You can jump on chat fuel calm. I'm not affiliated with them in any way shape or form


I Want To Help 100,000 People Workout | AI Powered Personal Trainer

but I really think messenger bots and communication is


Is where the next big boom is in tech and that's with voice and text chat


I think text chat will be first and then voice once it sort of picks up


It's slowly getting there that will that will come out that'll be really big in the next five years or so


Right now. I think the growth of the top four social media platforms whichever they are


Just got overtaken by the top four messaging apps, so I think that would be whatsapp messenger


Maybe we chat and line or something like that


So messaging apps are taking over social media apps


That's why I built a messenger bot not like a website or something like that so or a fully blown app actually


I haven't built a fully blown app yet


It's just it's just a messenger bot and I did it within within a couple of days and I watched a guide on YouTube


So if you want to get this stuff like started if you want to build your own and I encourage you to if you want


Like you can it's just fun to have I built in some some random features into mine that you can message it


Yeah, buddy, and it will send back an audio clip of Ronnie Coleman saying yeah, buddy


It's really fun. It's really fun. I got to admit


And even going through the chat history of of seeing what people actually say to it


And then I can I can see what people say to it


And then I go build that feature into the bot and then reply to them manually and say hey


I just built this feature in you can use it now


Thank you for for messaging the bot so that's how I plan on building it up for the start


It's just literally by hand going through


individual messages and then building in the feature as


As they as they come I might start signing off with my name


So it's actually not just a bot always messaging. It's a few. It's a human being if if I if they


Yeah, that's a good idea


Maybe I'm maybe add a personal touch to it if they message the bot something


It doesn't understand and then I can reply back with it, but where can you find this bot well?


I haven't got a customized Facebook page for it yet. It has a Facebook page, but the URL


I don't know what it is. I need to for some reason I need to


Have more likes or something like that or maybe invite another admin to the page or something. I


Don't know Facebook is weird. It's not letting me have a custom URL yet, but I will get there in the future


In the meanwhile, I've shared it on my social media so Facebook.com slash mr. D Burke mr. D


Bo you are ke I'll put a messenger code on my on my website or something like that mr. D Burke calm


I'll probably write a guide on medium on how I did it and


Within the next couple of days or something, but yeah, I'm really I'm really liking it. I'm having I'm having so much fun


I've got a lot of energy right now because it's I think once you've built something of your own you get this new


Newfound energy like all these features are running through my head. I've got like a little journal. I'm writing down all the feedback


I'm getting I posted it in a big Facebook group of all the


MIT bootcamp alumni and all of them are starting to comment on it and say hey you could try this you could try that you


Could try that you could try this and I've got a little lot of work to do a little to it, too


I just I don't know. I set myself a challenge over the weekend and built a built a messenger bot who knows


But it's still really early days. I've got a long way to go and I'm looking forward to it


What else is new so in the coming week?


I've got a lot of study to do so I'm gonna be splitting my time between study messenger bot working out


I didn't even work out properly today


It was actually probably a good day to rest because my legs are really sore


So resting up on the legs today


Working on my Udacity course tomorrow deep learning. I'm going to the library with one of my friends


That should be a good time


I've got into a habit recently of going to the library to do study and I take my laptop there without the charger


because I'm using a MacBook Pro and I


Know the battery life supposed to be all day, but


What I do on it some of the stuff is a bit programming intensive like a bit a bit CPU intensive. Sorry


So that she tends to drain in maybe four hours five hours or so


I don't know if that's


Bad or whatever. I don't really mind. It's it's I'm never really that far from a charger


And so that's my like limit to get out


So once once my Mac sort of says your Mac is low battery you got to get out


That's that's been my window to sort of go home have something to eat and have a sleep. That's my that's my tap out schedule


So I'll probably be spending a lot of time in the library


Over the next week. I've got four more days left after this of


Day 100 is gonna be this week. That's awesome. Yeah


Day 100 of the podcast experiment, but don't worry. I'm not stopping it there. I'm gonna keep going possibly not daily


No, actually not daily weekly, I'll set you up for success now. I haven't worked out which day of the week yet


I want to get it a scheduled routine as for YouTube videos more of them are to come


I'm trying to work out this whole process of being creative building products and whatnot


Loving it, but at the same time you gotta you gotta dedicate a lot of time to it, but that's a good thing right


I'm still learning. This is all the process


But whatever you do it. I actually was a daily challenge


Hmm try out my bot go to facebook.com slash mr. D Burke


And you can see the link the latest link that I shared you go to the move more bot


Give me some advice. Give me some feedback. I'd really appreciate it


And yeah with with your help we can make the world move more


Announcement: ☕⚔🍵 AMA with AI Coffee Break & Chai Time Data Science over @Weights & Biases #Shorts

Hi, we want to let you know about this cool event. This Saturday we are invited to a pair


Ask Me Anything session with Sanjam and me. And guess whom we'll have as a host? It's


Dr. Brian Bischoff, the head of data science at Weights and Biases. And because Sanjam


runs this Chai Time Data Science podcast, awesome by the way, we will have an epic fight


between chai and coffee. And of course we will also talk about educational content,


about videos, about the AI and machine learning communities in general. So Ms. Coffee Bean,


are we up for it? Of course we are, but are you? Bring your chai or coffee and join us


this Saturday at 5 PM CET, which is 8 AM PT. Find the link and details to the event in


Facebook F8 Event Highlights, Amazon Shareholder Letters and Sleep with William Bourke

And we're live! What's going on guys? We're back for another show, or actually it's really the first show.


First of many.


First of many, yeah exactly.


See how this one goes.


I think you may have just heard the intro.


Psst, we haven't actually done an intro yet.


Pre-recorded intro.


Yeah exactly. So we're going to do the intro based off what we talk about today, but otherwise the premise of this show is


we want to talk about, we go for these walks after work or at the end of the day, and we talk a lot of


technology, business, life in general, and we thought, why not record them?


And post them on the internet so other people can enjoy them too, and really just so we can look back on them in 20 years and go


what were we thinking?


Well maybe here's a brief intro.


I'm a business person learning AI, and you're an AI person learning business.


So kind of mash in that situation, and it's just good to throw around ideas.


I have ideas about business and how business can integrate AI in the tech side of things, so you share the spotlight on that side.


Still a newbie, still a newbie on that part, but I love learning.


Well you've got the AI shirt on man.


I should have got me one of them, look at that.


I don't know if you can see it, but what's that from?


MIT. It's artificial general intelligence.


Which is an epic series on YouTube, and by the way, everything we talk about, we'll wrap it up, it'll be in the show notes somewhere in the internet.


I was about to say, if we reference something, are we going to chuck it in the description so that people can...


Yeah, well that's enough of an intro, or a second intro, whatever.


But the show's going to be, we're not entirely sure what it's called, maybe Berkbrothers Podcast, something, if you've got any ideas, send them our way.


Or otherwise, three-ish things, because that's what we want to try and keep it to, just three-ish.


Three points every week.


Three points every week or something.


Alright, so let's kick it off.


Amazon shareholder letter.


Yeah, that's where we're completing each other's sentences already.


Alright, so what did you, did you read the whole thing?


Yeah, I read most of it, well all of it, sorry.


There's a few things that really stuck out for me, and I'd like to start off by...


Wait, wait, wait, what is Amazon shareholder letter?


So two weeks ago Jeff Bezos posted the Amazon shareholder letter for 2017, reflecting on their results and how they went, and updates on how the company's going as a whole.


Pretty much issued to the whole company, all their employees, and the wider community, so shareholders, to pretty much provide a general update on how the company's operating.


Yeah, here's what we've been doing, here's what we want to do for the future.


So the one thing that stuck straight out at me was Amazon Prime.


Prime?


Wow.


Yeah.


Like, 100 million people per year are now using Prime.


And 100 million.


Across, well that's globally, but initially when I read it I'm like, no way, if that's the US name...


It's not here in Australia, I don't think.


No, it's not.


Because while obviously Amazon's recently new, they just came to the Australia, so they obviously got a lot of distributions and...


I can't wait.


...logistics to sort out here, and obviously different legislation and pay wages, so they've got to sort out a whole lot of logistics to get their prices down to what they're known for over in the States, and obviously other places like Europe I would imagine, to being the low-cost delivery provider.


The owner of the last mile delivery, that's what they refer to.


Last mile delivery, what does that mean?


So the last mile delivery is like basically the last, so being able to get you a package very quickly.


Like owning that last mile from point A to point B in the delivery, like having the most efficient...


Okay, so being on that last step of the pipeline.


Which is so important because I think I read in the shareholder letter as well that in 2017 third parties sold more on Amazon's site than Amazon themselves.


So like Amazon Marketplace, which I'm not entirely across, but that's kind of insane to me that they just open it up, it's just this one massive marketplace for buyers, sellers of all people, not even...


And Amazon are just like the hub.


The hub.


They even do the fulfillment and everything.


Allow people to use their distribution centers and their logistics to sell their products and they just take a share of the profit, obviously, a percentage to cover what they spend and then also what the profit...


Obviously, to cover their expenses and then also a little bit extra for them on the side.


Yeah.


Pretty much just to own, just a way to own more of the market.


The Amazon takeover is real.


What was actually one of my favorites was day one, how Bezos says that Amazon's philosophy is that it's always day one, which I totally agree with, which is pretty much how we try to live almost at the moment is everything is a learning opportunity.


And he attaches at the bottom of... Because I made the mistake, I read the 2016 shareholder letter again. I'd already read it, I read it last year, but I think it's good. I took notes on both of them actually.


What were the main differences?


Last year wasn't as long, so this one was a bit more full of numbers, I thought.


Here's what we did. Last year was kind of like, here's four points that we think Amazon kind of giving a... Or Jeff Bezos and Amazon's insight into what makes a good business.


