call a fully connected Network So each neuron is connected to all the inputs and all the outputs of the previous and next layer Okay But there are different architectures, right and now you can you start to understand why it's so heavy From a computation point of view, right because it's you know, it's n1 times n2 times n3 times So it's it's a lot of connections and each of them has to be Optimized and computed Yeah, yeah training is we'll we'll do some live training on on a smaller dataset, right? You'll see you'll see that Okay, so now let's talk about what we do at amazon so Actually amazon has been doing lots of ai for i want to say forever. It's probably not quite true, but Feels right amazon was started in 95 And as a bookshop as you know And if you go to the internet and look for screenshots of the early website very early on you had Recommendation right and then you had per content personalization, etc. So very early on um You know they felt they had to have that Smart feeling to the website right that custom experience to the website and then as time went by Amazon started to use ai for their for the what we call the fulfillment centers So where the where the goods are actually stored and where they're shipped from you may have seen Those videos on youtube of the the amazon robots that pick up the shelves and and move the shelves To to the humans so that they can pick the objects and prepare your order, right? If you haven't seen this you you should take a look Just look for amazon robotics on On youtube and today we have more than 40 000 robots live Every single minute in all of our fulfillment centers Just moving around and autonomously and moving stuff So that you know we can all get our orders in time Um, of course there are tons of ai and machine learning on the website If all of us went to the same web page on amazon We would not see the same thing for sure right we would see different products different layouts different everything actually And i'm sure you've seen this Although i don't think it's available in italy But it's available in the uk and in in germany and in the us so hopefully it's And not in france Hopefully soon So the amazon echo family of of devices the personal assistants And you can just talk to them and order a taxi order a pizza ask for the news ask for weather information Every single day we have new new skills as we call them that come out for for the echo devices And it's it's evidently all based on deep running technology natural language processing Text to speech etc right so that's A consumer product that's i would say the visible side Of all the work that amazon is doing on ai right But there's of course we're developers so we want to build stuff and there's a There's a full stack of AI and machine learning solutions and services that are available in aws Right starting from of course the infrastructure the C the instances So obviously we have cpu instances. We have also gpu instances. I'll show you. I'll show you one in a minute We'll do some training On top of this We could run Your favorite deep learning libraries so today i'm going to use mx net But you could use tensorflow or keras or or something else Then if you want to actually Go deeper and and really do You know build your custom algorithms and your custom applications You could use Our emr service, which is the basically a managed service for the hadoop ecosystem To do spark and and and all the other hadoop friends You could do You could do amazon machine learning etc etc So we have a full set of services that allow you to build smarter applications But you need to be an expert right and although we all Have machine learning and And soon deep learning on our resumes Right not everyone is an expert okay, so We thought and you know our customers asked us to do that That it might be interesting to build some higher level services Right, which are just an api call away And and very simple to use and yet able to do very complex things And these are the three services that you see on top Lex poly and recognition and i'm going to talk about those for a minute and then show them to you okay So the first one is poly Well, it's easiest to explain poly is text to speech right so it's just one api call select a language select a voice and And you get the sound file in real time With a human sounding voice. So today we have 24 languages including italian so we can try that And 48 different voices. Okay, and we'll keep adding more The next service is lex. So lex is a chatbot service. So you can design a conversational interface using text or using again voice And and integrate that in in the AWS platform with Your web app or your mobile app or or on facebook or you know on external channels So pretty cool service if you're into chatbots And the last one is recognition So recognition is image recognition. So it can do object detection and face detection Face comparison etc etc and as you can imagine all these are obviously based on deep learning But all you have to do here is just call an api, right? So let's give it a try okay So of course we could play in the console here and you know recognition and poly. Oh, yeah, we want to try the italian. All right Um, could could have the mic for a second Okay, so that might be a little small. Okay. So here are all the voices that we support, right? And so for italian we have two voices. We have carla and george joel Very italian names. So let's try this Hopefully have some Oh, no, I don't It's coming. Oh, it's on the hgmi All right. That's okay.  Too bad. Okay. All right. Let's do icelandic Okay, all right. Well, we could do this all day, but that's not the point. Thank you. Okay. So as you can see This is really just This is just an api call. So I could do this I could do this on my on my laptop too. We'll do it on the robot afterwards. Okay. I can just show you Okay, so this is local here And let's okay, so that's pauli. Basically, I want to show you recognition now. So that's pauli, right 24 voices The 24 languages 48 voices And And it's extremely fast. So you can either play the sound file and have an interactive thing going or you can save it and use it in your applications So very very easy to use If you want to see what the api looks like after all, let's do this Well, this is it really right That's all there is to it. You select a voice and and the The format which is mp3 here by default and the text That you want to generate and that's it.  You don't have to be a deep running expert to do this Okay, let's take a look at the recognition now so Let's take my favorite image Should I show it to you? Yeah, of course Okay, so it's october fest but i'm sure we have a remini fest and bologna fest and We have the same thing all over the world, right? Okay, so that's my picture. It's a fairly complex picture so now let's Call recognition on that picture and see what happens Oh, and I Oh, sorry I need to copy that image to s3 So that's my bucket name Or could we have the sound on the hdmi? No, maybe not we don't have a tape. Yeah Oh, wow. So I need to go in my max settings. All right. I'm gonna learn something now. Yeah, it says hdmi. So Uh, just try this again Oh, okay. See what you mean Okay, thanks No, I'll be fine actually You can hear it, right? Or not. Do you want the mic? Yeah, let's have the mic. Okay, so Let's uh Let's send that image to recognition and see what happens. Okay, so Pretty immediately. I see some labels and confidence faces have been detected Here are some key words about this picture people person human alcohol beverage drink crowd female girl Okay, so I would say that's a fairly accurate description of that picture, right? And so like I said, we've got labels. We've got confidence scores and Then we find 15 faces, which is the maximum number we can find it's it's a Predetermined limit, right? We stopped it at 15 And for each of them we get some information like gender age range emotion detection and there's additional information on Where where the noses and where the eyes are etc, but I didn't print it out so If I show you my script and you know Highlights the faces that has been found that have been found and as you can see we see 15 faces, right? And we could check that. Okay face to here that lady here Is uh, where is she? Okay, here she is. Okay. She's female. She looks pretty happy Well, she looks to be closer to 23 than 14, but okay You know pretty pretty safe. Oh, by the way, never do this with your girlfriend.  So, uh, because it's way over there And it's it's hidden in this very complex picture And it's interesting to see that the The cartoon face is not picked up because it's not a face because no one has eyes eyes Half the size of their Face at least not where I come from Or or maybe after a very very long evening Abusing substances, but in general case, that's not a human face, right? Okay, so this is recognition Okay Pretty cool But and and you will find all the all the code and and everything again. It's it's on github I can just show you the the riko api. It's It's super easy as well Okay, so this is the one to detect faces, right? Again single api You literally copy the mh2 s3 and and point recognition to it Comparing faces needs a source image and a destination image But as you can see, you know, it's and detecting an image Again is as easy as this Right where the image is how many labels you want and what's the minimum confidence score that you want to to report?  