it's called ML self study tips. T series or PewDiePie? Definitely PewDiePie. What's the


Self-taught Machine Learning Engineer AMA (live)

most important way to get a job right now you're full to be a data scientist right


now I'm a full stack developer. Coldest trick, coldest trick, read the blog post that I put


down below three things for ML in the description. I wonder if I can link this in the chat. This


is what I do if I'd get Oh my god, everything's going haywire. Anyway, it's in the chat three


things for ML. First of all, you need skills. Second of all, you need to communicate them.


Third of all, you need to meet people in the industry. Thank you all so much for joining


by the way. Holy gosh, we got a lot of questions. Yeah, I'm trying to get to as many as I can


guys we've been going for about an hour. I'll keep going. Why did I choose BJJ? Why not


Muay Thai? Just BJJ is a non striking so I don't really want to cop punches to the head.


I need to use my head for thinking. So I just like wrestling. Where were we at? How can


you set up your own business in AI machine learning with deep miners spending millions


in the research area? Super Fox business. Don't over complicate business. This is what


I'm telling myself. I've never set up a business. But I'm going to this year. I think business


school can be summed up in one one sentence. Bring a customer or a person value and charge


the money for it. That is how you run a business and keep doing that. Right? So don't be scared


like deep, deep mind spending millions researching in the area. Start small. Right? Deep mind


even though they're spending millions of dollars researching and helping lots of people, there's


still way more people in the world that have problems that need to be solved, that are


willing to pay for those problems to be solved. So that's how I'm thinking about it. Choose


a really small area like a niche of whatever it is that your skill set provides for and


solve someone's problem in that and charge money for it. That's how you start a business.


That's how I'm going to be starting a business. Follow along on the channel follow along in


my blog post because I'm going to be sharing how I'm doing it right and it will it work.


Who knows, but I'm going to be sharing what I learned as I go. D hand. What what resource


would you recommend best for maths? I've spoken about this. I would say Khan Academy or Rachel


Thomas's compute computational linear algebra. But to be honest, I don't do enough. I don't


do much math. As a machine learning engineer, I most of the time and just coding. Have you


had a fight with your manager? What is your way of solutions to the same problem? I never


had a fight with with anyone I work with. How do I resolve conflict? That's a good question.


So first of all, the easiest way is to one of the easiest ways to diffuse any conflict


with anyone is to just take responsibility. A lot of people like to shift the blame to


someone else, right? That's where a lot of conflict starts is because some one person


can't take responsibility for something and is trying to offload it to someone else. So


a great way to resolve conflict with a manager or with anyone is to just accept the problem


that's at hand, right? Go, okay, I understand this is a problem. Here's what I'm going to


do to fix it. Or you can stop stop an argument by going, okay, I understand you've got your


view of it. This is my view of it. I'm going to leave it at that. Right that that's a way


to diffuse 99% of arguments is to either take responsibility for it, or accept the fact


that you're going to disagree with someone. Because at the end of the day, it's silly


to think that you're like everyone is going to agree with you. Right? So I would rather


put it this way. If I'm a manager, I would rather my employee disagree with me on on


something and have their own opinion than to just listen to how things are done all


the time and never raise or never bring up the fact that there's there might be a problem


somewhere. Right? So that's that's how I think about it. Now we still live I'm getting a


buffering signal. All right, where are we up to? Leave a comment below to say if we're


still live my channel things since it's our my channel is I'm getting a buffering wheel.


I'm going to try probably do like another 20 minutes of questions or another 10 minutes


of questions or something like that. Thanks, Aravin. All right, let's keep going. So yeah,


arguments take responsibility and say here's how I'm going to fix it or agree or just say,


I understand that we we we disagree on something, but I'm going to leave it at that. I'm from


Kenya and ML is a totally new concept. What would you recommend I learn first? If if learning


programming in Python is your first choice to get into ML follow that right? Learn programming


in Python and then start using programming in Python to to to build work on ML problems.


How many projects should I add to my portfolio before applying to companies drew us drew?


There is no right answer for that. How many projects I I didn't really have any. I just


had a YouTube channel and I was sharing my work on LinkedIn. You can have have at least


one. Right have one and have a good story about it. Right. So that's that's what you


can that's what you can do drew. What's your opinion using octave to prototype and converting


to Python as opposed to just using Python libraries? I've I when I use octave I hated


it. I only jumped in to use Python. So use Python. That's my opinion. We only use Python


at Max Kelsen. Are we the right? Are we using the right way? I don't know. But Python is


all I've ever had experience. Can you lay out last week your schedule when you train


BJJ extra machine learning? I'm curious to have you how train BJJ and do your job. All


right. This is how I structure every day practically. Wake up early. At the moment it's about 530


a.m. 530 a.m. meditate 10 minutes. Do you have to follow this? No. This is just how


I spend my day. Right. Wake up early 530 a.m. usually go to the bathroom meditate for 10


minutes. And then I do about 45 minutes of reading like whatever I want. And then I have


a shower and then I catch the train. And so this is about I catch the train at 656 a.m.


into the city. This is a Monday. The train takes about 40 minutes. So I live 40 minute


train ride from where I work on the train. I read or listen to an audio book or talk


to my brother because my brother also works in the city or just sit and think looking


out the window. I get to work. I know I get to the city about 730. I sit at a cafe and


write for an hour and a half or do more reading. So now it's 9 a.m. I work 9 to 5 at Max Kelson.


This is a Monday again. I catch the train home at 515. I have jujitsu training from


615 till 730. I get home at 745. I have dinner. It's now about 830. I have a shower and read


or write before bed and I'm in bed by 930 p.m. I do the same thing Monday to Thursday.


Friday Saturday Sunday I still get up early. I read in the morning whatever I want still


meditate and then I work on whatever creative project I have or learning learning project


I have. So whether that be doing a course or whether that be creating a YouTube video


that's how I spend Friday Saturday Sunday and training training happens at virtually


the same time every day. Emotions will say 5 to 6 p.m. I'm doing 5 till 7 p.m. is when


I work out. So that's going to be really soon actually once this stream finishes I'm going


to do a workout and then I'm going to have dinner and then I'm going to finish the the


the course outline that I'm building for data camp and then I'm going to go to bed probably


by 930 p.m. so I can wake up early. That's how I spend my my days. I've like I have a


I have a pretty strict schedule of doing nothing extremely exciting apart from working towards


my goals which is extremely exciting for me. Hi Daniel. How would I rate the following Coursera


Udacity Girish asks all of them are 10s. It just depends how you use them right. It's


like they offer amazing courses Coursera Udacity Udemy edX all of them are great right. So


use them to build a foundation and then work off that. There's no no better one than the


other and I've never had an employer or someone else ask me which one I've used. So really


just think about the question what did you work on last weekend. I actually wrote a core


answer of this. Instead of instead of worrying about which course is the best to do choose


one use it to build stuff that you want to build and if you need to learn more do another


course or find more learning resources. Juicy O do I recommend learn the tool first and


the math later. Yes that is that is how I'm doing it. I don't know the other way of doing


it. I don't know which one is better. I all I know is which one is best for me. I'm learning


the code first and going from that. So I love your medium and videos by the way. Thank you


so much. Super Fox asked how do I stick to my diet. How do I avoid eating bad when temptations


are around. Let's say shopping center and you avoid. How do you avoid eating bad. OK.


Is again same question I ask myself how do I avoid eating bad is what's the alternative.


I can eat healthily and be shredded and have energy to work on the things I want to work


on or I can eat bad not have energy and not be shredded. So that's my two two alternatives


for that. Right. That's how. And second of all I just avoid eating anything in a shopping


center right because shopping center food is trash and I make most of my own food. That's


how I avoid eating bad by making most of your own food or if you have a good meal service


around you if you have a good good place to go just go to the same place right. Eating


healthy is boring compared to how advertisers talk about other ways of eating. Right. So


one tip to eat healthily is to just eat boring. Right. If you think about your ancestors think


about primitive humans if they were out hunting they didn't have the option to eat fried chicken


and fried potato and a whole bunch of different sauce. They ate what they could find in the


wild. They hunted a moose. They ate the moose maybe had some shrubs or berries to go with


it. Right. That's that's all they had available. That is that is how I avoid eating bad food.


It's just just focusing on eating boring and eating towards my goals. How do I avoid distracting


social media trying to do the task in front of me. I don't have any social media. I don't


have Facebook. I don't have Instagram. I don't have Snapchat. There to me those apps don't


bring me any value. So I've just deleted all of them. I don't have a well I have accounts


there but I've just deleted them all. I use LinkedIn but I only post on it. I use YouTube


but I really only post on it or watch it for you for learning things. And that being said


like you don't have to like that includes me right. You if YouTube if my videos are


distracting you from what you want to work on like you can not watch them. I'm that's


that's perfectly fine with me. So just remove simple thing. It's like just cut it off. Cut


it off at the source. Do you really need Facebook Instagram Snapchat all those all those different


apps. Do you really need them. I personally don't. So I just got rid of them. Where do


I live. I live in Brisbane Australia. I love it here. Brisbane baby. Can you suggest ML


curriculum for two hours. Anything just study anything if you have two hours that's actually


a lot of time to spend studying per day like that is a good amount of time to study any


kind of machine learning. So just have get a foundation like the thing about studying


and learning guys it's not like a straight path. You're going to learn something and


then you're going to go backwards and then you're going to go up and down and then keep


going right. It's never going to be it's never going to be a straight line. So thinking that


you can study two hours a day and just keep getting better and better and better and better


like ideally it looks like it happens like that but sometimes you go like a whole week


of two hours per day and end up at the end of the week. I'm holy crap. I didn't even


learn anything that week. But it's only after like weeks months years of putting that putting


that effort in that you start to join the dots. We'll probably take two or three more


questions. So if you have anything that's burning burning burning something that's on


your mind leave it leave it in and we can we can we can wrap this up. Have you all had


fun. I've had a lot of fun but it's time for me to start working out in a bit. Would you


like me to do these videos more often. I try to do them at least once a month actually


once a month probably the maximum but would you like to to have them to keep having them


once a month. That's that's the main question. And here we go. Yeah. I WS or GCP either they're


practically the same thing just different brands in my opinion. Learn whichever one


you need for the project that you're working on. Thaves let's wrap it up. Yeah we're going


to do that. What activity gives you maximum energy. How do I avoid tiredness. Super Fox


sleep and food sleep and food give me maximum energy. So eight hours sleep minimum a day


and good quality food every day. I spend a lot of money on good quality food. Well not


a lot but I mean a lot of I did it. Yeah let's just say that I dedicate a lot of effort into


having quality sleep and quality food. They're the two foundations. Don't overcomplicate


it. Good food good sleep. Have a mission you're working towards. My mission is to is to combine


ML and health care. Right. And I don't know how yet but I'm going to figure it out. Ex


baller would have any girlfriend throw off your routine. I don't have a girlfriend so


I don't know if if a girl was to date me she probably find me very weird but that's fine.


