and I think you should start here. Figure out a reason why you want to begin.


In-depth deeplearning.ai Specialization Review

Why? Why should you start with why? Because when it gets hard that way you can come back to your


reason of why you started. Maybe it's to get a new job, maybe it's to build something amazing,


maybe it's just because you love new technologies but whatever the reason is they're all valid.


It's important to begin with one that way you have something to refer to when the motivation


is not there and you go oh why am I even doing this course? You come back to this, you write it


down on a piece of paper or a whiteboard so you can see it all the time and you go oh I remember


why. So to answer the rest of the questions like what, where, how and when, learning is a journey


right? So I figured why don't we go on a little trip? We'll answer them there.


All right so you've got your why. What do you actually study? The course is split into five


parts and so in part one it's kind of like an overview of deep learning. You get to know the


ins and outs of what makes up a neural network and if you're not sure what a neural network is


that's okay it gets explained in the course and you'll also learn how to build your own neural


network using pure python and NumPy which is python's numerical library aka it's used for


doing math calculations because that's in essence what deep learning is right? It's taking one


piece of information and using numerical programming to figure out some patterns and


relationships in that piece of information. The first programming assignment at the end of part


one you'll take that knowledge that you've learned through the lectures and then you put it into


practice. You'll build your own neural network using NumPy as we talked before. After you pass


part one you go on to part two which is hyperparameter tuning, optimization and regularization.


Now what what are all these terms mean? Well just think about it as little deep learning tidbits


that you can use to improve the performance of your model. So it's just kind of like pushing


dials on a car like say you wanted to get a song to play if you push the wrong dials


the song won't play but if you push the right ones... So part two you'll finish off with a


programming assignment which will introduce you to tensorflow which is one of the most popular


deep learning libraries. So you'll build off what you learned in part one with pure python and NumPy


and you'll start to use a deep learning framework which helps to shorten the amount of code you


actually have to write. So what that means is is someone else has written a bunch of functions


that allow you to write smaller programs that do essentially the same thing as what a quite large


program would do. That's where you see those articles that say state of the art performance


in 11 lines of code. It's because really those 11 lines of code are accessing something with


thousands of lines of code such as tensorflow. And of course once you pass the quizzes and


programming assignments in part two then you can move on to part three. Well actually you can you


can do them in any order but I think they work really well one after the other. I have to wind


down the window to get out of the car because the door handle's broken. Really need to get that door


handle fixed. What part three takes you through is how to structure a machine learning project.


Now this is this is one thing that that really separates the deep learning.ai specialization.


Can you hear that? It doesn't sound very good. That's really disappointing because this is


such a pretty clip. Look at that. You go through this and I walk along the waterfront for a bit.


Anyway what did I actually say in this one because I went through it all and the audio


is terrible. I gotta get a mic. I'll fix that for future videos. So part three is what really


stands out for the deep learning.ai specialization or one of the parts that stands out. I mean it's


all a great course but this one especially because Andrew goes through a list of things or goes over


some things he's learned structuring machine learning projects. And what does that mean?


Well okay you know how to build a neural network. You know how to build a machine learning model


but how do you actually weave that into a project? And that's what Andrew shares. It's his learns of


actually putting these techniques into projects. What do you do when it goes wrong? How do you


set your model up so it's robust over time? Or how do you improve it? These things you can really


only learn after some trial and error and that's what Andrew distills through this two-week part


of the specialization. And so at the end you'll have a programming assignment that you'll go


through a couple of case studies and you'll kind of be like a what's it called a flight simulator.


That's what they call it a machine learning flight simulator. One is you're a mayor of a city and


you've got to decide what type of machine learning project or products or what technologies you should


use to figure out how many birds are coming into the city. Hint it's a computer vision one. And


the next one is to do with autonomous driving. You get to think about should you use fully


automated or fully end-to-end deep learning in an autonomous driving situation. That's


essentially what I said in this in this really pretty section but you can't really hear it.


Back to the main video. All right so you've practiced building your own neural network from


scratch. You've been introduced to tensorflow and now you you've sort of got an understanding


of how machine learning projects work. By the time you get to course four you're really getting


your feet wet. Because course four is all about computer vision more specifically


convolutional neural networks. And when it comes to computer vision convolutional neural networks


they're a bit of a mouthful. Let's just call them CNNs. What is really driving the field


forward at the moment. And for the programming assignment well actually first of all you'll be


introduced to convolutional neural networks. The ins and outs of them what it takes to build your


very own. And you'll also be introduced to Keras which is a another deep learning library built


on top of tensorflow. And tensorflow is built on top of python. And so Keras helps you to really


get some some beautiful neural nets happening as soon as possible so you can test them out.


And one of the best my favorite programming assignments for it was implementing the YOLOv2


algorithm. And I don't know you can probably you can probably see this this little drone I've got


flying around with me. The YOLOv2 algorithm is specifically for object detection like forming


those little boxes around people you might have seen before. And so that helps follow people


follow things around. If you're a self-driving car it helps you detect what a street sign is what a


what a light is what a what a person is what should you avoid what should you not. So if you want to


learn about computer vision this is the course for you.


Oh yeah I forgot to mention you're working on a few other projects such as facial recognition.


It's not just the object detection like you will even build a small little program that that can


detect whether someone's smiling or not. So the the courses aren't just limited to the one project


that I'm mentioning. There's a few there's a few within but I thought I'd share my favorites because


I can I can only really give direct insights on on what what I found most interesting. So you've


wrapped up computer vision your computer can see now but what if you want it to be to be able to


hear or read what do you what do you do for that? It's where part five comes in.


All this review making is making me hungry. Let's go get something to eat.


I know just the thing we can use. Hey Siri, where's the nearest cafe?


So we've talked about CNNs and computer vision but sequence models help out whenever you've got


data over a time series. So imagine for for technology such as Siri you've got sound waves


happening over a certain period of time or other technologies such as Google Translate where you've


got words in a series or even natural language processing or NLP which is you could do a whole


corpus of text such as all the movie reviews on IMDB and try to work out whether they're positive


or negative.


It happened again. I was recording in a cafe and I didn't realize how loud it would be.


You can't yeah nah there's a baby in the background.


Yeah nah.


So we'll do it from here that's all right. Where were we? Part five. Yes sequence models.


So the main part of part five is learning to build recurrent neural networks. So just like


the other ones in CNNs and vanilla neural networks you learn to build the recurrent


neural network from scratch first and then you look into GIUs and LSTMs. Now GIUs are


gated recurrent units. Just imagine them as one layer of the neural network and same thing with


LSTMs. Long short term memory units. There we go. It takes me it took me a while to get all the


all the jargon and whatnot within deep learning. So if it takes you a while don't worry because


there's lots of terms and it's just really about practice just getting them in your head.


And once you've covered the basics of recurrent neural networks you'll then use that knowledge


that you've gained to build your own projects. One of the projects is machine translation. So


much like Google translate takes a sequence of words remember this is the course about sequence


models takes a sequence of words in one language and converts it into a sequence of words in another


language. So they're more than likely using the same technology that you're using recurrent neural


networks or some version of LSTM or slightly tweaked right. That's the beautiful thing about


neural networks is that you can you can chop and change different layers and and make it better or


worse for your specific task. And then probably my favorite project of the whole course because I


love everything to do with communication was the trigger word detection one and the speech


recognition just because I've been having so much fun with smart speakers and well I like what's


trigger word detection? It's this where you say hey Siri and it activates right. So you'll learn


to how to build a trigger detection model and subsequently lead on to speech recognition because


that's how most of these smart speakers work right they need a trigger word to begin with


they're not quite smart enough to to just pick up our our ambient voice and be able to react from


that they need that little kick start like hey Google there we go thanks you're welcome. All


right so you've got your why and you know what you're going to study where do you study it?


Now the beautiful thing about this course is that all the video lectures are available including the


interviews oh that's a little extra there's video interviews at the end of each course for the first


half with with a deep learning superhero like Jan Lacoon, Joshua Bengio, oh actually yeah I think


he's in there, Joshua Bengio and Jeffrey Hinton. If Joshua Bengio is in there or not in there I'm


I apologize for getting your hopes up but you get these interviews at the end of each of the courses


right and it's Andrew Ong talking about the future state of deep learning with with some of the best


in the field and all of the the video lectures as well as those interviews are available for free


on the deep learning.ai youtube channel but of course if you sign up to Coursera and do the


deep learning.ai specialization on there you'll get you'll again have access to all the video


lectures all the interviews the quizzes associated with each of the course and of course the


programming assignments associated with each course. Now what about location? Now because it's online


you can you can study wherever you want you could you could study in your own room like like I do


here you could you could go to the library or you could go to the the cafe and study I had a perfect


segue for this this cafe clip I was even I even had my iPad there as a as a show-off to to show


that you can study on your iPad at a cafe except the audio is not that great. So now you've got


your reason why you know what you're going to study through the course you know where you can


do it youtube Coursera library your room anywhere you want when can you do it you can close this


video right now all the courses are available right right away if you just search in the search


bar above deep learning.ai you'll find all the lectures you can you can jump in and start right


now give it a try right before you even sign up to Coursera you can you can try and watch them on


youtube and see if it's for you but otherwise if you do sign up to the to the Coursera cohort or


the Coursera specialisation every couple of weeks they have like an intake of students so realistically


you can you could start every couple of weeks and what that means is is you'll be starting with with


a group of students around the world at the same time so if you jump onto the forums you'll you'll


be having you'll you'll have people who are at the same level as you asking similar questions to what


you are it's it's a great way to sort of have a community to bounce off without exactly being


physically around that community because that's one of the things about online study is that it


can get pretty lonely sometimes right because if you if you are if you do choose to study alone in


your room it can be a bit hard if you wanted to reach out and ask someone so that's that's another


point we'll get into that in a sec all right that's you know when when you can do it how do


you do it if you sign up to it on Coursera how much does it cost it's 64 dollars per month


and the allotted time frame that they recommend is about a month per course but as you heard before