You don't have to be an expert Okay, but there's a problem with this Okay, and yeah, we have tons of customers. Oh, let me let me mention a few Uh, the the washington post Is using poly in their mobile app to uh to read articles, right? So you can you don't have to look at your phone like this. You can just you know Click play and and let uh let the the washington app read The article to you right and you can actually focus on what's outside, which is nice Um, and you know capital one is one of the top 10 banks in the u.s Very large bank and they have a a lex application Um For people to use to have information on their banking details, right? So you can just Instead of going to the bank website and looking at the You know the the detailed report that we never quite understand You can just use a chat bot and say, okay, uh, how much did I spend on the restaurants last month? Right that that kind of thing. So that's very very cool So again, like I said, there is a problem with what I showed you, right? In I would say in the context of uh, you know devices and robots, etc The problem is we need the cloud Right, we need the cloud connection. We need network connectivity And sure I could use recognition and And and everything on my on my robot over there. So maybe it's time to bring the robot now So, okay Here's my friend So it's a it's a raspberry pi robot so So sure I can I can connect to that little guy and I could use I could use poly to do tons of silly things like this Thank you for visiting us today. I hope you'll have a great time Now julian, could you please stop clowning around and get on with the talk? Right, thank you And thank you too far to our friends for not crushing the robot this time Yeah, it's pretty. Yeah, it's a pretty regular event. I keep fixing it, but now it's okay so, um So, yeah, you know, it's it's a raspberry pi. It has a wi-fi key. I can connect to the internet I can use anything I want with this. Okay, it's it's a Linux system But can we expect all the robots of the world and all the uh devices of the world to be uh, always on always connected to To the cloud probably not right. It's it's an unsafe assumption Autonomous cars and stuff, right? You go in the tunnel and then what happens? so, um We need something different, right something that could work and not be cloud-based Um, and this is what I'm going to talk about for for the rest of the presentation It's uh, we can build deep learning applications Um using mx net and embed them on devices like this which are not powerful at all, right? This has a one gigahertz Clock speed and one gig of memory. So it's a very very small device if you compare it to a typical computer or server, right? And and we're going to do local ai on this little fellow here without any cloud connection So if you were to buy mx net first so mx net is for programmers, right? It's it's really like I said It's developer friendly. It supports multiple languages Like of course python c++ javascript matlab And julia and I'm sure I'm forgetting something here It's an apache project. So it's open. It's not controlled by any company aws has committed to supporting this project because we think it's uh, it's the most appropriate and I will explain one in a minute Um, both for a cloud-based application and for smaller devices And in I think in the top 10 mx net committers we have Four people four or five people working for aws right now um It's high performance You know as will you will see even on a small device like this it runs fairly fast And it doesn't require gigabytes and gigabytes of memory, right? And like I said We endorsed it Because for all those reasons And also because in in a wider context It scales very well Right So why is scaling important scaling is not really important at this stage scaling is important is important when you train the model Right So when you actually take those million of images or those terabytes of sound Bites etc and you train the model, okay These these this is where the operations are the heaviest And so you want to be able to use as many gpus as you can to speed up training Okay, and mx net can do this very easily In in the code and it can do it very efficiently When it's running on as you can see here up to 16 gpus with almost linear scaling Which means if you train on 16 gpus, it's pretty much 16 times faster Than when you train on one gpu, right almost perfect linearity And it goes beyond this if you Why 16 because 16 is the largest gpu instance that we have right the largest instance that we have a 16 gpus So in one server, that's the limit But we can have multiple instances and we could go up to 256 gpus so 16 instances with 16 gpus in them And again as we scale on multiples training on multiple servers We see almost linear scalability again, and That's something you will not see in other frameworks, right Most other frameworks Can either Not do gpu at all or they can do maybe one gpu Or maybe maybe if you tweak your code like a maniac you can get it to run on multiple gpus in the same machine And that's just one line of code when you do that in mx net And then training on multiple hosts Then it becomes this becomes a real project if you want to do this with all the libraries for mx net It's almost as simple as sharing ssh keys Across the nodes so so that they can connect to one another and that's about it, right The data set will be split automatically and so on. It's it's really nice So that's one of the reasons also why we like Why we like mx net it's because you know scalability is very important for our customers And thus it's very important for us and we want to make sure we build services that scale to the max Right, so let's do some demos So let's start with something simple. Let's do some training for a second So here I'm going to use a gpu instance to train an image recognition model on the data set Which is called mnist and I guess Most of you or some of you have seen this before mnist. Yeah, it's very popular It's a 70 000 handwritten digits from zero to nine And of course the goal is to show an image and get their the proper result at the end. Okay So let's do this And you can see Where is my instance? Okay So here I'm running on the On a smaller gpu instance. It has only one gpu, but that's more than enough For what I need And I'm running an amazon machine image, which is called a deep learning. Am I which is built by us And you can use it for at no at no cost And the cool thing was this is that it comes pre-installed with everything So whatever framework you use, you know, uh, cafe mx net types of flow, blah, blah, blah Anything else it's already in there So you can just boot up your gpu instance with this image and everything is ready for you to work You don't need to go and install the kuda drivers and the nvidia stuff, which is a little tricky to do. Okay And so here, um, whoops, okay, I wrote Oh mx net, yeah All right So I designed A very simple model Right So it's Okay, it's 30 lines of code, right To do everything so it's like when I say it's developer friendly, it really is it's very high level, right? so you don't have to go and Coming back to your questions earlier You don't have to go into the details of every single neuron and just define layers connect them and that's it Okay, and that's what I'm doing here. So um I've got a series of blog articles on this with every single detail explained So I'm going to go a little faster here because I want to get to the point Um, basically here, um, you uh, you just load the data set, right? So you load those images There's a training set that we use for training and there's a validation set That we use to evaluate the quality of the model just like we do in machine learning. Okay This is the network definition Okay, so an input layer Uh, then um a first Hidden layer fully connected A second hidden with a 128 neurons A second fully connected layer with 64 neurons And then the output layer with 10 neurons and 10 is not a surprise. It's because we have, uh, 10 categories, right From zero to nine. Okay, so we need to figure out what digit this is And that's all it takes, right? That's all it takes to define my network Okay, define the layers Define how they're connected Define how many networks how many neurons are in each layer and that's it So we have multiple types of networks But this is um, this is the simplest one and as you can see it's only six or seven lines of code Okay Then I bind my data to that model. Okay data. I loaded I just say, okay, this is what you're going to train on And now you train Then I'm saving the results. So I'm saving all the weights for all the layers, right because I want to reuse it afterwards And then I use my validation set to measure the accuracy of the of the model Okay So Not a lot of code, right? So let's do this How do I train it just like this? So it's going to load the data And then it's going to run for I think it's 10 epochs. So an epoch is learning The full data set once Okay, so here I'm taking that data set and I'm sending it 10 times Into into my model. Okay batch by batch, but the full set goes 10 times In a row into the network and I can see my uh, I can see my training accuracy going up right And actually if I If I let it run for a little more Let's give it maybe 30 epochs You will see it gets to one Okay, that's that universal approximation theorem I mentioned Okay, so it's gonna it's gonna learn that data set perfectly. It's gonna learn the training set perfectly But then when I take the validation set and I run it Of course, I get a lower score because These are images that the network has never seen before Okay So again, yeah, we'll get to one Okay, so maybe I need 30 32 or 35 epochs, right Okay, so training accuracy almost gets to one and then validation accuracy is 97 Okay And then I could use Um, I could use some um handmade digits that you can see here. So I Did them myself and I could try and run them through the network Right, so I'm gonna load each image And and and load the model that I trained and just run it through there Okay, and do this and see what the scores are Okay, so well So you can see 10 Probabilities, right because obviously we have 10 categories. So they're pretty close to one. They're not perfect But they're pretty close to one. So the first image is a zero and the second and all these are pretty good And well, the nine is not so great. The probability is lower, but we're still okay with the fact that it's a nine Okay, so I could have a better network. I could train for longer. I could improve everything etc etc Okay But okay, that's just that's just a very simple model here Um Now I want to do something more complex, right? I want to be able to do that image net thing I mentioned earlier. I want to do it here I want to Use a pre-trained network, right training on image net takes a while. I