My Experience at CodeCamp, Intro to Keras and Failing Hard | Learning Intelligence 17

Key Takeaways

what's going on guys welcome back to learning intelligence episode 17 I'm speaking quietly this morning because it's nice and early I've got up I'm teaching some kids how to code this morning we're gonna I'm going to a it's called code camp and it's like a three day camp and the kids come each day and we learn how to build games and we learn some fundamental principles of coding so I'm really excited today I won't be able to film anything there of course because they're the young kids but when I get back this afternoon I'll let you know how it went and I've got a story about yesterday I failed really hard but I'll share that with you when I'm back from my videos you can really gather that I'm naturally high-energy like I'm excited to a lot of things but the kids at the coding cab they were unreal I've never met a group of kids so keen to do something they were just everytime we were on a break they were like oh we don't want to go on a break we don't want to accept we know what they were hungry they'll really keen to go on a break man after that I was so keen to get back into it and even at the end of the day we had kids that we sort of have to almost dragged away from the computer to stop them from building their games and the way you can continue building this at home and their eyes that I don't know can we can we keep building it they were so excited so if you didn't get that at all I just spent the day at a code camp so I was instructing all what I was a teacher's aide actually there was a head instructor and as she's doing an amazing job running the fort and essentially we're keep teaching these kids some some basics of coding him it's drag-and-drop block coding for now but tomorrow we're going into the line coding so we're going to be actually writing JavaScript and we've got a Anna and I were teaching a group of about nine kids and they're about seven eight nine I think ten is the oldest in our group maybe 11 I'm not entirely sure so around year four to six and the projects for them is building games small little games so the the stuff these kids are come up with is incredible like that they get a blank canvas when they come in and okay we give them some guidelines over what type of game they have and what should be the logic behind it but as for everything else they can they can implement it they can work on their own rules and whatnot in and then the best thing about it is at the end of the camp they get to export it and it's all touch screen compatible complain on their ipads or play it on their on their phones and whatnot a funny thing actually they were all working on laptops or most of them working on laptops but the kids it's incredible to see how they interact with the computer like i had one girl she doesn't call computers computer she calls him ipads and she was trying to touch that like the computer monitor it sort of seemed natural the way she was interacting with it but of course the the monitor wasn't touchscreen I don't know I think that's the future of computing like all all computers I know Apple they don't they haven't built a a touchscreen Mac yet but I think in the future they're going to combine these two devices in some way or another I don't know a blur but it's there are touchscreen laptops but you know how Apple are they usually late to the party with something something ridiculous but that's I digress so teaching kids is amazing there it's quite exhausting I get back so this is a hat tip to all the teachers out there you guys do an incredible job and I'm very grateful I have a newfound respect for for my teachers and I definitely I know I caused a ruckus in in all of my classes apology issue thank you for your patience for putting up with me outside of that I've got a couple of days left of code camp so I'm really excited to finish that up I'm having so much fun really excited actually to see what the kids final games look like and have a play of them it's good to be back as well I had an amazing holiday I'm well-rested I'm ready for 2018 I cannot actually I cannot tell you how excited I am for this year first plan first first Call of Duty is that what it's called maybe Call of Duty well first thing I'm gonna knock off for this year is the deep learning dog AI course I know I wanted to finish that before Christmas but you know how these things happen they get in the way I'm still well and truly ahead of the actual course schedule I believe if I did that the normal course pace it would take about four months I'm gonna complete it within about one month so I'm really excited for that and we're on convolutional neural networks in course for of it I believe I've got halfway through that and it is it's it's weighing me I thought I was most interested in natural language processing in terms of the field of AI but computer vision also seems very interesting so we'll see how that goes I might I might sort of see which one I prefer I haven't really tried either of them in depth but getting a good taste of computer vision through convolutional neural networks and Mac computers need to see right it's it'd be very handled second thing that's going to be happening after I finish the deep wound on AI specialization actually starting very soon is term two of the artificial intelligence no degree I believe by the time this video lands I will be well and truly into that I'll start that I'll probably a couple of days into that and so that's I'm really excited for that so that's going to be hard core deep learning working on building projects in computer vision natural language processing