I am not looking for a girlfriend at the moment. I like being weird anyway I want my girlfriend


to be weird. Yeah I'm definitely going to do this every month. Thaves. Yeah this is


going to be staying on YouTube. Python series first and ML tutorials. Yep that's coming.


Definitely happening. All right let's wrap this up. How do I get good quality sleep.


I've written a few articles on this. Look at my six tips for health video. I talk a


lot about sleep in there. Kushal or Kusagra. This will be the last question. Thank you


all so much for joining. I'm doing the deep learning nano degree from Udacity and Coursera


ML course and I'm middle of both course and I feel not proficient if I want to code anything


from scratch. What what. Any suggestions. So keep practicing. Start practicing coding


stuff from scratch. There's no good no perfect answer other than that. Right. So start up


a blank Jupiter notebook. Right. So you could go boom start a new note. Actually this didn't


work out well. That's not a good example. I don't have a notebook. And then start start


start. Start watching McCulloch. Start trying to build it from scratch. Practice practice


practice practice. All right. The current hardware I'm using is not enough to do deep


learning. Is there any other way. Harry use Cressl dot AI. That's what I use. Cressl dot


AI for deep learning online. Dude gives our heads up before doing a chat. Yes. Good idea.


Maybe I'll schedule these in like a week in advance. But yeah I've had a lot of fun. Thank


you all so much. If you have any more questions feel free to reach out to me on any of the


socials below. I mean I don't really use use socials but send me an email. You could probably


find it pretty easily. It's Daniel at Mr D Burke dot com if you want it answered more


directly. Otherwise I post a lot of stuff on my blog. I share I share my work fairly


actively. And yeah if you ever want to see like something from me never never be afraid


to ask like I'm happy to answer anything as best I can. And I'm more than happy to do


these kind of videos in the future. I don't as with anything that I put out I don't like


anything that I say I want you to take take into your make your own decisions with it


right. Just because I've said something doesn't mean it's the right thing for you. So think


about it. I can only share what I've done. So if you can learn something from that great.


If not you're capable of making your own decisions. You're capable of learning machine learning


on your own. You're capable of learning machine learning with others. There's amazing stuff


online and you're also most importantly capable of taking your health into your own cares.


I mean hands because these are the two things I'm most interested in the moment machine


learning and health. So I'm just sharing what I learn in that. But otherwise have a great


have a great have a great Sunday guys. And thank you all so much for tuning in. If you've


just joined this is going to be live on YouTube. I'm not entirely sure how to how to end it.


Learning about Generative Adversarial Networks on Udacity | 100 Days of Code 11

Good Morgan, Ahayo Gazzamas, or good morning.


It's day 49 of the 100 Days of Code,


and yes I know I wear the same hoodie almost every day,


but that's because, you know,


well if it's not getting dirty,


why do you need to wash it?


Like I don't try to use as many clothes as possible.


But, I'm kidding.


I just passed project four


on the Udacity deep learning course that I'm doing.


Check it out.


Meet specifications, so nothing special.


We got good feedback.


And then a whole bunch of resources here


on how I can learn more.


So, I've got an incredible amount of reading to do.


What am I up to today?


Well, I'm doing week nine,


all the classes for the machine learning course


on Coursera taught by Andrew Ng.


He's also started a new deep learning course,


well it's coming up soon, deeplearning.ai.


I'll link that in the description as well


so you can check it out.


It's just a front page, a landing page at the moment.


That's what it's called.


But yesterday I was learning all about Python on Treehouse.


Almost finished the Python track on Treehouse.


And so, yeah, everything's coming to an end.


So, the courses, I'm in the second last week


of the machine learning course.


I've got about three weeks left of the Udacity


deep learning nanobere foundations.


Almost finished the Python track.


So, I've gotta work out what I'm doing next.


Haven't got an exact route of where I'm going,


but I know it will be in the field of data science,


machine learning, or deep learning.


More specifically towards communication.


Something in that realm.


But I'll hit you up once I learn about it.


It's 49 days into the days, I've got 51 days left


of the 100 days of code.


But it's obviously gonna keep going after that anyway.


And you know the thing about filming on an iPhone


is that when you get a phone call, the thing cuts out.


So, yeah, 51 days left of the 100 days of code.


Still filming on the iPhone because my digital camera,


I haven't got a new lens for it at the moment.


Trying to spend less money, more than more money,


so I might try and find one at Ground Tree for cheap


or something like that.


But no excuses for getting it done.


I'm gonna go learn about some machines


and I'll catch you in a few clips.


A few seconds.


Big day of machine learning today.


I still haven't finished the programming assignment though.


That's the last thing I need to do.


But I went through all of these lectures here.


It takes me a while to go through them


because I like to take an abundance of notes.


Check out what I learned about today.


Anomaly detection, Gaussian distribution,


probability distribution, Gaussian distribution


is otherwise just known as normal,


so like just a normal curve.


What else?


What's anomaly detection used for?


Fraud detection, so like, say for example,


you're an online bank and you have millions


of okay transactions, then all of a sudden,


you've got one that looks a bit weird.


Or for me, exactly right.


Like if I was buying things in Brisbane for months on end


and then all of a sudden I have a massive charge


over in some other country like Tanzania


or something like that, I don't know,


somewhere in the other side of the world,


my bank might go, oh, that's probably fraud.


So we won't let that transaction go through.


Or in manufacturing, say you produce 100,000 iPhones,


like what I'm recording on millions of iPhones,


and all of a sudden one has some weird thing


going on with it, that's where you can use machine learning


and sort of anomaly detection.


And of course, how I just explained it,


there's a big overview, but that's essentially the gist of it.


Just a really small number in a large data set.


So only a small amount of positive samples.


What else did I learn about?


So when to use anomaly detection versus supervised learning.


So supervised learning, you have a large number


of positive and negative examples both labeled,


whereas anomaly, small number of y equals one


positive example, so zero to 20.


So really small amount.


What else do we learn about?


Collaborative filtering algorithm,


which is similar to what like Netflix or a book recommending


system would use if there's a lot of users,


they can take all the user input of ratings on movies


and whatnot and create recommendations for other people.


So say for example, you have a group of people


who are interested in romance movies,


and they're uploading a whole bunch of movies.


The collaborative filtering algorithm might realize that,


hold on, they're uploading these movies,


so these movies must be related to romance,


let's recommend them to other people


who are interested in romance.


So that's pretty cool.


And yeah, that's about it.


Collaborative filtering algorithm and,


what was that, anomaly detection.


Yeah, see I've done four or so hours of this,


and I don't know, it's gone up in one ear


and out the other.


That's why I like to take so many notes,


I have to review these at the, I don't know,


end of the course and go over it again.


I think I'll go through this course more than once.


But something really cool just got released


from Coursera as well.


Check this out, I'm excited for this actually.


$65 a month for unlimited access


of all the courses on there.


So when I plan my data science


slash artificial intelligence


slash machine learning master's degree


over the next six or so months,


Coursera may play a big part


because $65 a month for unlimited education,


like that's cheap.


Like my university degree, I still owe like $35,000


or something like that.


I don't even know how to check it, to be honest.


But yeah, $65 a month for all this access to learning,


highly recommend.


So I'm done for learning today.


I'm gonna go edit a video.


I should really be uploading.


I'm gonna upload on Fridays, a podcast and a V-log.


So that's what I've worked out is best for me.


Just fit in with my learning schedule


and then writing as much as I can


on the 100 Days of Code series.


Be sure to check that out.


It's on Medium and it's gonna be in the description.


But I'm gonna go play.


I'm gonna go have some fun.


Might play my brother in chess or something.


So we'll catch you tomorrow, day 50.


What's good, y'all?


Day 50 of the 100 Days of Code series.


And it is about 9.30 at night.


But I've had an amazing day.


I was working on, oh actually,


I was facing resistance this morning.


What does that mean?


Well, I just procrastinating, right?


When you don't wanna do something


or when you do wanna do something


but you're sort of doing everything else


instead of doing that one thing.


But eventually I got around to working on my Messenger bot


that I'm building which is kind of like


a personal trainer slash nutrition coach in a Messenger bot.


So I'll link that down here in the description.


It's called the Move More Messenger bot.


Now I'm doing some reading just in bed.


It's Ray Dalio's, a book by Ray Dalio


who's the CEO and founder of Bridgewater


which is the one, the largest hedge funds in the world.


It's called Principles, just a whole bunch of life lessons


that he's learned.


It's so valuable to learn from other people, guys.


Reading has changed my life.


Why would you want to learn these lessons


when you can learn them vicariously through other people?


And then for the rest of the day,


before I started reading, before I went for a dog walk,


before I went for a dog, had dinner,


I was working on my website.


It should be live now.


It's useanygym.com, also in the link in the description.


Ideally, if everything goes to plan,


maybe it will become the Airbnb for gyms.


Who knows?


Will it work?


I don't know.


I'm gonna keep working on it one every Friday


for the rest of this year at least


and see where it gets to.


But that's it for day 50, working on projects.


Friday I've reserved for projects.


Watch this space to see what comes next.


But tomorrow, machine learning assignment


and then, I don't know,


I think I'm heading out with my brothers.


But we'll see you then.


Good morning, y'all.


It's day 51 of the 100 Days of Code series.


And I just finished my eighth,


I had to think about that for a second.


