pro deep learning libraries help take out a lot of the code so a lot of these functions are built


Computer Vision Basics + More deeplearning.ai Progress! | Learning Intelligence 16

into to libraries like tensorflow and keras and that was a really long clip but we're going to


wrap up this video let's see i think it's the end of learning intelligence 16 we'll finish out with


some shoutouts and then my plans for the next week by the way did you finish reading that story i


linked i finished that story by andre kapatny it was a really great read took me a little while


probably about half an hour at least i won't spoil it for you it's definitely something as i


as i learn more and more about artificial intelligence and and smart agents and whatnot


smart systems it's definitely something that i can see happening in the future all right shoutouts


for the week these people reached out to me on various social media platforms instagram twitter


all the links will be in the bio emails commented on youtube and by the way you can email me at any


time my email is daniel at mrdberg.com if you have any questions if you have any emails everyone has


emails if you have any questions about ai or or anything in general you just want to talk you want


to meet happy to hear from you and i'll do my best to answer it thank you to savina shake gregory


kenny and madison i really appreciate it guys thank you so much for reaching out it means a lot


to me and so what's the place for the next week well as this video comes out i'm probably going


to be on an island so i won't necessarily be doing much much study at all i'm going to be on holidays


with my family and friends so i'm going to take the week off and then starting early 2018 i'm going


to continue on with the deep learning specialization on Coursera what you've been seeing in the past


couple of videos and then of course moving through my AI master's degree so we checking up the steps


there i think the AI nanodegree term 2 which is based on deep learning starts on January 11th 2018


so i'm really excited to that that's going to be my main main thing or main main squeeze main squeeze


is that even the right word to say probably not but oh well we're going to roll with it


main squeeze for the first few months of 2018 so happy new year and thanks for watching leave a like


Bourke Brothers hit the library | Learning Intelligence 41

We should gather that leverage.


Where are we off to this morning, boys?


Library.


We're gonna get bean water,


and then we're gonna go to a libonary.


Bean water is his code word for coffee.


You know, they put coffee beans in the water


and it tastes great.


But then we're off to the library to do some study.


We've got the whole clip today.


Josh, what are you studying?


I'm gonna study some more CoreML on Swift.


CoreML, Swift.


Billsy, what are you on?


Do some Python first, just introduction to Python,


and then do some data science.


That's what we're all about today.


Back to... What's a base?


I'm Python and data science as well.


That's what it's about. Just repetition, right?


Reps, reps, sets, reps, sets, reps.


If anything I've learned from the gym,


it's how to get good at something.


Just continual practice.


Rep it out.


You know how they get that shot?


Whether they're like this.


Am I doing it right?


You gotta hold it out front.


There we go.


We've nailed it.


How's it taste, bro?


A good bean should.


Oh, man.


Caffeinated.


Missed a car.


Ha ha ha.


MUSIC PLAYS


Yes.


Going away.


Me?


Don't look at me.


This is a quiet room, but it echoes.


Echo.


WHISTLES


KNOCKS


Hello, have you booked the room?


Yeah, for Daniel.


Daniel, so you were meant to be in room three?


BUZZER


Yeah, but there was someone there when we got here,


so we figured we'd take the other one.


Just see who's in that room now.


No worries. Thank you.


MUSIC PLAYS


Alright, hit roadblock.


Time to take a break.


I'll show you.


So, this is one of the biggest assignments I've ever done,


including all the nanodegrees.


Look how many tabs I've got open.


All these questions, all these different forums.


Let me scroll back up through.


Look at this.


What is it?


It's more pandas.


Go down, error codes, error codes, error codes.


Where are we up to?


Showtime.


There.


I've got about three notes on what I need to do next.


You can't even read here.


Create continent column by mapping.


Anyway, I've got some next steps on what I need to do.


My brain's fuzzled.


We're going to head over to Jim and solve it in the subconscious


and then I'm going to come back later this afternoon


and finish off the assignment.


But Josh has built something cool.


Check this out.


What have you got rolling, Josh?


This is basic at the moment.


This is an identifier type app using an ML database


that's already preset by Google.


They have a, like, it's called Inception V3, I think.


With the app, you click on the photo, the camera button,


it brings up the phone's camera.


Only works for iOS, but take a photo of what you want,


say this water bottle here, and then you click use photo,


and then it will read the photo through the database.


And it thinks, like in the database, not that big,


but it thinks that's the wine bottle.


That's the highest.


So you've built an image recogniser using order, no, ML, CoreML?


CoreML.


CoreML. That's awesome.


It's all just preset so you don't need internet or anything.


The database is in the phone with the app.


If we put a trained model in there, we can...


It's not that well trained,


but if we put something that's more well trained...


You know what I mean? If we put our own model in there,


CoreML can use it on the phone locally and recognise images.


Ooh, got some ideas.


Getting a workout?


Yep.


Nils, did we work out?


How was your prog?


It was good.


I'm up to nearly finish the first introduction course on Python,


and then go back to some data science after you finish the assignment.


You can help me.


Let's lift.


Would you look who it is?


Hey, man.


Keen to work out?


Yeah, pretty keen.


Yeah, boy.


I realised in that last clip I was pretty cooked


in explaining what I was up to in that problem,


because, you know, when you get so deep into something


and then you step away from it for a second


and you try to explain it to someone else


and you've just been learning it for so long,


it's like, I don't know what I'm doing.


That's what I use having a break for,


is letting the subconscious process the problem


and then coming back to it


and understanding it from a new set of eyes.


Look at those rock-solid arms.


Awesome.


What did we start off with? We did some ring dips,


superset it with some pull-ups,


and then we kept it simple, actually,


all about the rings and all about body weight.


You can reproduce this workout wherever you are


with push-ups and pull-ups,


and then after the ring dips and pull-ups,


we went on to ring push-ups...


..and then some just... What's it called?


TRX.


TRX bodyweight pulls.


And they were amazing.


Now we're off to get some food,


fill up these empty bellies.


It's what, 2.45?


Haven't eaten yet.


Yeah, look, sorry about that.


3pm, actually.


We're doing a bit of fasting lately.


That's how we eat.


We usually don't eat before 12.


12 till about 8pm.


It's an easy way to stay shredded and lose weight


if you want to give it a try.


You don't need to eat as often as you think.


Hot chicken.


Rice.


Done.


All showered up, you know.


Haven't had a nap yet, but that's all right.


I'm too excited to get back into this problem.


I want to solve this assignment.


I want to finish it.


I'm currently at 60 out of 100.


I need 80 out of 100 to pass,


and we're not leaving this room until I pass.


You keep your eyes on me.


You watch.


Oh, yeah.


Check it out.


A little bit of progress.


There we go.


Assignment three.


There was a submission today at the library.


60 out of 100.


Then again, 67 out of 100.


Then again, 74 out of 100.


Let me just quickly show you what my workflow is


for when I'm having some trouble.


See here, question 13.


Convert the population estimate series


to a string with thousands separated using comments.


Do not round the results.


So, AKA, get this number, return it from that number.


So, what I'll do, first of all, I'll come up in another browser,


and I just literally Googled,


pandas, separate column value, thousands with commas.


And one of the first results came up with this.


The person is working on the exact same function I am,


which is kind of crazy, but that's the internet for you.


And there we go.


Boom.


Now, I take that, plug it into my function here,


and it worked.


But what I didn't do is copy-paste it completely.


I copy-pasted the whole thing,


but then I re-written out the code to make sure I could understand it.