I did it a bit faster than a month per course so if you do it a bit faster you might end up


taking two months which means you only pay 64 Australian dollars twice 128 Australian dollars


for the whole course which is about 47 US dollars now there is financial aid available for those


who need it so if you reach out and when you sign up and ask Coursera support you may be eligible


for financial aid but but check out this in terms of conditions you have to satisfy to be eligible


all right I get it so if you do pay how's the how's this course structured how are the how


the lectures play out the course is broken down into five parts but each part has a week worth


of lectures followed by a quiz and an assignment each of the lectures is about five to ten minutes


in length or actually they can go up to about 20 minutes but usually I've watched them on about 1.5


speed if if I really didn't understand something I would go back to the normal speed but I got


pretty used to watching them at at least 1.5 speed and that means I could I could usually complete


a week's worth of lectures in in about four hours of of solid study all right I see so so there's a


few different styles of lectures there's short and longer ones but I'm up to a programming


assignment what happens if I need help when you need help the forums are your friend you've got


Andrew does an excellent job at explaining some of the things but but more than likely you'll


probably come across something you don't quite understand or there'll be a problem or a challenge


in one of the programming assignments where you need a little help and that's where you head over


to the forums by the time this video comes out there's there's probably a hundred or maybe a


thousand plus people I'm not sure the exact number that have gone through it like yourself and have


asked questions and more than likely what you're having a problem with someone else will have the


same problem so chances are your question has already been answered but if it's not don't be


afraid to ask because no matter how insignificant you think your problem is it's still a problem


in the past I've been held back from asking out for help because I thought oh I should know this


this is such an obvious question but that's held back my learning journey the only stupid question


or the only stupid thing is is thinking that your question is stupid there are no stupid questions


ask for help all right I see yeah use the forums but what happens if I'm really stuck like the


forums aren't helping and I'm getting frustrated because I can't do it don't forget when studying


gets hard take a break go read something you're interested in go do a workout that's one of my


favorite things to do take some time for yourself and let the let the knowledge of what you've just


gone over sink into the back of your mind and you'll probably find that while you're relaxing


the problem you had solves itself in your head so don't be afraid to take breaks when it gets tough


walk away come back to the problem all right dan it's one thing to to take regular breaks


when you when you hit a roadblock here and there like going for a walk or exercising


but how do you actually study it well it's going to be different for everyone and what's going on


here but I use Trello to track my progress which which keeps me up to date it shows me


what I need to do oh hold on there's a great example of computer vision here


those cameras in there are scanning my license plate to see if I need to pay


beautiful it worked like a treat that was actually a really good example so what the


camera was doing was using computer vision probably a convolutional neural network of some sort


to read my license plate and then compare it to a database to say hey you need to pay for your


parking that's a lot faster system than having someone there reading my ticket or something


like that but anyway where was I with Trello well you can organize your board any way you want but


I use it in a relatively simple manner on the left hand side I'll have all the resources that


I need for access to the course and then I'll have a column that's that's to do and then another


column that's doing and then a final column that's done and what I put in each of them is in to do


I'll go through the the original curriculum the whole course and make a make a little Trello block


for for each one each each lecture or each task that I need to do probably for the deep learning


specialization I'd make one for for each course so there'd be one two three four five and then within


each of those is a list of the lectures and assessment I need to go through like kind of


like a checklist right and then as I start part one I'll move it into the the doing column so that


way at a quick glance I go to my Trello board and I know what I need to do what I'm currently up to


and if I finish part one it'll go into the done column and so I finish that off that's that's done


so I know what I've completed they're my two tips on on how how I best study is of course be


concentrated get get into it do do little blocks at a time take regular breaks if if you're getting


stuck on something and let let the let your subconscious solve the problem for you and


track your progress so you know where you're up to and what you have to do next


oh it's been an epic day what a journey kind of like the learning journey you go on if you


start the deep learning.ai specialization I think to summarize the main question is would I do the


course again yes I would 100% I will probably even go back over it again in the near future


just to to re-re-cement the knowledge in my head now I highly recommend it it's probably one of


the best courses I've ever taken so if you're interested in getting into deep learning and


you've got a few months of python experience and about a high school math education or if you've


got about a year's experience in another programming language like javascript or java or c or something


like that it's definitely something you should check out it's it's one of the best ai courses


you'll find on the market there's a link in the description if you want to go and check it out I


highly encourage you Coursera offer a seven-day free trial if you just want to go and check it


out and if it's not for you well hey it's not for you there's plenty of other courses online


such as Udacity's AI Nanodegree, Udacity's Deep Learning Nanodegree and I can only really recommend


the courses that I've done so they're the ones that I've done so far and if you have any more


questions something I haven't really answered in the video it's a beautiful day outside I just


noticed notice that don't forget you can send me a message anytime via one of the links in my


description I'll reply back to you as soon as I can or if it's a common question I'll answer it


in a future live stream I encourage all of you to check out the deep learning.ai specialisation


Learning Data Science with My Brother! | Learning Intelligence 37

It's pretty loud out here. Can you hear me?


Morning team. Today we're on the iPhone camera.


And I think that looks pretty good.


My other camera's on charge but I'm not going to let that deter me from making a video.


Today, my brother and I, he wants to get into data science so we found a course on Coursera that we're going to be jumping into.


Why do you want to get into data science bro?


You've put me on the spot here man. I'm not ready for this question.


Well, I'm just interested to learn how to code and mainly because of the scalability of coding and what not.


You write it once and it can literally be played like millions and millions of times.


Like that code sticks forever.


Also interested in AI and what not in the application of business.


But this is the course. Coursera University of Michigan Python Fundamentals.


You've been through a few videos here.


Just the introduction stuff. Now it's starting to actually get into the fundamentals.


But yeah, got my tutor here.


My coding experience is pretty limited.


It's mainly the markup, so CSS and HTML and a small amount of JavaScript in there.


So, seeing how Python goes.


Hey, Papa and Harpy.


So, are we going to set up a desk in here for me?


What we're going to do, we're going to go through this course together over the next few weeks.


It's an introduction to applied data science with Python.


So, I'm actually really excited to jump back into some fundamentals and see what it's like from the beginning.


I've done a lot of deep learning and machine learning.


So, it'd be great to see what some fundamental data science principles are, which I think I need to work on.


So, we're going to jump into this. Do module one. I think that's our goal for today.


Finish module one and we'll hit you up with what we learn, eh, Bray?


Yep. I could probably do multiple weeks.


A little makeshift desk for today.


We're ready.


We can keep going. Keep going, finish week one.


We'll have a debrief at the end of week one.


Is that like a plan?


Well, when are you getting out to the quiz?


Well, I'm going to keep going until I'm at the end of week one and then we'll have a debrief.


Yeah?


Yep.


You're not really supposed to help each other with the quiz, but we're helping each other anyway.


I need to know if this is going to work.


What are you trying to do? Add two lists together? Remember?


Oh.


Sorry, just adding two lists together.


What do you think it'll be? Yeah, you've got to risk it for the biscuit. Hold on.


What is the result of the following code? One way to do it is to just try typing out that backboard.


Or you just go with it.


I think it's either it's one of these lists, right?


Yeah. Why?


I'm just taking a stab in the dark because it's got multiplication in it.


No.


No, comprehension.


Yeah.


I knew it was a list. I just didn't know. I forgot what the difference.


Remember square brackets you could think of. The square looks like an L. So square bracket kind of looks like an L. So that's a list.


Yeah, look, I don't think we are. We're bathed by this, but that's all right. We can help each other out a little bit.


Let's submit it, bro.


This is your first Python quiz. How's it feel?


Oh, man. I can't say I did that easy.


Hey, it's pretty hard. I found it pretty hard to submit.


What'd you get? Oh, you got one wrong. Which one was it?


String. A string is slicing.


Oh, no. It's a way to make a substring of a string in Python. So that'd be number C there.


All right. Week one done.


All right. I think week two goes into some pandas, which I'm really excited for.


How'd you find week one, bro?


To be honest with you, I didn't really understand a whole lot of it.


I had to watch the videos like twice to get around, to start getting around.


I think it's just like because obviously it's a new language and I have no Python experience whatsoever, like just trying to understand it.


And I think that obviously this is probably more complex than what you would normally start off learning in Python.


Well, you're diving straight into data science stuff.


Because what it says on the course is that it's for experience to... experience to what? No.


Intermediate?


