Graduating from the deeplearning.ai Coursera Specialization | Learning Intelligence 22

Key Takeaways

it's quarter past 8:00 at night and I just finished week two of the sequence models course I'm gonna did the whole entire week in one day so if we go through we start out this was lateral language nat rige natural language processing in word embeddings we go down I went through these classes induction into our introduction to word embeddings can you tell it means studying all day learning word embeddings so what's a word embedding I imagine like always when a computer wants to understand some data it can't understand a raw image or a can't understand a raw word what it has to do is convert that word into a list of numbers and that's what it's essentially a word embedding is now if you imagine a dictionary for example but if you go through the dictionary and you number the words from whatever it starts at a - zebra it's not zero but something close to zebra you label with numbers that's essentially at the basis of word embeddings it goes a bit deeper onto that but that's how that's how I understand it at the moment and then what did we do we we looked at word embeddings using word to vector and glove there two algorithms for forewarning beddings and then we looked at some applications for word embeddings so sentiment classification and D biasing word embeddings sentiment classification is for example if you imagine a movie review right and the movie comment there's thousands and thousands and hundred thousands millions of comments online about different movies and if you wanted to understand what the what the movie review was about humans can't always rate at a certain good or bad percentage I think this this may be how Rotten Tomatoes works or something like that be they analyzed or the comment and generator rating so sentiment classification goes through a comment of something or a bit of text and analyzes puts out an output whether or not the sentiment was good bad indifferent maybe one-star maybe five stars maybe four starts so it's sentiment classification and D biasing word embeddings is say for example you're looking at the relationship between a bunch of different words and the computer thinks that mail is more related to computer scientists whereas female is more related to nurse now of course that that has some gender stereotype there so what debasing word embeddings does is it removes that gender stereotype and put some at an equal weighting so man and woman are equally weighted towards computer scientist and nurse there's some really fun things there in natural language processing and then to programming assignments I'll go over these quickly operations are word vectors so deep icing we practice what I just talked about the D biasing and a merge a Phi which is really fun I just finished that one let's have a look here we'll go down essentially what this does is like in in you type on the iOS keyboard it brings up emojis this one assigns an emoji to whatever sentence you type in so I put in I love artificial intelligence and it put a little happy face there maybe it should have been a love heart but I'm not entirely sure my model was only about 85% accuracy and of course you all know I love working out so working out is fun that's the right emoji to put there the one last thing I found this awesome while this this was linked in the bottom of the assignment robot now this is the chat bot to help with mental help and it was built out of Stanford's research labs so it's all backed by science I think this is amazing so humans really seem to like me it's called robot IO I'll put the link in the description for you so you can check it out it's created by scientists and it's essentially to help mental health and the beauty beautiful thing is because it's a chat bot it remembers your history so no longer do you have to go to a doctor or a psychologist or a psychiatrist it well of course they're gonna remember it if they're a good one but you can talk to whoa bot 24/7 I was just practicing chatting to him it's it's incredible how how human-like it responds and I think that's something that that we may be looking into the future in terms of interaction with computers and machines it may move something more towards this and certainly that's that's where I'm looking to go in terms of their AI and health space from also health and fitness rather than mental health like these things can definitely help us out because there's definitely a shortage of doctors there's definitely a shortage of nurses and you can't always get to these people on time or only been on time but just our lives are busy right and so having these things on hand is really going to be beneficial for society our screens going for a morning walk I like started my day off like that and it's hot outside I've been listening to this really cool audiobook I don't know if you can see it 12 rules to life by Jordan Peterson I'll link here in the description if you haven't seen Jordan Pederson before check out his stuff on YouTube he's an amazing psychologist I'm gonna have a quick shower and then get into the code for today you know what the orange screen means means I've been staying up late and this little latest I've stayed up coding in a while what's the time here 11:30 but it is with all good cords because I set myself a goal today I wanted to finish week three I think I saw you this morning and I wanted to finish week three of the core serifs that final course or the sequence models cause I'll show you I'll show you where where I'm up to so this is a final assignment - trigger word detection welcome to the final programming assignment of this specialization watch trigger word detection well you ever seen Google home okay Google or Alexa Alexa or Siri hey Siri that's old trigger words and that's what sequence models can be useful if you train a data set or train a deep learning model on heaps and different audio on certain words and when whatnot you can create a microphone that listens to or oh sorry I've got a program that uses a microphone to listen in the air all those little noises coming through the air and picks out the exact sound waves at once if you imagine that is a sound wave and we want this one that means the microphone is listening for this one so it can todate and listen to what you're saying here we built our own trigger word detection so the trigger word was activated we play a little audio here it always is gonna work there we go so that's a positive example and then we'll give you a negative there free that's a negative example and then some background noise so just some ambient white noise and then we go right down here at the bottom I'll show you what the goal was the goal was to output a little model here that has a chime whenever someone says activate and this is the start of where models like Siri and okay Google this is how