So, I'm going to figure out what exactly this little function here does,


because I'm pretty sure that does the brunt of the work for me.


Back to it.


Whoo!


How good does a good yawn feel?


One of those wee ones where your eyes water,


your mind clears up,


and you forget where you just were.


No, I'm kidding. Check it out.


I've finally passed the assignment after about, what is it, a dozen attempts?


Have a go at that.


I'm going to leave it there.


I'm not fully at 100 yet, but I spent way too much time on this.


I think there's a conflict between the autograder.


Let me just show you the grader output.


So, I've read a bunch of the forums,


and even though some of my answers are correct,


some of them don't match up with the autograder.


I think I've only got one wrong.


Where was it?


Anyway, 94 out of 100.


That is good enough for me for now.


We're on to week four.


Let's check out what's in week four.


Oh, hey man.


What's going on, bro?


Sorry, were you filming something?


Yeah, I just passed my assignment.


Oh, f*** yeah.


Hey, Turkey.


Movie?


Yeah, and I was going to ask, what do you want to have for dinner?


Because I'm hungry.


Yeah, I'm pretty hungry too.


All right, let me show them what week four is,


and then we'll get on the movie and dinner.


You want to check out week four too?


No, I'm right.


Yeah.


Here we go.


Passed the deadline.


Oops.


Anyway, in this week of the course,


you'll be introduced to a variety of statistical techniques


such as distribution, sampling, and t-test.


Oh yeah, the majority of the week will be dedicated to your course project.


Yes, week three's course project took me, I would say, at least 12 hours.


It said two hours, but whatever.


It will take 10 hours.


This project is suitable for your data science portfolio.


Oh yeah.


Probably I'll upload it to GitHub.


Yeah, that's a good idea.


Once I finish, hold me to that.


Once I finish week four, I'll put up a version of it to my GitHub.


And then what do we got here?


Statistical analysis in Python, project.


What's this one take?


Four hours.


Woo.


Went in for a big one there.


So we've got introduction, distributions, more distributions,


hypothesis testing in Python, end of theory.


Beautiful.


And then that'll be part one of the applied data science with Python.


Oh.


Daniel, I'm sitting in that chair.


No.


I told you I was.


Hello, guys.


Week four.


Oh, geez.


Anyway, welcome to week four.


Let's go get dinner.


Hey, Adam.


Why would we watch a movie when we can just create our own entertainment?


We actually did choose a movie.


What is it?


Some gangster one with Johnny Depp?


Yeah, I don't know the name.


It wasn't Johnny Depp.


It was Tom Hardy.


Was it Johnny Depp?


Tom Hardy.


Anyway, check out my favourite dessert at the moment.


Coconut yoghurt, almond butter, and down the bottom there's mixed berries.


No sugar, and it's absolutely divine.


We just want to wrap this up.


Can confirm, absolutely divine.


Yeah, can confirm.


Can confirm.


Look at that.


Three yeses.


All right, we're going to sign off.


Three, two, one.


You both missed it.


Keep learning.


Yes.


Adam got it.


We'll see you next time, team.


It's going to be a lot.


Physique Update and 6 Things I Wish I Knew Before Starting Gym

What's going on y'all? So the other day I answered a question on Quora that was


what should I know before going to the gym? And I kind of answered it as if


like what I've learned I sort of put in my own perspective like what I'd like to


if I was just going back and starting now after sort of six or seven years of training on and off


well at least five years of solid training what would I have liked to know before I got into the


gym? So I answered it with a few points and I thought I'd turn this like that answer it got


turned out to be pretty popular so at the moment it's got about 40,000 views I'll put it in the


link in the description as well so I thought I'd turn this video into a sort of a physique update


as well as a video version of the points I gave and so there's some pretty I just finished gym


and there's a pretty good downlight here I haven't actually had a calorie in about 24 hours so it's


around 7pm, 7pm where I am right now I haven't eaten since last night dinner which is around 8pm


so I'm doing about a 24 hour fast so I'm probably going to look a little bit flat but that's all


right I'm not really trying to work on I don't know having the best physique anymore that was


a previous goal but not so much anymore so I'll just do an update this is what it looked like


I'm like intense in relatively good downlight like it's not the best but it's also not the worst


so this is front, side, back


do a few poses so this sort of stuff I used to live for I haven't posed in a long time


don't get me wrong every time I hit a mirror


I still managed to to bust one of these out


so what am I weighing right now I'm about a weighting this morning about 75 and a half


kilos I don't know what that is in pounds maybe 170 pounds I'm guessing 102.2 kilos times 2.2


calorie intake not tracking current diet protocol or food consumption protocol I stop eating


generally around 8pm and don't eat until after 12pm usually most days but within those sort of


that eight hour window I'm going to be consuming I'm guessing around 2,000 to 3,000 calories


and that will vary on days so I've changed my training philosophy from like building muscle


and being as shredded as possible I'm pretty lean at the moment but not as lean as I have been


to more of a functional type like if you look up edo portal any kind of gymnastics types workouts


like that's what I've been moving towards like gymnastic rings animal flow not so much like


bench press overhead press squat deadlift I still do them but they're not they're not my primary


focus so without further ado that's enough for the physique let's get on to the I think it's six


six things I wish I knew before I went to the gym so number one food is more important than working


out and I'll probably go back on this maybe update my answer because I would say rest is more


important than work than food and then food is second and then exercise third reason being I've


done a few a bit of research on sleep lately and I worked out like I started to realize sorry


how important that is like to get proper sleep proper rest so I would say sleep and food actually


on par and then working out is is sort of an addition to that so sleep and food are the force


multipliers of everything you do if you're not sleeping right and if you're not eating right


well then you're not going to get the results you get in the gym I'll link my video on sleep in the


description as well but just essentially think of sleep as your body's we all know it's time to


repair and the same thing is with food like when you're in the gym sort of a lot of people put too


much focus on on the workout they really kill themselves in the gym and sort of train all out


for hours on end and then all of a sudden they're not getting results well I used to be like that


right and that'll be another point in the future uh point three or something like that but since


focusing on my food intake and my rest I've got the best results in my life and I'm sort of I'm


in the best condition I've ever been in right now uh certainly not as lean but just amazing


physical condition no injuries uh but yeah focus on your food if you if you're not tracking calories


I suggest trying it out just so you understand how much you're you're intaking as well as your


micronutrient and macronutrient goals and whatnot and then sleep you may be getting


seven to nine hours but how's the quality of sleep again I'll put the sleep video in the


description uh there's a whole bunch of things you can do to improve your quality of sleep


because most of the time you may be getting enough quantity of sleep but you might not be


getting quality sleep which is fundamentally important so that's number one uh food and rest


are more important than working out number two you're supposed to feel better after a training


session so in my opinion that's that's what training is meant to be for right to make you


feel better like your body's going to be in a worse condition after training because you're


breaking down muscle tissue you're breaking down connective tissue you're breaking down


you're just breaking your body down right and the the way you get better is from the body healing


itself after the training session so if you're all going too hard like I used to like training


for hours and hours on end okay maybe you might be a professional athlete but for 99% of the


population you're probably not a professional athlete including myself but if you're if you're


busting yourself like let's go anything over two to three or two to in the realm of two to three


hours right if you're not getting at least 24 hours rest before your next session then you're


not going to feel too great and and that is if you're a natural athlete getting optimal food


intake as well as uh optimal sleep so I used to bash myself up in the gym being there for hours