cannot do it here um And I want to uh take that model say Train it in the cloud using the cloud scalability save it and then copy it in there and use it locally Okay, and that's what I'm doing here. So let's go back to Let's go back to my robot here So here's the model. I'm talking about it's the inception model. It's 44 megabytes. So it's not huge But it's it's a fairly uh, it's a fairly advanced model. Okay. It's been trained on image net And I'm going to do pretty much the same thing that like you saw here. I'm going to load the model And I'm going to ask the robot to recognize images, right But to make it a little more fun. Actually, I'm going to have the robot take a picture of objects, right using the camera down there and Recognizing that Okay So it's all in python. It's fairly easy to do and we just have to start that server And hopefully it still works Okay, yeah Yeah, the loudspeaker is on So can the thing move or not? Okay, so just to make it a little more even difficult to set up. Okay. I've got this thing here So it's an arduino Which is italian, right? Or something So it's an arduino, uh with a I guess it's a playstation joystick connected to it And here this has nothing to do with deep learning, but it's pretty funny. So why not? And uh, whoops, and I mean it's an iot thing. Okay, so I'm using the iot service of aws to through wi-fi here To send messages back and forth to the cloud. So from from here to the cloud to the robot, etc, etc Okay, so I can drive that thing Can you see it? Yeah So I'm making sure it's not falling off. That's why it's stopping Okay, so let's let's have an object somewhere I'll take my lucky object the one that should work and then if you want we can try something else Hmm, okay. I need to cheat It's not yeah, okay Yeah, you know I keep saying it's it's it's a running.  We're this one is very friendly It's got a twitter page. You can follow him on twitter Okay, so I'm gonna try something else that's probably not gonna work either, but okay fine So have you seen this before? It's the iot button You just click it and it sends an iot message Uh to a ws iot and so this one if it gets through if not, I will fake it That's right We'll send a message to uh to the robot asking it to take a picture. Oh, yes, it is working And telling us what it sees I'm 98% sure that this is a baseball. All right It's 31 centimeters away. Okay. Thanks All right, bring me your objects now. Okay. Yeah. Yeah, I'm I'm I'm up for that. Okay So I click here sends an iot message to a ws iot in in Dublin The robot gets it. So it's back and forth to island. As you can see, it's pretty fast The robot takes a picture The robot takes a picture with the camera and uses the local mx that model to To detect it this button has been giving me trouble. So I should Try something else. Oh, come on. Okay. I can fake it. That's okay. No worries Works only once Just use violence Yeah, see that works I'm 69% sure that this is a water bottle The object is 51 centimeters away Okay, pretty good, right so So, you know, it's all fun and everything and of course we're gonna try those and it's gonna fail Um Because this is a really small object. I don't know. I don't know what the distance should be maybe here So once again, what happens is okay, there's the iot thing going on But and there's poly right the voice comes from poly as you can understand as you can imagine So I really need to hit it right come on work Okay, I'll fake it. I can send I can send a message from here as well And so it takes the picture That's my complex protocol I'm 13% sure that this is a pole The object is 22 centimeters away. What did he see? Oh, yeah Oh lighter is in there. Hey, I get I get five remember I get five categories.  So if you have some other objects Do you have a laptop or something laptops usually work? We can try that and then conclude before they kick me off the stage You want to try? Yeah, I don't I don't have much in here. Oh Okay, we can try this. All right last one, right? Oh man. Yeah, okay fine. That's a tough one Now that's never gonna work Yeah, I'm very dependent on my phone here and it's not working great. Yeah, here we go I'm 67% sure that this is a thimble The object is 20. What? Okay, peel bottle man. It's in there. Come on Hey, I still win All right, they're gonna kick me off the stage So julian one remini zero okay All right, so oh, no, I don't need this. Thanks So I'm getting to the end. I mentioned the deep running am I already Again, I went very fast because there's so much stuff. I wanted to show you today You know keep you hopefully interested You will find all this stuff in detail on my on my medium blog. So just go to medium.com Uh Jules simon well, it's easy to find and and you will find All the tutorials to get started with mx net To do training etc. How to do the raspberry pi thing Etc etc. Okay, so it's all it's all out there All right, there are plenty more resources. There's one. I want to mention I recorded a w s podcast A couple of weeks ago With an introduction to mx net So just look for a w s podcasts mx net. There's only one and it's mine. So You can listen to that and and get some additional information. Okay. So I want to say Thank you Gracias and I'll still there, you know, I cannot do the 24 voices. Thank you very much euro python for having me Thanks for listening and if you have questions


Molo! Unjani! Long time no see! I am Jessaline and it's time to share with you some exciting DevTools updates for this quarter. Let's dive into some debugging, testing, and CSS improvements. There are 3 major improvements in JavaScript debugging. We enhance the locks point marking, redesign the breakpoint UX and we improve your stack trace by allowing you to ignore irrelevant scripts. Once we open the sources panel, let's say we want to lock the value of the page URL. Instead of sprinkling a console.log statement in our code, we can right-click on the line and add a lock point. It works just like the console.log. You can even lock multiple JavaScript expressions. Once you refresh the page, the values are locked with a mark to indicate that it is the lock point. In addition, it also shows you the code location. If you click on it, it will jump directly to the code. Next, let's shift our focus to the newly redesigned breakpoint pane. Did you notice the breakpoints are grouped by file now? When you hover over a file, you can remove all breakpoints at once or you can disable them. If you would like to edit a breakpoint, just click the edit button. Later, you can also change it to other breakpoint types. Next, let's talk about stack trace. For example, when you start debugging on this translate function, you can see a lot of framework generators frames in the stack trace, which could be irrelevant to you. We can improve this by ignoring the irrelevant files in the stack trace. To do that, go to the page tree, right-clicking on any files or directories. For example, the node modules folder and add it to the ignore list. Bola! We can see a cleaner stack trace immediately. Notice those ignore files are grayed out as well. You can view the ignore list's frames by clicking on the view ignore listed option. What about testing? The recorder is on fire. Now, you can use and customize the recorder shortcuts. Discover, view, highlight and copy user flow as code and customize the selector types if you wish. Open the recording in the recorder. You will see that we added a new options to export the user flow to Papyrtil with Lighthouse. This will make performance analysis easier for you. Click on get extensions to explore and install some useful extensions like I did. Here is one cool thing about the extension. You can view the code directly in the recorder. For example, if you select night watch option and hover over on each step, you can highlight the relevant code. To make it easier to record and replay, we added keyboard shortcuts. For example, to copy the script of a step, you can use the command C shortcut. Quite handy, isn't it? Let us know in the comment below. In addition, we added a new pure selector that can work better with Shadow DOM elements. However, you know your user flow better. So, some of these selectors might be irrelevant to you. At the start of your recording, you can choose the selectors you need. In this case, DevTools will only capture CSS selector according to your choice. Next, let's move on to new CSS improvement in DevTools. We added CSS documentation, support and HD color debugging. If you want to find out more about this CSS property, you can hover over it to get a quick overview of its function or click on the link to learn more about the syntax. Apart from that, Chrome now supports HD color. As an example, Lab is a new syntax to define HD color. Open the color picker. A gamut boundary line has been added. We're drawing a line between sRGB and DisplayP3 gamut, making it clear which gamut the selected color is within. To learn more about color, check out the High Definition CSS Color Guide by Adam. One of my favourite CSS preprocessor features is now built into CSS, the nesting styles rules. Without the nesting support, we need to declare every selector and separate them from one another. With nesting selectors, these declarations can be continued and the related styles rules can be grouped within. Currently, you can view nested styles in the styles pane. However, tracing the nesting and its full selector context is not yet supported. Stay tuned for better support in the coming release. Time for a bonus tip. Navigate your performance timeline using the WASD shortcut just like a gamer. In the performance trace, use the W and S key to zoom in and out. Then, use the A and D key to move back and forward. It is quite useful, isn't it? Apart from that, it is worth mentioning that we fixed the start profile and reload experience. The panel now navigate to a blank page first so you can see that the recording begins with a blank trace. That's all for now. Don't forget to check out my blog post for more information. Click on the link below. Thanks for watching. See you next quarter or in my latest Dev2Tips video. Ciao!