Intermediate, yeah, experience level. So like they expect that you already have programming knowledge, where I don't.


Oh, look what I got.


Look delivery. Let me set this stuff down.


I think you already know what this is.


Get it open. Yes.


Python for Data Analysis by Wes McKinney.


This is the book I've been talking about the last few videos of live stream earlier in this one, I believe.


I'm excited to learn some pandas going over that introductory data science course with my brother.


It got me pumped. Some of it was hard, but it's good when you're learning new things and progressing


and looking at something you thought you knew from a different angle.


It just feels like it's unlocking a different part of your brain.


But now I'm going to get into a deep reinforcement learning lecture.


Kind of switch gears a little bit from just straight up Python and data analysis into some deep reinforcement learning.


Because that's what we're learning about.


I said in the last video at Max Kelsen, we're working on some...


We've chosen to take on a deep reinforcement learning problem.


So there's a deep reinforcement learning bootcamp.


I'm saying so many words on YouTube, which you can check out.


There'll be a link in the description.


It's time to get in. I'm up to the second lecture.


I said I'd do three before we got back next Monday.


I'm going to get into the second one now.


Whoa.


Deep reinforcement learning is blowing my mind.


I just finished the second lecture of the deep RL bootcamp.


They're nice and short. They're about an hour long, but I watched them on 1.5x speed.


So maybe it takes half an hour or so.


But I've got some notes here.


Essentially, there's a lot of math to go through.


A lot of it was theory so far.


And I'll be honest with you guys.


Those equations, they scare me.


Well, not scare me, but just because I don't fully understand them.


But that doesn't mean I can't.


So I'm going to practice some of this stuff with actual code in the very near future,


like PyTorch or something.


And here's one of the slides that stood out to me.


This is the final one.


Deep reinforcement learning from pixels to actions.


That's what I'm really, really fascinated about.


So how do you take something from just like a game, for example,


like DeepMind did with Atari or did with Go, and turn it into pixels?


Because my dogs are...


Well, this is a good example.


My dogs are seeing some light particles from a stranger or someone walking along the street.


Oh, there we go.


Someone skateboarding.


Perfect example.


So my dogs are seeing the light reflect or reflect from her as she's skateboarding.


They're hitting their brain, and they've learned from previous experience of dealing with us,


my family, that that girl is not a part of our family,


and so she might be a stranger, and their instinct is telling them to bark.


Now, how does that relate to deep reinforcement learning?


Well, how do you get an agent to look at a game or to, for example, a self-driving car,


to look at the world, turn it into a pixel space, and then learn actions from that pixel space?


Such a beautiful problem.


Such a...


That's the problem of life, right, is how do you act in the world?


We get deep on this channel.


Deep learning.


I need to go work out.


Want to see something funny?


Check out that hat hair.


All sweaty, and I've been wearing that hat all day.


I definitely need a shower.


You know, I've been thinking.


I get a lot of questions.


Where do I start?


Where's the right place to begin?


What course should I do first?


I've always had a little bit of trouble answering that.


My bad.


Fogged up there.


That shower was nice and warm.


My face is probably all red because...


Oh, it was so hot.


I love jumping on beds.


The fun is, remember when you were a kid and you just bounce up and down on the bed?


But where is the best place to start?


Truth is, I'm not entirely sure.


In fact, I don't know how I got started


and the reason why I document and share so much of my journey


is because I'm really figuring it out myself.


I'm trying a bunch of different things and just exploring the world.


Trying to be like a little kid.


We have so much to learn from kids.


Just exploring the world, trying things out, seeing what works, seeing what doesn't.


And I mean, is there a best course you could do?


And this goes for learning anything, not just data science and machine learning.


Is there a best course you could do? Probably.


But is it likelihood that you'll find it based on everything that's on the internet,


based on all the different universities out there?


It's unlikely, right?


So that's why the beautiful thing about the internet is


you can jump on anywhere and try something.


You can check it out for yourself.


And the worst thing that's going to happen is if you don't complete it.


Well then you learn that you didn't like that and you move on to the next thing.


Let's go check in with my bro and see if he wants to keep learning tomorrow.


He better, right?


Wait, hang on to it. Let's get back up to the teleports.


Let's go in anyway.


There's one guy left.


He's streaming Fortnite.


Bro, are we on for tomorrow? Yes.


What's your advice to people who want to jump in and learn data science?


Just get amongst it.


You heard it first guys.


Two beds, one video. My brother just got a new bed.


It's a little bit comfier than mine. I'm a tiny bit jealous.


Wait, come back! Let me in!


Check out my brother on Facebook. He streams Fortnite.


If you're into that, I've never played Fortnite. I don't know what it is.


It does do damage.


Dude, the thing's nearly dead.


Foobs, what are you talking about? Unless that's you crashing all the time.


Or next week. Or whenever the next video comes out.


Something just come to me. I'm editing this video that you're watching right now.


And I realised, I didn't quite say enough towards the end.


I said, just jump in and try something.


I think the other caveat to that is, whenever you're trying something new,


something you've never done before, you're going to suck.


And sucking is hard.


When you're bad at something, it's hard to keep going.


What do you do when it's hard to keep going?


What other option do you have?


You can give up, or you can keep going.


Just keep going. Remember those three words?


Life goes on, just keep going, you're going to die.


Three words, whichever three you want to pick to be your motivation.


But, that's all. Here's my favourite one actually.


Embrace the suck.


When you try something new, you're going to be bad at it for a little while.


I'm bad at most of the things I do, but I know I can continually improve


step by step if I keep putting in the work.


So, if you're trying something new, if you're just jumping in,


realise it'll be hard, there'll be times it'll suck, but just keep going.


Keep learning. And this is definitely the final clip.


What I do when I don't feel like studying | Learning Intelligence 33

What is going on y'all? Welcome back to another episode of Learning Intelligence


and I just woke up from a nap. I was sleeping on this couch here for about


20 or so minutes and I'm about to get back into the voice user interface


capstone project for the AI nanodegree. I'll be honest with you guys


because that's what this channel is about. I want to share the good times and


also the not so good times, the hard times. I was facing a little bit of


resistance today. What does that mean? Well it's when you know you should


be doing something else but you're sort of filling in the time with other tasks.


So this morning I've done a workout, I've cooked some food, I've listened to a


podcast, amazing podcast on sleep on the Joe Rogan show actually with Matthew


Walker. I'll link that below so you can check it out as well. But I've been


doing a bunch of other things instead of studying finishing off the artificial


intelligence nanodegree because I'm going to a sticking point, I'm going to a hard point


and that's where you always hear roadblockers. When you get to


that point where it's not as free-flowing as what it is at the start


that a excitement sort of wear it off a little bit and it's just about


getting the work done, getting into it. That's where I'm at. Essentially it's


just me being like I don't want to do the hard thing right so I'm gonna not


listen to that resistance force anymore the part of me going Daniel you could do


all these other things today. I'm gonna get into it right now so that's the


project for today. I'm gonna spend at least three hours on it. I'm rested it's


about lunchtime now so the whole afternoon cleared to get back into some


study.


Alexa 25 minutes timer. 25 minutes starting now.


See you in 90 minutes.


Oh I can I can hear myself talk now. It's amazing what just 15 minutes of


dedicated work I mean why 15 minutes well that's that's what I find about


that once you get about 15 minutes into something you're you're on a roll right


so that time just flew by. I've made some progress on the project and I'm really


really happy with it so far. So what I got here is a is a LSTM or RNN model


training to recognize speech input from this sort of stuff so this is a what is


it called a Mel frequency septual coefficient so that's a mouthful there


but essentially what it is is a sound wave coming through the air getting


translated or transformed into an image by by different heat maps so this is


intensity of sound wave and whatnot and then we're using RNNs to to model the


sequences that it comes in off that and that's probably not the greatest


explanation of of how this model is working but once I finish the entire


project I'll be sure to upload it to github so you can check it out but


otherwise I've read two blog posts here that have really really helped me out


understand a few things this one is from machine learning mastery which is


understanding the difference between return sequences and return states for


LSTM and Keras. I use that where did I use that over here maybe return


sequences equals true so I'll let you read that blog post so you can get up up


to date on that as well and then one more over here by Kola or Cola I believe


Cola or Kola I'm not entirely sure understanding LSTM networks which is a


relatively old blog post in terms of this field how fast it moves but it's a


great explanation on LSTM networks with a bunch of different diagrams I'm gonna


have a 30 minute break then do another 90 minute cycle and hopefully my


networks finish training by then.