on end and I wouldn't get results that I wanted and then when I when I cut it back and sort of


tracked my training and was focused I could get in and out in 45 minutes to an hour look at look


at Dorian Yates he was Mr Olympia I used to I used to sort of have a goal of being being massive and


building lots of muscle don't get me wrong I have built muscle like over the past five or so years


not nowhere near as much as some of the big guys like Dorian Yates used to but he used to train four


times a week and 45 minutes to an hour sessions it's if you're putting in the work in that time


you can get enough results or you can you can stress your body enough to to make it adapt to


change there's no need to go those two to three hour long sessions uh really depending on what


your goal is but if you're if you're not walking out of the gym feeling like a better person you're


feeling like worse well then you're probably over training a bit too much so number two uh


don't worry too much about the longer sessions keep it short and concise number three don't forget


about mobility yes 100 percent so in the last I would say 18 months to two years 50 percent of my


training has been dedicated towards mobility and why is that well I got to a point where I was I


had a sore knee constantly like even walking up and down stairs um I had like back spasms in my


back I was I was stuck hunched like this for one side for for a weeks for weeks at a time uh because


my back was just so tight and I would still lift heavy deadlifts do heavy squats and I was putting


too much pressure on my back my hips weren't really firing my glutes weren't firing and this


is all due to my own fault not doing proper technique with certain lifts like deadlift and


squat and whatnot uh and I really could have educated myself to to do to do them better in


the first place um but once I started focusing more on mobility and by mobility I mean active


stretching so if you follow mobility what on instagram mobility w o d uh they're a great sort


of starting point to learning about how to move properly I also booked the book uh becoming a


supple leopard by kelly's direct that's an amazing book I actually fixed my my knee problem with


that after going to to many physios and uh even chiropractors and couldn't we couldn't solve it


I ended up fixing on my own and it was a whole like I tie it back to a lack of hip and a lack


of ankle mobility so it's too much pressure on my knee uh and so since getting back into mobility


all of my workouts all of my physical exercise in general even I play dodgeball once a week I


play touch football I play I could go to almost any sport and be very adaptable to that so that's


one of my goals at the moment just be uh an adaptable athlete so sort of like crossfit like


you can go to any type of sport and and perform well so for mobility is now 50 at least in my


training I'll spend 15 20 minutes before each session doing active stretching or some form of


yoga uh actually that's that's practically every day 10 to 20 minutes of active stretching or yoga


just to ensure my mobility and it's not good that's that's I don't see a point where I'll stop


that like I can keep doing that sort of stuff for the rest of my life so mobility fundamental if


you're lifting the heavy weights I've tried to treat it as an accordion like I think of the body


as an accordion right if you're like squatting under a heavy weight think of an accordion getting


crushed accordion doesn't work well when it's just just stable like that when it's when it's tight


like tight together it has to be pulled back out to work fully right so that's what I feel like with


the body as well this is just my thoughts right I'm not sort of trained in physiotherapy or whatnot


it's just my logic running through the body it's not going to perform well if it's if it's constantly


tight you have to also expand it as well that's when you get the best performance and that's just


what I've noticed over the past 18 months to two years focusing on that mobility training


so that's number three don't forget about mobility number four get a training partner all of my best


friendships uh forged through iron forged through the gym forged through some sort of physical


activity and you know who you are if you're watching this and so the reason why I say this


is because oftentimes we'll let ourselves down like we'll more easily let ourselves down than


we will let others down and that's I don't know what that's from I think that's maybe just wanting


to fit in in society but we'll we'll also we're more easily to say no like if we're if we're just


going to train by ourselves than if we've got a friend expecting us to come there so you've


acted like Greg Plitt says you have to be pretty a pretty low down person to let your friend down


constantly so you're not going to do that that's why having a training partner is good because not


only will you have that sort of uh that pressure to go there and and not let them down but you'll


also have that person who'll be able to motivate you through your workout like if you're if you're


struggling on some sort of weight or I don't know if you just want to talk with someone during it


because the gyms these days I see a lot of people with headphones in and I don't know training I've


trained in silence plenty of times but it's still it's still a good social social social thing to uh


to chat with someone lift some heavy weights and test your body and don't get me wrong like when


you're on endorphins like that's that's the greatest high there ever is it's free as well


like what and by endorphins I mean the feeling you get when you get the pump like when you're


when your muscles are full of blood and when you're sweating and when you're you just got


whole some great things flowing through your body um you have some pretty epic conversations as as


my training partners will attest to so number four get a training partner number five plus


minus equal so what does that mean I think this this can be related to almost anything in life


as well not just the gym and what I mean by plus minus equal is so spend 30% of your time with


someone who's who's better than you that way you can learn from them and improve yourself spend 30%


of your time or 33% you break it into a third each so a third of your time with a plus a third


of your time with someone who's equal to you and a third of the time with someone who's who's less


than you and so a third of the time with someone who's equal to you like on equal playing field


with just as good as you in terms of weight in terms of performance in terms of times etc that


will give you someone to compete against so spending time all of your time with someone


who's better than you may sort of I don't know disencourage you to keep going because you're


constantly getting beaten and then spend 33% of your time or a third of your time with someone


who's a little worse than you that way you can use cement the knowledge you've learned from someone


who's better than you as well as with competing with others and teach them and I believe that


that philosophy can be used in any aspect of life so whatever you want to learn think of plus minus


equal how can you learn from someone better than you how can you compete against someone on the


same level as you and how can you use that knowledge you've learned from from those two


experiences to help someone else because that's what it's all about right give them back so number


five plus minus equal and finally number six there is no finish line so in the past I've competed


in men's physiques competitions competing men's bodybuilding competitions probably probably too


early for if I'm if I look back and I'm honest because I should have let I'm only 23 years old


24 and actually two weeks I should have let my body grow and I was kind of looking at these


these competitions as like the end point like once I get there yeah I'll I'll reach my goal and I'll


be I'll have the body I always wanted and I'll look awesome and I'll have this this fake tan


and I'll be on stage in front of everyone and everything will be great well that's that's not


true like you get there it's a great feeling don't get me wrong uh you do have to dedicate


you have to pretty much have tunnel vision while you're while you're preparing for the show you're


putting your body to an absolute extreme level of of whatever of performance or something like that


um but realistically looking back the this getting on stage is just the I know just the icing on the


cake the rest of the cake is the journey and that's that's another philosophy you could almost


put on anything in life so you may have these goals of like you look at me okay I've got a


I've got a poster of Arnold Schwarzenegger on my roof above my bed here I look at him and you may


have a poster like that in your room you look at that body of whoever it is you know I want to


achieve that or you see someone do a dance move or a backflip or some sort of physical maneuver


and you know I want to achieve that okay yeah you may reach there one day and of course that's


that's great achievement but in my experience the journey is so much like you look back you're like


wow I can't believe I've come this far like looking back and and seeing the progress shots


and seeing where you were remembering how much you used to live remembering what you used to look


like remembering what you used to weigh remembering how you couldn't do a certain movement before


that that is like that's the best part the journey and that's that's with anything like you


because if you tie too much happiness to the goal if you don't achieve that goal it won't be