And of course, number one was true customer obsession. This is from last year's letter, so I'll just go through these points quickly.


Number one was true customer obsession. Are you delighting your customers?


Number two was resist proxies. So that means when big companies get big, they can kind of get into a space where they just think they're fulfilling the process, and they think that the same process they've been doing for so long should keep getting results.


But Bezos in the letter said, do we own the process or does the process own us?


Which I think is a very good point to think about, especially not even on a big company scale, but on any level.


Like, are you just going through the actions? Does the process rule over you? Or are you innovating on that?


So resist proxies, I'm not entirely sure what in terms of proxy that meant, but it'll be more in the shareholder letter.


And number three was embrace external trends, which I thought was massive as well. And one of them was rather than fight against technological trends, make sure you're using them to your advantage.


Because that's what he said, he's like, if you're going against technology and whatnot, you're essentially betting against yourself.


And so the two main ones were, of course, artificial intelligence and machine learning, ride the tailwinds of society.


And the fact that they keep struggling with that in mind, they're struggling to keep Echo in stock. So that's pretty insane.


Excited times. Exactly. And the last one is high velocity decision making.


So he said, if you're 90% sure on what decision you're about to make, you're probably waiting too long.


Like we like to keep moving fast and have this, this saying in house called disagree and commit.


So he gave the example of a couple of producers pitching him a pilot for Amazon Prime.


And he said, he didn't really agree with what it was. And he's like, well, it's going to take way too long for you to convince me.


So I'm going to disagree and commit and know that you're capable of good work.


So I just think that's, imagine having a leader like that. But that's the 2016 letter, which is a lot of value.


But my favorite for 2017, what you got?


Just before we move on back to the 2017, maybe you could share your experience with them being so customer focused with your AWS issue.


Oh, yeah, that's a good point. Yes, a customer focused.


I last year or so I've been learning AI technologies and whatnot using a lot of them.


A lot of these technologies require intense compute power.


So what that means is essentially, you use my laptop to build the basis of a program, let's say.


And then because my laptop doesn't have enough computing power or horsepower, you need to dial into the cloud to recruit a bigger computer somewhere in the world, somewhere else.


So a lot bigger than what I could store in my room.


Or actually, you could probably store in your room. It's just that you don't have access to it all the time.


And obviously, it costs a lot of money to have the hardware.


Yeah, it costs a lot of money to set up this type of hardware.


So I dial into Amazon's cloud server, which is AWS.


And I start up a computer that's somewhere in Sydney or the US, usually in Sydney because that's the closest to where we live.


And for some reason, I forgot to shut it down.


And this is a problem, actually.


I think it would be good to get notified if it's running and not being used.


Like surely that's something they could look into doing.


And so essentially, I had this computer running and it's quite expensive to keep running over a long period of time because it's just number of hours, right?


They charge per second.


So it's efficient if you're using it for just what you need it for, but not so efficient if you're just letting it run with nothing running, which is what I was doing.


I used it for an assignment and then stopped it about, I would say, two to three weeks later once I received the bill, which was 700 US dollars.


So that's almost a thousand Australian dollars.


And I kind of, I was like, far out.


I was a student.


I'm still a student.


It was a lot of money.


And I'm like, if I pay for this, I don't have much money at the moment.


So I reached out to the AWS support, told them my story, said, hey, I'm a student.


I was running this for an assignment.


The assignment finished.


And for some reason I thought I terminated it, well, terminate means just pretty much pull the power plug on it.


But I didn't.


So completely my fault.


But they turned around and said, hey, you know what?


We can see what you've been doing.


Thank you for reaching out to us.


Thank you for using AWS.


We're wiping the whole bill.


So I mean, yeah, that's no small bill.


Like, either that's 700 US dollars.


So I don't know what their margins are, like what it actually costs them.


But that's something that comes back to what he said.


He's like, obsess over your customers.


And I mean, they may take a hit on that, but I'll be a customer farmers for life.


Like I bought the Echo the other day and I use AWS almost every second day.


Yes.


Yeah.


Massive.


But 2017 letter, we kind of went on a ramble there in the 2016 letter.


Let's go back to my first point on Prime.


Prime.


So 100 million people, if we look at the metrics, that's 9.9 billion in annual recurring revenue.


Just in 99 US.


99 US.


Yeah, I should have said that better.


99 US a year.


100 million customers.


And 100 million customers.


And it's 9.9 billion in annual recurring revenue just from that section of their business.


Just that one revenue stream.


That is insane.


And that, the Prime members, I was listening, two and three households in the US have Prime.


I think I may have read some statistic that more people have Prime than own a gun in the US.


But I think, I don't know how accurate that is, but.


I wouldn't be surprised.


It's incredible.


I can't believe the scale of this company.


And we were saying, actually, let's talk about something technology based.


So, I read in the, we'll get back to that in a second.


2017 annual letter was, a big thing was Amazon SageMaker, or AWS.


Which is totally another business almost on its own.


That's 20 billion revenue.


Inside.


20 billion.


So, Amazon, AWS SageMaker, which was released about six months ago.


But I'm only just learning about it now.


I should be more across these sort of things.


Which is essentially, like, how do I explain this?


So, it's automating the machine learning pipeline.


Right?


So, if you're someone like me, true, who's running data science models or whatnot.


I can go to AWS, get into an environment, so like a web application per se.


Load in my data.


Excuse me.


Bless you brother.


Load in my data into Amazon's databases.


And then their machine learning algorithms that they've put in are made to run extremely efficiently.


I think they were talking about 10x.


10x, up to 10x faster on this dedicated SageMaker service.


And I can essentially train my model in one click.


Train the model on the data.


And then it even has an option to scale up to what you want it to do.


So, how much power you can dedicate.


So, you can just turn this little knob and say, I want this to run for X and I want it to use this much computing power.


So, that means it might cost this much at minimum or it might cost this much at maximum.


So, that's up front.


Because a lot of the machine learning at the moment is kind of guesswork.


Like, you run your model, you adjust your parameters in terms of your settings.


Like, knobs on a microwave or on an oven.


And you see how it goes. It's guessing check work.


But this thing, you upload it, it works, it tests a bunch of different models for you.


And it does a bunch of hyper parameter tuning as well, depending on how long you want to let it run.


And it's all packaged in little modules.


So, if you want to do the whole pipeline of up training your data, deploying it to applications, and then whatever.


Sorry, storing the data online.


So, storage, training, deployment.


You can do it.


But if you want to do the storage somewhere else, training on there, deploying somewhere else, you can break it up.


Like, that one service had 250,000 customers since launch.


And it was announced end of last year, November 29th.


And I'm like, that is insane.


It's making it more accessible.


And that's what I think is the end game for a lot of these cloud-based offerings like Google Cloud or AWS or Azure.


Is, okay, they're pretty accessible now if you have the know-how.


But for someone like yourself, who, and me, who are just getting new into the field, and we haven't been developers for five to ten years.


I think SageMaker is definitely very valuable, especially if you're a startup.


Like, you don't have the resources like a big business has.


You could get on there, store your data in one place, train your model in one place, and deploy it all in one place.


It's fascinating.


But that's enough from me in terms of the ML side of things.


Actually, what else did you find in the letter?


With AWS?


Yeah, let's go, what do you reckon?


AWS, was there anything else from the letter that stood out to you?


How they're really accelerating their pace of innovation in the areas of AI and machine learning, obviously, as you just touched on it.


But it seems like AI and machine learning within AWS is a very big focus point for them because they obviously see the potential in the size of that market.


And a lot of people are now starting to integrate machine learning into their organizations.


So, what's there to run it off? AWS.


Yeah, so AWS. If you look at a graph, I haven't got one for the screen at the moment, or for the listener, sorry.


AWS, just imagine in the top right, and then all the other offices, imagine them in the bottom left.


That's how far ahead they are in terms of numbers, services, etc.


But that being said, I went to a Google event a couple of weeks ago, and they're ramping up.


They're like, hey, I want some of this AWS pie.


And, yeah, well, obviously, they're making 20 billion in revenue off AWS alone.


And it's just crazy to think that when you look at Amazon, they're a retail technology company, essentially.


But in the background, they silently became the biggest cloud server provider in the world.


So, it's just crazy.


What were you saying the other day about them being split up?


So, if regulation were to hit Amazon, I would just imagine that it would be easier for them just to spin off the AWS side of the business.


Instead of just take AWS out of the picture, and have that as its own entity, and leave the rest of the business as a whole.


Because obviously, if it's still in 20 billion in revenue, it's worth nearly $200 billion, that section of the business.


What do you mean?


So, you just did a math equation there.


So, 20 billion means that you're worth $200 billion.


Yeah.


It's kind of like I was just throwing out a ballpark figure of what it would be worth.


Actually, I think I've heard that before.


If you're doing 100 million ARR, you've got a billion dollar valuation.


That was with Slack and all those other things.


Okay, I get that now.


So, you're just kind of 10X it for a ballpark figure.


Another massive thing I liked was Whole Foods, which I think is massive.


The acquisition of Whole Foods, Amazon's, that's a space I really want to get into, I think.


If I was to go over there, my food is something I love.


My passion, food's like my religion.


You know how into it I am.


And the fact that I think Bezos said, or in the letter it said, the acquisition of Whole Foods


was the fact that first and foremost, they wanted to get good quality organic food to everyone,


as many people as possible, which I think is a massive mission.


I totally agree with that.


And I think they've got the infrastructure and technology to do it, especially with Prime and Amazon Go.


It's just going to be a merger.


It also just made sense for them to buy Whole Foods in the first place.


Because they always wanted to go into the fresh food delivery side of things.


In grocery.