This video explains how to include books in your work cited list according to the seventh edition of the MLA Handbook. I'll show you how to format references for print books, including edited books, translated books, what to do with addition and volume numbers, and an individual essay or chapter with a different author than the books editor. For examples of in-text citations, check out our video, Citing Sources in MLA Style, A Basic Introduction. Here, the punctuation is in red for emphasis. In your actual work cited list, all font would be black. Start with the author's last name, followed by their first name. Next, enter the title of the book in italics. Then, enter the city of publication, followed by a colon. There's no need to include the state or province. Then the publisher's name, followed by a comma. And the year of publication, followed by a period. Finally, indicate the medium of publication. Medium refers to the format. For print books, the medium would be print. To find all of the information you need, look at the book's title page at the beginning of the book. Here's the title, addition number, and author's name. At the bottom of the page is the name of the publisher. Leave out words like publisher, press, books, company, or ink. Here's the city of publication. If more than one is listed, just use the city that's listed first. Sometimes the year is listed here, but if not, you can turn the page to find the year it was published. Previous editions are sometimes listed, so go with the most recent. And if it's a library book, the year will also be on the spine label. And here's a hint. Look up that book in the library catalog. All of the information will be listed there as well. What if the book was edited? Add the abbreviation ED period after the editor's name. If a novel has an editor in addition to the author, the author's name gets listed first. After the title, write capital ED period, then the editor's name. When editors' names follow the title, only ever use ED dot, never EDS dot. Also notice that here you don't put the editor's last name first. A book that's been translated is referenced in a similar way. After the title, add TRANS period, followed by the translator's first name and last name. What do you do with addition or volume numbers? For an addition of a book other than the first edition, add the addition after the title and after the name of any editors or translators. What if you need to cite an individual essay or chapter in a book that has a different author than the book's editor? Start with the author of the essay or chapter. Then the title of the essay or chapter, in quotation marks. Then the title of the book in italics, followed by the editor of the book. And remember here the first name goes first. After the publication info, but before the medium, add the page numbers for that essay or chapter. Thanks for watching. For more information about MLA style, watch our other videos or visit the Munn Libraries webpage for our MLA guide and to chat live with library staff.


Hi and welcome to the session, I am Shashi and I am going to help you with the following question. Question says, in a meeting 70% of the member favour and 30% oppose a certain proposal, a member is selected at random and we take x is equal to 0 if he opposed and x is equal to 1 if he is in favour, find mean of variable x and variance of variable x. Let us now start with the solution. Now we are given, we write random variable x is equal to 1 if member favours a certain proposal. We are also given that we take random variable x is equal to 0 if member opposed a certain proposal. Now we will write the probability distribution of random variable x. You know random variable x can assume two values only that is 0 or 1. Now we are given that 70% of the members favour a certain proposal and 30% of the members oppose a certain proposal. And we also know that we take x is equal to 1 when a member favours a proposal and we write x is equal to 0 when a member opposes a proposal. Now when x is equal to 1, px is equal to 70 upon 100. You know we can write 70% as 70 upon 100 which is further equal to 0.70. Similarly when x is equal to 0, px is equal to 30 upon 100. We know 30% can be written as 30 upon 100 which is further equal to 0.3. Now for x is equal to 0 we have px is equal to 0.3. So here we can write 0.3 and for x is equal to 1 we have px is equal to 0.70. So here we can write 0.70. Now we know mean of random variable x is equal to ex which is given by summation of xi pxi where i is equal to 1 to n. Now mean of random variable x is equal to x1 p1 plus x2 p2. So here we can write 0 multiplied by 0.3 plus 1 multiplied by 0.70. Now we know 0 multiplied by 0.3 is equal to 0 only and 1 multiplied by 0.70 is equal to 0.70. So mean of variable x is equal to 0.70. Now we will find variance of variable x is equal to ex square minus ex whole square. Now first of all we will find out ex square is equal to summation of xi square multiplied by pxi where i is equal to 1 to n. So we get ex square is equal to 0 square multiplied by 0.3 plus 1 square multiplied by 0.70. Now square of 0 is equal to 0 only and 0 multiplied by 0.3 is equal to 0. Now square of 1 is equal to 1 and 1 multiplied by 0.70 is equal to 0.70. So we get ex square is equal to 0.70. Now we will find variance of variable x is equal to 0.70 minus square of 0.70. We know value of ex square is equal to 0.70 and value of ex is equal to 0.70. And we have written this square as it is. Now this is further equal to 0.7 minus 0.49. Now subtracting these two terms we get 0.21. So variance of x is equal to 0.21. So we get mean of variable x is equal to 0.7 and variance of x is equal to 0.21. This is our required answer. This completes our session. Hope you understood the solution. Take care and have a nice day.


You need a financial advisor with the freedom to focus on your specific needs. Whatever your goals, Reid Potter can create a game plan tailored to you. Call Reid at 4-3-2-0-7-7-7 at Pikeville, Kentucky to learn more. The Appalachian Center for the Arts is gearing up for the 46th Annual Hewbilly Days and for the first time ever, the Center is hosting a selfie museum. And the best part is only $5 to enter. So the selfie museum came about, this idea came about in October and I've been talking to anybody who will listen to about it. So it's a bunch of different picture stations, basically. And you can come up and take as many pictures as you want. There's tons of backdrops. There's this cool cloud backdrop. There's a flower backdrop and a fun donut wall. There's a Bob Ross painting backdrop. And we've partnered with local businesses and things like that. And so some of the businesses have come and decorated their backdrops. Like our positive project is the one that did the Bob Ross one. We've got Mountain Girl here. So yeah, there's a lot of cool stuff. Not only will the visitors be able to snap unlimited pictures, but they'll also have the chance to win free show tickets by posting their photos on social media with the hashtag app art selfie and tagging the center. And to add more to the festival atmosphere, they've also invited a band you do not want to miss. Laidback Country Picker is going to be here on Saturday night. I believe we start at 7pm and it'll be cash bar. We are dragging in old couches and lawn furniture and anything that we can find we'll push in our seats back. So it is going to be a Laidback Country Picker style concert.  And it's going to be a cool party. The cover charge for the concert is only $10 and there will be a cash bar available. Furthermore, every day of Hibbley Days, the Parkful Farmers Market will be set up in the downstairs lobby giving visitors the opportunity to start their own garden or pick up fresh produce. Reporting from Mountain Top News, I'm Ethan Miller.