I don't know you'll sort of become unhappy and that's that's not the point right if you have if


you set it up so that your goal is to just keep doing the things you enjoy which is maybe working


out building muscle then you can you can't lose right so just remember that there is no finish


line don't think that getting getting a six-pack is is the be-all and end-all I've been on both


sides I was overweight as a teenager and now I'm relatively lean it trust me it is good I do prefer


being lean but it wasn't like it was an absolutely life-changing moment once once I achieved it you


sort of get there and you realize okay what's the next goal what's the next goal they say this is a


saying in the the lifting and the bodybuilding community the day you start lifting is the day


you become forever small and I I agree to that my goal isn't to build as much muscle anymore but


I still look in the mirror sometimes and think hey my body could always be better so that's number


six wrapping up there is no finish line uh that was that was about half a dozen things I wish I


knew before going to gym and of course there's probably a few more out there if you have any


you'd like to add I'd love to hear it in the comments and as well at the start of the year


that was a bit of a physique update as I said my goal at the moment is not to sort of have the


best looking physique I'm more enjoying being functional being able to do some things like


dancing and uh going on gymnastic rings and doing flips and a whole bunch of movement that's that's


what I'm about now movement um but yeah thank you so much for watching leave a like comment


subscribe I'd love to put more videos out for you guys and and those things really help but


we'll see you next week and uh I've got to go eat some food because I've spent 24 hours almost


Finishing Term 1 of Udacity's Artificial Intelligence Nanodegree | Learning Intelligence 13

What's going on learners? Welcome back to Learning Intelligence episode 13.


And this one's going to be a good one because I should by the end of this video, I will be,


I will be finished term one of the artificial intelligence nanobere offered by Udacity.


And today was a big day actually. I worked through probably about 40% of project four.


So I'm about 90% done, but I've run into a little problem. See this W-E-R here?


That's word error rate. And that's 90, almost 99%. My model for detecting American sign language


only got two out of 178 correct. And that's across the board for all of my functions.


So I've got to work out where the errors lie, but it's getting late here as you can probably see


that my screen was going really orange because it was flux. So I posted some questions in the forums.


Word error rate never under 90, 98%. And I've got a snippet of my code here, but that's, it's,


it's hidden out because of foreign policy. We stick to the rules around here. Not really, but


that's an important point guys. If you need help with anything, make sure you ask for it.


That's something I've got to learn in my own journey. I need to, to need to work on my ability


to reach out and ask for help because usually I'm quite stubborn and I like to do things on my own


and just push through. But once you run into a roadblock, leveraging, leveraging the help of


others gets incredible things done. Human beings work better when we work together. So if you're


ever in need, reach out to ask for help. You know, you can reach out and ask me any question anytime,


comments, email, Twitter. That's lesson of the video or lesson of the day actually.


Reach out and ask for help where you need it. But otherwise tomorrow I'm going to get back into it.


Hopefully I've got some replies on this discussion forum. Also on the Slack channel I posted in there,


but otherwise if not, I'll just have to do some debugging and finish off this project.


What's going on guys? I'm at the library so I can't really talk that loud, but this morning


I got up and I had some awesome feedback from the forum mentors and from the Slack channel.


And so I've managed to implement that feedback into my project so far and we're on the last leg.


So I'm going to finish off this algorithm here. This is part of myrecognizer.py. As you can see,


the forum mentors are always, always really helpful. Here's my question. I got some feedback


from, I think it's Letty? Letty Rodry? I'm sorry I'm pronouncing that name wrong, but she's amazing.


She's all over everything. Every single post I check out, most of the constructive feedback is


from her. So shout out to Letty. And my friend's donating blood so that's why I'm at the library


and I'm going to be a bit quiet. But that's my goal for the next hour or so and I'm going to


submit this project and we'll check in when I'm done. Check it out guys, submitted the final


project of the Udacity Artificial Intelligence Nanodegree Term 1. And check this out, my model,


it ended up recognizing sign language at about 47% the best one got. Now there are ways to improve


it, but my, the best model that I was able to build or the intelligence system with the guide


lines from the course by the way. The GitHub repo that I downloaded, that's with all the projects


in the artificial intelligence nanodegree is I'm not building these models from scratch, is that


they have the outline of the framework and I have to fill in the gaps to build to fill in the some


lines of code and whatnot and build some algorithms step by step. But the majority of them, I would say,


I would say almost at least 50% of each project has already been implemented, like the background


and stuff. And the students, we come in and we implement some top level algorithms. So all the


testing, all the database and whatnot has been done and we come in over the top and build our own


custom algorithms to solve a specific problem. So as you can see here, given some instructions, so I


had to do the, make the recognize function which takes in two parameters here and then we've got


some details here what those parameters are. So I'm excited, the review should come back shortly,


they may be, I got a message before saying that Udacity may be, the reviews may be a bit slow


because a lot of nanodegrees are finishing up about this time of year. Because it is, it is


November 28th and that's when, that's when the Udacity, the last project was actually due. You


can't even see that, it's not focusing. But good news is we can tick this off in the Trello board.


I'll put the link in the description by the way. So project using hidden, use hidden markup models


to recognize American Sign Language. It's all done and it should be up on my GitHub by the time


I post this video. So I'll put a link down in the description, you can check it out. As for what's


happening now, I'm going to go celebrate with a workout and then I've got to wait for my review


to come back, make sure I pass term one and then we'll, I don't know, we might have to spend, yes,


tomorrow, sorry, planning on what we're going to do next and where, what I'm going to work


towards next in the Artificial Intelligence Master's degree. Drum roll please. Check it out


guys. We did it. We met the specifications for project four. Build a sign language recognizer,


needs specifications. Excellent work, you've passed all the sections and we've got some resources here


for the optional part four of the final project of term one of the AI now degree. I'm really


excited. So today was a big day, last week actually has been a pretty big day working up towards this


and I think that's the first time actually that I've passed the project on the first submission


for all four of the projects so far in the AI nanodegree. So I'm really stoked with that. I just


had a shower, I'm getting ready for bed, I checked my emails before I jump in because I was really


curious to see if the review had been there or not and it was and we found it and passed it. So


what does that mean that I'm doing tomorrow? That's a good question because I'm pretty sure


there's a little break in between term one and term two of the AI nanodegree but I'll hit you


up once I find out more details. I should receive some sort of email or something saying that I've


completed term one relatively soon. I'm not sure what the process is there. Look how much my plan


has grown already. I called it planting McPlantface by the way. I'll poll on my Instagram story which


is actually really cool that you can poll people on Instagram now. So if you put something on a


poll you can put a little one there pop up or a little widget that says vote for this or vote for


that and the crowd decided to name it planting McPlantface or it's going to be now plant backwards


of the silent T a planting McPlantface one. It's growing so quick but I think I'm going to have to


move it into my front yard soon because it is a sunflower and sunflowers grow really big and the


pot it's in is not that big. So back to back to code back to what I've been up to today this morning


I spent about half an hour on hacker rank otherwise for the rest of the day I've been working on on


my writing on my novel that I signed up for nano re nano rhyme-o which is the challenge is I've got


a video that explaining what I was going to do. My challenge was to write 50,000 words in a month


and I think I don't think I'm going to reach 50,000 words I'm going to end up about 40,000.


So that means I averaged about 1300 words writing a day for the month of November when really I


should have been averaging about 1700. Something I should have mentioned in the last clip is the


power of daily practice. So I've been doing Python every day for the past 10 days or so.