I really need to get ready when I do these videos.


Actually, it's the first of July.


We're almost halfway through winter, which is exciting.


I'm still in my pyjamas, as you can see.


But I just finished, first thing I got up,


Saturday morning I got up and I finished


my eighth machine learning assignment.


Check it out.


Submitted 9.14 a.m.


That's gonna be the majority of my coding done.


There's two weeks left in this machine learning course


and then I'm not sure what I'm gonna do after.


I'll figure it out though.


I'm gonna do a, I don't know, a syllabus for myself


in terms of learning data science


and machine learning and AI and whatnot


over the next six months or so.


But I'll fill you in.


So, what am I up to for the rest of the day?


Oh, I'm gonna do my newsletter.


So, check out my website, MrDeBirk.com


if you wanna get that.


And then a workout, oh, I gotta learn about GitHub,


clip for V-log.


I have to write all these little reminders


to make sure I get everything done.


I keep it visual on my whiteboard.


But that will be the majority of coding done for today.


We'll catch you tomorrow.


So day 54 of the 100 Days of Code series


and I've been learning about, where is it?


Go up here.


GANs, Generative Adversarial Networks.


So what's the whole principle behind GANs?


Well, essentially, you've got a generator network


and a discriminator network.


So you've got two neural networks


that work against each other


to work out some sort of input, output.


So they take an input, some sort of real data will go in


and a sample bunch of data will go in.


And then the generator network,


its goal is to fool the discriminator network


by generating fake data.


And then the discriminator network has to decide


which is the real and which is the fake data.


And look here.


So this is the exercise I did.


We started off with some MNIST data set.


I keep forgetting what they're called.


And then so that imports a whole bunch of numbers,


handwritten digits.


We go down here.


And so I trained two networks to work against each other


and this is what it produced.


It produced it out of nowhere.


So you've got these numbers, you can sort of see a one here,


a nine here, a nine there, and we go down.


And this is what it came up with at the end,


some scrambled digits.


It starts with nothing.


And the one, one, seven, seven, one.


Gets pretty good by the end.


And here's the final output, nine, eight.


So what's the idea?


Well, the generator tries to produce fake numbers


and then the discriminator looks at the original data set


and goes, hey, are these fake or are they real?


And it tries to match them with the probability distribution.


And of course, my explanation is not the best at it,


but an analogy I like to think of is imagine the police


and some criminals.


And so the criminals are making fake money,


but the police can keep finding out


that they're making fake money.


So you can imagine the police


as being the discriminator network


and the criminals as being the generator network.


And so over time, the generator gets better and better,


which is the criminal, at making fake banknotes


and the police get better and better


at detecting the fake banknotes.


And then eventually, the criminals have no choice


but to make fake banknotes that are virtually


the exact same as real banknotes


so that the police can't differentiate from it.


And that's the whole idea with GANs,


is that the generator becomes so good


at generating new data that the discriminator


can't tell what's real or fake,


and then it produces this new data completely on its own.


And there's a lot more to learn about GANs,


but that's what I've learned so far.


I've gone through the GANs class on Udacity


all of today, and I'll probably go through the videos again


a couple more times just to get my head around the concept


because it's so new.


But concept like, this is really powerful stuff.


This is like one of the newest things in deep learning.


It's only came about in 2014.


So imagine like a generative network,


and by generative, it means bringing this stuff


out of nowhere after training on so much data.


Imagine it like you can apply it to anything.


Like imagine you could go,


hey Siri, get me a,


get me a,


sorry, I got interrupted.


But yeah, imagine if you could just ask Siri


to generate you a video of 2PAC teaching you calculus,


and all of a sudden it goes to the internet,


finds videos of 2PAC,


finds videos of like Khan Academy of, I don't know,


them teaching calculus and combines them together,


and all of a sudden you've got 2PAC on your screen here


teaching you calculus.


So that would be amazing.


I mean, of course, that's a few years down the track,


but they're already, if you watch Siraj's video,


I'll link it in the description,


he's generating completely new video clips


out of using GANs.


There's videos of people turning horses into zebras.


Yeah, this is crazy stuff.


But for the rest of this evening,


I'm gonna do some writing.


I didn't do any filming yesterday or the day before.


Yesterday I was learning all about GitHub,


still trying to get my head around using Git


and GitHub and whatnot.


I wanna get my projects that I'm working on


onto my GitHub portfolio.


There's nothing on there at the moment


except a few basic things,


but I'll work it out eventually.


And then Sunday, of course, I spent writing.


You can check out what I did actually.


It's on my Quora profile.


I'll link that in the description as well.


But we'll catch you tomorrow.


It's day 55 tomorrow.


Yeah.


Machine Learning and Movement Part 2

Why isn't my model training? Look at this. Excuse me. Just had to take a quick nap because


Machine Learning and Movement Part 2

here we go. Jupiter lab. I've disconnected it. It was supposed to be on Google Cloud,


Machine Learning and Movement Part 2

but I've got a runtime error. CUDA runtime error out of memory. I just went to the bathroom as


Machine Learning and Movement Part 2

well and my urine was a colour that it shouldn't be. So I forgot to drink water for like the last


Machine Learning and Movement Part 2

six hours. Don't do that. I've been working on this modelling problem for my Airbnb problem and


Machine Learning and Movement Part 2

it's continuing from what we were doing yesterday. Running a massive experiment. It's working on


Machine Learning and Movement Part 2

Colab, which is great, using transfer learning, but I wanted to train a model from scratch on


Machine Learning and Movement Part 2

my own GCP instance, so Google Cloud Platform instance. But as you can see by the red, red


Machine Learning and Movement Part 2

means not good and we're getting server connection errors. So whatever. I'm going to monitor this


Machine Learning and Movement Part 2

training while we do another workout. But the theme of this one is, is I was gassed, right?


Machine Learning and Movement Part 2

You saw me begin on the floor, tired, laying down. And you know what? When you need to take a break


Machine Learning and Movement Part 2

sometimes, a wise man once told me the pump is the cure. So yawning my absolute skull off before,


Machine Learning and Movement Part 2

but there are things to do tonight because it is Saturday night and you know what that means. So I


Machine Learning and Movement Part 2

have to move. Now, as I said before, I haven't planned anything today. So today's theme is


Machine Learning and Movement Part 2

randomness. We're day 12 for Revsarona, almost two full weeks of moving. So thank you for joining


Machine Learning and Movement Part 2

in. And randomness is so valuable. I think it's undervalued these days. Too much. I have a list


Machine Learning and Movement Part 2

of things that I have to do rather than just inject randomness. I follow this routine instead


Machine Learning and Movement Part 2

of just trying things for the sake of trying things, following curiosity. You know, routine


Machine Learning and Movement Part 2

is the death of joy. So today we are going to start the clock. We're doing 10 minutes today.


Machine Learning and Movement Part 2

We'll monitor the model training, but we're going to do random exercises. Ready? Number one,


Machine Learning and Movement Part 2

star jumps. Every time it's going to come straight from the top of the dome. Remember,


Machine Learning and Movement Part 2

we're starting this one feeling very yawnish, very tired, but we're going to keep moving.


Machine Learning and Movement Part 2

Oh, look at that. Cameraman's even yawning. Got him. And now he's laughing. Come on,


Machine Learning and Movement Part 2

we're doing star jumps. Maybe this could be a great little series. Movement. Oh, there we go.


Machine Learning and Movement Part 2

I'm feeling it already. Feeling it already. Maybe this could be a little series. Movement


Machine Learning and Movement Part 2

and modeling. Come on, have a look at what we've got here. So I've kicked off this for,


Machine Learning and Movement Part 2

we're up to iteration, just zoom in on this for me, up to iteration 25,000. I'm not sure if you


Machine Learning and Movement Part 2

can see that, but you can just take my word for it. 25,000. There's some curves here. The curves


Machine Learning and Movement Part 2

are going in the right direction. Oh, back to it. Random exercise. We're going to do sit-throughs.


Machine Learning and Movement Part 2

So we'll come here, start in a plank position and then throw your leg through like that. Arm up in


Machine Learning and Movement Part 2

the air. Note, this is different to a kick sit because we're staying up high. There we go. Kick


Machine Learning and Movement Part 2

through, make it nice and fun. So come back, kick through. Boom. Boom. That's it. Little shout as you


Machine Learning and Movement Part 2

go through. Squeeze those glutes, thrust those hips towards the roof and then come back. Look at that.


Machine Learning and Movement Part 2

Look at that. All right, come back in. So what I want this to do, average precision. It is currently,


Machine Learning and Movement Part 2

you won't be able to see that because I can barely even see that, but it's at 35. I want it to reach


Machine Learning and Movement Part 2

50, meaning average precision, because that is what Airbnb's MVP was for their object detection.


Machine Learning and Movement Part 2

So let's say for example, you took a photo of this room. I want my model to be able to identify that


Machine Learning and Movement Part 2

that is a chair. What move are we going to do? Push-ups. Let's do it. No, this is slow. Actually,


Machine Learning and Movement Part 2

cobra's because they're more fun. Down. Yeah. Down, right, left, up, down, right, left, up, down, right, left, up, down, right.


Machine Learning and Movement Part 2

Oh, I tricked you. A little bit of randomness. Left, right, up, down, left, up, down, right, left, up, down, right, up.


Machine Learning and Movement Part 2

You see, what else is there to do in quarantine and lockdown than to train machine learning models


Machine Learning and Movement Part 2

and do a little bit of dancing in your bedrooms? And so what my experiment is currently at,


Machine Learning and Movement Part 2

it's at 25 or 26,000 iterations. What I want it to, it's going to go up to 100,000. Now that may take


Machine Learning and Movement Part 2

maybe a late night for me. I think from zero to 25,000 took about two hours. All right, what are we on?


Machine Learning and Movement Part 2

What are we on? Let's do froggies. So come down, elbows between your knees and go up like that.


Machine Learning and Movement Part 2

Oh, that's so good. Sit nice and deep down. That's it. You can even hold it there. That feels great


Machine Learning and Movement Part 2

for me because as I said, I sat down training my model or trying to fix my errors on Google Cloud.