Hello everyone, my name is John Hammond and welcome back to another video in order to be taking a look at the WGAL CTF from TriHackMe. So I have this over up on my screen here. This is a free room so you don't need to be subscribed in order to access it. It says simply have fun with this easy box and the only problem is that we have our user flag and root flag. So once our machine spins up, we should just have an IP address to muff around with. I'll clean out some of my notes from a previous video. Looks like that shell super broke so I'll reset this here. And let's make a directory for WGAL CTF. I think that's how you say that. I have no idea. Let's create a simple readme file for this so we can keep track of our notes. I will go ahead and export that IP address here, slap that into my shell. Let's see if I can ping that machine if he's up already for us. Still taking his time. So let's go ahead and build out our tasking things just to have this here. User flag. That should be one and two should be your root flag. So let's burn through this here and see what we got. That machine is still spinning up. So let's take a little bit of time anyway. We can go ahead and make our end map directory to stage that and get that ready. We'll also grab that export IP command and we will start to end map tech SC tech SV tech on end map initial. Let's run that on the IP address and let's kind of hang tight until that machine comes up for us. Maybe it's also just actually not kind of, I don't know, showcasing things. We can just go access the web page itself and see if that is alive. Nothing at the moment. So we'll stand by. We are still connected to our IP address or our VPN. Are we not verify? Okay, it looks like it's good. I'll just hang tight and wait. Stand by. Okay, it looks like he is responding now. So how's that web page look if I were to refresh this guy? Still taking his time. Let's fire off our end map scan regardless. See if he comes up soon or if our end map scan dies as well. Okay, so now this web page seems to load. It's weird to me, because it's an Apache to default page, which we see all the time, but it's weird that some of the notions here aren't like some of the files here aren't filled out. Let's just view the source here. See we have a lot of CSS kind of default stuff. Items are commented out. Jesse, don't forget to udate the web. Is it? Okay, so update the website. Jesse, that's a peculiar name. Let's start some other enumeration on that. Let's go ahead and need to attack each HTTP on that IP address. He's going along. Let's start go buster as well. Oh, is that end map scan done? Didn't find 80 but found SSH and some funky ones. So I guess I'll rerun that. Let's grab this again for this new shell down here. And let's do some go buster. Let's do go buster attack you on that IP address. I'm going to specify my word list for opt directory listing. Let's see if we can figure any of that out. Site map. What the what? Okay, it's worth seeing if that's a thing. Maybe there's some other information that we have not seen before. Site map. That gives me a 301 and redirects me somewhere. So let's see if that page loads unit template.  So this looks like a whole directory. This is a this is a folder thing. Let's actually restart that go buster and see if we can figure out anything else inside of site map. And I'll bump around in here works goes to work HTML services. Okay, so these are all pages. Seem to have anything interesting contact info.  Go buster found images. Good enough. I'm just going to slowly poke through these about UNAP. Is this actually a video? Let's see. Oh, that's a thing. That's a real thing. That's got timestamps and everything. Let's not play that on my YouTube channel. Dorothy Murphy. No one else and Adam Moore. What are you doing, dude? That's not how you drink drinks about. Okay, the page does not change when it's services is still saying that thing shop. HTML contact HTML. Can I be used contact? Maybe we can muff around with that form too. That's an option. CSS JS, automatic backup data. Let's check. Is there a robots dot text file hidden over and any of these sometimes it's supposed to be in the root directory. But it's not always. Is there a dot get directory in there? No, but like, is it BZR or what is it for bizarre? There's some weird stuff in there, fonts. So you're just getting stuff at the moment. It works as grid. Let's look back at our other enumerations because we still have necdo running and map finished. Looks like it found those other options that we have. We can still run Derb because go buster is good to have that, but Derb also has some other worthwhile dictionary files like some of the stuff that they share for Apache. These are really cool. If you go take a look at these, it'll have some other files like that might be more likely on a web server. So they have one common, I think it's called cat. And that's in Derb word lists, common dot text that has some good good stuff. So let's try him. I'll do the same go buster tack you on my p address, but I'll use the word list from common and see if he finds anything else there. Okay, so HDA pack. Oh, let's do that in the site map because we know that we have some stuff in there, but we did see the ht password. We can go take a look at that ht password. I cannot read that. What about ht access? Maybe still no. Okay. Oh, what the what? There is a dot SSH directory, seemingly within site map. So let's try and navigate to that if it loads. Whoa, okay, we have an ID or a save file. So that's a private key. Nice. Sweet. So we could try. We don't need really you need to anymore. Let's just make that ID RSA file. We know that username was Jesse, right? Let me check page. I thought it was Jessica that I misread that earlier. Where to go? Jesse, Jesse, Jesse. So that's the name. Okay, so let's actually move. Let's call that Jesse ID RSA and let's make that 600. So it is an SSH key that SSH would be willing to use. Let me grab. Accidentally nerfed my shell just a moment ago. So SSH tech, I Jesse to Jesse at the IP address. See if that connects. Oh, and it logs me right in. Okay, awesome. So seemingly no flag. Oh, that's a lot of stuff. Find grep flag. It's in documents. Cat that flag out. There we go. Let's slap that in here. There is our user flag. And we could go ahead and SSH copy some stuff over. So scp tech, I Jesse at that. Let's move Lynn P's over into Jesse at let's grab that IP address. This guy in dev SHM, see if that will work for us. Okay, copy it over just fine. So dev SHM, let's try and prevask. I'll run Lynn P's and see if we get any good stuff. Whoa. Okay. I already felt like I saw some potential prevask techniques in there. Stuff in my path is potentially writable. I am pseudo. I'm in the pseudo group. What is in that? Whoa. User Jesse may run the following commands. No password on W get. Okay. Let's go check out GTFO bends and see if we can do things with W get. W get file upload. So it's pseudo. Fetch a remote file via HTTP get. Hmm. Can only file download and file upload. So we could get a root flag, but we could also get a root password. We could set a root password and that might be kind of cool. Let's do that. Let's, let's see. Let's see if we can pull that off. So, okay, you can stop that little Lynn P's. Let's take this et cetera password and let's fake a root password we could use. Let's create a simple new entry or let's modify the root entry and it's that root password right here with our own password that we want to supply. So we could do that with Python. I'll get to a regular shell that's on my host. Let's go ahead and Python import crypt. And I think it's crypt dot crypt and the password that we want to use. So I'll just say please sub as our password and let's copy this string. Yep, yep, yep. Let's paste that in. So let's call this a password file. So let's get back to the victim. Let's try and actually make a copy of et cetera password to et cetera password dot back. And I can't do that. So let's put it in devshm. So we have a place we can write to. So if we could download files, we could host our new bad one or new bad et cetera password that has our custom command or password for root in there. And we could overwrite that so we have our own password set for us because we can sudo w yet. So sudo w yet looks like it works. Let's go spin up a server on our attacker machine. HTTP dot server port 8,000. I believe I am still 10, 8, 9, 1, 1, 12. Yep, yep, yep. Let's get HTTP 10, 8, 9, 1, 1, 12 at port 8,000. Let's grab that password file. And it needs to have tack capital O to where it wants to store it. So let's put it in et cetera password and it wrote it. Okay, so let's check out cat et cetera password on the victim. And now we have our fudged password for root. So we could su to root with please sub as our password and now we're root. So now we have a root shell and we wouldn't need to, we could have just exfiltrated like, okay, grab the flag with getting the file and sending it to our own machine. But I think it's much more fun to actually get a root shell. So that technique just clobbering et cetera password with our own set password for an account will help us do that. Cause we could write to et cetera password which is pretty cool. Let's go ahead now into root and grab that root flag. Cat root flag. Thank you, thank you. And we're done. That's that machine. So pretty cool, super nice, pretty easy. I'd like that W get previsk. I hope that technique is kind of neat for just clobbering that et cetera password entry. Again, the other option is just exfiltrating that flag out, but kind of neat to get that initial access with finding the username and finding that directory in the flag or in the website. So that was very, very cool, I think. Hope you guys enjoyed this video. Hope you guys enjoyed this room. I thought it was kind of neat. But again, just wanted to showcase it to you. If you did like the video, please do press that like button, comment button, type things in and hit enter.  So thanks everybody. I'll see you in the next video. Take care.