I was supposed to do it all month but I really only caught up to it about 10 days or so and just


just small 25 minute blocks one pomodoro and my confidence in Python has gone up dramatically and


I'm only even doing the basic challenges of hacker rank I haven't really moved on to the


advanced algorithms yet but even just small little little pieces of practice here and there


incredibly valuable. I spent the rest of the day planning what I'm going to do next in my AI


master's degree and so I chose for the month of December I'm going to do the deep learning


specialization on Coursera as well as the linear algebra course on Khan Academy which is free and


why I want to do those in parallel because deep learning has a massive linear level not massive


but a fairly substantial linear algebra component. It has a lot of math actually involved in it.


However a lot of the pre-existing libraries such as TensorFlow and Keras and Python and NumPy and


Pandas and all that have all those fundamental math principles built into the library so you don't


necessarily need to know the math inside and out unless you're trying to to push the field forward


and in in the case of maybe you're doing a research PhD or something but in my case I'm looking to


be able to use the existing libraries to build something and that's I'm not necessarily trying


to make the deep learning model and tweak it so that it's a couple of percentage points better


than the best model that's in existence. I'm happy to use what's existing and build upon the shoulders


of others or stand it. How's Isaac Newton saying going? I would not be able to accomplish what I


did if I wasn't already standing on the top shoulders of giants so that's what I plan on doing


and of course all of this is in the Trello board and the rest of it is in the medium document that


I created of the artificial intelligence master's degree that I'm doing. Both those links will be in


the description and last but not least guys we do have a few shout outs of the week so these guys


these people reached out commented on my videos sent me an email there's plenty more and if I


missed you I'm so sorry send me an email or twitter and you'll be you'll be in one of my


future works or I'll make sure I get you out there but thank you to Chris, Ben, I can't pronounce


this one but snoonham01, Nexus Wars, Avanesh and Ming. Thank you guys so much for reaching out to


me I hope I did my best to or I hope you got some value out of the the questions that I answered or


the comments that I replied to. If you have any questions for me at all guys feel free to leave a


comment if you want to see a future video of any kind just let me know leave a comment below or


send me an email or hit me up on twitter my email is daniel at mrdivert.com and I'll see you guys


This book will help you get a job in machine learning (ML Interviews Book) | ML Monthly June 2021

Friends, welcome back.


It is that time of month again.


I'm super, super excited for this episode.


Now it's a beautiful rainy day outside.


I did a rain dance this morning.


And it looks like it must've worked.


This is Machine Learning Monthly,


where we cover the latest and greatest


of machine learning for the previous month.


So this is the June 2021 edition.


It's the latest and greatest of machine learning,


but not always the latest,


because there are some things out there


that are evergreen and have stood the test of time.


Without any further ado, this is the article.


We've got a lot to cover today.


If you'd like to read it,


you can jump down the description.


The link will be there.


Of course, if you'd like this delivered to your inbox,


you can put your email in there,


and then you'll get this at the start of every month.


And the video version, which is what you're watching now,


comes a few days after.


Let's jump into it.


So my work, the Zero to Mastery TensorFlow


for Deep Learning course has launched.


Yes, yes, yes, I'm super excited about this.


The biggest update from the last month


is that now all of the materials are available


in a beautiful book.


Look at this.


So if we go learn tensorflow.io, boom, jump into it.


Look what you get.


You get this amazing, amazing formatted book.


We have 00, 01, 02, they're the fundamentals.


Then we have notebooks on computer vision,


transfer learning, a massive, massive important technology


in deep learning, natural language processing.


And finally, the time series notebook,


beautiful, beautiful, beautiful.


Go to learn tensorflow.io, it'll redirect you to here.


This is a lot easier to read


than a Jupyter notebook on GitHub,


which is where the current materials live.


So if we jump in here, and if you click on one of these,


sometimes it loads, sometimes it doesn't.


Yeah, see, we're waiting for that to load,


versus this resource also has beautiful headings.


We jumped down into here.


So we wanted to learn about uncertainty and dating.


I'm not gonna spoil this, but you can read that if you like.


But learn tensorflow.io, my new TensorFlow course


is now available as a book, beautiful.


So now let's jump into the internet.


We have some amazing resources here.


Do I have a booger in my nose?


Maybe, hopefully my faces are small enough


that you can't see that.


Anyway, this is a beautiful blog post by Uber.


Uber's journey towards better data culture


from first principles.


So let's jump into this.


If we have a look here, did our notebook even load actually?


Yeah, see, look, this is what happens


when you try to load big notebooks on GitHub.


Something went wrong, reload.


When you can just now go to this version,


and it's going to be really quick.


Fundamentals, zero, zero.


We jump into this, boom.


All right, matrix multiplication, so important.


Anyway, we're talking about Uber.


So this is a great blog post on how Uber


are becoming a data, well, they already are a data company.


All right, so a lot more companies


are sort of heading towards that data first.


And if you imagine a timeline of data companies,


Uber's probably one of the biggest big dogs


or one of the elders.


I mean, they're only 12 years old,


but they've basically, from the get go,


Uber's business depends on data.


And so they've shared some principles here,


which are really great.


And if you're looking to build some sort of company


or add a company that wants to start approaching data


from first principles,


you'd be wise to take a note out of Uber's book.


So one of the first ones, I like these,


data as code, data is owned, data quality is known.


So we go back to data as code.


Data should be treated as code.


How did it get created?


How did it get changed?


Who owns it?


So if you have to go to talk to someone


about a certain data set, who's the best person to talk to?


These things should be tracked,


especially in a large organization like Uber.


Organize for data, I really love this one as well.


Teams should be aimed to be staffed as full stack.


So that means that everyone in the data engineering pipeline


or all of the steps should be covered.


That way, within individual teams, there's no bottlenecks.


And then if we go back here,


I also linked why this matters,


because it relates back to software 2.0,


which is being covered in a previous issue


of Machine Learning Monthly,


but I'd highly recommend reading this article.


Software 2.0, the main distinction between software 1.0


and 2.0 is that software 1.0,


you would write a set of instructions


for a computer program to complete,


whereas software 2.0 is your,


not necessarily writing a full set of instructions.


Of course, there are going to be some instructions there,


but your programming data.


So that's the big paradigm shift.


And this is what, essentially,


this is what I got from this article,


is that Uber are really heading towards


that software 2.0 milestone.


Let's jump back in.


So the next one up is


test text-based video editing research paper and demo.


I got sent this demo.


I wonder if we can check out the video demo


without getting in trouble.


I might just put a clip here.


Let's watch this together.


We introduce a practical tool


for iteratively editing talking head video.


Starting from this video,


this was easy for us.


We edit the transcript,


and within 40 seconds, we obtain this.


This was entertaining for us.


What?


Have you, when I was first sent this,


I was like, no way.


No way.


And as someone who's in the machine learning field


and sort of looked at these things daily,


I didn't even, and someone who also makes videos,


I didn't even think of this.


When they sent this to me,


they sent this amazing research to me


and sent this demo video.


When I edit videos,


I don't even think of editing them with text.


Because it's just such a different paradigm


to what we've done in the past.


If I make something wrong,


usually now I just roll with the punches


because mistakes are gonna be made


and I just keep talking.


But if I make something actually wrong


and I need to edit it out,


I usually just cut it and rerecord it.


But this is a massive paradigm shift


to how I think about editing videos.


And I think there's a beautiful company as well


called Descript who are doing something similar to this


with video editing.


They use transcription so you can upload a,


this is not sponsored by Descript or whatever,


I just like their product.


You can upload your audio or video.


Yeah, there we go.


A video word processor.


Edit video by editing text.


So they, this one's more like of a cut.