During my breaks I try not to go on my phone because that thing is is just a


distraction device I mean it's okay for a little burst here and here and there


around around downtime and whatnot but when I'm in study mode I want to be in


study mode I don't want to get distracted by by anything too heavy


usually I'll go for a little walk as you just saw in the backyard I'll have


maybe a snack or something maybe maybe not I just had an apple because I felt


like having an apple now I'm gonna have the rest of my break you know let me go


back to this couch again and sleep and let what I've just learned sink into my


brain because I just listened to that podcast on sleep seriously listen to it


you'll love it and then we'll get back into another 90 minute cycle hey Siri


stop the timer hey Siri stop the timer now back in the code


Alexa set the timer and there we are three hours done and why was I


procrastinating so much who knows but my advice is is when you're in one of those


funks just just get in set yourself a timer for 15 minutes or something like


that and once you get that initial momentum done the hardest step is


usually I'm sorry yeah the hardest step is usually the first part and then you


get the momentum done and all of a sudden I'm 75% through this project I've


got my second last model training here it's gonna take a while but it's only


through one epoch I'm gonna gonna stop it early though because I've got to go


somewhere and and look after a house but I've got a few errors here I'm gonna fix


them up I've left myself some notes of what to do I do this every time I'm with


a project so I know what to do when I come back next and we can knock this off


the to-do list today so see here I had artificial intelligence an agree times


six Pomodoros is done one two three and Pomodoros are 25 minutes each so an hour


and a half equals three and then another hour and a half so three hours done of


concentrated work on this for the rest of the week I've got work at Max Kelson


for Wednesday to Friday so I'm really really excited there I'm not sure what


we'll be working on still concentrating on trying to finish the artificial


intelligence and I'm gonna agree we're gonna wrap this video up and as always


keep learning but a side note to that don't forget that even people like


myself we get into funks as well we're all human we're all on this this


learning journey together when the times get tough just remember what's the


alternative I just not do it and just just continually have that feeling but


it's like no I'm gonna get in I'm gonna get this project done and I'm not gonna


listen to to that voice in my head that's like oh but there's so many other


things you could be doing not this time I win leave a comment below what do you


do to get over those points where everything is difficult and it's like I


don't really want to do it


Completing Google Cloud Platform Machine Learning Fundamentals | Learning Intelligence 44

I'm going to eat some coffee for today, it's a big day on tour.


First of all, I've got the Google Cloud Platform training that I'm supposed to be doing.


And then after that, do that for a little bit.


We've got to talk with Shaik and Ahmad.


The magical D&D.


Okay, so let me, let me start recording.


Yeah.


And then we'll do a workout with Josh and by the end of today, try and figure out whether


I should continue on with the data science assignment four or keep going with the little


project we were working on yesterday, Banana Not Banana.


He's not a banana!


At the moment, I'm more excited by the project I'm doing.


My own project.


You know, when you're really interested in something.


I mean, I know I need to do the data science assignment, but you got to chase what you're


excited by, right?


Let's do it.


Morning, bro.


What are you up to today?


Me?


No, the other person in the room.


I've got to go for a coffee, I think.


Go for a coffee with the rent.


Beautiful.


Good idea.


So the Google Cloud Platform certification is a data engineering course of some sort.


So I can figure out how to use the Google Cloud Platform like AWS, like Microsoft Azure


to host data engineering pipelines.


So that might be a machine learning model or something like that, because it's a part


of what we're working on at Max Kelsen.


We partner with Google Cloud and they've given us some free courses to do on Coursera, which


I'm really thankful for.


I'm really excited about.


Because if we need to run data science pipelines or machine learning models, whatnot, and get


them into production, rather than just having them sit on the cloud, we want people to be


able to use them.


So that's one really valuable thing about getting a product to market is having it accessible


to people all over the world.


And Google Cloud Platform allows for that.


I mean, they all do, but I'm going to be studying Google Cloud Platform today.


So I'm going to go ahead and turn on the computer.


Alexa stop the alarm.


This is a real cool module already.


And where's the value in learning stuff from here?


Well, I think one of the use cases that I think his name is, is Tuck or Fluck?


Luck.


He's an awesome instructor.


He said when he was an academic, he used to run machine learning models on weather and


whatnot.


And he used to run all the computing on the universities on premise.


And now when he switched to a startup, they use the cloud and they could run the same


things.


And this is, I mean, if you're new to machine learning or new to data science or new to


any type of computing, you might be thinking, well, the cloud is the only way to go.


And yeah, I agree with you.


But in the past, he said what used to take them four years to run, they were running


every two weeks at startups and using the cloud because that's the access we have today,


right?


In the past, you might've had to have your own machine, have a really big piece of hardware


sitting in your room before you could even run something.


But now we can use this baby little laptop, dial up to the cloud and run it on some awesome


computing hardware that we don't even own and then pay per second.


And so where am I up to now?


Well, the next is GPUs on demand.


That's a lecture that we're up to.


And I'll run through some of the curriculum in a sec after I've talked with my boys.


So we'll do that and then we'll get back into this course.


So what's going on in there?


Yo, what's going on, team?


So I want to start off with some brief introductions and I'm hoping that both of them can tell


me about where they're from, are you employed or in school and what you're currently interested


in right now.


Daniel first, ladies first.


Ladies first.


All right.


Thanks, man.


Hello, everyone.


Nice to meet you.


My name is Daniel.


And I am currently a machine learning engineer at Max Kelson, which is a technology company.


We have about 25, 27 people in Brisbane.


I like it.


I like it because it's small, but we work on big projects.


I'm also a student because I think studying is a lifelong journey and it's all about just


continual learning.


And at the moment, I always see myself as a beginner with whatever I do.


So no matter how much experience I have in something, there's always something more to


learn, which is exciting.


And that was really fun.


You'll probably be able to see the full video on Hamad's or Sheikh's channel or something


like that, but I'll link it in the description once it's done.


Really good conversation about how to get started in AI, data science.


Sheikh's 14 and 15, sorry, from India and learning AI.


It's a fun place to be right now.


Back to study.


All right, my Pomodoro time is telling me it's time to take a break.


So let's start that.


I'm going for a little bit of a walk, shall we?


It's been great fun so far.


Running through two labs and the first one was compute engine.


So getting Google's compute engine.


I really want to sit down actually.


I've been sitting down for a past hour.


So first one was Google's compute engine and then the second one was Google's storage or


GCS, Google Cloud Storage.


And why is that?


Why are they important?


Well, that's what every computer is made of, right?


It's made of something that can do compute, CPU and something that can store things, RAM


and hard drives.


And so what essentially the labs are go through is just showing you how to get one started.


And I mean, it's a few clicks of a button and that's what's important.


That's why I'm all about things that are simple.


And if something is too hard, takes too many steps to use, then I'm not going to use it.


So I think Google making their cloud being easy to use is a big plus in my book.


So the next time I want to install something, it's as easy as Git clone, just like you would


on your local machine, but you can do it on the Google cloud platform.


I need to take care of these parties.


What you barking at?


What's going on?


And then we'll get back to some study.


Break time's over.


Back to these parties.


Come on.


Break time.


We're going to finish all of these modules today.


Trust me.


All right.


Time for another break.


I think my brother's waiting to go.


Hey, bro.


What are you doing there?


What do you want to do?


What have we got there?


You got the drift trikes ready?


Everybody ready?


religion course it's like bumper cars and drift pricing one


not a bad way to spend a steady break hey bro


that was bad how's your course going?


yeah good um just about to wrap it up actually almost finished about to move on to the next


one yeah


but josh is doing ios doing angela's ios


actually you yeah yeah sweet


young young


she's a good teacher hey yeah yeah if you want to learn ios jump onto that course but


now it's time to back to the back to the google cloud


oops sorry man


check it out we've we've worked through a large portion of this course what's next


next is carrying out machine learning with tensorflow and i just learned about data lab


which you may have been you may have used it before but data lab is is a jupyter notebook


that runs on google's cloud platform and allows you direct access to their compute power so


that would come in pretty handy we're going to go to gym now and then come home and finish


the rest of it who's going to lift zeno you in?


you coming jim?


yeah i'm coming jim josh you coming jim?


yeah you look keen for it bro


woohoo


all showered up finished jim how cool are my slippers and pjs now what is it it's friday


night i think it's like 9pm 9.37pm we're going to finish this course tonight i've got probably


an hour worth of study to go but i promised myself i'm going to finish it and i don't


break promises with myself so let's get into it i'm up to machine learning with tensorflow


and google cloud


we did it check it out congratulations you've successfully completed course one of five


google cloud program google cloud platform big data and machine learning fundamentals


oh yeah the last course or the last part of it was my favorite it was the ml apis and


what what could you use those for so they're pre-trained and google's google machine learning


models on a bunch of google data so for the problem we were doing yesterday josh remember


banana not banana we created our own model but if we wanted to to use one of google's


pre-trained ones say on the vision api we could use that and check it out they give


demos of it in the in a jupiter style notebook so here's a vision api for translation it


will read that sign and then give you the output here boom and then it will translate


it for you translate it please care for and protect the health to create a beautiful water


environment what a lovely sign and then we go down oh here's the vision api so if you


go to cloud.google.com vision you can test out the api and now we're going to see how


it how it works you can drag and drop an image here's our banana from yesterday how's the


google cloud api going to go oh much better banana detected 87 so maybe maybe we'll look


into that to see if it can do do more than one thing but it's definitely past my bedtime


now it's it's 10 57 p.m it's time to rock and roll we got the whole course done in a


day that's not even going to focus but there we go all all done all ticked off can go off


the notebook boom there we go done i learned a new saying today um i think it's it's not


it's not my first language so forgive me if i get it wrong but it's my friends hamad and


shane taught it to me it was pana ben caro now i really hope that's the right sign off


message but even if it's not even if it's saying it's not wow see how tired i am it's


been a good day pana ben caro peace out


not right


Foundation Models | On the opportunities and risks of calling pre-trained models “Foundation Models”

Hello there! Perhaps you noticed that Ms. Coffee Bean is fascinated by lots and lots of data.