Machine Learning and Movement Part 2

Coup to runtime errors, man. You ever had them? Leave a comment below if you've ever had a coup


Machine Learning and Movement Part 2

to runtime error. That's it. Come back. You know, I got to think of these exercises before


Machine Learning and Movement Part 2

the clock starts, but that's the beautiful thing about having a deadline, is it? Okay, sometimes,


Machine Learning and Movement Part 2

yes, you will be in a rush, but other times you'd be amazed. How many times have you had a deadline


Machine Learning and Movement Part 2

and you sort of, I think we spoke about this in another one, in the last video maybe. You've had


Machine Learning and Movement Part 2

a deadline and it's sort of somehow the entire universe has come together to help you. Oh,


Machine Learning and Movement Part 2

what are we at? Exercise. What we're going to do? We're going to do kick sits.


Machine Learning and Movement Part 2

So these are some of my favorite exercises. You know, I should actually mix it up.


Machine Learning and Movement Part 2

Keep it up. That's it. Movement and modeling, episode two.


Machine Learning and Movement Part 2

I'm pretty sore from yesterday, actually. That was a good session. Keep it up. Kick sits. Let's go


Machine Learning and Movement Part 2

through. If you're training your machine learning model, remember, what's the rule?


Machine Learning and Movement Part 2

Rest. That's my new rule. If my model moves, I move. Oh, I got a good one. A good one for what's


Machine Learning and Movement Part 2

next. My friend, Andre, who we teach my machine learning course on Udemy, shout out to Andre,


Machine Learning and Movement Part 2

absolute, complete, absolute and complete living legend. Hope to visit you one day. He lives in


Machine Learning and Movement Part 2

Canada. I live in Australia. He gave me this workout to try and I haven't tried it yet,


Machine Learning and Movement Part 2

but we need to practice. So what do we got? Burpees. Come down, all the way in the bottom,


Machine Learning and Movement Part 2

up, tap, that's it. Up, clap. Oh yeah. Down, up, take a peak at your model. Yep.


Machine Learning and Movement Part 2

Keep it up. Come on.


Machine Learning and Movement Part 2

Rest. Nice. All right, where are we at? So we're getting about 100 iterations because I'm running


Machine Learning and Movement Part 2

a, in Google Colab, the Google Colab gods have gifted me a P100 GPU. So thank you, Google Colab


Machine Learning and Movement Part 2

gods. Because you know how, if you go to Google Colab, you get a free GPU, but sometimes it's


Machine Learning and Movement Part 2

random as to what one you're going to get. So I'm going to go ahead and do a little bit of a


Machine Learning and Movement Part 2

break. So we're at the big dog GPU. So it takes about one minute per 100 iterations. And speaking


Machine Learning and Movement Part 2

of 100 iterations, let's do some knee taps. So tap your elbow. We go back, tap elbow and back.


Machine Learning and Movement Part 2

That's it. Tap your elbow and back. So that's it. So tap your elbow,


Machine Learning and Movement Part 2

and back. That's it. Tap your elbow, back. Keep it up. Nice. That's it.


Machine Learning and Movement Part 2

Keep going. Hold it. Rest. Beautiful. You know, we've been mixing it up. We're seven through.


Machine Learning and Movement Part 2

That's seven different exercises. We're injecting randomness. So that's a bit of a challenge,


Machine Learning and Movement Part 2

actually, is that every so often, I like to have a random day. So I've got my personal assistant


Machine Learning and Movement Part 2

here. And this is just a whole bunch of crap that I need to do today. Reps for Rona day 12.


Machine Learning and Movement Part 2

I put that on the list and that's what we're doing now. But every so often, I like to,


Machine Learning and Movement Part 2

I like to inject a random day where I just say, screw the list. So now we're going to do bear


Machine Learning and Movement Part 2

crawls. Unleash your inner animal. Oh yes. Feels good to move. Okay. Now back. All the way back.


Machine Learning and Movement Part 2

It's a bit hard going back. You need to concentrate on where each limb is moving. Keep it up. Come on.


Machine Learning and Movement Part 2

Nice. Rest. That is beautiful. So where are we at? I really wish we could have had an evaluation


Machine Learning and Movement Part 2

around here. As you see, actually, my loss curve is just bouncing around. Have a look at that.


Machine Learning and Movement Part 2

See how wildly it's going there. So what I think, I've got it scheduled to do that. I need some


Machine Learning and Movement Part 2

learning rate decay. This is my theory. I need my learning rate to be smaller and it's set to decay


Machine Learning and Movement Part 2

at iteration 60,000. So you might have to wait for that. Okay. I'm going to do split jumps. Lunge.


Machine Learning and Movement Part 2

That's it. Lunge into a jump. Pump it up. Just keeping these movements nice and simple.


Machine Learning and Movement Part 2

I got a great one for the next one. I just thought of it.


Machine Learning and Movement Part 2

And I just thought of a way to fix my CUDA error. Keep it up. Come on.


Machine Learning and Movement Part 2

Rest. Boom. Okay. Try my CUDA error. I got to record this so I come back. I need to try a smaller


Machine Learning and Movement Part 2

model because I'm running out of memory. The original Detectron 2 models, which is the computer


Machine Learning and Movement Part 2

vision library that I'm using, were trained on 16 to 32 gigabyte GPUs, but I'm only using


Machine Learning and Movement Part 2

eight gigabyte GPUs. Okay. Now I'm going to fall to the floor and push back up. So fall. Oh, yes.


Machine Learning and Movement Part 2

Come back. You got to fall. Nice. Come back up. Now if you can't fall, that's all right.


Machine Learning and Movement Part 2

Walk out. Walk back up. Walk out. Walk back up. That's it. Watch those wrists if you're falling.


Machine Learning and Movement Part 2

Walk forward. Nice. I think that's it. Ten sets. All right. Where's the model at? We need to finish


Machine Learning and Movement Part 2

the video with a little summary. We're at iteration as of this little speech part, 26,840.


Machine Learning and Movement Part 2

We've worked up a sweat. Our model has been moving the whole time.


Machine Learning and Movement Part 2

Running it in Google Colab so it might time out. I'm not sure.


Machine Learning and Movement Part 2

We're going to get the little victory with the ad.


Machine Learning and Movement Part 2

Great workout. You spent 10 minutes, seven seconds exercising. Beautiful. So that's,


Machine Learning and Movement Part 2

you're awesome. Five stars. And you get that little ad plug in there for natural dry tomato.


Machine Learning and Movement Part 2

I don't need that. It just wants me to upgrade to the paid version of it. So we are now at iteration


Machine Learning and Movement Part 2

27,000. Total loss is 0.328. We want that number to get as close to zero as possible. Average


Machine Learning and Movement Part 2

precision is 35. Hopefully that increases. I've got it set to schedule. Evaluate itself every


Machine Learning and Movement Part 2

20,000 iterations. So that won't be until 40,000. But I feel so much better. That workout, the theme


Machine Learning and Movement Part 2

was randomness. So question for you, how have you, or how do you inject randomness into your life?


Machine Learning and Movement Part 2

Me at the moment, it could be something as small as doing something that you haven't planned.


Machine Learning and Movement Part 2

That's nice and simple. That's one way. If you've ever done machine learning, if you've ever done


Machine Learning and Movement Part 2

deep learning, you know how valuable randomness is for a model to learn. In fact, most of it,


Machine Learning and Movement Part 2

the entire thing is based on randomness. So there's your challenge. How can you inject some


Machine Learning and Movement Part 2

randomness into your life? And how can you get moving a little bit every day? Even when you're


Machine Learning and Movement Part 2


Machine Learning INTERNSHIP Day 2 | Learning Intelligence 30

Hey Mum, what's your favorite voice assistant?


Voice assistant?


Yeah, Siri, Alexa or Google?


I like Alexa.


Alexa? Really? How come?


Because she's organising your life at the moment.


What about Siri?


Siri's good.


What does she help you with?


She helps me with timing, cooking, reminding me.


So, yeah.


Mum's dropping us to the train station. Me and my brother.


What's up bro?


What's up, Mierzi?


We're heading off. He's heading off to work.


I'm heading off to day two of my internship at Max Kelsen, which I'm really looking forward to.


I'm not entirely sure what I'm going to be getting up to today, but once I find out more, we'll check back in.


See you Mum. Love you.


Morning.


My hands are so full right now.


I've got coffee.


Lunchbox, because I don't like buying food when I'm out.


I like eating my own food.


I'm in training for Spartan Rays now, so I have some pretty high standard nutritional requirements.


So, I make my own food.


Backpack, laptop and whatnot.


I'll tell you a little story.


I'm just about to walk into the building where I'm doing the internship at.


The people at the cafe I went to, I walked in and they were really upset.


I was like, what's going on?


It was just a little cafe that I hadn't been before, because I like trying new places.


I walked in and I'm like, what's going on guys?


They're like, we're being shut down.


So, apparently the council came along that morning and told them that they can't use their storefront anymore,


because the building they were in were complaining about the tables and chairs being on the street side.


So, essentially they've got no space at all now.


They're just a hole in a wall and that really sucks.


But, they insisted on giving me this for free.


I was more than happy to pay for it, but they gave it to me and said to have a good day.


They were upset, but they were still in good spirits.


They knew whatever they were going to do next.


They were going to try their best.


So guys, I wish you all the best.


It sucks you're being shut down, but I know you'll get onto something new and just smash it.


Thank you so much. Cheers.


Alright, I'm washing this back.


And, don't do this.


Don't do what I'm doing.


Either computer or eat.


Don't do them at the same time.


Back to the video.


So, we're kind of stuffed up.


I got on the wrong train to get home.


And now, I was replying to messages on my phone and I ended up in the complete opposite direction to where I should have been.


But, I figured while I wait for an Uber to come get me, what better time than to tell you what I got up to in the internship.


So, this morning I got in and it was about a week since I'd been there.


Well, exactly a week.


It was Thursday to Thursday.


And, the two guys I was working with had made some epic progress on the machine learning project that we were working on.


They were doing a lot of data cleaning.


And, why did they have to do so much data cleaning?


Well, when we got the data or when they got to the data from the partner company, they didn't really look at it before they got it.