The everything store, as they're often referred to.


So, obviously, fresh food was their next target after they owned other parts of retail.


So, buying Whole Foods pretty much saved them almost 10 years building out all the distribution channels.


They've also got 100, however many stores it is across the US.


So, the money, what do they spend on it?


Like, $13 billion?


Yeah, like $13 billion to save them 10 years of building out those distribution centers.


Yeah, so now they've got Whole Foods positioned in all different places around America.


And they can just use that as another hub for people to pick up their Amazon parcels


or do their grocery shopping or whatnot.


With the Whole Foods acquisition as a whole, I'm surprised no competitor tried to throw out a counterbid


to Amazon's $13 billion thing.


Obviously, you're not going to be able to outbid them.


But, throw out a counterbid.


Who would compete?


Off the top of my head, Walmart.


Yeah, good point.


Obviously, Walmart's got a lot of money as well, but bid the price up.


They can pay a bit more because for $13 billion, they stole Whole Foods.


And we were saying, imagine if when they come here,


so in Australia we've got a supermarket chain called Woolworths,


and we're just like, Amazon come here, swallow up Woolworths.


Just buy Woolworths.


And then they don't have to build the distribution centers, they've got stores everywhere.


Kind of like what they did with Whole Foods in the US.


I see that happening, but I don't at the same time because


Australia, 25 million people population, we are growing very fast.


I'd love to see it happen personally.


Yeah, I'd love to see it happen.


Alright, little hiccup in episode one.


We've got the camera paused, but we're going to jump straight back into things.


Thanks for being patient, podcasting.


But yeah, where were we?


So we were saying, on-device fingerprinting technique,


which is to do with Amazon Alexa.


My Alexa's about to go off.


So if an ad is playing on TV and it says Alexa,


Alexa knows that it's not your voice and won't activate.


So when the Super Bowl ad was playing, which was amazing,


your Alexa won't activate.


And then there was a few more things, a new Kindle.


Which skill do you want to enable?


Sorry, we don't need you.


There we go. I thought it was going to come on.


Not quite the anti-activation yet.


Just walk out shopping, just walk out, that's Amazon Go.


That's their new terminology for Amazon Go, is just walk out.


And they've cracked half a million employees.


560k. But you were saying?


Yeah, so it's funny that you say that.


Amazon's a hiring machine right now, but on the flip side,


with them hiring a lot of people, at the same time they're patenting


and also developing a lot of technology that's going to automate a lot of jobs.


Yeah, so they hire one person.


But that way you might not get a job.


Exactly.


The biggest example I can think of of what they're doing is Kiva Robots.


So I don't know if you've heard about them.


Kiva Robots?


Yeah.


So they're the robots that Amazon uses in its fulfillment centers.


In their warehouses.


No one working there.


And a scary photo would be inside of an Amazon warehouse, because there is not.


You don't see photos in the warehouse?


You don't see any photos or videos inside an Amazon warehouse,


because it's all robots and automated.


So what happened with Kiva Robots was, there was a new startup outside of,


I think, Massachusetts.


Amazon was one of their biggest contractors, obviously.


One of their biggest buyers of their robot technology.


Amazon ended up buying them, taking them over for about $800 million.


And as soon as Amazon bought them out, they canceled all their other contracts


with every other company that they were supplying to.


Just so that they could own the whole...


They got all the robots.


No one else is doing robot warehouses, just us.


That's the great white shark of Amazon.


But I think that's enough for the AWS shareholder letter.


Facebook had its F8 event for 2018.


Did you watch the highlights at all?


I watched a few.


You want to kick it off with what you thought was good?


Yeah, so let's go for the tech side of things.


I think there was one...


I don't know whether to be like this or to be scared or to...


I'll get your insight and what it might look from a business point of view.


But Facebook, they essentially, they're going all in on camera, right?


They love cameras. Zuckerberg loves camera.


And I'm a bit wary of time here, because we want to keep these episodes


within 30 to 45 minutes or maybe a bit less.


So, long story short, they want to grab your old photos.


So old photos on your devices or just photos of...


Say you took a photo of this point of the room,


and then we took another photo of that point of the room.


And if you make an album with that, Facebook want to use computer vision


to reconstruct the room.


So you can take a 3D tour of the room and see the photos all in one kind of image,


which I think is pretty awesome as far as computer vision technology goes.


But in terms of what that means in how they understand literally the items


in your home and from an advertising point of view,


is this going to be like they know what brand of blender you use now


and all of a sudden you're seeing that brand of blender pop up in your newsfeed?


Well, yeah, from a business point of view, just hearing that Mark says


that they're obsessed with the camera and being a camera company almost


is just another dog shot at Snapchat, because what does Snapchat claim it is?


A camera company, essentially.


So it's just like another reason why...


Well, another stranglehold placed on Snapchat is that Facebook just continues


to go after it and after it and after it, kicking Snapchat while it's down almost.


Yeah, but I mean, as well...


Yeah, as you said, if they have a model of your room,


they know the inside, all that details, they know what sort of brands


you're using in your kitchen.


So that data of knowing what's in your kitchen is so valuable,


because they could go...


What food you eat.


They now have more quantifiable data where they can go to a big retailer.


Say, for example, a fridge producer.


They can now go to you and say,


we know this many people don't have your fridge.


We can target ads at these people on our platform.


So that becomes a lot more valuable to the supplier of fridges.


This is just a bad example, but you know what I mean?


It's not a bad example at all.


It's exactly the kind of example...


Look, we may be overthinking it here, because if you just watch the video,


I only saw about a minute or two minutes of what it actually does.


But that being said, with what's happened recently,


with the Analytica thing, coming back, I think a good point on the event I saw


was the ability to clear your history on Facebook.


I think that's massive, almost.


That's been there for ages though.


What do you mean? You can clear your entire browsing history


of apps, products and external websites.


Yeah, that's been there for a while.


Well, there you go. I'm not even on Facebook at the moment.


But I guess obviously on the flip side, people just didn't know about it.


Well, that seemed like it was a new feature that was being announced.


Because after the Congress hearing...


There you go.


Speaking of that, I'll just quickly...


After the Congress hearing, when I first logged on Facebook,


I got a notification at the top to say that you can now do that.


Oh, that's good. Yeah, well that's recent.


It's fairly recent, yeah.


But then they just...


We've got enough data. We don't need any more.


How much data do we need?


And then there was a couple more things, which was...


They're hiring 20,000 people to work on manually auditing ads,


newsfeed items, content, etc.


Yeah.


My opinion on that is just to avoid regulation.


What do you mean?


Well, like, it depends on how you take it.


The Congress hearing obviously didn't go well for them.


If you look on the flip side, after the Congress hearing,


their share price didn't take a hit.


Their share price is still on the column.


So they're going to hire 20,000 people to be like...


But just how good does it look in their perspective to come out


and address those points and just go,


we're now going to make 20,000 new jobs.


Yeah, that's what I was saying.


Like, hey government, we're hiring 20,000 people to curate this stuff


so you don't touch anything from us.


Yeah, and we'll just keep it all within the Facebook world.


It's not going to make a dent on their profits either.


Yeah.


Like, the 20,000 people, what's that, 50k a year, let's just say.


That's a billion dollars.


Yeah, but...


I suppose that's nothing compared to like...


What's spending a billion dollars to protect their monopoly?


Yeah, that's true.


Nothing.


Yeah, to protect their complete pipeline control of what information people see,


which is crazy.


What else today?


Otherwise, there was Oculus and a few other things.


Watch the video if you want.


Otherwise, it was like group video chat for Instagram,


which was, I thought that's going to come eventually.


Group video chat for WhatsApp, group video chat for Messenger,


just the same features across, it seems three different chat apps almost now


with Instagram with a focus on...


But I think that's going to be the next big platform.


Yeah, I was about to say, yeah, Instagram's their big play right now.


Yeah.


And their big money maker that captures the most attention


and has a lot more people.


I use it.


I don't even think it has half the people on Instagram that does Facebook,


but the attention it catches is...


Yeah, it's catchy.


And I think Instagram's always in the top, either the first position


or the second position for the most downloaded iOS app.


Yeah, always one and two.


And so where does AI come into this?


Well, of course, with the computer vision they're using in the room


for the Facebook camera app,


they're also using AI to fix the news feed,


like trying to detect fake news versus real news,


which I think that's a very hard problem because...


I think AI's still blowing out of the water in the fact that


it can do some superhuman stuff, which it can,


but if a human expert struggles,


a machine learning model is also going to struggle a bit.


Yeah, because obviously the machine learning model is developed by humans.


Yeah, exactly.


So if a human expert on content curation


can't tell whether something's fake or not, it's going to be hard.


But that being said, Facebook, on another AI side, released...


Never underestimate the power of these big tech companies,


AI firms and research,


because they essentially replicated AlphaGo Zero,


which is DeepMind's Go playing engine.


If you haven't heard, DeepMind built an engine which can play Go


better than any human player in history,


and better than its own engine it built a couple of years ago.


It beat itself a hundred times,


and essentially it learned from scratch.


They didn't teach anything, they just said,


here's Go, a few minor settings maybe, and it learned.


It played against itself, and just got better than anyone it'll ever be.


But DeepMind is Google,


Facebook AI Research built their own version called OpenGo, I think.


And the reason they called it OpenGo is because they open sourced it.


So the code for it is all online.


You can look how they did it.


I'd love to see their one play AlphaGo Zero, like it'd be a contest.


Imagine that. I'd watch that live.


I would 100% tune into that.


I think a lot of people would.


Yeah, and the code's online, and they also released it along,


maybe the framework's been out called ELF,


which is something experimental, maybe, I don't know what the E stands for,


but experimental light framework,


which is a reinforcement learning framework,


which is essentially what they used to play AlphaGo.