Yo, you got it, dude. Oh, you got mad in the night, man. Like you just got it You just got it. We just got it dude. You just got it Boys no money spent it's back Here it is. I wish I could tell you I had crazy upgrades to make but I haven't really played this game This team that much with all the tournaments and all the other I've been doing with the guy squad and everything I probably haven't finished the weekend league in like a month with this team But this weekend we're gonna finish so make sure you guys check it out live on twitch already done about half the game Is what let me show you right now if you guys forgot are you just watching your first no money spent video the actual team I remember I never played any solos. No head-to-head just weekend league Started with silvers and golds and this is where we're at now. I said I haven't finished the weekend league in probably a month But we'll finish this week. I'm glad I took some time off and really let this team get super outdated But let's take a look Herbert you know you know guys know he's at the helm 91 Herbert I actually have past lead elite on him and deep range that I which I mean I don't think does anything good, you know But I have it because I have AP because my players aren't good enough to have the abilities that I want Would you see Herbert right there? Not the full Herbert? I still need this this limited right here That's the full one I need but honest I don't think there's a big difference between this one and the full full limited So I'm cool with 94. What am I at 91 Herbert? He gets the job done I'll tell you with no gunslinger and he's super slow in the game now But here he goes Thurman Thomas. We have it running back. We actually have a strategy We have one strategy card for running back speed. You see him there with 93 speed. He's pretty solid I'll be real man. He's got that that backfield master. He's receiving back. He's tough Thurman He's tough. We are a Dolphins team team. You don't see that too often 25 dolphins I could probably work on getting the 50 but I have that because my two outside receivers are dolphins will fuller and Jalen waddle remember. I don't have any strategy cards for our receivers here. This is my fastest player on my team 94 speed Jalen waddle. He plays outside you so we are that deep out of lead on him 94 speed Jalen waddle then I have 93 speed will fuller not the full limited another limited that I need to go get But like I said, these illimiters are expensive man. They really are that's wild card Will fuller team in a week. That's expensive card But he'll get the job done with 93 speed, but I'll be honest when I look at this team What's the weakness is those two receivers? I'm used to having 99 speed 98 97 right now We want a 94 93 just not getting the job done over the top on the defense nowadays, but we have this guy Who's carried us all year? We're on a retire's jersey and then no money spent Hall of Fame Christian Kirk 92 speed somehow he just you guys watched you guys have watched he's Dominated dominated then we have pits went on this video the games you'll watch here I had regular same rookie premier pits I actually just upgraded to this 89 pits with 90 speed in these videos Remember you're watching you got 88 speed pits out there. All the line is coming together. We got Trash Jason Peters Hall of Fame are probably one of the best left tackles ever So he's a stud, but I have edge protector because I don't really have enough like I would love route tech Well, right on my real team. I use route tech then I use obviously gun slinger, so it's more AP on the quarterback more AP on the virus is I don't have that AP to use here So I use edge protectors give them an extra boost You know, I don't use it on my real team, but I have extra AP on this team. So might as well use a defense You're looking at it boys, this is it and we got Derwin James is a really good card for kind of cheap Justin Simmons good zone safety big body Sean Taylor and Brian Dawkins at linebacker good to see Brian Dawkins has an upgrade Might be able to get this, you know, I actually get them up there some respectable speed at linebacker right now I'm running around with 89 speed Brian Dawkins That's actually worse than I thought it was but that's my linebacker Sean Taylor Brian Dawkins Second they're got Jesse base Jeremy Chinnick corner. Yes, they're safe. He's they do okay in the slot We have Eric row and we have Jason McCordy going along with that Dolphins theme team And there's the D line. We got Vince will fork big Vince.  He's a run-stop in the tackle. That means he gets inside stuff for one So we're gonna throw inside stuff and unpredictable on him.  I'm not even want to try to say his name and then Dante Fowler From the Falcon speed rushers, but I actually had this away away. Do we away? Well, I had him if you guys remember way back when I had his base Silver card playing outside linebacker. This was put him in a game with 91 speed. He's a stud, you know Get used to speed against Randall and these other running scrambling quarterbacks So this is my squad. This is what we're working. We're here boys. This is I mean, I don't know what else more to tell you It's not the best, but I guess it done you guess the job done But this is no money spent this one right? We're gonna finish this week because my team definitely is upgrade I want to be able to play at least have the option to play two weekend These are some weekend like I said like the video leave a comment if you love the no money spent this year I love playing with like low-key cards that you don't always see I know you guys always watch Randall cutting ham People on my tiktok and YouTube I say why does everybody have the same quarterback? I like having a team with different cards, but I'm it is rough with this team right here So let's check it out. Check out these games. These are already beginning a weekend league So I still have tons left to play. Make sure you guys check my live Let me know you're from YouTube. These are the best no money spent games so far this weekend league The fat wolf or bro, he got all these linemen abilities, dude He's gonna go. Oh, he kicked the field. Oh, thank God, bro. Why check way You could just throw that in the middle like that's so bad apex early tonight apex early tonight It's no way I play there's no money spent bro. Oh good luck.  Oh, cut Couldn't move it all right. Yo, I swear to God this game would be so good if everybody to use Justin Herbert as quarterback, bro Hey, I can't move Oh My god, I bumped into my own.  He was going crazy with the moves You're that random kind of bagging Randy Moss, bro.  He got no line.  One of them is gonna get you a touchdown one of them. I hate this game I swear to God.  It's just the easiest pass in my life, bro I had a Russian touchdown. Well, we will catch a pick this drive, dude like cuz Can we get off the field? Oh My god, this is brutal, man.  Like why would you call timeout? Oh, you won't go for the game Respect them right? It's about to be ugly. I got a feeling You got mad in the night man. You just got it. You just got it. We just got it, dude You just got it man. You just got I really lost this game, dude I really love how did we lose this game? Face mask oh one play for all the marbles for all the marbles from the 26-yard line. Oh my god Get in there 34% completion dude 44% completion percentage from my man 13 to 29 Just actually actually amazing Look out like why why do we not line up the right way boys? Do my dudes are getting steamroll, dude. I don't know how to happen, bro I really just like I did indeed spot this man seven.  Cam chancellor was so good right there. Holy All right. All right pits.  This is the defense That's gonna be trouble when they just run when they just run this man coverage. I mean, he's got the tight end I'm gonna do this.  Thank you.   He's definitely just gonna go for the bomb play any Intentional grounding. I mean this is just brutal, bro You should just be banned from playing this game.   Oh, yeah, I'm the only one here I didn't had an in row. Oh, he had nothing alert.    They should ban his account. He should be banned This is worse than buying coins on side kick. Who would have known I'm telling you banned. Oh Gun empty. God. You're Yeah, where's Bo Jackson at now? Yeah, I form wing right back today So bagged. He's so bad. It's scary right now. Come on.   Yeah, we're gonna take all the time in the world. You this is what pisses me off He's as smart as a box of cereal Good blocking boys. Good work Good blocking boys That's why it pisses me off cuz he just lost cuz he rushed one person Yeah, I see you in the hub. You're not going 25 and oh this weekend.  I'm gonna call time. I'll take some more knees. Trust me We won't we're gonna take some knees up out here boys. Trust me as a performance from J Herbo, that's a performance from Justin Herbo. That's a performance right there That's a performance for touchdowns for touchdowns to regular pits, bro This is the team I played with right here boys. Just watch it. Just just taking the team right here It's what we're working with right here boys. That's what we're going on YouTube We are 20 we are a couple subs away from 30,000 subs on the YouTube boys. We're almost there. We're 192 subs away from 30,000 subs on YouTube