So if you say the wrong sentence,


so I tell you to don't think of a green elephant,


what are you going to do?


And I wanna cut that sentence out,


well, I can do it with that.


However, this demo video goes a step further.


And if I said the wrong word,


if I said don't think of a purple elephant,


but it should have been don't think of a green elephant,


I could just cut that out.


And then it would generate a video of my own face


saying that word.


Now, of course, this is still research,


but if you'd like to check it out,


the link to Descript is there.


But there's a project website for the paper.


There's a paper itself.


And of course the video demo, which is this video,


check that out.


That's wild.


If you make videos, that could be big for the future.


The next one, this was a massive one.


So GPT-3 came for writers,


and now it's coming for coders via GitHub or to co-pilot.


I was about to say autopilot,


because I'm thinking about Tesla's autopilot.


But let's have a dive in.


You've probably already seen this.


I mean, it's all over the internet.


Your AI pair programmer rip all of the programming jobs.


No, I don't think so.


Let's have a look at this little demo.


So what it is, is you enter some natural language


describing what you'd like the code to do,


and then co-pilot writes the,


this is Python, the code for it.


Now, there could be,


I can understand why some people are like,


wow, this is gonna take all my jobs.


However, I think, well, it's saying more than autocomplete.


For me, it's like, yes, it's a very smart autocomplete.


However, I think this will be most helpful


for things like helper functions, debugging and whatnot.


You'll get co-pilot to help you out,


because you still can't yet go to co-pilot and say,


write me an app that replicates Airbnb's functionality.


Or maybe you can, I'm not sure, I haven't tried it out.


I've signed up for the test.


But that's what I think.


It's like, yes, it will be able to probably write code


better than me for small little helper functions.


But the overall logic of a program,


from top to bottom, where data goes in,


how it gets manipulated, how it comes out,


you'll still have to put those pieces of the puzzle together.


Co-pilot will just help you out.


It's similar, imagine this, those tools like Logic Pro.


Logic is a tool, there we go, Logic Pro,


a digital tool for creating music.


And of course, it can produce digital sounds


of like electric guitar or something like that,


but you still have to put the pieces of the puzzle together


to create a beautiful sounding track.


That's how I imagine Co-pilot's gonna help you out.


Yes, wow, I've got a little bug flying around here.


Come on, buggy, don't wanna kill you live on camera.


Yeah, you're still gonna have to put the pieces


of the program back together.


But this is really cool.


And a little tidbit from this is that I think


this is a beautiful synergy happening between


when Microsoft bought GitHub.


So Microsoft, look at this, we've cracked the code.


Microsoft buys GitHub.


Microsoft decries GitHub, look at that.


So GitHub, notice that.


Microsoft partners with OpenAI.


OpenAI forms exclusive computing partnership with Microsoft.


And then the final nail in the coffin is,


oh, we don't even have to search for anything.


How is, boom, GitHub Co-pilot is powered by OpenAI.


I believe it's called Codex.


Codex, which is a cool name.


Yeah, so GitHub Co-pilot is powered by Codex.


I think it'd be very similar technology


to what GPT-3 is using.


So some sort of code language model.


Just an amazing synergy there.


We see it all comes together now.


Microsoft partners exclusively with OpenAI.


And now GitHub is partnering with OpenAI


to create Co-pilot.


That's a pretty cool setup.


But now let's jump back in, go check that out.


Sign up to the wait list and we'll try it out.


The next one, oh, this is absolutely amazing.


This is probably the book I wish I had


when I was looking for machine learning jobs.


Is an introduction to machine learning interviews book


by the amazing, is it here?


Yeah, Chip Heughan.


So Chip's an absolute and complete living legend.


One of the OGs of machine learning.


So there's Chip's website.


Graduated from Stanford.


Teaches a course, Machine Learning Systems Design.


We've seen this in a previous machine learning monthly


where yours truly did the guest lecture


for machine learning deployment.


Boom, there we go.


Guest lecture by one.


But back to the book.


So this is an amazing book that if you're looking for a job


in machine learning, Chip has interviewed


at a dozen big companies and startups.


Got offers for machine learning roles


at companies like Google, Nvidia, Snap, Netflix,


Primary AI, Snorkel AI, just keep naming big ML companies.


Chip's probably been there.


As an interviewer, also involved in designing


and executing the hiring process at Nvidia and Snorkel AI.


So Nvidia, you've probably heard of that company.


Pretty big.


And what she's done is she's collated all of the things


that she's learned over that process


of both being an interviewer as well as an interviewee.


Is that the same thing?


I'm not sure, you get what I'm saying.


And then collated it.


So such as some of my favorites from here


is describing the different machine learning roles.


This is all online and free by the way.


So you can read through this.


If you're going for a job,


I'd highly recommend this resource.


I get asked all the time, what should I do for an interview?


I don't really know.


I've never had a traditional job interview.


In fact, I'm not even going for a job in machine learning.


I'm sort of, I would rather build my own business.


But I understand that's not for everyone.


So if you'd like to go for an interview, read this book.


And so some of the roles that she breaks down,


machine learning engineer, data scientist,


if you're new to the field, it can be,


you see all these role titles and you're like,


I don't even, personally,


I don't even know what all these people do, right?


So when you run, when you do sort of the things that I do,


you kind of a little bit of everything,


depends what the day is.


But it breaks them down.


My other favorite one was four, yeah, four.


Where to start.


Oh my gosh, we've got so many tabs already, team.


Where's number four?


Look at this, look how comprehensive this book is.


Where to start, but that's a beautiful thing


is that it's all HTML, so you can just jump in and out.


And I love this one.


How long do I need for my job search?


Well, this first sentence is completely true.


Getting a job is a skill in itself.


It's kind of strange, right?


The skills you need to get a job


are different to the skills


you would actually use on the job.


Right, so by the time, when you get like,


you get good at trying to look for a job,


but then you get an actual job,


and then those skills wane for looking for a job


because you don't have to look for a job.


It's kind of counterintuitive.


But it is a hard problem to start.


Now, this is my favorite one.


Can you see this?


Am I blocking this out?


I don't think so.


Boom.


How to become a machine learning expert in three months.


You can't, right?


If I was to advise someone


on how to become a machine learning expert in three months,


spend the first 87 days building a time machine.


Day 88, take a rest


because you just spent 87 days working straight.


Day 89, go into the future, 10 years or so,


trick your future self to swap with you,


and then there you go.


I've been doing this stuff for four years,


and I'm still nowhere near an expert.


I'll be the first one to tell you where I can improve.


But becoming an expert in anything takes years,


if not decades, yes.


At best, they teach you bad machine learning.


At worst, it's a scam.


So anyone who claims to give you a shortcut


to becoming a machine learning expert,


I got trapped in this.


I got trapped in this in the past.


I thought I could do everything in three months,


six months, or whatever.


That's the curse of the internet.


It speeds up everything.


But it is a scam.


You take enough shortcuts, you end up taking the long cut.


I love this article by Peter Norvig,


which is teach yourself programming in 10 years.


We're kind of digressing here, but this is worth it.


Read this article.


It's just one of the many hidden gems


that you're going to find in this book.


There's stories of how other people did it.


And then there's do's and don'ts for ML interviews.


I love this one, do's.


Start your preparation early.


I like this one probably the most.


Number two, get someone to refer you.


All of the jobs I've had in the past


have been through someone else.


So that's a big thing, but how do you do that?


How do you get someone to refer you


if you don't know anyone, right?