So, it was sort of like, here you go, here's all our data.


See if you can run some machine learning stuff on it and find some insights for us.


So, that's probably a learn for the future.


Before you go into any project to partner up.


Well, that's all we were talking about today.


Me and one of the machine learning engineers, Athen, who's an absolute wizard.


Shout out to you Athen.


Before you partner on any project actually, it's probably best to meet up and discuss in person what you guys are both looking out of it.


And, my Uber's just here, so we'll continue this in a second.


Hey man, how are you?


I'm just going to put this in the back and I'll get in the front.


Welcome to San Geo.


San Geo, please my man. Thank you.


Hey guys, this is my friend, Zagwinda.


You were just teaching me about investing. What's the number one tip for people here?


How can I help them out?


Number one is never put borrow money.


Second is never put credit card money.


Only saving money.


Only savings money.


So, no credit cards.


No, no, no.


No credit cards, no loans or anything.


Minimum you have to practice one year.


One year.


With anything, right? Not just investing, but you've got to practice with anything you want to do.


You have to keep in touch every day.


Every day.


Well, thank you man.


Good to meet you.


Have a good night.


Thank you.


Okay, if you need any help, just call me.


Thank you. Have a good night.


See you later.


Finally made it home.


And where was I?


We were talking about when you first meet up with a client, it would be ideal for you to both talk to each other


and get on to terms with what it is you're actually working on.


So, that was a great learn.


I think it's even though the stuff that we were working on today was quite difficult,


I think that's the best thing to do.


I mean, that's when you learn the most, right?


When you're working on the hardest projects.


What did we do after...


Oh, my puppy's saying hello.


This is Seven.


Say hello to YouTube, Seven.


You're famous now.


What did we do after that?


Well, they've been spending most of the project, Connor and Ath, and the two machine learning engineers


cleaning the data and essentially getting it ready for putting it into a machine learning model.


So, that's the last little bit of it.


What was I personally working on?


Well, I read a paper on archive about entity embeddings, which I'm thinking they're similar to word embeddings,


but they might be a little bit different.


I'll link the paper below so you can check it out as well.


And then I implemented the code from the paper onto our server, I believe it is, or Docker container.


I put it into there and made sure it can run.


So, we're going to use similar code from that paper to run a machine learning model on the data


that we get once it's fully cleaned up.


We've got a whole bunch of new data today that we had to add to the already processed database.


So much fun, lots of learning, and I think I'll be back there next week.


I should hear from them soon.


Exciting times.


I'm going to go lift some weights.


No, we ain't having none of that.


I'm going to go tell Dan I'm watching this video in 2048.


We can't have it just ending on no hashtag.


What are you thinking, Dan?


Yeah, I know, but I didn't make any outro clips.


I forgot.


Hold on, I've got an idea.


What do you think?


It'll do.


I didn't eat food for 3 days, here's what happened

Morning! I'm about 13 or so hours in to a three-day fast.


I'm gonna check that out there.


I just weighed in at 76.4 kilos and the reason why I'm doing it is because


my friend messaged me the other day and was like


We're doing a three-day fast. Do you want to join in? And I was like sure why not?


So we have the power of the crowd. I'm about to go for a long walk. Probably about two or so hours with some water and salt.


The rules of the fast is


Water only. Well for me personally water only. Salt is okay for electrolytes and whatnot. So no coffee, no tea, no nothing. Just a water fast.


So we're gonna film what happens, how I feel and


But the reason of the walk is to try and kick-start my body into ketosis.


So I've done a five day and a four day fast and usually when you do that you get into ketosis.


Your blood ketones go up. Things start feeling good.


So I'm gonna chill out for the next three days or so and we'll see what happens.


Warning.


I just got back from that huge walk that I was telling you about.


It's only 17 hours in so apparently we're in the fat burning stage.


That's what it's saying on the zero fasting app. So 17 hours into my last meal. My last meal by the way was


just something simple steak and onions. The walk I did was about twelve and a half thousand steps.


I'll just take a screenshot of this so I can put it on.


Twelve and a half thousand steps. Two hours fifteen. Now I'm gonna measure my blood ketones.


Let's uh, I did a measurement this morning around 9 a.m.


So it is about three hours since then. It was too low for the for the reader.


So when the reader has no value, it just puts up the letters LO for low.


You gotta get a good draw. Need a little bit of blood on the pinky.


It's a little bit cold outside so my hands are quite cold.


So getting blood out a little bit difficult.


And if you don't get enough the machine won't get angry at you.


Oh!


0.3 millimole. I'm gonna take a photo of that. Boom.


0.3 millimole. I don't think that's quite yet ketosis. I have to look this up every time.


I think it might be 0.5. I'll correct myself if I'm wrong.


But yeah, I should be starting to remove the stores of glycogen because that was quite a long walk.


And then shifting into ketosis. So I'm feeling pretty fine. I'm just gonna keep drinking water and salt.


No different to how I'd usually feel. I know some people, I don't have a headache.


I know some people get headaches when they stop drinking caffeine or don't have coffee or whatnot.


I haven't had any of that of course. Yeah, I'm just feeling chilled out.


I think that's the big thing that I notice whenever I do an extended fast is I'm just chilled.


It's very zen. You start to feel really...


It's hard to explain. I mean when I have a cup of coffee in the morning sometimes I just feel like I need to


do, do, do, do, do to satisfy something.


But right now I'm just, I'm chilled out. There's some pretty birds flying around.


I'm gonna do some reading, probably clean out my closet. Who knows?


The sky. Very pretty colors. It's a beautiful afternoon. Can you hear the birdies?


We have an update time. It is 21 hours, 29 minutes and 15, 16, 17 seconds into the fast.


That's probably not gonna focus. It doesn't matter.


I'm going to measure my blood ketones. It's been a very chilled day.


I went for the long walk this morning and then cleaned my closet out a little bit.


Did all those little things of, you put off for a while and you just...


I've just been chilling out really all day. I did a little bit of computer work.


Otherwise just on my couch doing some reading.


Now let's see where my ketones are at. Oh, and I'm rugged up at the moment.


I've got a hoodie on. I've got track pants and some thick socks.


Because I find usually after 16 to 18 hours into a fast, I...


I'll just get this on. I start to get quite cold.


And so it's a strange coldness. It's hard to explain.


Oh, we are at 0.7 millimoles of ketones.


Again, not sure if that focused so I just take a photo of that.


0.7 millimoles per litre of ketones.


So not quite fully into a deep state of nutritional ketosis.


That is the goal. That's when things start to cruise.


But back to the feeling cold part.


Yeah, I find I typically start to feel cold basically after 18 hours.


And I rug up, but it's like a deep cold.


It's... And from what I've read and from what I've heard,


it's from a lack of thyroid activity. So the metabolism is slowing down.


So if the metabolism slows down, you produce less body heat.


Hence why I'm rugged up in Australian weather.


It is winter though.


Essentially, the reason we're feeling cold is because the fat that would normally generate heat


is getting turned into a fat that is getting burned for fuel instead.


So pick your poison.


Would you rather have some fat that makes you feel a little bit warm


or would you rather have some fat that allows you to feel cold but goes away faster?


I've got jujitsu training this afternoon.


I was contemplating whether I was going to go.


I probably will just because I have so much time when you're not eating.


You just have...


It's amazing how much time throughout the day gets dedicated towards eating.


I'm reminded of this every time I fast.


It's a new appreciation for food every time.


I'll go to jujitsu training after that.


By the time that's finished, I will be about 24 hours done.


Not hungry at all.


Of course, I would like...


There's a big car going past.


Of course, I've been thinking about food,


but not in the sense of like,


oh, I need food.


It's more like I just enjoy eating food during the day.


You have those times as rituals, it gets to a certain time.


It's fun to eat food.


So we'll check back in later.


A little update on the fast.


Just pretty sure it's 8.07,


so I'm just about half an hour, 45 minutes or so over the 24 hour mark with no food.


Just finished Brazilian Jiu-Jitsu training.


And I would say I went about 50%.


So I had a chilled out day.


And usually I'm pretty...


I like to train fairly intense.


I'm pretty fit.


But today I just wasn't...


I think if I pushed myself, I could have done it,


but it wasn't like I'm trying to push myself.


I just took it easy, trained, did the techniques,


had a bit of a flow roll with my friend,


which is basically like just practicing what you learned during class.


Nothing too extreme or intense.


We'll see how the next couple of days goes.


I've got training on tomorrow and Saturday.


Not sure if I'll do that yet, but who knows?


I'd like to. I like training.


Now just going home, shower up,


some salt and water, dinner.


Yum, yum, yum.


And then we're going to sleep.


The good news is you wake up on the morning of day two of a three day fast.


And if you started after dinner on day zero,


well, the morning of day two is 36 hours in or thereabouts,


and you're halfway through.


So we'll check in then.


Well, hello there.


It's morning day two, morning of day two.


It's actually like 10.21.


I've been up for about an hour and a half,


but I just woke up.


I was pretty busted up.


Not gonna lie.


I weighed in at 75.2 kilos,


which is down like 1.3 or something kilos on yesterday.


I think it is mostly water, though.


So maybe everything's a bit...


..tied up or whatever.


But it's 38 hours, 40 minutes, 30 seconds into the fast.


Now, why do I think it's dehydration?


Well, because my urine this morning was a shade that you don't really want.


As for the other event that you do in the toilet,


it was pretty uneventful.


So I've been sipping on salt and water again all this morning.


Now I'm feeling a little bit perky now.


It's a beautiful rainy day outside.


I'm interested to see what my ketones are, my blood ketones.


So I'm going to measure that.


And today is going to be very similar to yesterday,


a lot of chewing out.


Not like this, though.


Big, big, big tip on how to feel better.


When you're fasting, just start dancing.


Now I'm kind of looking at myself there.


I can't play that music on YouTube


because it's 50 Cent, window shopper.


So I'll probably just put a little something else on that groove.


Let's... Very exciting.


I'm feeling a lot better now that I did some dancing


and that I've had some salt and water.


Tick, tick, tick.


My winter... winter tan is very much showing.


It's raining. It's not that cold today.