Alright, and what can be dubbed as a true symbol of all island schoolboy football supremacy in Jamaica, Manning Cup champions Mona and the Costa Cup winners Clarendon College will do battle on Wednesday for the oldest schoolboy trophy in the country, the Olivia Shield. The match will take place at the national stadium and will be live on Sportsmax Plus, Sportsmax 2 and CineTV, starting with a pre-game show at 4.30pm local time, 5.30pm ECT. Schools will be in high spirits heading into the contest, with Mona securing their first-ever schoolboy football trophy by beating Heidel 1-0 in the Manning Cup final last Friday, while Clarendon College emphatically put away Glenmer High 6-2 in the Dacosta Cup final on Saturday. This will be the second time the teams are meeting in two seasons. Clarendon College won 3-1 when they met in the Champions Cup quarterfinals last year. So let's quickly preview this one with Dwight Jeremiah, head coach of the Costa Cup last 16 qualifiers, William Nibb, and our very own Sportsmax analyst. Good afternoon, how are you? I'm good, very good. Just getting off the work. Of course, really a pleasure to chat with you, Dwight, on the Sportsmax Zone. We have a mouse-watering matchup, and for me, as I said in the opening, it's the best of the best clashing against each other. For me, I'm a bit nervous for this one, so I'm going to take… Nervous because you're supporting Mona. We're going to leave it up to you with the expert advice, Dwight. How is this one going to go? It's two teams of two different styles. So I think each style won't really adversely make the other team very uncomfortable. Mona can play a lot without the ball. They don't mind not having the ball. Clarendon College, they will like to have it, and I suspect they will have the bulk of it. Mona is okay with parking the bus as their head coach or their technical director says he's quite willing to do. He's more pragmatic. I think Lenny Hyde is more a philosophical coach, that he likes a particular style, but we'll do what is necessary to win a game. Whether or not it will be enough against this Clarendon team is another matter. I certainly think it may not be because if they turn up as they did in that finals, when they want to play, I honestly believe there's not a schoolboy football team that can live with them. However, we know this is football and no two games are ever the same, and tactically, yes, Butler will try and set up to really frustrate and play on the counter. So, yeah, it could be, it could yet still be for Butler, but for me, I think it's Clarendon's. All right, Dwight, are we to expect a lot of goals in this match, or do we expect the teams to be a bit more cautious? I think not teams, but team. I think Mona will be cautious for this one. I don't think they'll play as open as Glenmure did. When Pirate said he went with two strikers, I think that might have been a misjudgment to think that he could counter it with two upfront, needed more numbers behind the ball, because I think in the wide areas, I did say it on the show before that Clarendon will exploit, that they'll look to do the same against Mona. I think it'll be difficult. Mona will sit in low blocks and try and frustrate them, but I think no matter how well organized the difference is, if you do have a pass in the movement, one touch football, they can be unlocked and there's a lot of match winners in this Clarendon team.  They do well on the counter. Two, three, four passes, the max, and they're into your final third, looking to threaten goals. So Clarendon will be mindful of that. But I think, I think Clarendon will go for it from the goal, from the opening whistle. Mona, I think will be the one that will apply the little cagedness about this encounter. Yeah, Dwight, your thoughts on the turnaround time for the Olivier Shield, Manicop Final on Friday, the Dacosta Cup Final on Saturday, Olivier Shield playoff on Wednesday? Always, you require at least 72 hours maximum, or minimum rather, for your recovery. When you check the time span, I think they will border just on that, but I think both do these two teams understand the signs of the recovery and rest, and will do all they can to get their boards ready with rest, elevation, flushing, and all of that to get the fatigue out. It wasn't about on the footing in terms of the playing. Yes, Clarendon did exert, but in moments in the game, they toned down a bit, and then as Glenmore threatened, they tend to change gear as well. I don't think it will be a major factor for these two teams, maybe a little bit on the intensity with which Clarendon will want to play with this short turnaround, but Mona in their style, being a bit more reserved, will not look to expound a lot of energy in this contest early on. So if anybody would affect his Clarendon, but when you have the ball, you dictate the pace of the game, so yeah, I think it will be minimal this factor. Interesting that you say that, Dwight, especially suggesting that maybe Clarendon College is the one that would be more affected by it, because I asked that question against the background or thinking about the fact that this is the first time Mona would have won the Manning Cup. I don't think they held back on the celebrations, and at least if not physical, and maybe it can be physical, but from a psychological standpoint, it can be difficult to come off such a significant high, and then get yourself back into focus mode for another major assignment just 48 to 72 hours after. Well, the more I think about it too, Ricardo, and what you said there could be a factor, but then I think about it a little bit more, and Mona in terms of their squad depth, yes, they have the players on the bench, but I think really and truly has relied on probably 13 or there about players going through the season. So in that regards, if it requires the bench, I think Clarendon College will have the fresher players or the players that are more equipped to affect the game than a Mona. And these are the boys that have been doing it all season when you look at Mona. So yeah, I think that that that would be a factor. And Clarendon College, they do get back both. Thompson did a really good job, but both is returning from his red card for this one. So yeah, they do have more quality, I think in depth. So if anything, as I said, Clarendon would be the one that because they're pushing the game, but then again, you look at their squad depth. And that's why I think it probably will be a non factor overall. I think Butler is quite good at getting into the head of these boys. Whether or not they will feel like, you know, we won the Manning Cup, we've done the great things. So the Olivier Shield is a bonus. Well, that we'll see how they approach this one. But on social media here, the Clarendon fans just begging the boys to be up for this one. They want this one. They really want to prove that they're better than everybody else. Yeah. And just one quick one on this Clarendon College team. And maybe it is just me. But before Clarendon College went down by two goals to one against Glenmure in the Champions Cup final, I felt that this was a team playing within themselves. Now, after they went down by two goals to one against Glenmure, I saw a different Clarendon College, which transitioned into the Dacosta Cup final. And part of that is their big three, Christopher Hall, Malachi Douglas and Kahim Dixon seemingly playing closer together. If that happens again, come tomorrow, will Mona have a genuine chance against this CC team? I tell you, that is their plus Clarendon College with those big three.  I think their soft underbelly was through the middle. They were quite good with the ball, but without it, they weren't. That was against Glenmure and Glenmure number 10, Gordon and White had a field day through there. I think they corrected that for the finals of the Dacosta Cup. They were very good without the ball. They pressed, they worked hard. So that, I think, nullify the chance of exploiting them. They're very good creating overloads in wide areas. And that's when those three boys get close together and really create those triangles and diamond if Ashley gets involved in it. So, yeah, if they are firing like that and come out with the middle operating as they do without the football. Yeah, I think it's a long day for Mona. I keep saying that they're in the game. It's going to be played so they do have their chance and tactically, they will set up to make it difficult. But, I mean, you'd be a very brave betting man to go again, scan and college after what you saw with them on Saturday. Well, it will be for sure some really exciting football fight. I cannot wait for tomorrow. I want to thank you so much for your time and we'll talk again soon after whichever team walks away with the Olivia Shield. Well, I'm looking forward to it. Hopefully, they make it a good game because I'll be on call. Thank you so much, Dwight Jeremiah there. He is our very own Sportsmax analyst. We're taking a quick break. We still have football to talk.