But this is the power of the internet.


We go down to appendix B.


Of course, you could read through these.


These are some more great tips, but this is a summary.


B, building your network.


If you didn't think that having a network


mattered much before,


I hope that this book has changed your mind.


Having the right network can open you up


to new opportunities and bring you closer to your dream jobs.


How do you create a network?


Well, if you're like me and you're in a bedroom


and you just want to make things online,


you just make things online.


And then at the start, yes, there'll be crap,


but people can tell that you've just been honest.


You've just been upfront.


Hey, I'm learning.


I go back to all of my original videos


and all of my original articles, bunch of crap, right?


But it was at the time, it was the best I could produce.


That's what I'm doing now.


I'm still creating the best I can do


with the skills I have now.


And then eventually people will find that.


If they can see your honesty,


interact with other people's work,


that's how you build a network.


And it's really, network is to me a scummy word.


It's just you're building friends.


To refer someone, this is like, you'll read back through.


There's a story of like the best applicants


are the ones that are referred by someone else.


That's what you're doing.


You're building friends in the field by sharing your work,


sharing your ideas and interacting with the same


that others have put out there.


So highly, highly recommend this book.


I'm now going to send it to anyone who asks the question,


what should I do about getting a job?


My favorite is Daniel Burke,


how does a beginner data scientist like me?


Yeah, here we go.


My favorite is, I always say this one,


start the job before you have it.


Right, that's my best piece of advice.


I'm now gonna say that along with this book.


So go check that out.


Thank you Chip for creating the amazing work that you do.


Up next, we have Tesla inching towards full self-driving.


This was amazing talk by Andrej Kapathy,


who was the head of Tesla AI.


Oh my goodness, pause.


But it's a beautiful video.


It re-highlights the use of the data engine.


And it also goes through what Tesla are now using as,


they've gone vision only.


Oh, look at this, their supercomputer.


It has eight, no 720 nodes of eight times A108 gigabytes.


So 5,716 Nvidia 80 gigabyte GPUs


to power their full self-driving model,


which is absolutely insane.


Where's the architecture?


I believe it's, yeah, here we go.


There we go, neural net architecture.


It's got some backbones in the talk.


Andrej also mentions that these are sort of ResNet 50 like.


Of course, Tesla are going to customize them


for their own use case.


And they combine them and they have multiple different outputs.


So it's just one big architecture


with a whole bunch of image feature extractors


that go into different trunks and put out.


So this is a detection output, attributes,


attribute output, kinematics, future trajectory,


direction, et cetera, et cetera.


So I would highly recommend looking at this talk.


It's absolutely amazing that people like us


can see how the best companies in the world


are building their tech stacks.


And of course, can we replicate this in our backyard?


Probably not, but it gives us an insight into the skills


that we have to learn to work towards potentially getting


a job at Tesla or creating your own machine learning startup.


Go check out that beautiful talk by Andrej Kapathy.


Next.


Oh yes, this is another thing that Tesla,


why they're leading in self-driving


is because they've created a data flywheel,


a concept that we also covered


in a previous Machine Learning Monthly,


build a data flywheel.


So if you'd like to check that out,


that's Machine Learning Monthly, January, 2021.


Now we are up to,


hit me baby two more time.


That should be times, times, series,


and there we go.


Okay, so that's, I worked out what I did


with my own writing.


Hit me baby two more time series analysis


and forecasting libraries.


Doesn't quite work as well.


But these are two libraries that have come out of Facebook


and LinkedIn for time series analysis and forecasting.


If you're, why, this is the beautiful thing I love


about machine learning as well,


is that a lot of the best practices


get released as open source,


and then it's your skill,


or it takes you to sort of apply those to your problems.


So let's have a look at Facebook.


CATS, cool name.


Maybe it's an acronym for something.


Is a kit to analyze time series data.


Oh, okay.


CATS, a kit to analyze time series.


Cool name, we just worked that out on the fly.


But there's some great, great examples here.


If you'd like a tutorial,


it goes through time series basics


or CATS basics forecasting.


So if you'd like to predict a number in the future,


detection, which is like time series classification.


This is one of the extracurriculum resources


that I've linked for, if we go back,


for the time series module of the TensorFlow course.


Of course, the course is,


of course the course is focused on TensorFlow,


but there are other libraries that I've put here.


If we go other kinds of time series,


this is extra resources.


If we see here, Facebook CATS, beautiful.


We can leverage the technologies used


by the best in the business,


as well as LinkedIn Greykite, which is very similar.


And we have a flexible, intuitive forecasting library.


We have Greykite, Quickstart, tutorials, et cetera.


If you're doing anything to do with time series,


bookmark both of these,


and I'm sure you'll find something useful in there.


Speaking of open source best practices,


we have all kinds of data augmentation


with Orgly by Facebook.


Now this is a beautiful library


that offers over a hundred different kinds


of data augmentation for text, image, video, and audio data.


And it's open source, of course, all in Python.


I used it here, example workflow of Orgly.


We have an input image, 10 lines of code, barely.


And then I turned my book into a meme.


This book is awesome, very biased meme here,


because I wrote the book and I said that it's awesome.


But this is the type of input and output that Orgly offers.


And now it's geared towards a lot of different problems


that Facebook have with content, copyright breaches


and stuff like that.


So if you imagine people reposting photos


that have copyrights, but changing the colors


on Facebook's timeline,


of course that still infringes copyright.


So Orgly was created to help train models


that can detect those types of images.


So look at this beautiful image of a turtle.


And we have some screenshot, augmentation, overlay text,


saturation, crop, rotate, brightness, blur,


et cetera, et cetera.


Once again, some beautiful tutorials in here as well.


We go into the examples.


This is how I created my book meme.


We jump into this.


This is the Orgly image example.


And of course you can open this in Colab


and try it on your own images.


There's over a hundred different augmentations


you can try out.


So bookmark that if you would like to build


more robust AI models.


To finish off, let's finish with the fantastic four,


rapid fire resources.


We have two times ML Ops.


So this has been covered a few times


in Machine Learning Monthly, but there's a reason


because it's amazing.


This is the ML Ops course made with ML.


I'm personally going to be going through this


as I start to work on my next project.


Stay tuned for that.


It's by the incredible Goku Mohandas,


who has some amazing machine learning experience.


Experiments is a big part of machine learning.


I've been saying that a lot, recording videos


for the TensorFlow course.


But you can check out Goku's awesome name by the way,


Dragon Ball Z, am I right?


Awesome experience here.


But the main part of the course is look at this.


We have product, data, how do you label data?


How do you pre-process it?


Modeling, baseline, scripting.


How do you organize your Python scripts into a package?


How do you take your things from being a notebook


to being something like a Python library?


Interfaces, how do you use a command line,


a restful API, testing, reproducibility, production.


How do you deploy stuff?


Dashboard, infrastructure, CI, CD workflows.


So continuous integration, continuous deployment.


That is actually how the book is built


with a little bit of CI.


So if you go to the GitHub,


and this is how this book is being built


using GitHub actions.


So if I make a commit to this repo,


change one of the notebooks, it runs this code here.


Copy Python notebooks to docs,


which is another folder over here, docs.


And then it hosts these docs on a GitHub pages website,


which is what you see here.


So check this out, go through it.


The lessons are all done with beautiful code.


Let's have a look at CI, CD.


Intuition, GitHub actions.


Wow, that's a beautiful graphic.