They used this framework.


So if another developer like me and my team,


who we've been working on an open AI competition


to build a super playing Sonic the Hedgehog,


we can use Facebook's framework, which is all open sourced.


I haven't dived too much into that, but I think that's really cool


for Facebook to go, hey, we've seen DeepMind do this really cool thing.


We're going to replicate it with our own engineers and our own scientists,


and then open source it for everyone.


I think that's how everyone wins.


If it's open source, okay, sure, you might not make as much money, whatever,


but is that the goal?


You contribute to the development and the progress of the field


by making it open sourced.


Yeah, exactly.


Think about if you're a young developer,


you're just getting into the field, and you go online,


and you want to learn this stuff,


and you jump in and you use Facebook stuff,


where are you going to work later?


Because it's all open sourced, where are you going to work?


You're going to be, I want to work for here,


because that's who got me a leg up.


That's the 10-year rule, the 20-year rule.


You should always be nice, always whatever,


because you never know when it's going to come back.


Exactly.


But I think otherwise, I think that's enough.


I've got the Amazon shareholder letter,


Facebook F8 announcements,


Facebook open source is its own version of AlphaGo.


We did have one more thing, but I think for now, we'll leave it.


It's essentially the Sleep podcast from Joe Rogan with,


I think it's Matthew Walker, I'm not entirely sure.


I don't know.


What do you think of the Sleep podcast with Joe Rogan?


I think everyone should definitely check it out.


What was your takeaway from it?


I'd probably say that I really was drawn by the fact


that when they changed the school time.


Originally, a school in America used to start at 7.30.


Their SAT scores or something like that.


Test scores.


They start at 7.30.


The average test score was like a 1200 or something,


which apparently is still very good.


But when they moved the start time to 8.30,


the average test score went up to 1500, which is just insane.


Yeah.


I think the other one I was blown away with was the doctors


doing 30-hour shifts or something, which is inhumane.


It all started from this one doctor back in the day who did coke.


No, he was addicted to coke because they used it as a pain treatment


and he was testing it on himself.


This is about 100 years ago.


Essentially, the podcast was Joe Rogan, who's an awesome dude


with Michael Walker or Matthew Walker.


Sorry, bro, I forgot your actual name.


But he's a PhD researcher in sleep, essentially, for 20 years.


He's wrote this book and it's just blown me away.


I already value my sleep highly, but this thing...


Anyway, this doctor, 100 years ago or something,


they used cocaine to treat pain.


Suddenly, he got addicted to cocaine.


He would do 30-hour shifts because people were like,


this guy's insane.


He's working so long.


He's like, well, the student doctors need to keep up with me.


The interns, something like one in 20, because they're so sleep-deprived,


one in 20 ends up killing someone accidentally because...


One in 20. New doctors, student doctors.


One in five injures someone, which is ridiculous.


Or you're 400% or something more likely to make an injury


after a certain period of time.


The data that they had, and I'm probably misreinterpreting


some of the exact numbers, but when you listen to them,


they're just ridiculous. Absolutely ridiculous.


If I go for another any surgery, I'm going to be asking the doctor


how much sleep have you had?


Because there's something like...


Yeah, a major surgery.


If the doctor hadn't had X amount of sleep...


This is no malice towards doctors. It's just the one case example.


It happens everywhere.


How much sleep have you had?


Because there's something like 170% more likely to make an error


if they've had six or less hours sleep in the past 24 hours,


which is, I think, something so easy to fix.


But who knows? Who knows?


I'm going to be sleeping more. Let's put it that way.


With that being said, it shows the value that AI and robotics


and machine learning will bring to the health care world.


We'll talk about that in the next podcast most likely.


Yeah, maybe in a future episode.


But that was fun. We had a little bit of business,


a little bit of technology, a little bit of health,


and three-ish topics, Amazon, Facebook, and health.


But otherwise, what do you think the question of the week is?


What can we give our viewers to go out and do?


That's a good one.


Yeah, question of the week.


Question of the week is...


Go check out one of the things we've talked about today


and send us in something.


Actually, that can be challenge. Challenge of the week.


We're kind of thinking of this on the fly.


So challenge of the week, go through one of the resources


we've shared with you and share your favourite thing with us.


If you want to ask us a question or if you want to talk about a topic,


send me an email, danielatmisterdeburg.com


and we'll get it on one of the next shows.


If you can send in audio, we'll try to play it on the show


and we'll go off that.


So if you've got any questions, any advice, whatnot,


send it in.


Feedbacks. Appreciate it for the first one.


Yeah, actually, what worked? This is the first episode.


So what worked? What didn't?


What do you want to see more of? What do you want to see less of?


What do you want to hear more of?


More technology, more health, more business?


Who knows? We're still working this out.


But otherwise, if you want to find out more,


all the links will be in the description, all the show notes.


But otherwise, do we have a sign off?


No.


Oh yeah, next episode, Will's going to do upside down.


He promised me.


So he's going to do the whole thing upside down.


We'll see you next week.


Bro, you going to sign off?


Self-taught Machine Learning Engineer AMA (live)

Alright, I think we're live. Are we live? Alright I think we're getting some updates


here. What's happening? Raphael Aravind, give me a heads up if you can see me live because


I don't get massive notifications here to say what's going on. But anyway, says we are.


Got a green light. Let's get started, hey. What's happening everyone? My name is Daniel


Burke. I'm 25 years old and own machine learning engineer from Brisbane. I know the title says


self-taught but that is kind of like just a just a way of explaining things. I've learned


a lot in this room by myself but it's only because of the beautiful and amazing resources


and work that other people have done and put out online so graciously. Now the purpose


of this stream is to answer some of your questions. You can ask them live. I'll get to them as


best I can in the chat. If not, I do have some pre-made ones that people have asked


me over time and I try to do at least one of these videos once a month so I can answer


the questions I get most commonly asked and yeah just hang out with you all right. That's


what I like to do. So where can we start? Oh actually, first of all thank you all for


5000 subscribers. I mean in the last month or so we've got about 800 of you so that's


amazing. If you're new to the channel, welcome. I'll try to address who I am and I just did


that actually and some of the stuff I'm up to throughout this stream but it'll be a bunch


of fun. Happy Sunday, let's roll into it. Whoa, we have lots of people joining the chat.


What's happening? 1am, 1am in US. Raphael, what's up man? Sri, path to becoming a machine


learning engineer. Sam, how are you my friend? Carlo, what books do you recommend? Shaik,


what's up my brother? How are you? How many months does it take to become good at ML?


All right. Okay, can I turn up the volume? Is it not loud enough? How's this? Let me


know if this is loud enough. Can everyone hear? Leave a yes or a thumbs up in the chat


if you can hear properly because I'm still getting this live stream stuff sorted out.


All right, so we're going to jump in. I've got a list of questions here. I think you


might be able to see it and look, whoa, there's my face. I'm trying to monitor the chat at


the same time as do some questions. We've got some here. The first one is from Ray Lowe.


At Daniel Burke, would love to see how you are improving yourself on the ML application


skills and how I am benefiting from the AI ML online course in my job. Keep it up man.


Well, first of all, thank you. Thank you, Ray. I really appreciate it. How am I benefiting


from the ML? How am I improving my ML application skills? Ray, there's really no best formula


for this one. And it's a caveat for every answer that I give in this guide. It's all


coming from my experience, right? So everyone's path is going to be different. I can only


share the things that I've done, the things that I've experienced, and the way I look


at things. So that is the role of a good data scientist anyway is to not take the opinion


of one person or the opinion of all the information from one source of data. A good machine learning


engineer and a good data scientist, as you all know, combines different sources. So whatever


I say, feel free to disagree with it. Feel free to argue with me. That's fine. I'll see


how we go. But anyway, how am I benefiting from AI online course in my job? Well, before


I got my job as a machine learning engineer, I had no kind of education other than online


courses. So that really set the foundations, right, for me to get into machine learning.


But what really, really took my skills up, I think there's a couple of gaps at the moment


in terms of ML education. The first one is going from online courses into applying what


you've learned from online courses to real data sets. So that's it. That's a gap there.


And then the next gap is, so once you've even worked on a real data set, right, so maybe


a problem for a client or a customer or something like that, the next gap is how do you deploy


that model? So rather than it just being living in a Jupyter notebook or something, how do


you get that live and so customers can access that on the fly, whether that be through a


cloud service or through a website. So they're the gaps that I'm currently trying to bridge.


Online courses do a great job of that. But really, where I've seen the best gains, and


it's always the most difficult part, is applying what I've learned online to some kind of problem


that no one has really done before. So you can follow all the tutorials. And I mean,


the online resources that we have are all great. They're all great. I'm struggling to


find a bad one for learning resources online. So the best knowledge that you'll get is by


choosing some kind of problem and then working towards that. And it sounds so cliche because


you hear that all the time, but it works because it's true. When I started working as a machine


learning engineer, I had all this experience from online courses. But then when I actually


started working with a client's data set, that's when I started to learn, holy shit,


right? I need to figure out how to fill the holes in this data, how to ask some questions


about fixing it up even more, what they want to look for in it, and stuff like that. So


no matter what you're interested in, I would suggest if you've done a few online courses,


you've got some foundational skills, pick something and try to build something from


that, whatever kind of problem it is. It actually doesn't matter what the problem space is as


long as you're trying to work on your own project. And then even better, the next stage


is deploy that. So get it live on a website that people can go to and access your work.


So that's the way I'm improving myself with ML application is by literally just continually


working on projects that don't necessarily have a guide. We might go to the live stream


now. So what I'm probably going to do is bounce back and forth with some of the question bank


that I have here. And then I'm going to, so you can see the question bank here. So we've


done Ray Lowes. Thank you, Ray, for that. And then we'll bounce back to the chat. I


think that's a good way to set it up. And here we go. Kushik asks, how many months does


it take to become good at ML? Oh, man. It really depends what you classify as good.