Too fascinated if you ask me. With the scaling law paper in Vision Transformers and now with


How to Train Your VIT, we are seeing impressive performance of big transformer models on lots


and lots of data. With the release of How to Train Your VIT, more than 50,000 models, yes 50,000,


have been released by the Google Brain team. Which is great, because these models do not have to be


trained again and the whole community can start using them. Ms. Coffee Bean, did you sound the


foundation models alarm? Oh come on, who invited the term foundation models into this video?


Ms. Coffee Bean, we need to talk about the question whether VIT counts as a foundation model,


about the term foundation model itself and about some interesting points we saw in the


foundation model paper, like the question of understanding and generalization power of


models under distribution shift. Ms. Coffee Bean, are you ready for this? Should we sound the rant


warning alarm? Feel free to skip through the video chapters when it gets too much.


While reading the abstract of this new, well who are we kidding, it's machine learning, so this is


already old, I was saying that I was reading the abstract of How to Train Your VIT, which explains


to us how vision transformers can be adapted to many downstream tasks. Oh, so this must be one of


these foundation models, right? You know, this paper, rather a book, published by everyone at


Stanford, where they rename pre-trained models into foundation models. But there is no mention


of VIT or DIT, just a reference, among many others, to the paper about emerging properties


in vision transformers, that's it. So why didn't they mention VIT? Honestly, no idea. Let's check


the definition of foundation models to see for ourselves. A foundation model is any model that


is trained on broad data at scale and can be adapted, fine-tuned, to a wide range of downstream


tasks. VIT is trained on more than 300 million images, but is this broad enough? How broad


is broad data at scale? Is something like ImageNet with 1.2 million images large scale?


I mean, it seems like 2009 it was, but nowadays ImageNet counts as academic-sized data set,


and it is put into contrast with the hundreds of millions of images VIT was trained on. So


is ImageNet now big or small? What scale does scale have? Well, let's suppose that 300 million


images for VIT, a scale that not many have, is large enough. So condition of broad data at scale


for VIT, check. Second condition, can be adapted to a wide range of downstream tasks. But again,


how should we know from this definition that this enumeration is wide enough? What about now?


How wide is wide? Well, how is this definition of any help in making terminology more accurate?


You see, me and Ms. Coffee Bean have a love-hate relationship with the term. Ms. Coffee Bean,


the YouTuber, loves it. I am the PhD student and I am mad at this as a small bystander to this PR


stunt that seems to have worked quite well. Even if there are enough people in AI who criticize


this move, there are people already happily starting to use this term of foundation models.


Small PhD student rant here, but was it really a necessity that now every time one uses this term


that fits everywhere and just replaces some older, non-attributed terms, all these researchers have


to receive a whole citation? Okay, but forget about this. Let's look specifically at the section


where they explain why they chose the name they did. They say that this term, foundation models,


will fill a void. A void I was not really aware of? We cite, existing terms capture the technical


dimension. Because the grandeur of a paradigm shift is not visible. So their idea is, let's replace


exact technical terms through a vague term, which is literary and metaphoric in its interpretations,


because on one side we have the confused researcher about when to use or not to use the term


foundation models, for the VAT example or ResNet. But on the other side, the uninformed broad public


and possibly supporters of this new center for research on foundation models should suddenly get


it that it is about models laying a foundation, but that a foundation does not build an entire house.


This is deep. Wow, I'm making Ms. Coffee Bean angrier and angrier, because she does not agree


with me. You know, Ms. Coffee Bean, who was created by a PR move herself, actually loves this. And


it's nice. It's more drama, more PR, more catchy terms for grant applications and paper titles.


So do not forget to add this one to your list. Even more, from the perspective of someone who


is not involved in research on these models or from the perspective of someone who likes to


educate the broader public about AI, what is really, really great about this paper is that it


is an extensive place to learn all about these big models for anyone not closely familiar with them.


It's like a manual. So she wishes you happy reading of a document where researchers have no space


limit. Now, not really to spare you the reading, but more to give you some spoilers. Here are Ms.


Coffee Bean's favorite takes from this book or paper of 200 pages, whatever. It was really scary


to read the section about incentives where they say that, we cite, just as the pharmaceutical


industry has little incentive to devote significant resources to the research and development of


malaria treatments, because poor people cannot afford medications, the tech industry has little


incentive to devote significant resources to technologies designed for improving the condition


of poor and marginalized people. So in contrast, universities are much, much better. But I don't


know if things are so black and white. When Ms. Coffee Bean sees the broad spread aversion of


academics for actually doing science communication and that there are companies that do better than


described here, we're not that sure. Then Ms. Coffee Bean and me both liked the section on


understanding, where it is all about the questions that are going on lately after seeing how good


these models become. But do they understand? Does GPT-3 understand? We even had a video


previously about the restricted question of whether a text-only system could ever understand,


asked by Bender and Coller, and a follow-up on what more is required to get to understanding,


based on a paper by Bisk and collaborators. The view in the Foundation Models paper is a little


broader, with snapshots taken from different philosophical sides. And you know, when it gets


philosophical, opinions tend to diverge. The paper tells us that there is the metaphysical


question of what understanding really means, and then people's answers diverge. But supposedly,


we get an answer there, on which we all agree, then we need to go further into the epistemological


question, which is a fancy word for saying that we are unsure about whether we would ever be able


to exhaustively test and analyze and dissect the model to know whether it has understanding. This


means that even if a model would have understanding, we would not be able to say whether it does so,


because we might not have the right means or even the right measure for detecting understanding.


The authors highlight that it's the same with intelligence, with the Turing test that has been


passed many times already, but we still don't call these systems intelligent, because we keep moving


the goalpost. We even like to leave the question open of whether understanding even matters in


these models. Right now, we do not know, and people keep disagreeing, exactly like me and


Ms. Coffee Bean. My opinion, me as a researcher, is that understanding is a crucial component for


these models. Only then we can trust them, be comfortable with them and rely on them when they


do difficult things, like driving our cars or caring for us. And then there's Ms. Coffee Bean,


the incarnation of pragmatism. She thinks that we do not need models to understand for us to


comfortably interact with them. We interact with things that we do not understand daily. When


browsing a website, most people do not know about IP addresses and DNS servers or any other


technicalities of the internet, but they use and trust it nonetheless. Ms. Coffee Bean thinks that


in the future, at the moment when these models will get close to perfect, it will all become a


risk assessment. Supposedly, we know from lots of data statistics that a self-driving car crashed


100 times in the last 20 years and otherwise was driving perfectly throughout the whole world.


But we know that there were 1000 human drivers that crashed due to alcohol consumption or whatever


in the last 20 years. Do we really need to know that a self-driving car understands to trust it


more and to use it? What do you think about this? Let us know. The comment section is for you to


crash. Otherwise, in the paper it was nice to see more emphasis put on the data creation process


and the push to create a data hub for the whole community, because these models rarely, and by


that we actually mean never, get any better than the data they are trained on. Then it is even more


surprising to see how the point on distribution shift was set up. So this is a point we really


thought we need to discuss. It's not about the message itself, with which we think we agree,


but about how the message is presented. We cite, Existing work shows that pre-training on unlabeled


data is an effective, general-purpose way to improve accuracy on OOD, which is out-of-domain,


test distributions. This, and almost every other machine learning talk Ms. Coffee Bean heard,


makes it sound like increasing the amount of training data increases the out-of-domain


performance. Don't get us wrong, we completely agree that scaling up the data increases performance.


But we do not really agree on this strong emphasis on OOD. So OOD means that we have types of data


that the models have not yet seen. This is of course in contrast with in-distribution,


which contains types of data that models have already seen. I mean, look at the example here.


In-distribution is satellite data of the whole world, since the model was pre-trained on that.


Then how is satellite data of Malawi out of distribution, since Malawi is part of the world?


We are OOD skeptics in a sense. We have not yet seen these models perform well out of their comfort


zone, which is OOD. What we have seen is how expanding the amounts of training data,


the in-domain distribution was expanded a lot too. We just make the training data so huge,


that most of the testing you do is actually still in training distribution. In other words,


nothing will be ever out of training distribution if the whole world was seen during training. And


combine this with our incapability of really imagining huge data amounts and we are ready


to think that we have created something more powerful than it actually is. Why can GPT-3


write HTML code and perfect this in few-shot learning? Because it had HTML code during the


pre-training stage already. But why do we talk about data samples like they were out of


distribution when they are mostly an interpolated version of a sparsely sampled in-distribution?


The authors give the example of Clip performing well on ImageNet sketch. But of course, it was


trained on 400 million images from the internet, where enough of them were containing sketches. To


us it seems like foundation models are not really a solution to the generalization problem, since


one just completely resolves the need for generalization by letting the model interpolate


or even specialize during fine-tuning, but not generalize. We do not negate the advantages of


scaling up data, it works very well to increase the data so much that everything becomes in


distribution. But we just advocate for more honesty about where the performance gains of


foundation models actually come from. If you're looking for real alternatives and real out-of-domain


generalization, where your model can really handle new data that is substantially different from what


it has seen before, look in other places. Someone in this community proposed Jeff Hawking's HTML


model. Check it out. Otherwise, we really enjoyed and appreciated the section on alignment, where


the authors say that one challenge is the misalignment between the foundation model's


training objective and the desired behavior. This really speaks from our heart. And if you want our


take on this in relation to CLIP, watch our video where we highlight how alignment has already


started to be a problem, when CLIP's normal behavior, following its training objective,


was blamed to have been the victim of an adversarial attack. Well, wow, this was the video.