it all starts off and then you can use instead of the time it'll be listening for for your voice of what to tell it to do and then reply with a certain action so let's look look here activate activate bake and a really cool thing I made my own so check it out this this little shower kinda sounds like that chime that comes on on the seat belt sign when they come in their line well that's what I think it sounds like anyway last night I broke the door on our oven upstairs shattered the glass completely so I was trying to soar somewhere to fix that for us for the family room so we can eat food I'm kidding with what we can still eat but we just can't use the oven I was also helping at my brother with these AI Simon he's doing a course from MIT actually that's using AI in the business world so he's going into an accounting role so that was really fun to look at what the perspective was of using AI in the business world of course I'm excited about AI in any aspect but my my passion and foundation lies in AI and health anything to do with AI excites me and I tell you what I'm also really excited because check this out so the Coursera deep learning specialization we can officially mark it off well this is a try board and my screens orange because it is it is again well past sunset but we're gonna take this off here and we're gonna take this off course five sequence models is officially done and then we can drag this over into the done in the February common Lords a little few days overdue but that's alright we've still got it done congratulations you have successfully completed sequence miles and the deep running specialization I have no idea what I'm talking like that my final grade for this course was probably the highest one I got actually ninety-two seven point two ninety seven point eight ninety nine point five for the final course deep learning I finished the entire thing look at this beautiful certificate that you get at the end deep learning dot AI Daniel Burke sequence models signed by the man himself the legendary adjunct professor Andrew on computer science what an amazing call skies on top of that it's now on my LinkedIn you guys want to add me on LinkedIn I'll put the the link in the description so you can we've got nine certifications now sequence models of convolutional neural nets improving deplored networks etc etc and of course we're still going with the Udacity artificial intelligence nano degree there is no shortage work to do here guys as I said well as as my philosophy goes I'm not entirely interested certificates are great don't get me wrong I really love it but the skills is what I'm looking for that's that's really important the certificates are just something that you can you can sort of prove to someone else like if I was to go for a job I would look I've got this certificate rather than just saying hey look I know about sequence sequence models but I'm a big advocate of of letting your work prove that you've got the skills so that's where these videos come in and that's where these blog posts that I like to do come in and that's where contributing the open-source that's really important so if you're going for a job it's it's showing the employer what you can do I'm sorry what you've done sort yeah that's right what you've done rather than what you can do because anyone can string together a cent and say oh yeah I can build an AI model that can decrypt the human genome and decipher what you need to eat every day every hour of the day but it's another thing to have actually built something that can do that that's a really cool point I just finished watching two lectures of the MIT AGI course the first one was on future of intelligence Foraker as well and the second one was on building machines that see learn and think like people with george Tenenbaum and there's photo of Ray Ray Kurzweil and Josh Tenenbaum I believe is there and those both of those lectures it wasn't even oh he excited me I didn't even feel like it was a it was that stressful I mean it was explaining some hard concepts but it wasn't I was having fun I mean that's a really good place to be in when when you're learning something that you're actually really enjoying and so that's that's where I was with these two lectures and these two courses offered by MIT for free are incredible I'll leave a link in the description for both of them so you can check them out self-driving cars course and this one is artificial general intelligence but I had one one major takeaway from both both videos so the first one was from Josh Tenenbaum video and the idea was using modeling the how children learn on how babies discover physics in the world and to reproduce general intelligent systems or intelligent systems that can progressively learn on their own because that's where we're gonna end up right if we well if we want to build systems that can or robots or something like that that can help us in the physical world I mean now AI is is great at doing things in terms of a data processing on a computer parallel heaps and heaps and heaps of CPUs across major massive cloud computes for things like image recognition or speech detection or natural language processing but if you want to have an actual physical robot in the world like something something here that can help me out my little pal robot yeah doesn't exist quite yet but maybe maybe future Dan knows one that we have to model how children think or how babies learn and how they come into the world and that the concept or the concept to take away from that is how do you develop the game engine of of intelligence with a game engine of your own mind now I thought that was really cool so if you imagine like video games they have a game engine so don't have to hard code every single piece of graphics into the game they have the game engine which generates it on the fly and that's a really similar like analogy you could use how do we develop our own game engine for the minds that rather than an intelligence system reproduce every single scenario it's ever been through how we create one so it's got some principles that can adhere to and make something on the fly that was really cool and the second one was right up my alley in the healthcare space from the Ray Kurzweil workshop and if you've never heard he's he's got a really good book called the singularity he's got a few books actually he works for Google he's a boards company after it was only started five weeks or so he's a really really smart guy in terms of the technology field his books are are all about predicting the future or not in terms of predicting a future but just using mental models of today to try and understand what's coming next and he's got some very good very good predictions very good track record but here's was on escape velocity in life expectancy and what does that mean well at the moment life expectancy is a measure of how