I went from in my, this is just what I've done. I started my AI master's degree. There's


a link below into all the courses that I've followed. I started that in nine months after


I started. I was hired as a machine learning engineer. Was I good? Not really. When I first


started, I had a very, like the first few weeks were like a very, well, it's still a


big learning curve actually, right? Because as much as online courses, this is back to


that first point. As much as online education prepares you for like a foundational level


of machine learning, it's a totally different thing once you start to work on your own problem


or a customer's problem, right? Because there's no guide. You can't just look up stack overflow


or tutorial on how to solve a customer's problem or how to solve your own problem. You kind


of just have to work at it, right? So how many months does it take to become good? And


I would say you can become, if you start at zero and work hard, you can become pretty


good after six to 12 months. However, it depends on what your scale is. So I'm about 18 months,


two years in now, and now I realize how much I don't know, right? So after the first year,


I was better than when I started, but now that I'm getting a bit further, I realize


how much there's still to learn. So I still suck. But there's really no timeframe. These


skills take decades to learn, like the math, the code, well not decades, I mean years,


right? So there's no real timeframe, just keep working at it. Stay fascinated, don't


get complacent. I mean, these are all things I tell myself, right? These are just, it's


always been a good thing if I've continually followed what excites me. That's it. That's


the only formula that I really have. Just stay, well, and what's practical, right? At


the end of the day, you can follow what excites you or you want, but at some stage, you have


to turn that into practicality. And that's where, what's that called? I don't know, passion


or something. Excitement meets practicality. You get that crossover, and you're in a good


place. So that's, we got one from the chat. We'll go back to the little notebook here.


All right, so let me ask a fair few questions here. So we might just quickly rattle them


off. I mean, there's five or so, so we'll just bounce through them. So we'll go nice


and quick. So number one is, how many Pomodoros do you do per day? And specifically, so a


Pomodoro, if you don't know, is 25 minutes of straight continuous study or whatever task


you're working on. Just do it for 25 minutes. So how many do I do a day? Well, I aim to


do a little bit each day, right? But I mean, it's hard. Most days, if it's a big day, I'll


do eight to 10. So eight to 10, 25-minute sessions. And then other days, smaller days,


it'll be four, four 25-minute sessions. And these numbers don't really matter. Like it's


just about doing a little bit each day. And if you miss a day, that's fine. You need rest.


I take rest. And so it's important. But never more than really four to six hours of intense


study. After that, it's just law of diminishing returns. You can look that up on Wikipedia.


Do I do multiple courses and topics at once or the same time? I used to do multiple things


at once, but now I've really cut back trying to do less and just focusing on one specific


thing and trying to get better at that. At the moment, it's, well, I'm lying at the moment.


It's NLP, so natural language processing, PyTorch, so these kind of PyTorch with NLP


with PyTorch and health and data science. So you could really, I mean, that's three things,


but you could almost combine them because you could do data science with health with


PyTorch and NLP with PyTorch. Anyway, I'm having trouble staying consistent with the


math. Can you give me some tips? I'm not the best person to talk to about the math. In


my day to day, honestly, I don't do much math. It's really just code. My job is ML engineer.


So I'm not necessarily a machine learning researcher. I'm applying existing frameworks


to existing problems rather than building new frameworks. But that may change in the


future. If you want to stay consistent with the math, treat it as how you'd treat anything


that you want to stay consistent with. You have to make a game out of it, right? I mean,


there's a big divide, and I wrote an article about this on how do I study. I mean, I think


there's a link below in study tips how you can stay consistent with anything is to treat


it as play. If you look at a three year old on the playground, they're having an awesome


time. But there's a big divide, it seems, in society at the moment to treat work as


work and play as play. But if you see the whole world as play and just if you're working


on math, all you're doing is trying to connect the dots between two different things, right?


The same as any other kind of study. You're just trying to connect the dots. And if you


treat that as a game, like you have to get to the next stage every day or however long


you want to do it for, then that's how you stay consistent. Are you learning every day


or do I take breaks? I take breaks. I try to study a little bit every day. But when


it's... You sort of... You got to listen to yourself, guys. There's no real formula to


taking breaks, slash studying. You'll hear different opinions from everyone. But at the


end of the day, if you're working hard, you'll know when you need to rest, right? Just write


down how you feel at the end of the day. I do that in like a journal, right? I've got


a notebook that I carry with me everywhere. Like literally 500 words a day on just what


I'm thinking. And so I'll know when I'm getting to the point. Like it's like if you're training


too hard, like physically, you know, when you go into a gym or you're weightlifting, you'll


need a break. Same thing with the brain, right? In the learning how to learn course, on Course


Air, I did a video on that. There's two ways of thinking. There's focus thinking and diffuse


thinking. The best learning happens at the crossover. It's always at the crossover. Like


this is a formula for life, guys. The best stuff happens at the crossover of two awesome


things or three or more. Like machine learning is the crossover of compute, math, data, etc.


So learn when you're reaching your limits, right? If you're young, you should be pushing


as much as you can and just trying to figure out, okay, what's my limit and then work backwards


from there. All right. I started a YouTube channel. This is all still from Salim. He


asked five questions. I'm just trying to get through them. So thank you, Salim. I started


a YouTube channel documenting my journey. Can you give me some tips based on my experience?


To be honest, I don't have a formula for YouTube either. I just kind of make stuff. If people


have requests for videos, I try to get to them. I don't have much time to make. I like


to make videos good, like stuff that I would watch. So that's the best information I can


give for YouTube. Bring value to people and if you're stuck there, make videos that you


would like to watch, right? So that's all I really have for YouTube. In the future,


I'm going to be making plenty more YouTube videos. I definitely want to start teaching


some stuff. So like a series of Kaggle from scratch. I think that's going to be my next


big series is like let's say a dozen videos of going to zero to Kaggle submission. We'll


see how that turns out, but I definitely want to work on that. All right, back to the chat.


Thank you, Salim, for the questions. All right, where are we at? You can hear, everyone can


hear. Wow, I should have checked that way earlier. How to learn math for ML? Suresh


asked how to learn math for ML. I think just go to Khan Academy and just learn a whole


bunch of stats. No, I'm kidding. As I said, I'm not the best person to learn to ask for


questions for math. On my day-to-day job, I honestly don't do too much math. It's mostly


to do with code. So any math I've learned is from Khan Academy. So that's linear algebra.


I was watching three blue, one brown the other day for linear algebra. Actually, there's


a course from Rachel Thomas called Computational Linear Algebra. I've been meaning to do that,


but if you Google that, Computational Linear Algebra by Rachel Thomas, that should be able


to help you out. Do you have any AI beginner projects to start with? Anything you can recommend


if you have programming experience? Sayem, definitely something on Kaggle. I mean, that's


a brilliant place to go. If you want, I have a Kaggle video which walks you through how


to enter the Titanic, which is kind of like the level one project. After you've done that,


I'd suggest finding the next one and going to that. And then after you've done maybe


three or four, try something of your own. Sahil, but then again, there's no exact formula


for that. If you want to start straight away on your own, go for it. Sahil, can you tell


me what you think about OMSCS from Georgia Tech? Is it worth it if you're already a software


engineer? Sahil, honestly, I've never even heard of OMSCS, so I'm sorry I can't help


you with that. Good question, can relate. Practice hacker rank. Hacker rank is good


too. And here we go. Participate on Kaggle. Yep, I'm not up to date with the chat here.


What's the first step you should take to getting into a career as ML? I wrote an article on


this as well, if I got fired. So if I got fired tomorrow, the three things I would do


is get really good at what I was doing. So I'd get really good at machine learning, really


good at data science. The link to this article is below as well. Three things for ML. I get


really good at what I do. I get really good at communicating what I do. So either through


a GitHub repo or multiple blog posts or videos or something like that. And then the final


one is I'd meet people in the industry. So whether that be going to meetups or posting


online or reaching out to different people who are already in the industry. So they're


the three things I would do, right? So number one, you need skills no matter what. So that


involves study and hard work. Number two is being able to communicate those skills. So


this is for any field, not just machine learning, right? You need to be able to communicate


your skills. And number three is people. Because at the end of the day, you have to be able


to work with people. That's the kind of species we are. As much as I like being in this room


alone and working on my computer, like at the end of the day, to get problems solved,


you need to at least interact with people at some point. Because if you build something,


you're going to have customers, et cetera, et cetera. It can't just be all code, right?


So skills, communication, people. So work on those three things. I can't hear enough.


You don't have headphones. Man, I'm trying to turn this up. William, Zhang, Kaggle is


very, very good compared to working on other resources like fast AI. 100%. Kaggle is definitely


fast AI and Kaggle are both great resources, right? Kaggle comms are good for your resume.


I don't know, to be honest. But William Zhang asked, are they helpful as a machine learning


engineer? Yeah. I mean, you get practice of applying skills. It's not very many places


you can get. So the reason why competitions are valuable is because you can see what a


good result is, like a really good result. So you can compare, right? So if you're entering


a Kaggle competition and you see that your result gets 50 points and the top one gets


85 points, you know that to get to number one, you have to do a lot of work to get up


there. So that's the benefit of competition. Don't let it scare you off though, right?


Because the number one people, I've obviously had some experience, but as the courses in


fast.ai show, you can get some pretty good results relatively quickly. All right, so


it's about setting those foundations, getting those baseline results, and then improving


on your work. That's the benefit of competition. Carlo says, how do I overcome procrastination?