and speech recognition systems so of course I'm going to bring you all along on the journey but that's that's the two major plans for the for the start of 2018 otherwise I'm going to keep working through my artificial intelligence master's degree the link will be in the description as always and one more thing before we get into the rest of the video MIT just announced a artificial general intelligence course and a few other details it's going to be some lectures on virtually the State of the Union what's what's known in terms of artificial intelligences as well as how it's going to affect different fields and it's it's free it's open to everyone so I'm really excited for that that starts January 22nd I believe if not the link will be in the description as well I'll put something maybe on the screen or something like that I don't know I'm really excited for that though you've probably gathered I'm dressed for bed I just had a shower my hands feel wet my face is fully all read from the hot water these things happen I'm gonna jump in a bed I need some rest for the kids tomorrow and then yeah we'll be back into coding soon and learning about AI let's do it have any of you seen this meme I love it I love this seen an exception to by the way oh maybe not this this scene I can't remember the scene but this meme is everywhere we need to go deeper and this is actually part of the the Coursera deep learning specialization see and Rhian and this meme was the inspiration for inception networks so essentially inception network is just layers upon layers upon layers upon layers upon layers upon layers of convolutional neural network layers and the whole model itself was inspired by that meme I think that's that's really cool ahead just just because that meme is amazing and the network a pull paper was published on it and it was by some researchers at Google and they named at Google net and they started a funny way I'll let you look up the paper to pay tribute to Lynette which is I think it's y'all the Anna Lou Coons net which was the original image net way back in the day if I'm correct but if you want to check out that paper that's why I'm not gonna try to pronounce this name but it's 2014 going deeper with convolutions and I'll leave a link to the description in that second of all I forgot yesterday that I'm all you know in a previous clip of this video let me just put you down here I forgot to mention what I completely failed at so I applied for an internship at landing day I which is Adrienne's new AI company to help manufacturing companies or manufacturing businesses with incorporate artificial intelligence into their workflow and so I applied for an internship there which is based over in the US and I had to do a coding challenge for it and I failed miserably at the coding talent and I felt pretty down about it after I first did it but after reflecting back I think it was a blessing because oh well bless and whatever one in your core really whatever you want to call it really because it taught me what I need to work on it showed me where my weaknesses are and that's what I think is a valuable lesson is to if you fail at something or if you try something that's sort of out of your reach rather than sort of being down by it which I was and I think it's inevitable to be down by a failure or to feel down even just a little bit but if you look at it at a different way you can flip it and go okay I need to work on these two things and so I took screenshots and whatnot of the coding challenge so I can come back to it and try it in the future if I need to and I wrote down the two specific topics that I should work on in Python and so first of all its algorithms or one of them algorithms is is everywhere so that's important to work on and number two is data structures and number three I wrote down as a as a follow-on from that is coding from scratch and now the challenge the internship challenge was on hacker and hacker rank provides some great opportunities to practice those things algorithms data structures and coding from scratch amongst whatever else I'm learning I think I might need to update my AI master's curriculum to incorporate some hacker ranked challenges I know I've been doing a lot of Python on there but I think I should start to branch out into rather than just doing Python based challenges into applied challenges so the algorithms and data structures and whatnot on to the next thing I'm currently building a model for a programming assignment of the Coursera convolutional neural networks course and this Clips going to be a bit long but that's alright so it's in carers and essentially what carers is is if you imagine python has been the lowest level programming foundation or programming language that you can use or framework and then tensorflow is just one step above Python and then carers is even one step above tensorflow and what the purpose of carers is is so deep learning engineers like you and me can build a deep learning model quickly implemented try it out get some feedback on it and then if we need to go deeper we need to deploy this mean if we need to go deeper we can use the basis of the model that we've built in Carris and improve upon it intensive low or something custom coded in Python and so just check this out that's you can build a model and caris look how quickly you can def model and then you go define your inputs define your padding define your convolutional layers convolutional 2d define batch normalization or add batch normalization to your previous layer apply activation layer apply a max pooling layer flatten it and then define your model as the whole thing and then return model and