Congrats for making it until the end. But what do you think about foundation models? Let us know


your perspective in the comments to help settle my divergence with Ms. Coffee Bean.


How to get experience as a beginner data scientist

Look, I just finished recording this video that you're about to watch and I realize it's,


I said that we're going to go for about 10 minutes but it goes for longer and so clearly


I need to work on that but I kind of had my verbosity set to two. That's a nerdy machine


learning joke. You probably get it if you've ever built a carer's model. Anyway, there's


timestamps below if you want to check them out. Otherwise, there's also, I'll remind you in the


video but there's an article below if you prefer to read. Treat this video as something like you


can listen to in the background while you're cooking a nice meal or going for a walk or


something like that because you don't necessarily have to just sit there and watch me talk about


the points throughout it. But yeah, enjoy. Manuela sent me an email the other day and it said


something along the lines of, how can a beginner data scientist like me, actually, let me just


find it and I'll read it out to you exactly. Mail, Manuela, here we go. Hey Daniel, I have a


straightforward question. How can a beginner data scientist like me acquire experience? I did some


courses and did some projects but some interviewers said, I liked your formation but you don't have


the minimum experience we want. Come back later. Nowadays, many companies hire freelancers through


freelancer companies. To me, it seems I'm running in circles. So the main question was,


how can a beginner data scientist like me gain experience? And so what I did was, I wrote back


a fairly lengthy email with some points of, now this is a big disclaimer on this video actually,


is that I'm not going through any kind of job at the moment. I work for myself and I'm happy doing


that for the time being. So you should take, you should be very cautious of anyone offering,


including myself, this is a disclaimer, anyone offering advice that they're not actually pursuing


themselves. However, because Manuela asked me this question directly, I replied as if,


or I replied with the steps I would take if I was going for a job. So I've turned them into


an article as well. So if you want, you can read that article. But what we're going to do in this


video is the next 10 minutes or so, I've even got a stopwatch here to kind of make sure I'm on time.


We're going to just talk through some of those points and I'm going to rip on them


as soon as I get my dogs. Puppies, gotta love them. Now, where were we? Well, the first point was,


how does a beginner like me get experience? So if you were going to apply for a job,


a lot of them say, you'll read the job postings and they'll go, required, here's your skills,


you might require, let's say, Pandas, Matplotlib experience, data analysis, modeling, etc, etc.


Python code, maybe some SQL, if you have to query databases, etc. etc. You might go, yep,


yep, I've ticked, I've got some experience doing those things. But then you'll see the dreaded dot


point, the one that says minimum three plus years experience. And of course, if you've been studying


like many of us do online by yourself, chances are, you don't necessarily, or like Memoella,


doing some courses, doing some projects, you don't necessarily have three plus years experience. I


mean, me personally, I don't have, I think I just crossed the three year mark of studying machine


learning full stop. Like I started in maybe July 2017. And it's, it's now July 2020. So I've got


three years of just starting to now. And I've got about 14 months of hands on experience in an actual


role. So if I did want to apply for one of these roles, that said something like you need three


plus years experience, what would I do? Now, the first point you have to remember is, Memoella said


that he'd done some courses and projects. There's a gap, there's a gap between courses and


experience. And now I can say this, because I'm someone who, first of all, has hands on machine


learning experience, and second of all, teaches a machine learning course. So what happens in a


machine learning course, or any type of online course, is the instructor has done a lot of the


thinking for you. So they've, of course, you can't cover everything in a single course, otherwise,


it'd be far too long. So they've taken the entire corpus of whatever topic it is, say data science,


or machine learning, they've compressed it into they compress the knowledge in machine learning


terms, they'd be like an encoder, compressing the knowledge into a lower feature space,


making the knowledge understandable for someone who's like a beginner going through a course.


Because if you start too far, too outlandish too far, the students are going to be like, well,


I don't really understand what's going on. So you have to build it up, you have to it's almost like


you're holding a student's hand as they're going through. And I've experienced this both on both


sides. So going through a course and, and then finishing a course and try my own project and


going, wow, it's actually quite hard to, to bridge the experience you get in the course


to working on a project of your own. Now, this includes the projects that are within courses,


because for a project to be within a course, again, same thing has to happen, the instructor


has to do the thinking for you, they have to get the knowledge of what the project takes,


and compress it into an understandable form to step you through it to go, hey, here's what you


do first, here's what you do next. And here's the outcome you should you should probably be looking


for. Whereas real life projects don't really have that, you might have some idea of what the project


might you might want it to look like. But chances are, if you've ever worked on anything, anything of


your own, it doesn't usually turn out exactly how you imagine it when you first begin. Because of


course, as you go, you're going to learn different things. Now the example I had here between the


courses and experience gap is, remember how many times when you were little, maybe your mother and


father told you how hot the stove was to, oh, don't touch the stove, don't touch the stove,


don't touch the stove, it's hot, hot, hot, hot. Maybe you're two years old, three years old,


or something like that. And you're reaching you, you're exploring the world you, you can't really


see but you're trying to feel around your own what's this? And they're saying, stay away from


the stove. Now chances are, you probably heard them say this a number of times. Similar to how


an instructor steps you through a project and go on at this step, you want to make sure your data


is in the right shape before you pass it to a neural network. But chances are, you didn't really


learn that the stove was hot until you touch the stove. So you you're down here and you're reaching


around and you're going your parents hot, hot, hot, hot, and you're you're still reaching around.


Right? And that's a lesson that you're never going to forget, are you? And now, luckily, that has,


that doesn't necessarily have to happen for everything. Thankfully, we humans can learn that


it's not a wise idea to get hands on experience crossing the road and get hit by a car to know


that it's it's not safe to cross the road when you don't look both ways. But the lesson here is,


or how we can relate that back to the courses and experience gap, is that the the courses,


courses are the equivalent of your parents telling you don't touch the stove.


Whereas getting your own experience is the equivalent of you actually touching the stove.


So do you see how those two different types of or those two different experiences are different


types of knowledge? So courses are yes, important to begin with, you get foundation knowledge,


they're nice to have. But that knowledge really won't be cemented until you put it into practice


yourself. And this leads us into point two, which is start the job before you have it.


Now, I posted the article that I wrote to go along with this video on LinkedIn, and someone commented


on on the post, giving their their exact hands on experience with this point to start the job


before you have it. Now this is this one's lying in plain sight. This one here is that if you have


an ideal role that you can think of, whatever it is, whatever company it is, whether it's not even


a company, to be honest, like you can do this. The beautiful thing about a lot of these points is that


it they relate to not only getting experience as a data scientist, but even if you weren't going


for a data scientist role, it can be related to almost any endeavor. So the advice is the


advice contained in here is generalizable. Start the job before you have it. Now, the comment on


the LinkedIn post was something to do with freelancing. So I mentioned freelancers,


so many companies hire freelancers through freelancer companies. So let's say you're on


a freelancing website, and you saw a job posting there. This is what this person did on LinkedIn.


And they said they saw a web scraping role. And they didn't have the full on experience that they


needed to apply for the role. So they looked at the job posting. And they looked at what it required.


And say it required these three steps. Well, they went and did those three steps over the next week


or so, and then contacted the person who posted the role and said, Hey, I've actually got a little


bit of a head start on these three steps. I don't have I may not have the exact experience that


you're you're you're asking for this, but I've actually started this project already. And so


that's what you can do if you want hands on experience with anything, with any job with


any role with any activity started before you formally have it. So let's say for example, for


my concrete example that you can actually refer to is a couple of weeks ago, I worked on an Airbnb


project. So let's say I wanted to Airbnb is really one of the handful of companies that I'd work for,


because I just like I like what they offer. I like their experience. I like their design.


And I think it's a it's a cool thing that they're doing the cool thing that they're building.


If you're interested, the other ones are probably any of Elon Musk companies. But at the moment,


as I said, I prefer to work on my own and build my own. Anyway, I replicated to get experience,


I replicated one of Airbnb's machine learning projects. So they posted an article. It was


amenity detection, which is using object detection computer vision to understand different pieces of


furniture in a photo. So say for example, you uploaded your house to a photo of your house


to Airbnb, what amenities do you have such as a fridge, a microwave and oven? Airbnb use computer


vision to see if that could assist in filling out the property listing. So I just spent six weeks


replicating that I wasn't going for a job at Airbnb. But if I was I could go Hey, you know what?


I could apply to their team and say, you know what, I see what your what your machine learning


team is doing. I actually started this already. I actually started working on this project. I


replicated it myself. I even I personally didn't end up improving on theirs because I didn't have


their internal data. But you can at least say, Hey, I went through these steps. This is what


I've done. We'll get to that point in the moment. But that goes with whatever role that you're


after. Do your research. If you're smart enough to learn the basics and foundations of data science,


you're smart enough to, to, to research and figure out what particular roles require.