So if you treat procrastination as a habit, one of the biggest things for me for procrastination


is not, so I had all this energy, right? You can have all the energy, you can have all


the enthusiasm in the world to study something, but where do you apply that? Right? So oftentimes


procrastination comes from a lack of direction, which is why every morning I get another notepad


here. Every morning, sometimes every night, I write down a list of things that I want


to do. I don't know if you can see that, but I've got live stream there, right? So that


was what I wanted to do today, which I'm doing. So I'm one of those people, right? I procrastinate.


I don't know if I don't have direction, I just sit around and do nothing, which is not


necessarily a bad thing. Like it's important to sometimes do nothing. But if you want some


direction, write yourself a list. It could be one thing. So say maybe if you want to


start small, if you're really stuck, write down one thing. It could be study for 25 minutes


in a whole day. That's it. That's all you have to do, and you're done for the day. Or


if you want something smaller, maybe start with 10 minutes, right? So the way to do it


is to start small. And if you've got a habit of continually procrastination, so you've


got habit, you can break it down into four steps. One, you've got the cue, right? So


the trigger. Number two, you've got the routine. So the thing you do after you get the cue.


Three is the reward, and four is the belief. So say, for example, I've got my phone here


so I don't get distracted. Say the cue is your phone, right? It goes off. And the routine


of watching your phone go off is that you check it. Oh, you got a message from Sally.


Sweet, I checked from Sally. And then you get the reward, which the reward is you get


to check your phone. And now the belief is that you believe that you're someone who gets


distracted. So how do you fix that? How do you fix that habit of procrastination? You


remove the cue, which is why I put my phone in my drawer and turn it off, frankly, so


I don't have anything going on. But that's one way to avoid procrastination is to write


down a list of things you want to do so you have direction of where to apply your energy.


And two, to remove that procrastination habit, get rid of the cue that causes that habit.


Here we go. Aniruddha says, hey, Aniruddha, I'm sorry if I pronounce these names wrong,


guys. Hey, how are you, man? Thanks for joining. What do I think? I've already answered that


one. You can't hear enough. Can everyone hear? I thought people could hear. CNN versus Capsule


Network. I've never had experience with a Capsule Network. Because ML is required to


enter AI. To be honest, I don't have a great definition of the difference between machine


learning, artificial intelligence and data science. To me, they're all much of a muchness.


They're all a bunch of the same thing. So don't get too caught up on definitions. Everyone


you talk to will have a different definition of something. Like someone will say we're


using artificial intelligence to do this. At the end of the day, they're using sophisticated


machine learning techniques or data science. I mean, it's they're just words, right? Just


words. But they're all the volume we can hear. Increase the volume. It would be nice to be


louder. Man, I don't even. Sorry, guys. Hopeless. How about this? We're getting better. We're


getting louder. My bad. A bit quiet. Merlin, how's the sound now? One job. I had one job.


I'm still getting still getting used to this live stream. Live stream stuff. All right,


I think the mic is practically on on extremely loud. We're back. Still quiet. And I didn't


even know how to I'm just gonna pull the mic right up to my face. Okay, there. I don't


know what else I can do apart from this. It's going to take too long. Keep this stream rolling.


Siddharth asked, and hey, Daniel, my question would be how to effectively search for job


opportunities after completing mutes. What are places where recruiters and developers


are like a quite active LinkedIn, LinkedIn is the the best place hands down. Share share


your work on LinkedIn, whether it definitely projects not just not just not just certifications


like certifications are great. But if I'm a recruiter, I'm I don't have a company so


I can't talk about what I would do. But when I do have a company, right, my first question


is going to be, what did you work on last weekend? Right, I'm not going to care to be


honest about how many certificates you've done or whatever. I'm going to ask you, this


is this is how I'm going to interview for people in the future. What did you work on


last weekend? That is going to be my question. And if it's if you've got your own project


or, or something, then that's that's a bonus. That's a tick in my books. Right? I'm I don't


really care like because courses, courses, everyone can do them, right? They're well,


not everyone. I mean, they're a good way to show that you can commit to something over


over X amount of time. But the next stage is applying that knowledge. So I'm gonna ask,


what did you work on last weekend? Because I want to hear what excites you? What are


you fascinated by? What is something that you've taken, taking your own time to dive


into? So that that is my use LinkedIn and share what you've been working on. Right?


Not just courses have your own project because that that that's automatically put you in


a different bucket. Alright, we're gonna go back to the we'll come back to the chat in


a second. Thank you for all your feedback on the mic. And thank you for being so patient


with the loudness. I'm still getting still getting used to this. So we have a question


from Kamal. Kamal, I'm a robotics student. And I'm interested in machine learning in


general, as I'm more curious to get a job as a machine learning engineer. I'll be graduating


soon I'm not confident in my ability of programming and Python. Well, this is a long one. Is there


any suggestion which will help me help help land a job? Okay. So you're a robotics student


interested in machine learning? All right, he wants to get a job as a machine learning


engineer. And of all the people I work with, we're all say I have we have a team of 15


machine learning engineers or so. Everybody codes, and it's all in Python. So that's my


experience. If you want a job as a machine learning engineer, I would, I don't know how


you could get one without learning code. Honestly, you need to like if you this is out of my


experience. No one I work with doesn't code put it that way. So yeah, Kamal, if you're


a robotics student and want to get into machine learning, definitely work on your coding skills,


my friend. And Python, Python is our language of choice at Max Kelson. Is there other better


languages? I don't know. I've only had experience with Python. I know you can do it with others


like R and Java and JavaScript even now with MLJS or something. But yeah, Python definitely


get some if you if you've done robotics engineering, I mean, you could probably definitely learn


Python code. Get into it, Kamal. All right. So we've done the done one from the from the


question bank. Let's jump into back into the chat. Now it's louder. Here we go. Data Sciences


Insight Machine Learning Prediction artificial intelligence accurate action. Oh, that is


a major Carlo. I really like that. How do I start a comment? I can't. All right. And


Carlo, that's an amazing definition of I'm gonna I'm gonna put that in the chat. I wonder


if I can pin that. I'm gonna put that in the chat. Boom. Thanks, Carlo. All right, here


we got Hey, I like your video, man. The question I have is important is data structures and


algorithm to get started in ML. So Samas, Sukar, I like your video. How important is


data structures and algorithms to get started in in machine learning? So data structures?


Look to be honest, we I haven't dealt firsthand with data structures explicitly. The algorithms


that we that I use most most right are the ones that you'll find in something like hands


on machine learning that book, right? So if you Google hands on machine learning, all the


things like random forest, logistic regression, for XG boost, all that sort of stuff. How


if you want to, I'm not entirely sure if you your question is targeted as to designing


algorithms from scratch, or applying them to get started in ML, I would my my experience


is only applying them. I've never had, I've never had experience designing a machine learning


algorithm. So I would say, if you want to get started in ML, don't get too held up on


trying to design your data structure or algorithm from scratch. Learn how to apply them. And


then once you get stuck, once applying the existing ones aren't working for you, then


it might be an idea to start digging into the data structure and fundamentals of each


algorithm and how they're applied. But again, that's only that's only my opinion, you might


find something different else. Could you list your favorite machine learning YouTubers?


To be honest, I don't watch much machine learning on YouTube. Of course, Siraj, Jeremy Howard,


which is fast.ai. Three blue one brown. I most of my learning happens in docs, so documentation


and blog posts. And I, and of course, myself. So yeah, there we go. Siraj, Jeremy Howard,


and three blue one brown, and Daniel Burke, they're my favorite machine learning YouTubers.


Because after all, you got to be your biggest fan, right? But yeah, I plan on making more


content as I said before, in the future on ground up machine learning. So like Kaggle


competition is this is just one I'm thinking of like it's a zero, zero to one machine learning


and use a Kaggle, we actually enter a Kaggle competition as as the as the basis of the


narrative. Stay tuned for that. Praful asks, would you recommend DataCamp for getting into


machine learning over Coursera? I have a year of experience on working in Python. Okay,


so before I in my experience with DataCamp, they are they are great introductory. So I


would say, if you've got a little bit of Python start with DataCamp. So DataCamp give you


a good little foundation, right? So they're very, the videos are very short, very quick


concepts, most of the courses are only about four hours long. I mean, that's the actual


limit of their courses, I'm pretty sure it's four hours. So use DataCamp to get a foundation


and then Coursera to dive deeper on things you want to get in because Coursera courses


are generally longer and more in depth. So DataCamp will give you that that that spark,


right? So that that little bit from going from Python for web development into machine


learning, and then Coursera use that to go deeper on more specific topics. Siddharth,


awesome, awesome work. Keeping that in mind for LinkedIn. How do I get motivated to study


art? Okay, this is a two from G H K K K. This is a two because I've got one from my boy


Aravind. Aravind is in the stream. How are you my friend? How to stay motivated while


learning data science ml from scratch and at the same time we have we have this question


over here, which is the same thing. So we're going to do both of these at the same time.


All right. How do I stay motivated? Okay, so this is a this is a tough one, right? Because


it's going to be it's going to be different for every single person. How do I do it? Well,


the first of all, I have one question, right? It can be summed up in one question. What


is the alternative? And what do I mean by that? Well, if if I wanted to get into machine


learning when I was driving Uber, right, I was driving Uber on the weekends to pay for


my courses. This is 18 months ago, or however long it is. I've it's it's on the internet


somewhere. If you Google, how do I start machine learning Daniel Burke, you'll probably find


the story anyway. And I was getting fatigued with studying my question I'd asked myself


is what's the alternative? I either keep studying and keep getting better at these skills. Or


I stop and I keep driving Uber, which I didn't want to do. So that is the one question I


I asked myself, every every day when I find something that's difficult. Right? And because


studying studying frankly sucks. Like, it's great to learn new things, but it's hard.