that is your entire model built-in carers how cool is that I'm gonna finish off this week's programming assignments and then I'll check back in with you guys oh yeah check it out 100 out of 100 and you know what that means we just passed the programming assignment for residual networks let me show what we did a residual network is essentially a combination of different blocks and each of these blocks are called a residual block and then we go up here and I'll show you what it's a combination of this one was a convolutional network or or ResNet and we use convolutional blocks so if you can see in there I don't know if you can but these are individual convolutional neural networks let me see if I can zoom in there we go so we've got a common 2 d convolutional neural network there some Bachelor ization some reloj activation and what we do here is we pass the inputs all the way up to here and add it back into this main chain here and what that does is it allows you to create really really deep networks so for example the one we just built in this assignment was 50 layers deep well that passing of what that that shortcut is what it's called here is passing the inputs over to here and what that does is it helps prevent your gradient from diminishing and if that doesn't make sense I'm not gonna go too in depth to it but I'll put a link in the description of where you can check out something something about what diminishing gradient and what exploding grading and trying to put those two words together exploding gradient means and essentially it's a problem of losing losing valuable parameters as your neural network gets deeper and deeper and so what this sort of step does is help to prevent that and helps make your models better essentially I want to show you another thing is carers and did the tutorial here what we built was a neural network that detects whether someone is happy or not and so there's a very pixelated picture of me and you can see here it outputs one for happy and zero for not happy and that's that says I'm happy there and then there's my friend not such a happy face how can we make you smile and there's another one of him smiling there what a happy chap and it didn't really work for these two so they both got unhappy which so said there's some improvement there's an improvement that can be done in that carers network there but I really want to emphasize how amazing carers is and I was just reading their website I'll put a link in the description here Cara's is used by Netflix uber Yelp instacart Zoo Doc Square and many others and look at this archived mentions in 2017 it's very popular so tensorflow is probably the number one deep learning library and carers is a close while actually not very close second but definitely well but these other Network other libraries here I'm finding an incredibly simple like if you want to use carers to start building a deep learning model to to get something up that you want off the ground I would I would 100% start here if you're familiar with anything to do with Python carers is is where you should start that's it for this this week of coding and next week I'll show you what I'm going to get onto it's still on computer vision we're going to wrap up course for of Coursera next week or the deep learning today's specialization which is all about computer vision and this week three is on object detection week four is on facial recognition so I'm very excited for both those object detection if you haven't heard before it's it's used in say for example you're designing a self-driving car a self-driving car means very good object detection because if you imagine when you're driving you've got a lot of objects you have to avoid you have other cars in the road you have cyclist you have pedestrians cats dogs everything so object detection is something that's going to explode in the coming years and facial recognition well if you're watching this on an iPhone X I don't need to iPhone 10 it's really hard to say ten when they put an X set anyway if you're using iPhone 10 you know what facial recognition is or if you've ever used a camera all cameras these days pretty much have facial detection so they can make sure they focus on on your face and not something else that's gonna wrap up this week's episode of learning intelligence 17 I got that one right this time I really hope I did it's time for some shout outs of the week alrighty then it's time for some all these people either reached out to me by email Twitter our social media or wherever commented on YouTube and guys thank you so much reaching out I really appreciate it I hope I answered all of your questions that you have and I really appreciate the the kind words that you give me the the feedback it it really makes me excited to keep making these videos so thank you so much to all these guys and as well if you're watching thank you so much to you and don't forget if you want to reach out to me anytime my email is daniel at mr deburr calm but nonetheless in no particular order we have Barney Alex rode ciao Aaron Sarab Gregory Lewis and Demetrios and I apologize if I pronounced any of those names wrong but thank you so guys thank you so guys thank you so guys thank you so guys much for thank you guys so much for reaching out for well I can't even talk anymore but you know what I'm trying to say next week's video you heard about it otherwise I'm gonna go for a walk now I think it's about the storm where I'm at so I'm gonna be excited for that I can read a book in the rain who else loves reading a book in the rain that's it for this week's episode thank you so much for watching and we'll catch you next week and don't forget