And so the next one is the follow up. That was point two, by the way, start the job before you


have it. The follow up. Now, and while I said, some interviewers said, I liked your, your formation,


but you don't have the minimum experience we want to come back later. And this is another very, very


forgotten, forgotten point. It's in a lot of human relationships could actually be improved by


the follow up is that how often have you had an encounter with someone and then it's kind of,


you thought, wow, I'd like to potentially meet that person again. And then it's kind of fizzled


out because neither of you reached out to each other. Now you might have, let's say for example,


might be your crush or something like that. You might be going, well, they should reach out to me.


We had this encounter and I'm not going to, I'm not going to message her or him, whoever it is,


until they messaged me. You know what? They're probably thinking the same thing. Or in the case


of an interviewer who's dealt with, let's say a hundred candidates, which is probably a conservative


estimate, depending on where you're applying for a role. Let's face it, they're not going to


remember you. And the same thing happens. My experience with this is when someone emails


me and when my email inbox is clogged, I try to get back to everyone. Sometimes some slip


through the cracks. You know, the ones I reply to most often are the ones who remind me, hey,


Daniel, you know, you've been pretty slack. You forgot to reply to my original email. Just


wondering if you got it. The likelihood of me replying to that email, the person who followed up


goes up exponentially. So this is, the follow up is nonlinear. So if you think you're first


action of going, getting an interview was stage one, and then if you follow it up, that's level


two. No, it's more like level 10. If you follow up and go, Hey, if you, if I was an MNWLR and I


went and went, went and got experience, went and started the job before I had had it, the follow up


would be, Hey, you know what? I took on your feedback. I went and got some experience. Here's


the project I worked on. Just thought I'd let you know. That's the follow up. That's, that's as simple


as it could be. Nice and simple, nice and direct. And that's what MNWLR is. I'm more, I'm more likely


as well. This is the directness. This is where it's worth practicing some, some sort of business


writing is and being specific with your questions. I'm more likely to reply back to emails that ask


specific questions or a succinct and go, Hey, here's what I did. Would you like to, here's my


question. That's it. Nice and simple. Keep it simple. So the next one is after you followed up,


or again, these points aren't sequential. You can do them in any order is get to know someone. And


now this is, this is really important is that what I found in my experience, again, all of these are


biased with my own experience. So you might have found something else. If you have, leave a comment


below and share your experience is that a lot of job postings, a lot of jobs are actually never


posted in a company. When I was working at Max Kelsen, which is the machine learning company that


I worked in my local city, we had, we had a lot of highs. Our team grew, I think by about three


times, maybe four times the size. Our machine learning team grew. I think about a quarter of


times the size. Our machine learning team grew. I think I was the third. By the time I left, we had


maybe 15 total machine learning team. And a lot of our hires were not from posting roles online


and like a job portal or something like that. They were from people that we knew, like directly knew,


had had contact with. And maybe they didn't have hands-on. Yeah. They didn't have hands-on machine


learning experience. But see, this is, this is human nature. People like to work with people


that they like. That's, that's it. Regardless of, okay, and it might be different for things like


remote work and all that sort of stuff. But if you're in the office interacting with someone,


let's face it, you want that person to be someone you enjoy spending time with.


Now you might go, oh, well, Daniel, what if someone's skill is higher than someone else? And


yes, of course, in a perfect world, that's how it would work. We would hire the person with the


highest skill level. But frankly, the way humans work, and if you're telling yourself, like we're


completely rational, we hire on the, we hire on the best talent for the role, et cetera. Not in


my experience. Okay. You might be different. You might be different, but people usually hire


people they like. And the example of this is that a lot of the hires that we, we brought on,


we didn't even post. We just said, Hey, yeah, we might need someone else. Do you know anyone? Yeah,


actually I do know someone from, from my math class and they've done, they haven't done much


machine learning, but they're pretty quick at learning stuff. So let's, let's get them in and


see what they can do. And again, might be different at different, different companies.


But that's how we essentially hired a lot of our new team members was from someone who knew someone.


And my story for this is I went to an artificial intelligence meetup. I can't remember the date,


but let's just say at some certain point. And then seven months later, I was working for the


company. So Max Coulson that ran that artificial intelligence meetup. And when I went to meet


people, I didn't even know that they, they were hiring. When I went to the meeting, I didn't know


that they, they were hiring. When I went to meet people, they said, meet people at the company.


They said, Oh yeah. Well, I was able to say, sorry. I was able to say, yeah, I went to this


meetup and I was fascinated. I've been, I've been following your guys' work ever since. And so as


much as us nerds who like to spend time at computers like to believe, people still like to


interact with their, their meat bodies. And again, remote work. Yes, I work mostly remote at the


moment, but this is again, different roles require different things. So the way you get to know


someone is when social distancing rules and all that sort of stuff allow you go to events, or we


have a lot of social distancing rules. And the next point, which is share your work. So before we


elaborate on that, getting to know someone, I treat job portals on the internet as dead. So I, if I


click a button to apply for a job, I pretend there is no chance I'm getting that. So I have to seek


out the right person to talk to. And if you want to do this digitally, you can search again, do


your research. If you've got the skill to learn data scientists, data science or machine learning


to look out and research the people who you should talk to contact them, message them directly, just


like Manuela emailed me, be succinct, be direct. It's like, Hey, here's what I'm working on. I see


you've got this position for this role, or they might not even have a position for that role. Just


say, Hey, I'm just touching base. I'm interested. I enjoy your work. I'm looking for experience. I


just, just wanted to get to get to know you or just say hello. That's all it has to be nice and simple.


But now if you're not great at outbound, make the opportunities come to you. This is a,


the other point, share your work. So right now this is not to brag or anything, but because I have


so much work, again, I'm still a newcomer to this field. Like if you look at me and you look at these


videos and you go, wow, Daniel must be an expert. You must realize that I'm still learning as well.


I'm three years, I'm about three years into studying machine learning and working on it full time.


So that in this grand scheme of things, that's basically nothing because real skill comes only


after about 10 years of doing anything hands-on. So I'm about 30% the way through to getting to


real skill. But the reason is I appear that I have more skill than I do is that I, I'm not


afraid to say that I'm a bit more skilled than I am. And that's not a problem. It's a problem.


Is that I, all of my work is public. You can see everything that I've done. If you find me online,


well then it's all there. So a very important point is that there are probably lots more people


that appear more skilled because I've communicated my work. And now is that fair? Well, I don't know.


How would you make that fair? That is, that is my strength is communication. And now again,


could always do with some improving. I'm not here to brag. I'm just telling you that is why I appear


as having skill. And that is why I have at the moment more inbound opportunities. So like people


say, would you like to work on this? I have to reply to, no, sorry, I'm working on other things.


So if the opportunities that you're applying outbound aren't coming in, make them come to you.


Again, I'm not, I'm not doing this to brag. I'll be very upfront with you with the things I'm,


I'm good at, the things I'm not good at. I'm just showing you an example of why it's important,


or one of the points is share your work. So if you are working on something, if you did work


on a project, if you did start the job before you have it, let someone know, even if it doesn't work


out, you could share the lessons and go write it up in an article. The best thing I've done in the


last three to four years is write about what I've been working on. Hands down, that has brought me


more opportunities than anything else is just by writing out, even if it doesn't work, I write


about the story that, Hey, I'm doing this. This is what I'm up. This is what I tried. These are the


steps I took. So that, that is your experience. And if, if someone looks at that and doesn't call


that experience, well, Hey, you probably don't want to work there, right? Like if you've gone out of


your way to go research, I've got some foundational skills with courses. I've started the job before I


had it. I wrote about what I was doing because this is, these are all things that you're going to


actually be doing on the job, right? You're going to be starting a project that you don't know how


is necessarily going to finish. Again, you might have some sort of idea in mind of how it looks,


but likelihood of that changing along the way is very high. And then at some point you're going to


have to communicate to others, here's what I've actually been doing. And so you can do all of


those steps before you sign a contract and start an actual role. No one's holding you back. Like


these things are all out there, all the projects, they just require some effort from you. And so


we'll just quickly go back through what we've talked about the courses and experience gap.


Remember my question back to Manuela would be, okay, you've done some courses and some projects.


Have you touched the stove yet? Because no matter how many times your parents told you,


careful, the stove's hot. My guess is you didn't learn until you got burned. Number two,


we'll start the job before you have it. Nothing is holding you back from starting whatever role,


such as like if you want to make videos, point your camera on, start shooting. If you want to


write articles, type on your keyboard, press publish. The first ones are probably going to be


pretty crap. You can go back and watch all of my original videos, me sitting in my car,


pretty crap. You can go back and read my beginning articles, pretty crap. They got better


with experience. If you want to work at a tech company, research the kind of role you may want,


doesn't have to be exact, then start it. The first time you work on a project like that,


it's probably going to be pretty bad, but you're not comparing, compare yourself to where you were


at the beginning of the project and the end of it. And if you want to shorten the time,


so and give yourself six weeks, worst case, it's only six weeks. Best case, you learned a whole


bunch. That's what I did with my Airbnb project. Number three was the follow-up. Remember steps


in communication are often nonlinear. You might have one interaction with someone and it went


great. Then the amount of times that those interactions sort of just faded off into the dust


because someone forgot to do the follow-up, you probably can't count them. So follow back up. Hey,


here's what I've done. Just following back up and then will it always work? Maybe, maybe not.