Right? So it's always and look, I get times where I'm demotivated. Right? I made that


I made I made videos about this. And I talk about this in a lot of my writing as well.


And I'm going to be honest, right? I'm not going to lie to you and say that it's always


easy. At the end of the day, these things are hard to learn. And frankly, it shouldn't


be easy. Because if you're learning something new, it's by definition hard because you don't


know about it. Right? So it's so it's it's gonna be hard. And now the thing is, what


I do is I just asked myself that question, what's the alternative, when something gets


hard, the alternative is to not do it. And then what happens? And I know the outcome


of that if I stopped doing that, if I stopped doing the thing that's going to get me towards


where I want to go, I'm not going to get there. So say my goal is to be a machine learning


engineer. Right? That was my goal, right to work in in technology field. And I was driving


Uber, and I could have stopped, right, I could have stopped studying and just said, Hey,


I'm just going to be an Uber driver now. But that wasn't my goal. So I kept studying, I


kept making things online, I kept putting my work into the world. And eventually something


came off. And now, honestly, I don't have like, and I could not explain how how things


play out. All I knew is that if I stopped, I already knew the outcome of what that would


be. Right? So I wouldn't wouldn't ever become a machine learning engineer, if I stopped


studying, I would never be able to build a technology business if I stopped studying,


etc, etc. So that is the one question that I asked myself, what's the alternative? Alright,


and when you know the alternative is, you're not going to get to where you want to be,


then it's pretty easy to go, Okay, well, I know that so I'm going to keep moving towards


this. And then when it gets hard, see, there's things you can control, right? So you can


set the system up. So you always win a tough goal, right is to or a non specific goal is


to, I'm going to finish this assignment today. You can't control that. What if something


comes up? What you can control is by going, I'm going to study for three hours today.


That's it. You can study for three hours, whatever you get done, you get done. Tomorrow's


another day. Right? So that's that's what you got to think about set the system up.


So you always win. That's that's how I approach things in my daily life. Everything. Think


about what you can control. Think about what you can't control. So what you can control


is putting in effort, what you can't control is whether all of your goals and ambitions


come off. So ask yourself, what's the alternative? And think about things that you can and can't


control. They're the two things that I do when when I'm lacking motivation, right? Or


three, take a rest. Maybe you maybe your body is telling you that you need to take a break


from whatever it is you're doing. So you can recharge and work on something, something


else. Or you just need more energy to hold off for a second you realize, oh, wow, we


work on the wrong thing. And then you get back into it, you've got more energy to keep


going. Right? So focus on those three things if you lack motivation. All right. So thank


you all for that for that question there. That was from our Aravind and GHKKK. All right,


now we'll go back to the back to the chat. All right, if you've got any questions, guys,


feel free to feel free to drop them in. I'll do my best. We'll probably been gone for about


37 minutes. We'll probably go for who knows, if you want me to keep going, leave a leave


a comment below. Otherwise, I've got plenty of questions to keep going. So yeah, just


let me know. Does maths is maths really required for ML? How to learn maths for ML from meat.


Machine learning is completely based on math. Do you need a fundamental understanding of


it anything past eighth grade calculus to apply ML? No. Right? So you can apply it.


But if you're looking so I'm an ML engineer. So I my focus is to apply machine learning


techniques and build things. Do you need a massively in depth knowledge of the math to


do that? I don't you think you do. But if you want to become a machine learning researcher,


maybe you want to work on different machine learning algorithms. I don't know enough about


this to give advice, right? Because I don't do this. I'm not at university working on


this amazing, this amazing technology trying to trying to better new algorithms. I'm happy


to use existing algorithms. And in depth knowledge of math is definitely required. And so Darth


hands on ML with scikit learn TensorFlow is a book mentioned. Yes, that is an amazing


book. I would highly recommend that. What us UK? Any any rude us what us UK showing


universities are good for master's machine learning to join. Honestly, I don't know any.


I don't know any. Well, I know my university, but I never I'm not going back to university


ever. I don't plan on it to it. So I don't know any Australian universities. There are


definitely some great ones out there. And I I just don't know enough. Honestly, I'm


not going back to university. I think I can do everything I want to do without ever going


back to university. But that's me. Your goals you might need to go to university for. So


that's goal. What do I need to? Oh, so that's us. What blogs do I read? I read Seth Godin's


blog, Derek Sivers blog, everything on medium. What's the other one? pi image search. So


it really depends. There's a lot a lot of resources for for machine learning. But I


also read just a lot of a lot of stuff that isn't related to machine learning, right?


Because at the end of the day, I think that's that's where it's important to not just get


focused too much into one thing. If you want to build, build something awesome, it's good


to combine different different areas of knowledge, right? So that's that's my take on it anyway.


But there's a plethora of blogs that I like reading. I like reading Cora. Anything on


medium because mediums just got some great articles. And then of course, yeah, Seth Godin,


Derek Sivers, Jason Fried and DHH. Their signal versus noise. So so essentially, I want to


start my I'm going to start my own technology business probably within the next year or


so like a like an app development. And I really like their take on how to build companies.


So they've built an amazing company called Basecamp doesn't necessarily use much machine


learning. I don't know if it uses any, but I just like the way they run their company.


So that's how I would probably run my company in the future as well. And Merlin, how long


does it take for someone who's studying programming for half a year to learn machine learning


and land a job? Merlin, my boy, how long does it take to someone who studied programming


for half a year and learn machine learning and land a job? Well, again, that's a million


dollar question. There's no right answer for this, right? If you've studied programming


for half an year, well, all right, let's put it into my terms. I did it for nine months


studied machine learning for nine months and then got a job. So maybe you've studied it


for programming for half a year. So let's say six months, you could probably study machine


learning for six months. And if you're getting really good at your skills, and sharing your


work and talking to people in the field, who knows, you might get hired. But at the end


of the day, my formula might not work for you, right? Or how I did it might not work


for you. But they're the three things you're going to need anyway, is skills. So get good


at machine learning to share your work. Three, talk to people in the field, build relationships


in the industry you want to get into. Curthy asked what I think about capital competitions,


love them. Already got to that before the stream. Subrat Gupta, I want to get a job


in machine learning, which course should I choose? Subrat, there's no there's no best


course. They're all great. Choose one, keep going with it. Get good at machine learning,


build something, share it, show people at the workplace you want to get hired at. Do


you think AI will destroy humans? No, but we certainly will not have control over it.


Our event, that will never happen and we are the creators, we will control them. I disagree.


If AI existed, I don't think we would have control over it. But then again, like we're


talking about AGI. I haven't devoted much thought to this, to be honest, right? Because


I did at one point and then I realized it's just like, so pointless because well not pointless,


obviously, there's a point to thinking about these technologies. And it's just, I'll put


it this way, I didn't have the intellectual capacity to understand or I was just on the


fence with both sides. Like one side of me was like, yes, AGI will be amazing. We will


totally get there in our lifetime. It'll be the best thing in the world. And then the


other side was like, oh, wow, this is this is terribly terrifying. AI is just going to


control us, etc, etc. And I found I was devoting too much time to trying to think about which


extreme will win. So my opinion is both of them at the same time. And also, also, I just


think it's a lack of relevance to present day. Right? So whatever AGI, like technology,


I think it's a fair while off. I think our next stage is really ingraining some sort


of AI system with our biology. I think that's the next stage before we have this this rampant,


like AGI that just terrorizes human race. I think we merge with it before that happens.


That's just that's just where I'm at. And really, to be honest, I don't know enough


about AGI. Here we go. Just taking Udemy on course, Udemy. Udemy, I've had some great


experience with Udemy. I haven't used it much recently. But yeah, you could use Udemy. I


mean, honestly, there's there's no there's no best course. The best thing is to just


choose something. Follow it like don't even you don't even have to give a reason like


just choose anything. Follow it and then just get good at it. Just get good at what skill


like and what does good mean? Well, that's why Kaggle competitions are great because


you can see how well you go against each other. That's why reading stuff on the internet.


That's why looking at people's work is is look at there's a whole bunch of open source


work right? Look at look at that and look at the quality of the stuff that's been published.


Now yours of course, if you're just starting won't be on that level. It won't. Right? Mine


isn't on that level. Right? But that's the sort of stuff you know you're like, okay,


I need to slowly chip away at getting towards this. Maybe I'll start with with a problem


that's 10% of what that that big project that I just checked out is. And then maybe I'll


do a little bit more and a little bit more. Right? So the best value is coming from learning


a foundation and applying those skills. Here we go back to the chat. All right. I'm an


electrical engineer and knew a little bit about machine learning and somehow got hired


at him and no Python only Java. I learned everything on the job. Same as me my friend


xt 73. Well, actually, I knew a little bit about machine learning. I knew Python. So


I was probably a few steps ahead of you. But a lot of the stuff I've learned on the job.