long statistically you will live once you're born right but it's it's sort of flawed in the way that it the the number itself is based on the fact that there will be no scientific progress in your life but that's that's completely wrong right every other week we're hearing of an AI breakthrough or a biotechnology breakthrough and so Ray said the life the explosion or the escape velocity of life expectancy is about ten years or so away so if you're diligent and you can you take care of yourself and you make it through the next ten years or so life expectancy or your life expectancy in my life expectancy a kid who's born tomorrow their life expectancy will increase faster than time itself what does that mean so say say for example life expectancies eighty years old or something like that now within ten years based on Ray's prediction and the advancements in technology in biotechnology AI every year my life expectancy will increase more than my age I find that mind-blowing and because I'm sort of interested in the AI in health space that is my why those two things or well that thing from reins talk is my favorite takeaway what's for next week well I just checked over the NLP natural language processing section in the artificial intelligence nano degree here's the classroom here I updated the Trello board to have some of the some of the tasks I need to do there's the lesson plan if you want to check that out natural language processing lesson plan and there the class is a part of it of course this link will be in the description now that was a lot of rambling on but it's time to some think I'm getting better at that and now these people reached out to me via youtube comment by email by Twitter you can contact me anywhere if you like all my links are in the description below of course otherwise my email is daniel at mr deburr comm and my twitter is at mr deburr first of all a cache from the email space thank you so much and well done my friend on learning for free that is amazing that is one of the best things you can do in this world and that is why we are so privileged to have access to the resources we do I have paid for some of the courses I've done but I do not regret that because they have been high-quality courses but that being said you can certainly learn a lot of of whatever you need to know almost actually let's just say with the internet you can probably learn exactly what you need to know without paying a cent because it is that great of a resource because we are so lucky I think I can you imagine 30 years ago trying to learn something without the internet I don't even know where I would start right now I talked to my mom about my assignments and whatnot in high school I had to go to library and handwrite things and look for books and stuff like that or didn't have the power of being out a search through Google I digress thank you so much a cache from the YouTube space we have Demetrios yes I totally agree do I ever feel like I'm learning something but not actually learning it do I not get there the concepts and I think as Hume we are very visual right so if we if we take in something on a screen whatever it may be I tried to pick up an example there I don't have one close but if we take something on a screen and we we visually see it we can see it we see lots of things right but then if we see it again because we've seen in the past it can be sort of a trick that we understand it but really we don't we just recognize it visually we don't understand underlying concepts behind it and I think this is where compound learning comes in for me at least it's where I learned something I like - yeah it's it's like learning something for the first time you know you sort of you get that you get the visual picture of it and then learning something for the second time you sort of start to understand like the the wheels behind the wheels that are actually making that beautiful car move and learning from different sources this really helps with compound learning and I think that's that's where myself in my case combining the deep learning native degree with the deep learning specialization on Coursera it's two different approaches to a similar topic and I like that has cemented my knowledge I've I'm so thankful that I've gone through both of those courses and I really recommend that to you if you want to if you're doing the nanodegree if you're doing the Coursera try to learn from different sources it doesn't have to be the exact same ones I've done but try to find different sources so someone else's point of view and then most importantly create your own point of view on it and so that's that's the idea of compound learning and well that's what I like to think of it as Demetrius yes I definitely get that thing of learning something but not actually learning it but the way I get around that is just keep going with it keep revising it teach it to someone else it's kind of the reason I like to make these videos as well although I haven't made really in-depth educational ones just talking about it helps me to learn more and finally Sarah Van Daan thank you so much and the computer I use is a MacBook Pro it has the touch by the touch ID probably the best feature on it I actually I really love it 13-inch MacBook Pro 512 gigabyte solid state storage I 5 Core processor I'm pretty sure however all the major deep learning models that I run on it through the cloud so through AWS through Google cloud through Floyd hub a lot of deep learning models far too complex to run on my local machine and that's what I think you'll find with any with any laptop almost it's unless you have an external GPU it's gonna be very hard for you to run deep blowing models at speed or at least big deep learning models on your computer until we work out a way to make simple models or faster computing in a smaller space so thank you to all of those who have reached out to me and of course guys feel free as I said before you've got my contact details reach out ask me any question I'll do my best to answer it we have one last thing which is the question of the week I'm starting this and trying to remember to do it every video question of the week this week the best comment leave a comment I'll shout you out in the next video when we can talk about it more what are your thoughts on AGI does it exist or can it ever exist AGI is artificial general intelligence right can we solve it by 22:45 why 2045 well that's the date when Ray in one of the videos I I watched this week Ray Kurzweil predicts the intelligent singularity if you've never heard of that google it because this video is far too long already so I'm not going to explain it what are your thoughts on AGI and can we reach the singularity by 2045 leave a comment below I'll talk about the next or the best comment in next week's video thank you so much for watching as always keep learning you [Applause] you