Starting Term 2 of the Udacity Artificial Intelligence Nanodegree | Learning Intelligence 19

Key Takeaways

what is going on learners welcome to learning intelligence 19 and this week's episode is going to be really cool I'm into that second term of the artificial intelligence nano degree on Udacity and this term is all about deep learning so we're building projects using deep learning and right now I'm up to the first module and I'll show you what it's about we're looking here the first project is computer vision capstone building a facial key point detection system and I'll show you that in a second I'll show you an example of what it is I've been through all the initial classes here so this is what you get when you start so you've got to set up an ending and a combat environment for your computer set up some cloud computing on AWS really cool thing about their AI no degree much like the deep learning no degree you do get $100 worth of AWS credits a hundred US dollars that is that's really good to use amazon web services such as ec2 GPUs which are really good for deep learning and so that's that's one thing sort of if you imagine the cost of the course you could essentially take $100 off of it because you get those AWS credits like then we go into here so we started off with intro to computer vision mimic me is a project an optional project I'll get onto that I'll show you what it's about in a second and then they did image representation and analysis image segmentation features and object recognition and now up to the project I'm a hundred percent beautif and I haven't completed the project now one really cool takeaway I took from the intro to computer vision class is that the basis of any AI system is that number one it can perceive its environment and number two it can make actions on its own will based on those perceptions so if you think about it in two part phrase it's or imagine a self-driving car is an example so self-driving car needs to use computer vision as well as other systems to perceive its environment and then it has to be able to take actions based on those perceptions so it's a really I think cool way to introduce AI to someone if they're not familiar with what it is and perceptions about environment can be anything in environment if you imagine a voice recognition system it has to be able to perceive sounds in its environment and then make actions on that two really important points I think in terms of describing what AI actually is now you see something really cool is this facial key point system let me set this camera up now this piece of software is called f decks me which is made by Affectiva and I don't know if you can see but in close here it's got on on the side next to next to my face it's got all these labels it's got anger joy sadness disgust surprise and fear and on the on the right side here all the webcams back to front for me it shows an emoji of what I'm feeling so let's try this out wing face who's got tongue out we'll smile see if we can get angry so that's really cool and this is essentially what we're going to be building for the first capstone project in computer vision so I'm really excited for that if I can leave a link in the description so you can try out using this piece of software I will but if not head over to the effective web page which I'll leave a this link in the description they're a start-up using computer vision to detect emotion in faces which i think is really cool alright guys so we just finished part 1 of the computer vision capstone project and I'm gonna get onto part 2 and part 3 later this week so I'll show you here what I've done I've essentially created just a document here like a just a bear no it's just a simple note and gone through the project and just wrote down what's required for the set up what's required for each different part some links to the rubric and then some option optional tasks that I can make my project stand out and then a whole bunch of nodes for each part what I want to do with this this project or each of the projects in the the term to is to once I finished them make a blog post or a video about it so how a guide of how you can implement it yourself that's what I'm taking extensive notes on this one and working through it as hard as I can and sort of writing down everything that I found out how to help me so you can work on these things as well stay tuned that'll come out in the next few weeks but if we look here this this project is all on computer vision and we've started out right up the top just detecting a face in the image and then we detected the eyes of that face and then if we go down here we detected a whole group of faces in a blurry image and then unblurred or ordi noise fi the image and then re detected it so if you see up here in this one it's missing that face of the girl in the purple dress then we took some of the noise out and made it a bit clearer I'm not perfectly clear clear enough so that you could detect that extra extra face and then I just finished off Oh actually sorry then we did some edge detection to see here we've got a face there and then detected the edges of it cleaned up the edges so they were a bit cleaner and then we did I learnt how Google so if you know if you've ever used Google Maps in in person view you know how everyone's faces blurred out well we did the equivalent of that if you come down here we go into this so there's the original image and then I learned how to oh I detected the face in it and then I blurred out the face so you see their load out the face but you can still see the rest of the image I'm really excited I'm really liking this project I think the next stage or the next part of it let me just check off I'll fill you into the next stage is part two so be training a convolutional neural network to detect facial key points and then part three is putting parts 1 & 2 to get up so I'll hit shot once I've done signal progress I need a break from it I'm gonna get something to eat oh one more thing don't be afraid to reach out for help look at this on the udacity and this slack Channel see I asked a question here I'm not sure if you can see it but that's 3:14 p.m. I got a response back by 3:18 p.m. and the same goes for the forums if we come over here where is it I got a response back 29 minutes