Number four is get to know someone. Treat, you might not have to think of it like this,


but I think I personally, I'd tell myself, job portals are dead. If I click that button,


it's always a no. So I have to go out of my way. I have to go above and beyond to figure out a way


to get to know someone because most job postings probably not listed. Number five was share your


work. If you're outbound applications aren't working, make them come to you. Share your work.


That is experience. And if you want some further reading on this topic, a couple of things I would


recommend is the book So Good They Can't Ignore You by Cal Newport. Basically self-explanatory


by the title, but if you're not sure, it's just improve at your skill so that, and share your work,


of course, so that you're so good that people can't ignore you.


But if you want to read 300 pages on that, you can. Next one is another blog post that I did,


which is how to communicate and share your work. So a lot of courses and material,


it's actually part of, like that blog post is part of the machine learning course that I teach,


because a lot of them are very good at telling you what to do, like what code to write, but not


very many of them teach you the how around it. So I'm not talking about how to write the code at all.


I'm talking about what to do outside of the code, because what you'll find is that,


okay, a lot of actually building things and delivering products and whatnot is, of course,


you need the code. That's fundamental. I'm not delusional. You need that before you show people


your work, but there's a lot of steps that go around that to getting something in the world.


And I assume that's probably what you want to do is build things that build the code.


What you want to do is build things that bring other people value. And the final one is don't


call yourself a programmer, which is another blog post, which is basically the read me, read me.md


of starting a role. You might be thinking, Daniel, this video is on how to get experienced before


starting a role. But yes, we talked about this. If you're smart enough to learn skills,


you're smart enough to read that article, don't call yourself a programmer and reverse engineer


what's in that article to start doing that. Start the job before you have it. So there we go.


Touch the stove, start the job before you have it. Don't forget the follow up. Get to know someone,


treat job portals as dead if you want and share your work. And there's a few further reading tips


if you want. So Manuela, I hope you enjoyed that reply. If you've read the article, don't forget,


actually, there's a link below. You can read the article. If you have any other questions like


Manuela's, which was how can a beginner data scientist like me acquire experience,


you can always email me. That's pretty easy to find. Do your research. Otherwise, leave a comment


below of any questions that you may have similar to that could be related to this video. And I'll


probably answer all of them. But as always, keep learning, keep creating and touch the stove.


In-Depth Udacity Deep Learning Nanodegree Review

What's going on learners? Welcome to my deep learning nanodegree review video. So in this


video I'm going to break down the what, when, why, where and how of the deep learning nanodegree


or at least share with you my experiences of each of those. So I've created my own artificial


intelligence master's degree essentially just grabbing courses from the internet and combining


them into a bit of a structured curriculum so I have an idea of what I'm doing and the deep


learning nanodegree was a major part of that. Everything I talk about in this video by the way


I'll be linking in the description so if you if you want to find anything it'll be down there.


As Simon Sinek says let's start with why. Why would you want to do this course? Well if you want to


get into the field of AI deep learning is where it's at and I'm just breaking into the field at


the moment I'm still learning but from all the research I've done and that has been my main


focus for at least the past year or so it's that deep learning is is the forefront of AI and as


Andrew Ng says AI is the new electricity. Artificial intelligence in the future or already now actually


will eventually break into every industry so if you imagine how electricity changed every industry


that's the same thing that's going to happen with artificial intelligence. Once our machines


they're powered by electricity now they're going to be powered by intelligence and so that's going


to deepen our interaction with them further and further. So whatever industry you're in whether


finance, health, anything artificial intelligence will affect you in some way and at the moment


deep learning is the forefront of that that artificial intelligence so having an understanding


at least an overview of it I think will be very helpful no matter what industry you're in.


So before you sign up I think it's important to think about why you want to sign up so I'm just


going to talk to you about go over briefly of why I signed up to the course. So in the past I'd done


some programming some well it wasn't really programming it's just like web design web


development I did the first section of free code camp if you've ever heard of that before and then


after I did more and more research into the world of tech I've always been interested in it. I


graduated in 2015 with a nutrition degree so I've been interested in health but tech is something I


finally decided to get into and start actually making things but I found that programming was


moving more towards from giving the computer instructions rather than telling it what to do


it's moving to showing it what to do. That's a quote from Jeff Hinton by the way is programming


is transitioning from telling a computer what to do to showing a computer what to do and that


transitioned into into the what actually and we'll get to that in a second about what is deep learning


and so I stumbled upon this this course on Coursera I was browsing through the nanodegrees and then


I saw Siraj's videos and that really encapsulated me the way he was teaching the way he was


so passionate about these things I hadn't I heard about artificial intelligence but not in the way


that he was talking about and so he really inspired me to sign up to the deep learning nanodegree


and then as I got into it further and further I got a few months in I realized that artificial


intelligence is the place I need to go to and that's how I created my AI master's degree and


then that's where we are today. So we're finished with why let's move on to what. First of all what


is deep learning? Deep learning is just a neural network with more than one layer so if you imagine


a lasagna right you got sheets of pasta and say a normal neural network is just a lasagna with only


two sheets of pasta not a very fun lasagna but then it becomes deep once you add layers of


layers of pasta right you can imagine deep learning as being a lasagna with three or more sheets


of pasta and what does this mean what why am I talking about sheets you can imagine the sheets


as layers in the neural network if you've never heard of a neural network just imagine a neural


network is very simply just being something that you you take an input you do a bunch of calculations


aka move the input through different layers and the calculations are a mathematical calculations


and then as you move it through the layers it refines that input into an output and then the


output is something that you can use that gives you more value than the original input. So what


are the prerequisites of the deep learning nanodegree? From my perspective I signed up I'd


only had three weeks of python experience in a high school math education you could probably do


it but beware I found it pretty difficult. What would the actual prerequisites be? If you can if


you have any programming experience at all let's say six months to a year in python java c++ or


something like that or if you have a high school math education or an undergraduate math education


you'll be more than ready to take on the deep learning nanodegree however if you don't have


those things you can do what I did I upskilled myself on places like treehouse they did the python


programming track on there and for the math I've learned it all through Khan Academy so linear


algebra uh math sorry matrix multiplication or matrix math a bunch of calculus as well the guys


at Khan Academy this is a this is a bit of a digression but that's an amazing place to learn


anything else that you that you need online and it's completely free don't worry too much about


the the programming needs and the math needs you've had a high school math education and you've done


a little bit of programming before aka a month of python be more than enough jump into the deep


learning nanodegree. So what's covered in the deep learning nanodegree? Well it's broken down into


five main categories first of all is neural networks so building a basic or your first neural


network and it's convolutional neural networks there you classify different dog breeds using


actually convolutional neural networks imagine them used for computer vision so anytime you see


a facial recognition system or something classifying something as a or you'll if you have


a drone that classifies different objects or if you're taking a photo on your camera and it can


see faces that's probably a convolutional neural network and then the recurrent neural networks


so recurrent neural networks imagine it to do with anything to do with language so google translate


is powered by recurrent neural networks in the recurrent neural network project you'll work on


generating a tv script what that means is you'll take a corpus of text and create a brand new tv


script what i did is i worked on took 22 seasons of the simpsons text and created a totally new


scene at moe's bar which was actually really fun four is generative adversarial networks so this is


actually relatively new in the field of deep learning so it came out in 2014 by ian goodfellow


and it combines game theory and deep learning and puts them together and you create some awesome


things in the project and the deep learning category you'll work on generating faces with


gans or generative adversarial networks so what that means is you'll take a data set of different


celebrity faces and use them to generate new faces it's mind-blowing and then the new content which i


haven't done yet is deep reinforcement learning so this is number five which is actually really


exciting for me i wrote an article on how google's deep mind are using deep reinforcement learning


to build smart agents to play starcraft 2 in the deep reinforcement learning from what i've read


i haven't done it yet you will learn how to teach a quadcopter or a drone how to fly some really


exciting stuff in there to work on one of the great things i love about the projects is that


you dive straight in and start building these deep learning neural networks straight from the start


so from the project one which is your first neural network straight into convolutional neural


networks you're building these deep learning architectures that are world class so it's


something that would be in in production level deep learning so that companies and and startups


and whatnot these are the same type of networks that they would be using there and that's what i


really like you're sort of you're diving into the deep end rather than you do build a foundational


knowledge but then you're going off the cliff you you take the training wheels off set yourself on


the path of of building a deep learning network of your own what that means is that if you're like me


and you've had a little bit of you haven't had much programming experience you will probably find


it a little bit difficult but you can get through it if i can get through it so can you i'm not


special at all i just just work through it ask for help and that's another thing you get a dedicated


slack channel to the deep learning that i agree which is incredibly helpful there's thousands of


people in there so so make sure you if you go in you say hello i'm pretty sure you can join it


without actually being a a member of the deep learning that agree i'm not entirely sure so don't


quote me on that and the forum has plenty of questions trust me if you if you think of a


question when you're working through a project or working through a class chances are because there's