I'll probably start feeling cold again in the afternoon, though.


Let's do a little testy test


on the right pinky this time.


My fingers are probably too cold, maybe.


What are we at?


Ooh, 2.4 millimoles per litre ketones.


That's very exciting.


I'm going to take a little photo of this.


Boom.


So that means I am in nutritional ketosis,


but that would be representative of what the Xero app is saying.


So if we go into the Xero app,


it's saying that we're at ketosis.


Again, you probably can't see that, but that's all right.


I'm going to record this.


We click on ketosis.


So from 24 to 72 hours, you're in ketosis.


Zero to four hours anabolic, catabolic, fat burning,


and now ketosis.


72 plus hours is deep ketosis.


So what's happening? Here we go.


Liver glycogen stops being a source of glucose.


Gluconeogenesis reaches its peak.


Gluconeogenesis is making glucose out of different types of molecules,


reaches its peak and begins to drop off.


Blood ketones increase.


BHB is usually 0.5 to 2 millimoles.


Well, that's about where I am.


Total ghrelin output decreases.


So ghrelin is a hunger hormone.


So hence why the deeper you get into a fast,


the less hungry you get because ghrelin is not triggered.


So that's another thing.


I woke up this morning. I don't feel hungry at all.


I am excited for food, though.


I do miss having... I'm missing... I think I'm missing milk the most.


I do like having a glass of milk in the morning.


And then diminished growth signals and increase AMPK activity,


further promote autophagy.


Autophagy, I believe, is cells, old cells clearing themselves out.


But I've got a few little things that I want to do today.


Again, it's just like I don't have much on


because I'm just going to be chilling out.


I'll do some reading, clean out the other closet,


and we'll check back in soon.


Sipping on a nice lemonade.


I'm kidding. It's sparkling water.


San Pellegrino, to be specific.


But little update, it's been about six hours or so since the last update.


41 or 45... I don't know why I said 41.


45 and a half thereabouts, hours in.


So 45 hours, 21 minutes, 18, 19, 20 seconds.


It's been another relatively chilled day.


I went for quite a large walk this afternoon.


Not as big as yesterday morning because it was a little bit rainy today.


But now I'm reading the Lindy newsletter,


which I would highly recommend, by the way.


The Lindy effect is if something's been around for a long time,


chances are it will stay around for a long time.


And there's a specific paragraph here on fasting


from the issue on diet part one, feasting and fasting.


I'm going to read this out


because it says what it needs to say better than I could say it.


So when we look at most ancient religions,


we tend to see one commonality.


Fasting.


Refraining from food for a certain period of time.


If something is in almost every religion, it is probably a signal.


Before the modern era,


food availability was unpredictable and highly irregular.


Fraught, war, insect infestations...


That's a little bit of a tongue twister.


And disease all played a part in restricting food,


sometimes to the point of starvation.


So did the seasons.


During the summer and fall, fruits and vegetables were plentiful,


but during the winter and spring, they were scarce.


Periods without food could last weeks.


There's a reason that one of the four horsemen of the apocalypse is famine.


As human societies developed in agriculture,


these periods of famines were gradually reduced and eliminated.


However, ancient civilizations recognised something deeply,


intrinsically beneficial to periodic fasting.


As periods of involuntary starvation faded,


ancient cultures replaced them with periods of voluntary fasting.


Kind of like what I'm doing now.


These were often called times of cleansing,


detoxification or purification.


And so there's another point here.


Religions do a lot of things,


and one of them is to be a vehicle for activities that help humans survive.


So again, the Lindy effect.


If something's been around for a fairly long time,


religions, chances are it will stay around for a long time.


And if something's strange but been around for a long time,


chances are there's a little bit of truth in there somewhere.


And so this is a great tweet by Lindyman,


or Paul Scalase, who's the author of the Lindy newsletter.


When it comes to things like diet, architecture and health,


tradition is smarter than you.


So this is really big, and there's another paragraph down below.


I'll let you read it, I'll link it below.


What fasting does.


So what happens when we fast?


Insulin goes down, adrenaline increases and metabolism speeds up,


which is contrary to what I would have thought.


Growth hormone goes up.


So there's some beautiful links, there's a beautiful article.


I'll let you check it out.


I'm gonna keep chilling out this afternoon, do some more reading.


And I guess it's time for another Makita measurement check.


Brad!


How about the left thumb this time?


Oh, we got a bleeder!


2.4 millimoles, so not...


I don't think that's too different to what it was before,


I can't remember exactly what it was.


I'll take a photo of that.


So still in nutritional ketosis.


I'm feeling fine.


Interesting to see how tonight's sleep would go.


Usually the deeper you get into the fast, the worst.


Well, for me, the worse your sleep gets.


But the benefit is I haven't had any caffeine


for at least the last 48 hours or whatever it is.


Probably a little bit more because I didn't have it before the night.


I'm gonna go to Jiu Jitsu training later.


Before then, gonna keep reading.


End of day two.


It is 9.05pm.


I am driving home from the family, went over for dinner.


Of course, I didn't eat anything.


I just kind of went over and said hello after Jiu Jitsu training.


Today was a lot better at Jiu Jitsu training.


Me and my brother had a great tussle.


So shout out to Josh, thank you for that.


I found I was a lot more stronger, I had a lot more energy.


Maybe because my body's now fully kicked over into ketosis,


whereas yesterday afternoon, I have a feeling that it was


kind of on the halfway point.


It was in between using glycogen and ketones as fuel.


At the moment, I'm not feeling hungry at all.


I've been sipping plenty of...


Because I've been training, I've been sweating a lot,


been sipping plenty of water and salt.


When I go home, probably gonna have a magnesium tablet before bed


just to generic a magnesium tablet


to bring back the electrolytes and whatnot.


I am excited for food, though.


So we were talking, there's a group of us doing this fast


and we were talking in a group chat today saying


there's a restaurant booked for tomorrow afternoon,


tomorrow evening.


So we're just over, just under 24 hours left.


Very exciting.


So we'll see how the sleep goes.


We'll check in the morning.


48 hours, no food.


Just got touted.


Good morning.


Super excited for today.


It is day three of the fast.


I woke up about an hour ago, did the same as yesterday,


stayed in bed chilling, a little slow to get going,


but drinking lots of salt and water.


Very excited because it is 60 or almost 62 hours


through the fast.


So that means I do get to break it tonight.


So last night's sleep was pretty good.


I woke up, I think, about three times


and it was a kind of a strange feeling.


Like I was alert, but not tired when I was going to bed.


I think it could have been because of jujitsu before.


I woke up about quarter to six


because the garbage truck arrived and whatnot.


And then I just sort of went back to sleep,


slept another few hours.


I've been in bed for probably the last hour or so.


Again, I wouldn't say my sleep was restless,


but I did wake up and I was a little slower to get going.


I weighed in this morning at 74.4 kilos,


which is by far the lightest across the whole fast.


I think it's down almost a kilo from yesterday


and probably two kilos from the start.


So that's pretty cool.


It is a beautiful sunny day today.


Very windy, as you might be able to hear.


But today is going to be much the same


as yesterday, chilling out.


And I get to break the fast tonight.


I was going to sleep like a little kid


before their birthday last night.


I was like, yes, I get food tomorrow.


So very excited for that.


I like this stuff.


Again, I can't stress this enough, major key.


If you want to feel better every morning,


we begin with dancing.


It is time to measure the keytones.


According to the Xero Fasting app,


I am still in ketosis.


So current fasting zone is ketosis,


24 to 72 hours, which makes sense.


I've been fasting for about 62 hours.


And we read that yesterday.


So the summary is that the primary fuel source


is fatty acids and ketone bodies,


plus glucose from gluconeogenesis,


which again, gluconeogenesis is the process


of the body breaking other types of non-glucose molecules


into glucose to use as fuel.


You are now entering a multi-day fast.


Between 24 and 72 hours, you rely more and more


on fat and ketone bodies for fuel.


Your body will prioritize burning fatty acids.


Yes, we can get more shredded.


And sparing ketone bodies to be used in the brain.


Any remaining liver glycogen will be fully used up.


Glucose production from the gluconeogenesis peaks


shortly after the 24-hour mark at around 150 gram per D.


I'm not sure what the D is.


And then drops off, settling around 80 gram per D


after 48 hours.


As ketosis gets underway,


levels of ketone bodies in your blood will begin to elevate,


indicating you're in a state of fasting ketosis,


which is somewhere down here.


It says BHB levels to rise somewhere between 0.5 and 2 millimol


is safely in the fasting ketosis range.


I think that's a different...


BHB is beta-hydroxybutyrate.


I'm not sure if this measures the exact same thing.


But let's measure the ketones.


I forgot to mention this last night,


but my sense of smell or my senses in general


have gone weapons hot.


What I mean by that is I walked out of jujitsu last night


and just finished training,


and there's a wood fire pizza store just across the road.


It's smell good.


Real good.


Okay, we got a good bit of blood.


2.8 millimoles.


Maybe you can see that, maybe you can't.


A little bit of glare coming in off the ocean.


That's really bright.


Have a look at this.


It's so bright.


Look at that.


Zoom in.


Bright.


It looks darker on the camera.


But that's all right.


I'm gonna chill out today, do some more reading.


We'll check back in soon.


Let's do a final update, shall we?


It has been a big day on tour.


Let me just readjust this.


How's my face look in the candlelight?


Let's move this over to here.


You might be wondering, why get a candle?


And I thought, well, since my sense of smell


is elevated through the roof, why not buy a scented candle


and enjoy the smell twofold?


This is a bergamot, cedar, and tobacco candle.


My apartment smells of rich mahogany.


I can smell the smoke.


Rich mahogany.


I can recommend it.


I'm enjoying the smell.


Also, I like to light a fire while I'm writing.


So I wrote down some thoughts.


I'm currently 69 hours, 28 minutes,


and 31, 32, 33 seconds into the fast.


You're not gonna be able to see that.


But very excited.


We've got a dinner booked in about an hour or so.


So it probably won't be exactly 72 hours,


but give or take, whatever.


Today has been a massive day on tour.