ago and my question I only asked one hour ago that's a note to self if you're having problem with something don't be afraid to ask out and that goes for you guys as well if you need any help with anything ask me I'll do my best to help leave a comment below and I'll try my best three quick things from today's coding adventures so the main one is I'm still working through the computer vision project aka to train a facial recognition system to detect facial key points ideally what we want is some sort of outcome like this see the blue dots there and so what I'm doing I'm building a convolutional neural network you can see the epochs here and the training output the losses there and I'm doing it in carers where is it where's my implementation there it is there so starting off convolutional layer max pooling layer dropout layer however it's got some tweaking to do but I'm gonna save that for tomorrow because I'm gonna go work out and clear my brain and sort of let their subconscious do the work I've got my model pretty accurate but I think I need a few more iterations before I get it perfect and ready for submission this is how I'm tracking what I'm doing so but iteration one I put down the neural network architecture here and then for each iteration so iteration two I use the same network as above but I change to the atom optimizer I'm just writing down and taking screenshots of the results writing down the changes that I implement and taking note of what results and how it effects the results so I can update it better in the future the second thing for today as I went over this lecture on deep learning with with my brother and I'll leave a link in the description it's a deep learning lecture from the MIT self-driving cars course and it's it introduces deep learning from the eyes of self-driving cars so it's I found it really valuable I'll put a link in the description and it's by Lex who's also a wizard so check his channel out I mentioned him in last episode Lex Friedman and the third and final thing is if we go here playground tensorflow org so this is where you can tinker with a neural network right here in the browser you can just change the architectures see I've got mine running here it's running almost 4,000 epochs learning rates their activation regularization look at all this you can add more layers add hidden layers etc and it shows you what it outputs I think that's a really cool visualization for our neural network looks like intensive flow what it's actually doing behind the scenes that's it for today it's really hot here as you can see I'm sweating just standing in my room I'm gonna go gym and sweat a bit more apparently 33 percent of the test miles driven in the MIT self-driving car course test cars which are all Tesla's are in autopilot check this out and apparently that reflects the actual use case of Tesla's on the road and I'm not entirely sure how true that is I tried tweeting Elon Musk to see if that was correct but I didn't get a response that's alright I imagine Iran's a very busy guy I've left a left a note here of what I have to do next week just so I can I can get on top of things I've got a few questions to answers I've got a few checkboxes to answer and I'll show you how far through it I am in the Troy Board this is how I keep track of all my projects so we go down here I've got a checklist of all the tasks I have to do I've done up to step four Step five I've probably done about 50% of Step five so create a convolutional neural network to recognize facial clear points put it on about 50% of that I have to finish off that and then get through the rest of that guys it has been a big week from deep learning with self-driving cars out of history general intelligence to building a facial keypoint detection system I'm loving this and next week is going to be no different as I work towards finishing the first major project of artificial intelligence not a degree term to building a facial keypoint detection system using a convolutional neural network combining OpenCV and carrots to to build a system that can recognize seven I think it is seven also key points on a face and stay tuned in the future once I finish the project I'll release a video detailing the steps I took to do it as well as I'll put out a blog post of how I work through the the computer vision pipeline but stay tuned for that subscribe to me on on medium or subscribe to my channel and and you'll be the first to see those videos and and blog posts when they come out but without any further ado it's time to get out to some shout outs of the week so these guys either commented on my videos reached out to me via email or contacted me in one way or another you can always do the same my emails daniel at mr deburr comm my twitter account is at mr. d burke if you want to reach out to me I respond to everything and I'll do my best to answer any questions you have or any advice you want or if you want to give me advice feel free to reach out as well I'd love to hear from you in no particular order we have Carlos so all the best with your AI nanodegree program and thank you for that Kegel learning resource I'll definitely be checking that out in the near future ruff I hope I'm pronouncing this name right but thank you so much for your kind words and all the best with your machine learning now agree Shreyas again I hope I'm pronouncing these names right guys and if I don't I apologize but thank you so much for your kind words and I'll definitely keep these videos going I'm loving it so much it's I love I love learning and I love sharing what I learned so I'm I'm gonna keep making them don't you worry about that just over I hope the note-taking advice I gave in my deep learning nanodegree review video went well it's worked for me so hopefully does the same for you Quran thank you for your consistent advice your feedback and your words of encouragement plus that link to the deep reinforcement learning video that's available on Lex Friedman's channel the links in the description and that's a rapid learning intelligence 19 thank you so much for tuning in I'll be back next week with learning intelligence 20 can you believe it up to the 20s already it's been an awesome journey so far and I can't wait to see what's next but as always keep learning you [Applause] you