Welcome to the School of Languages, Cultures and Linguistics. Here at SOAS we specialise in a range of languages and cultures across Africa, the Near and Middle East, South Asia and Southeast Asia. Our postgraduate programmes provide exceptional opportunities for advanced study in a department with a strong research culture and collaborative research links with universities and NGOs across the world. We are dedicated to decolonising the curriculum and to exploring perspectives developed beyond Western construction of knowledge. Our staff have extensive experience and engagement with the regions we cover, teaching over 20 languages including Arabic, Swahili, Persian, Hindi, Turkish and Vietnamese. We encourage students to engage with research with opportunities to speak at conferences through the year. You'll study in the heart of London where we hope to provide lots of extracurricular activities too, such as film screenings and museum visits. Studying at SOAS means joining a diverse community of people from around the world. Our graduates lease SOAS not only with linguistic and cultural expertise, but also with skills in written and oral communication, analysis and problem solving that are valued by top employers. Study at SOAS to gain the skills and culture understanding to help you further your career.


Here we are in our custom zero home page opening up the new company file we set up in a prior presentation that being get great guitars. Thus far we set up the new company file we entered some of the beginning balances imagining we're pulling that information in from a prior accounting system as we did so we also tried to set up some of those foundational type of items necessary to make data input in the future easier now we're just going to do some last little tweaks to those opening balances made mostly within the equity section of the balance sheet but before we do let's duplicate some tabs to put reports in by right clicking on the tab up top and duplicating it we're going to right click on the tab up top again to duplicate it again as the one to the right's thinking I'm going to go to the tab to the left open up the balance sheet report which is under the accounting and then the balance sheet as it's thinking we'll tab to the right go to the accounting and this time the income statement the profit and loss as it's thinking we'll tab back to the balance sheet and we see that we're on December 31st so that looks good this is what we have thus far when I go to the income statement nothing's in there as of 2023 which is basically what we want so let's go a recap and then look at our issue here so if I go back to our beginning balances our strategy has been that instead of entering these beginning balances as one giant journal entry we put them in one at a time and as we did so the other side of the transaction for each of them we put in some way shape or form to the equity account down here so we put the checking account we increased debit of the checking account the other side went to equity we increased the accounts receivable debit the other side went to equity and so on and so forth we did it that way so that we can accommodate any special needs for particular accounts such as the accounts receivable needing customers to be able to populate the sub ledger inventory needing items so that we can track a sub ledger by item and unit of items accounts payable needing the sub ledger tracking by vendor for example so that all ties out now everything looks good and we set up some of those fundamental fundamental items necessary for data input in the future but we could have some discrepancy in our owner's equity account so quick a quick thought about the owner's equity account down here note that your owner's equity is often the most confusing part of the balance sheet for many people because when you think about different types of companies the owner's equity section could look different let's go back to our balance sheet to see what that means it's also confusing because the equity accounts basically roll into or the balance the income statement accounts basically roll into the equity accounts so let's just quick recap on what equity is you can think of it as like the book value of the company meaning it's going to be the assets minus the liabilities equals the equity so if I was to say okay my assets my total assets here in dollars not in terms of units of fixed assets or equipment or something right in dollars that's how we measure things or whatever currency we're using one one five eight nine six minus the liabilities which is thirty eight thousand here thirty eight thousand gives us the uh the seventy seven eight ninety six total book value so when we think about the value of the company the equity is kind of like the value of of the company so the other way you can think of it and the reason we have it set up in the accounting equation is assets equal liabilities and equity the two balancing out is because you can basically think of liabilities and equity as the other side of the coin to assets assets in other words is what the company has in total dollar amounts here liabilities and equity flip side of the coin represents who has claimed to those assets either it's a third party like the bank liabilities thirty eight thousand in this case or it's the owner who who has the seventy seven eight ninety six if you closed up the company in theory then you should be able to get sell everything and get one hundred and fifteen thousand eight ninety six of cash pay off the liability thirty eight thousand and have seventy seven eight ninety six for yourself right but that's just in theory because obviously these are cash is what we're trying to measure things in dollars but when we actually liquidate a company we might not have the same amounts that we would receive when we're paying off or selling the assets for example all right so that's the general idea also if you were a sole proprietorship then equity is quite easy because you only have one person so all the equity is then basically yours as the sole owner of the business however if you have a partnership two or more partners then the net equity of the business has to in some way then be broken down to who has claimed to the equity side of the business which would then be broken out to different capital accounts which is actually oftentimes the most confusing set or thing to do more confusing than a corporation sometimes because the capital accounts could differ meaning you could have partnership agreements that have different profit sharing so you so you'd have to break out your equity to the partnership accounts if you're a corporation then all of the equity instead of having different owners having different amounts which are attributable to them with different capital accounts we say the corporation is just going to this was the genius of a corporation we're going to make it one lump sum corporation and we'll divide out ownership in equal units kind of like currency equal units of the corporation and then different people own different amounts of the corporation by owning a different number of equal units within the corporation that makes it easy on the bookkeeping side because now I can just record everything as basically retained earnings versus the capital stock the investment so whatever your your corporate your structure you might have to do a last minute adjustment down here to get your equity accounts to be lined up one last thing that's confusing on the equity accounts if I go to the income statement we did some things where there might be an impact on the income statement in the prior year so if I go to last fiscal year for example I can see here I've got 20,500 because I recorded income I'm okay with that because that 20,500 it's going to roll into the balance sheet and we can see it here in current year earnings and it's not going to impact the income statement as of the current period I'm going to be working with going forward as we can see if I change the date again to this fiscal year and that's that's fine anything prior to the cutoff I'm going to say I have the data there from the prior accounting system however that might be either I still have the prior accounting system up uploaded or I've printed everything out and downloaded everything so I'll go there for the prior stuff I'm using this system for January 2023 going forward on the balance sheet you can see it here they give you this current year earnings this account will change to retained earnings when we when we flip to 2023 so if I go to this year to date end of last month end of end of last quarter let's say if I do something in 2023 and then you could see now they call it retained earnings so retained earnings is usually an account name that we use for like a corporation we call it the earnings that have been accumulated over time which have not yet been distributed to in that case the owners which are shareholders in the form of dividends in that case in our case if we're we're going to assume we're a sole proprietor then it might be more proper to call this like the capital account like we did up here so I don't want this one basically maybe to be the capital account and then maybe I should change this one to like draws so in a in a sole proprietorship one owner we're going to have an increase you could keep it retained earnings but it probably be lined up to a to a sole proprietor better if you changed it to like owner's capital and then we'll have our investments and then we'll have our draws instead of dividends because we draw the money out and we don't have to go through the board of directors or anything to do so we could just take the money out as a draw as opposed to a dividend and we don't have the same tax impacts in the united states okay so with that said let's go over here and do those changes so I'm going to go let's go down here and say chart of accounts and I'm going to go into my equity accounts which I can do up here which is kind of cool they have this little thing up here