I've done some reading, sat on the balcony in the sun


because I was feeling a little bit cold.


Check out the name of the chapter I just read.


Hunger was good discipline.


A Movable Feast by Hemingway.


Goes on to talk about how your senses get


turned up when you're hungry.


I think I can attest to that.


After the reading, I came back inside.


It's very windy here today.


So because I was feeling cold, I had quite an extended long hot


shower.


That warmed me up a bit.


Then I did basically nothing and sat around on the couch,


went for a long walk, had a nap, and some grass.


I mean, this is what happens when you fast.


You just don't eat.


So you have so much time to just do whatever.


I did some writing, which is what I basically just finished


then.


And it's just clear my thoughts to sort of align everything.


But I did note down that I'm ready to finish.


I'm excited for dinner.


I'm excited to eat food with people.


That is probably my number one favorite activity in life,


is to eat food with friends and family


or just eat food with loved ones.


I've been sipping plenty of salt and water,


having a magnesium tablet here and there.


I had one last night.


I had one this afternoon because I did feel as though I


might get a cramp in my leg if I go for a long walk.


But let's do a blood ketone check.


And I was asking when I was writing down, why fast?


Why not fast?


And so of course, I found it's not


hard to find all of the beneficial points of fasting.


But I wanted to do, as a scientist,


I wanted to explore what are the arguments against fasting.


And of course, we saw in a previous clip


that all religions have some component of fasting.


So chances are, if it's been around for a long time,


it has some benefits.


And you can read up on all the other benefits.


I'm not here to preach fasting to you.


I'm more here to preach.


And take your health into your own hands.


Think about it.


And that was one of the things that I came up with,


is why fast, why not fast?


One of them is why fast, why not fast?


Stress.


What is stress?


Where does stress come from?


How do you even define stress?


You read a lot of articles on good stress and bad stress.


What is good stress?


What is bad stress?


How do you?


For me, if you categorize stress as a lack of energy,


I can't get any blood out of this finger,


then I would have had a highly stressful last three days.


Because my energy hasn't been rock bottom.


But it hasn't also been, I haven't had the urge


to really do anything except chill out,


which is good or bad.


The only stress I've really felt,


I mean, as I've said before,


I haven't even till right now,


three days in without any food, just water and salt.


I don't feel hungry at all.


Three millimoles.


We cracked it.


We cracked the three millimoles.


This is very intimate by the way, the candlelight here.


I'm enjoying this.


For me, the stress hasn't really come from


the fasting itself.


It's come from what I've not been doing


because of the fast in terms of not spending time


with family and friends.


Because typically the most time I spend with family


and friends is during a meal time.


And of course I went to visit my family last night,


but I didn't eat.


And so when you're not, it's not the same.


It's not the same, right?


When you're not partaking it.


Then of course, why fast?


Become proactive about a health situation.


If you've got something going on,


if your health fasting has a lot of health benefits,


one of them is it can help with weight loss.


Of course, everyone's going to be different.


But it also can help with if you have gut troubles.


So just give your gut a break from food


so it can repair itself.


There is the concept, a very, I like this concept.


The concept of, well, I don't like its effects,


but I like knowing about it is itrogenics.


And I may be saying that wrong,


but it's the way I understand it is


if you go to seek help from someone


or health help from someone,


and they introduce something worse to your health


because they're introducing something at all.


It's the adding any kind of,


like it's a doctor going for a surgery


and the doctor, the surgeon leaves scissors


inside your stomach and then zips you back up.


So that's itrogenics.


Your health situation was probably, might've been minor,


but it's now quite major


because you have scissors inside your gut.


And so fasting is kind of the preventative way of that


is that if you have some sort of gut trouble or gut problem,


one way to approach it would be


approval by removal.


And is approval a word?


I tried to type it,


but it kept telling me that it wasn't a word.


Doesn't really matter.


So there are my notes on why fast, why not fast.


I'm probably gonna write an article on something,


why fast, why not fast.


That's gonna be the last update.


I'll probably update after dinner, after I break the fast.


See how I'm feeling.


Maybe tomorrow as well.


We'll see.


Boy, do I feel good.


On the way home from that fast dinner,


three day fast has officially been broken.


I had ribs and coleslaw as the first meal.


And it was absolutely delicious.


The ribs were like pull off the bone.


Beautiful, beautiful weight ended.


And even up until the point where I started eating,


I never felt hungry.


Like the last couple of hours,


I haven't felt hungry at all.


I knew I wanted to eat,


but I wasn't ravenously hungry.


I mean, that was the opposite of one of my friends


who was at the dinner.


He was ravenously hungry for day three,


whereas yesterday, so day two, he had plenty of energy.


Kind of like the opposite of me.


Today I felt like I had plenty more energy


than the second day.


And so, I mean, that's what you're gonna get


with different people and their different experiences.


But I know some people commented on previous videos


saying you should break the fast with something simple.


To me, it's like bring it as the individual.


If you're fasting because of some sort of digestive issue,


yes, it would probably be a smart idea


to break the fast with something simple.


For me, I've never had any issues in the past.


So you can define simple as what you want, by the way.


So I was fine to take down some ribs and some coleslaw.


And of course, we got ice cream afterwards.


I had a double scoop,


so I'm 100% probably out of ketosis by now.


But I'm gonna check that in the morning.


Also be interested to see what I weigh in at.


So three-day fast done, 72 hours in books.


Feeling amazing.


Let's see how I feel.


SIREN WAILS


Welcome back to Grand Old Day.


Seems like I'm checking in every single time.


It is about 12 or so hours after the fast.


I just woke up and I weighed in at 74.9 kilos,


which is 500 grams or so, half a kilo up.


So I'm still down 1.5 kilos from when I started.


Not that weight loss was a goal or anything.


For me, fasting, the goal of fasting,


is just to have a break from the normal way of doing things,


to relax, to chill,


and to develop a new appreciation for food.


And of course, as you can see,


I'm very happy that I can eat again.


So this is what I look like.


Three days fasting plus a meal last night, I think.


Might have tightened up a little bit.


So not a dramatic difference,


but a little bit of a difference.


Now let's do one last blood ketone measurement, and then...


MUSIC PLAYS


What? Just because I can eat doesn't mean


I'm not going to begin the morning without dancing.


I think I'm going to make that a new habit.


Let's check the blood ketones and wrap this up.


What's it going to be? Am I out of ketosis?


I think I am.


Ooh, 0.9 millimoles.


So could still be quite in ketosis.


A very light ketosis, let's say that.


But that is a wrap. It's been a fun three days.


It's quite a long three days.


Time just feels a little longer


because you've got all that extra time in the day.


There wasn't too many points where I felt dramatically terrible.


I felt quite good most of the time.


And breaking a fast feels like the opposite of a hangover.


And speaking of which, I've got to go to breakfast with my family.


See you next time.


Windows sharper. Mad at me.


An image is worth 16x16 words: ViT | Is this the extinction of CNNs? Long live the Transformer?

Hello there!


Perhaps your academic social media exploded too, or maybe you are still unaware of the


news, but it's true!


A pure transformer beats the state-of-the-art in image recognition.


Yes, you heard right.


Images, vision, the realm dominated for centuries by the convolutional neural networks, or short


CNNs.


In this video, we will explain how the transformer was able to beat the CNN on its own image


battleground, and we will discuss whether the rise of the transformer will really mean


the extinction of CNNs.


So it happened.


The transformers, which we explained in a previous video, link below, took over the


field of natural language processing during the last couple of years.


It recently moved on to integrate vision and language in multimodal transformer architectures,


find the explanation link below.


Now after some CNN-transformer hybrid attempts, it happened.


The pure vision transformer is the new state-of-the-art, and this ruins the CNN fascination of Ms.


Coffee Bean.


Or really?


But how does the transformer do it?


Well, by doing better than CNNs for a start.


A convolutional neural network uses kernels to aggregate very local information in each


layer, which then is passed to the next layer that aggregates again local information, but


now with a larger field of view, because it looks at information that was already aggregated


by the first layer.


The take-home message here is that CNNs start to look very locally, and their receptive


field becomes more global in each layer and after quite some training time.


But the vision transformer, presented in this very anonymous paper, succeeds at having a


large field of view from the very beginning.


It overcomes the limitations of CNNs that look very narrowly at first, limitations which


help where there's not much training data, but about this later.


The transformer looks at the image by taking the input image and splitting it into patches


of 16 by 16 pixels.


These patches are then flattened by a linear transformation, a matrix, so that they become


vectors.


Then everything else what happens we know already from the language transformer, because


the same thing is applied here.


The vector of each patch gets positional embeddings and the usual transformer is then used to


predict what the image might represent.


Done.


This was the method.


Why does it do better than CNNs?


Because the anonymous authors can train on an awful lot of training data for an awful


lot of TPU days.


That's something all anonymous authors can do.


Or not.


The transformer is, unlike the CNN, very data hungry.


Especially because it has the freedom to look everywhere onto the image from the start.


In other words, it is very unfocused at first and it needs huge data amounts to learn how


to focus and what to focus its attention on to make the right predictions.


On many amounts of data, the transformer can find very original and unexpected ways to


look at the data, because there's no element in the architecture that tells the model how


to do that exactly.


The CNN, however, is focused from the beginning by the convolutions towards a local view.


The given focus patterns can be a limitation, but we can also spare a lot of training data


on this, because we do not have to teach the model how to focus, only what to focus.


So will transformers replace CNNs?


For heavyweight, state of the art winners, benchmark crushing, GPU intensive, data hungry,


energy consuming, environmental unfriendly kind of AI?


Yes, most probably, yeah, possibly.


For lightweight machine learning in production, for computational and energy economic settings,


for green AI?


CNNs and even far more classical approaches will still be out there, at least for a while.


The paper discussed here is at iClear still under double blind review.


We will update the video description with the full paper information after the authors


have been revealed.


Until then, try to figure out yourself who the authors might be.


But do not tell the paper's reviewers, otherwise you might ruin the double blind peer review.


Or was the whole setting ruined already?


deeplearning.ai Progress! | Learning Intelligence 14

What's going on learners? Welcome to Learning Intelligence episode 14.