Do I work full time machine learning engineer is Max Kelson from purple. No, I work four


days a week. So Monday to Thursday, I did get offered full time. But I said no, I'm


I'm happy with four days a week. And so I can create and work on my other projects for


Friday, Saturday, Sunday. I think that's honestly, when I run a company I I am very big on optimizing


the working hours that you do rather than just working more hours. Right? So how does


this let me flesh this out for like two minutes. So the I spend a lot of time just trying to


cultivate my energy, right? Because if you think about this, you need energy to be able


to do anything. Right? So what does that mean? Right? So if you want to get up and study


throughout the day, it's not going to help if you're tired, or your nutrition isn't that


great or you're in a bad environment with surrounded with the wrong people. So my a


lot of my time is dedicated towards optimizing energy so that when I do do something, I can


do it to the best of my ability, right? Because I'm more in favor of say you have a 24 hour


work day, let's say eight hours, I mean, 24 hour day, eight hours of sleep, eight hours


of work, eight hours of recreation maximum, right? And now sleep and recreation are non


negotiables. Of course, there'll be days we need to do more work. But to me, there comes


a point if you're working an eight hour, like nine to five, I would say like, up to 50%


maybe more of that can can be just wasted because of just just little things like that


are happening around the office and just whatever. So I honestly think like if you shortened


a full time work week, let's say Monday to Friday, from Monday to Thursday, but it was


only even six hours a day. So instead of five days of eight hours, what's that 40 days,


40 hours, and now I've never done this in practice. This is only just me thinking when


I run my own business, I might try it right because I obviously can't do it with someone


else's business. I probably will try it to be honest. Instead of having a business, instead


of having five days of eight hours a day. I think you could get the same amount done


in four days and six hours a day. Right? Because there's some law, right? I don't know whose


it is. Right? That is when you have a time period and you're working on something. The


thing you're working on takes up the amount of time that you allow it to. So if you shorten


it from 40 hours, so five days, eight hours a day, from 40 hours to four days, six hours


a day, 24 hours, I honestly think you could get the same amount done. Right? Because you


just take away, people know that they've only got four days of six hours to get something


done. So just all that time that was spent wasting and fluffing around gets taken away.


Because if you've ever worked in an office from nine to five, there's a period after


lunch like from one to three p.m. where just almost nothing gets done. Right? Because people


have had lunch and it's lethargic and it's just, it's the natural circadian rhythm. Right?


So if you look at Google circadian rhythm, I'm probably going to do an article on this


in the future. People's energy levels dip, right? In the hottest part of the day, which


is one to four p.m. and then they come back up towards the evening time. Right? And so


in a nutshell, I don't even, where do we get onto this? So yes, do I work full time? No,


I work four days. And I think in terms of productivity, people would get the same amount


done in four days at six hours a day, then five days and eight hours a day. Plus, they'd


have more time to spend with their family, more time to work on their own projects, more


time to sleep. Right? So just all of this extra stuff that's not necessarily needed


gets taken away. I'm probably going to experiment with that in the future when I have my own


business and of course when I have control over running a schedule and stuff like that.


That's just my thoughts. Let me know what you think about that. But honestly, I think


you can get the same amount done in less time, as long as it's focused work. How do you find


a good internship? I don't have a great formula for that. I've only ever had one internship


and it turned into a job. A good internship, again, skills, share your work online, talk


to the companies that you'd like to work for. Literally, find a way to communicate, go to


their events, talk to their staff on LinkedIn, write a blog post or something, make a website.


If you want to get a job for Airbnb, I really like this idea. If you're a data scientist,


find some Airbnb data or something like that, or a machine learning engineer. If you want


to get a job for Airbnb, maybe make a website, why Airbnb should hire DanielBurke.com and


then send that to Airbnb recruiters. But when they hit that website, they've got a data


science pipeline that you've built. You could apply that for almost anything. I'd like to


see that done. If you do that, send that to me. Big company or small company from Harsh?


I personally like small companies. I like 50 people max. That's how I plan on building


my business in the future. But yeah, 50 people max is for me. What do we got here? Good course


to learn, meet, I've said all the Khan Academy stuff like linear algebra, calculus, Khan


Academy. If you just search Khan Academy, calculus, linear algebra, matrix multiplication,


that is, they're all good resources. Udacity, good enough for learning Python. It's great


for learning Python. But again, you'll learn the most by combining different sources. Here


we go. Super Fox says, I find that the most effective way to complete a task is to ignore


all activities, even exercise. I don't agree with that. Never ignore exercise. Super Fox


says I find the most effective way to complete a task is to ignore all activities, even exercise.


I definitely do not agree with ignoring. There is no task for me that requires that much


concentration that I have to forget about my health. Health and relationships in life


should come first and foremost. The only time you should focus full concentration on something


and neglect everything else is if you have some kind of health condition or one of your


relationships is falling apart. Everything else almost doesn't matter to me. That's my


opinion. The health and relationships first and foremost. Work, YouTube channel. If I


had a choice between my YouTube channel or the company I want to build staying alive


or the health, like my own health or my mother's health or something like that, it's a no brainer,


right? Absolutely no brainer. There is nothing more important to me than health and relationships


because if you don't have them, what else do you have? So I don't agree with ignoring


those, like ignoring everything to focus on such a mammoth task. Of course, there are


going to be times where you need to focus, but if you have to start trading your health


for the project you're working on, I think you need to start rethinking the project you're


working on. Jay Yoon said, I heard that machine learning is at its limit working in the industry.


What do you think about this? Is ML still the hot item on the market? Jay Yoon, I think


I wouldn't have a job if ML wasn't in demand. Honestly, I think we're still learning on


how it can be best applied. I talked to a lot of people when I was driving Uber, I did


like a thousand trips and I told people I was studying machine learning and I think


about three out of the thousand knew what I was talking about, right? So that just goes


to show like that although, see it's hard for us to think about it because we're in


the industry and unless something blows our mind about how machine learning has been applied,


we don't get excited about it. But for someone who's never seen the potential of machine


learning, there's a lot more people like that than us who have seen it, right? So if you


imagine how many people can know how to code, first of all, which is a low percentage of


the population and then how many people of those people who know how to code know how


to do machine learning code? Then how many people of those machine learning codes know


how to apply what they know to an industry problem that helps people around the world?


Right, so this is, it's just a law of numbers, right? It's so small, like you keep going


down and down and down. So I definitely don't think that ML is at its limits, right? I think


I wouldn't be in the industry if I thought it was at its limits. Put it that way. That's


how I think about it. Here we go. What's a big? Praful asks, you're absolutely correct


about the working hours. Post lunch dizziness can be really controlled and it's just a waste


of work hours. Neural, I've seen DataQuest. I think that's a great resource as well. What


is the next big tech after machine learning and AI? I honestly don't know. If I knew,


I'd probably be working on it, to be honest, right? So I think it's going to be something


to do with, it's going to, to me, I think healthcare is a massive play, the crossover


of technology. And look, this is biased by my opinion. I think the crossover of health


and technology like biotech is, is going to be massive, right? Well, of course, because


what else do we have apart from health and technology? You combine those two, we got


some fireworks, baby. Would you suggest some beginner level projects for machine learning?


Definitely, definitely anything on Kaggle Praful or like the Titanic, the Titanic Kaggle


problem, something like that. Raymond, what's up, Daniel? I once attended a meetup with


Max Kelsen here in Auckland. Oh, that's awesome. How did you get awesome to hear? Max Kelsen's


going global. Yeah. I hope you had fun there in Auckland. And then by the way, how did


I get awesome sponsorship with Coursera? So Coursera reached out to me. They said they


would like to sponsor some of my videos. And it just went like that, because I was making


videos in the past of just literally saying, hey, I've been using Coursera courses to learn


this stuff. And here's what I've been learning. You can you can watch all the videos, right?


I've used Coursera for the last, for most of my online learning. And they said, hey,


we'd like to pay you to make another video and just say this is mentioned by Coursera.


And we'll give you the course to learn for free. And to me, I was like, sure thing, right?


I'm not going to promote, I'm never going to promote anything I've never done myself


or anything I wouldn't continue to use in the future. Right? And so Coursera, you can,


the proof's in the pudding. I've used it for the number of years, I'm going to continue


to use it. And that's why I said yes, any other, any other website that reaches out,


like I get a lot actually, like in any other place that reaches out for a sponsored shout


out, I'm never going to, to, to put their website or whatever in my videos, unless I've


used it unless I've got value from it unless I would continue to use it in the future.


That's it. Right? So honestly, anything that you hear from me, I've tried myself. I'm never


going to talk about anything I haven't done. Daniel, where do I train jujitsu? Are you


getting into competitions? Raymond, I train at Infinity. Infinity Sandgate. I'm getting


into competitions. Yes, I did my first competition in Byron Bay. I've got videos of that if you


want to see, actually, leave a comment if you want to see my first ever three jujitsu


fights, right? I won one and I lost two. I'll make a video and put them together as like


a my first ever jujitsu fight. I mean, I know a lot of you guys are here for machine learning


and data science and stuff like that. But if you want to see me fighting someone, I'll


turn that into a video. Otherwise, Raymond, I'm actually coming to New Zealand in April


6, I believe I have a fight there in jujitsu. No Gi. No Gi New Zealand Nationals in Auckland,


April 6. So I'm excited for that. I've got to train. I've got to train hard. McComard


says please tell us about nutrition strategy, what type of food to eat to make your brain


stay sharpened throughout the day, even after nine till seven workday. McComard, the best


nutrition strategy I have, right, so there's going to be different for everyone is to just


avoid processed food at full stop. Don't the less the less work your food takes to prepare,


the better. Right. And what I mean by that, if it comes in like packets and all that sort


of stuff, if you can't like hold the whole food in your hand, right, if it needs to come


in a packaging and all that sort of stuff, avoid it. So I like to eat vegetables, meats,


fruits, plants, nuts, oils, things that you can find at like a farmers market. That's


the sort of stuff I stick to. And now that's going to be different for everyone. But majority


of cases, you will find that is the best stuff. Right. So just avoid processed foods. All


the diets work like veganism, paleo, etc. because they have the one thing in common


and that is they avoid processed foods. So don't listen to anyone who's saying, take


this miracle supplement, or follow this wonder diet, or go on this eight week program or


etc, etc. Just stick to the basics. Don't overcomplicate it. Right. Stick to whole foods,


stuff that doesn't require someone in the lab coat to make. Last day you posted that


someone asked Nicholas, how do you get started in ML? Oh, Aravind. Yeah, thank you for that.


That post, the one how do you get started in ML? That's in the description. I believe