And today has been a really fun day. I've been trying out this new


learning platform that Google have released called Quick Labs.


And why is that? Well because in a few days


Google are hosting an event called Google On Air I believe and they


there's an event they host every so often where they have


speakers and talks about stuff that's going on in the Google world.


And this one is on the topic is Big Data to AI.


And so one of the prerequisites for attending the event or watching the


event is that you have some basic idea of the Google


cloud platform. So that's what I've been doing here. I've been learning the


where is it here? Google cloud platform essentials.


I went through all the modules today seven out of seven labs complete


and it's I must say it's one of the best hands-on lab experiences I've ever had.


And I'll put a link to this in the description by the way.


Google.quicklabs.com so you can check it out. There's a bunch of free modules


on there so you can try it out. Use the Google cloud platform.


If you've ever used AWS before it's similar to that but I found the Google cloud


platform to be much more user-friendly. So that may be


something you want to look into in the future to use for your applications or


use for hosting a website or something like that.


And the reason why I'm doing it is because I'm going to need to


to run my future applications on something. And cloud computing is where


it's at at the moment especially with Google and Amazon


and all their all their APIs that they have their machine learning APIs.


I'm looking to to eventually take advantage of those and put them into


some applications so that people can use it and bring value to the rest of the world.


I want to show you one more thing. I just had a shower and so I'm about to go to sleep.


You probably saw how orange my screen was because it fluxed in full effect.


But this is my it's my journal and every every morning I write in the date


and about a paragraph or so of of just random random thoughts.


I start off with one truth so today's truth was humility is the essence of life.


And I believe that was that's a quote from a former Disney CEO.


I can't remember his exact name but that's where I read it this morning.


It's a quote. So humility is the essence of life.


And I don't know I write those down so it reminds me that I can take from the best


and learn and try and apply these things in my own life.


But what else is on here? I put in six goals for today.


So right I've still got to do that actually that's going to be my next step.


Workout completed that. Google training finished.


One Pomodoro Python. Two Pomodoros on Learning Intelligence 13.


So editing it and a VEGALOG clip which is what we're doing now.


So I'm going to do a few more points in the journal before I go to sleep.


Otherwise tomorrow I think I'm going to start the deep learning course on Coursera.


So we'll check in then.


Check it out guys. We're on the deep learning course on Coursera today.


Just started it.


About 90 minutes through the first module and so far it's been amazing.


I just had a break. I went for a walk to break up my study schedule.


And so now I'm going to get back into another 19 minute block


and then I'm going to call it for the day.


But I'll check back in with my favorite parts so far.


All right so have you ever done Andrew Ong's machine learning class on Coursera?


The deep learning specialization takes all the good things


all the the good teaching mannerisms from that and goes above and beyond.


I don't know whether it's because I've got a bit of foundational knowledge in deep learning now


and this is sort of reconfirming all that knowledge that I've learned


through things like the deep learning nanodegree and various other online resources.


But so far I'm about three four hours into the course and it is incredible.


That's that's going to be enough for today.


But I've written down a few things that that I picked out that were my favorite from it.


So let's go through.


First of all is from the welcome video with Andrew


and his passion for the AI as a topic is incredible.


And he believes that AI or his philosophy is that AI is an electricity.


So if you imagine how much electricity changed the world when when devices


when objects could suddenly have power to them.


That's the same thing that's going to happen with artificial intelligence.


Or well that's what he thinks at least and I tend to believe that as well.


If you imagine how many things are being connected to the internet these days


from from these from tablets to water feeders to plant feeders to everything.


Everything is being connected to the internet.


When you have a connection to the internet you can suddenly start to stream


things like intelligence and that's that's where AI is going.


Suddenly things that were not intelligent can have intelligence injected into them.


And become intelligent and that's how interaction with them becomes deeper and deeper.


Number two was the interview Andrew did with with Hinton or Jeffrey Hinton.


And Hinton is is quoted by Andrew as being the godfather of deep learning.


And he's such an interesting individual.


I didn't know that when he when he graduated university he did an


undergraduate in psychology or something like that.


He was always interested in how people think and how the brain works.


And he left university when he got a job as a carpenter for a bit.


And then came back and did a PhD in artificial intelligence.


So if you're on a break from studies or whatnot if you're doing something just remember


one of the godfathers of deep learning went and became a carpenter.


Before he even was interested in AI.


And his philosophy on AI was much much aligned with with Andrews.


And he had some words of advice on anyone who's looking to get into it.


Is that read enough of the literature so you understand the concepts.


But not too much because then you'll start to think like how the industry already thinks.


So if you want to change things read enough so you understand the fundamental principles.


And then start start your own work start implementing your own point of view on it.


So number three is a little simple one and number four simple as well.


Is I didn't I didn't all this time I've been using logs in my life like logarithms.


And I didn't know what an actual logarithm was.


It's funny how you you miss these certain concepts in math and they you sort of you can use math as a language.


But you don't necessarily understand what what the language is what the language means.


Like when you use a word in English but you know the context and that it is relative in that certain context.


But you don't exactly know the definition of the word.


And so I think I googled what a logarithm actually is because it was used in some of the calculations we were doing.


And a logarithm is essentially what multiplication is to division.


So if you imagine multiplication is the inverse of division.


Logarithm is the same thing but for exponentials.


So if you we take the number of thousand.


So ten times ten times ten equals a thousand.


That's ten to the power of three.


And so a thousand to the log base ten is three.


So it's essentially just the inverse of the exponential.


Fun fact about logs.


Look to my first assignment in the deep learning Coursera course.


It's Python basics with numpy.


And it says it'll take about an hour or so to to go over.


And although it says it's optional I'm going to go through it anyway.


Because you can never have enough Python or numpy.


So I'm about halfway through the actual programming assignment of week two of the deep learning course on Coursera.


And it's really fun.


I must say like this is this is going on above and beyond this course is amazing.


And it's later at night.


I don't usually code like till this late at night because I'm getting tired.


But essentially what we're doing is we're building a neural network.


Where is it?


Oh there we go.


That's the track I'm up to.


So I figured out if I put my put myself to it I can do about a week's worth of of lectures and programming assignments per day.


My goal is to have the entire entire deep learning specialisation done before Christmas.


So let's see if we can do that.


But the project I'm working on is we're building a logistic regression with a neural network mindset.


So essentially designing a neural network to decipher whether a photo is cat or non cat.


So for those who've seen it or or heard of it Silicon Valley is a TV series.


And the guy on there built an app called Hot Dog vs Not Hot Dog.


And we're doing the same for cats.


So cats are the animal of the internet.


So what better way to start off learning about neural networks.


Oh by the way there'll be a link to to Coursera.


It's such a deep learning specialisation.


I did partner with them as an affiliate.


So I'll put my affiliate link in the in the description if you want to sign up to Coursera to do the course that I'm doing or do any of their other course.


They do have 1600 plus courses on there.


So definitely worth it.


And if you go through my link full disclosure I will get some sort of kickback from it.


But this is what I'm trying to do.


I'm trying to bring value to the guys through making these videos.


And then if I can make something to sustain my my way of doing my way of living through through these courses.


That's how I plan on doing it.


Made some progress today guys.


I just passed the shallow neural networks quiz.


So this is week three of the deep learning course.


That was my third attempt by the way.


So don't let a perfect score get in the way of someone failing the first two times.


Well at least that's what I did.


I got seven out of ten on the first attempt and then nine out of ten on the second attempt.


And then the third attempt I got ten out of ten because I knew what my previous answers were and I sort of just just went off that.


But I was pretty happy with my first attempt of being seven out of ten because some of the concepts that went through in this this week were fairly math heavy.


But the explanation that Andrew Ng gives is incredible.


He goes through the fundamentals of of what you need to know for things like forward propagation and back propagation with deep learning.


And then gives you sort of the the minimum effective dose or the yeah that's it the minimum effective dose of what you need to actually implement these algorithms.


So don't go fretting if you if you're not sort of a wizard at mathematics.


I've only had a high school mathematics education and then I've upskilled myself on Khan Academy and just watching various videos and reaching out to people for help.


So don't worry too much about the math.


Andrew Ng was a great teacher.


And second of all this every time I go through this course it keeps getting better and better.


So I'm up to the the second programming assignment so far.


And as I said before in the previous clip I'm looking to finish the entire specialization before Christmas.


So let's do it.


Now I'm not sure whether or not I mentioned it in a previous clip but a couple of days ago I attended an online event by Google Cloud.


And it was called Google Cloud On Air from Big Data to AI.


And there were some amazing lectures some amazing talks from people who are really high up in terms of the Google Cloud platform and really great at what they do.


And so they were sharing their knowledge of how the Google Cloud platform can be used and how it's been used already.


But I had a few favorite takeaways.


My favorite actually yeah let's go through what it was that a couple of them.


One of them was the access that we have to this platform.


So first of all we have access to the production grade like artificial intelligence APIs that Google uses in their products on their free tier.


So I'll put a link in the description or somewhere here.


It's cloud.google.com.com.com. You can check out all the free stuff they have there.


I won't go through it all in this video I'm probably going to make one in a video in the future going through some of the ones that are my favorite.


For example you can use their speech to text API.


So say for example you built an iPhone app and you wanted it to summarize your meeting notes.


you can have that iPhone app link up to the Google Cloud API,


set your iPhone on the desk,


and it will use Google's production level


speech-to-text algorithm to convert the words


that you're speaking into text.


And that's something, not specifically that example,


but that's something that I want to try in the future


is go onto the Google Cloud platform


and use their APIs to build some sort of application


that can be used in everyday use.


So if you're interested in that,


I'd definitely check out cloud.google.com slash free.


That's their free tier.


Otherwise, check out the Google Cloud channel on YouTube.


They have a lot of great stuff there.


And as I said, as I begin to use it


more and more in the future,


I'm sure Amazon will have a similar offering,


but to me, Google is, from what I've used AWS in the past


and what I've used Google briefly,


Google's a lot more user-friendly,


and I think that's something I value as really important.


So I'll make sure I bring it out to you guys


or show you guys if I'm using Google Cloud platform


for anything in the future.


But that's enough for this video.


It's time for some shout outs.