That's like graphically showing you


what I just kind of told you.


Components, we've got a YAML file, workflows, events,


jobs, steps.


Look, this is beautiful work.


This inspires me to do better work.


So check this out.


Bookmark it for all of your MLOps needs.


Speaking of MLOps, we have MLOps.toys,


which is another curated resource.


So I like these little URLs that are easy to remember,


MLOps.toys.


If you want training orchestration,


you've got tools here, tools for data versioning.


Boom, have we got experiment tracking


is my favorite weights and biases here.


Neptune, weights and bias, boom, there we go,


weights and biases.


All different tools on MLOps.toys.


If you got something, you can contribute as well.


So big shout out to MLOps.toys.


And finally, we have two beautiful blog posts,


training compact transformers from scratch


in 30 minutes with Pintorch.


So when transformers first came out,


I remember we had a problem in my machine learning role


is that they took way too much data,


but this beautiful research out of the University of Oregon


and PixArt AI research goes through


an incredibly well-written tutorial,


which first of all defines what attention is


and replicates it in code.


Attention is the main algorithm


behind transformer architecture,


many different flavors of attention,


but they use, I believe it's self-attention.


So there's the Pintorch code for self-attention.


Vision transformers, vision transformers is a recent paper


from, yeah, there we go.


And images were 16 by 16 words,


which achieved state of the art results


on image classification and whatnot.


It beat out convolutions.


However, of course, they're data hungry.


So what this paper solves, compact transformers,


is allows people like you and me


who don't have great big clusters of GPUs


to train our models on, so that we can train it.


Where is it?


Here we go.


Vision transformers for mere mortals,


compact and efficient transformers.


This is my favorite kind of research.


Oftentimes you read a research paper and it's like,


yes, we train this state of the art model,


but it took 300 TPU days or something like that


on across 240 TPUs.


I don't have that.


I have a GPU and I have a Google Colab.


So I like the fact that the researchers


have allowed us to train, where's the stats?


30 minutes, boom.


Well, it's in the title, isn't it?


30, 30, 30, there we go.


You can train on a CPU.


30 minutes to 80% accuracy, but go check that out.


Beautifully written blog posts.


There's a paper to go along with it.


That's how I like to read research in this sort of style.


Finally, we have a beautiful blog post by Inez Montani,


who's one of the co-founders of explosion.ai,


the founders or the creators of Spacey, Spacey NLP,


which is an industrial strength, natural language


processing library we've seen in the past.


But this blog post goes through how you should think


about applied NLP.


As you could imagine, or you might not, you might not,


applied NLP, which is applying NLP to a setting


in a production setting is a little bit different


to research.


So in research, you might have, I like this table,


by the way, how versus what.


Find the best trade-offs.


That's my favorite highlight from the whole thing.


What are the trade-offs?


So if you imagine in research, you can build the biggest


model you can ever imagine.


And it can use all the compute you ever need.


Well, maybe depending on what your limitations are.


But in production, that's often not the case.


So I remember when BERT came out, 2018,


we were trying to deploy that as machine learning engineers,


and we didn't have the resources for it.


So we ended up going with a smaller model that performed


at 95% the accuracy of BERT, but was like 50 times smaller.


I'm making those numbers up, but you can imagine that.


That's the, that's the about the scale.


So find the best trade-offs.


Potentially, the biggest model doesn't perform as fast


as you need it to.


It may have better accuracy performance,


but its speed is slower.


So just keep that in mind.


You can read through this blog post.


It's phenomenal for developing a mental model


about thinking about how to put your NLP models


into production.


With that being said, we have reached the end


of machine learning monthly.


Once again, a massive month for the ML world in June.


If you'd like this article delivered to your inbox,


sign up, otherwise the link will be below.


And this article of course includes the links


to everything that we've covered.


If you have anything, let me put me back to full screen.


If you have anything that you would like to,


or if you'd like me to share your work,


it can be whatever you want, beginner tutorial, any level.


If you have something you'd like to see in a future video


and what's this text, text version of machine


learning monthly, send me an email to daniel


at mrdberk.com with the subject machine learning monthly


submission, because I'd love to see it in here as well.


With that being said, keep machine learning,


keep creating and most importantly, keep dancing.


Pew, pew, pew, pew.


Got 20-minutes? This whole body workout will help you get shredded

Yeah, mate, we always record.


We on?


Yeah.


Yeah, fish on.


Morning, everyone.


It's nice and early, an excellent morning in the Northern Beaches.


I'm a little bit rusty because my godfather came round last night with some tickets to


Tool.


If you've never heard of them, they're an amazing rock band.


I wasn't familiar with their music until last night, but now I'm pumped for a workout.


Justin, we've got a plan.


What are we doing today?


There is no plan today.


This is going to be an impromptu setup.


Obviously we got to do battle rows.


We know that.


Battle rows are the default.


It's part of life.


So since we have a jujitsu tournament coming up in a few weeks, workouts likely going to


be to mimic that, similar to the last week's video where we did fight gone bad.


So this session we'll probably do, let's do three times five minutes, I reckon.


Yeah.


We're up to that.


Yeah, yeah, yeah.


So we're going to do a set of five minutes and then in that five minutes it's a different


exercise every minute.


So for one, we'll do battle rows.


Let's definitely do that.


And I've been craving some kettlebells, mate.


Let's use some kettlebells.


Kettlebell swings.


I've been craving some hip thrusters as well.


I don't know why I suddenly get in that 15 kilogram bag and just kind of using that to...


Beautiful.


Done.


So that's three.


So we've got battle rows, kettlebells, we're going to do some hip thrusters.


I've also, technical stand, I think we should practice that for a minute.


Okay.


Yeah.


Good idea.


Yeah, love it.


Technical stand for a minute.


That's like a jujitsu move where you're on the ground.


Basically, you're wrestling with someone here, you've got to stand up like that.


So we'll do 30 seconds each side.


And then to finish off, what can we do?


Slams are always good.


Slams are good.


So let's go through them.


We've got some battle rows and the heavy battle rows.


Kettlebells.


We've got some hip thrusters on the bag.


Technical stand for one minute.


And then finally is the medicine ball slam.


So first things first, always before you get into any workout is, because this is nice


and simple, right?


Then we'll probably finish off with some farmer's walks.


Warm up.


Because I didn't get as much sleep as I usually get last night, warm up is more important


today.


So my philosophy with warm up is I like to get sweating before I move.


So we're going to spend five to 10 minutes stretching, moving, getting warm.


All right.


Cool.


Love it.


And we're back out again with some garage fitness.


Again, I can't stress enough how important it is at the beginning to warm up.


So that's how every session that I do, I'm spending five to 10 minutes just getting active,


moving around, not really following any structure, but just figuring out where my body is stiff


or sore or something like that and trying to get that part of my body active.


So here just doing some push ups, anything to get that sweat pumping.


Because once you get that sweat pumping, look at that, some hip thrusts, all kinds of movements


here.


Once you get that sweat pumping, that's when your body starts to help you work out.


When you first begin any session, because you haven't been moving, your body won't be,


your muscles won't be oxidizing oxygen very fast.


But as soon as you start to sweat, things kick in just like we just did here.


And again, we're keeping these workouts nice and simple.


A few exercises on their own that aren't too difficult, but when you put them back to back


to back, that's when they start to get nice and hard.


You've got me in the background there doing some stand up, doing some ball throws, Sam's


doing some kettlebell swings.


Again, we're trying to hit every part of the body in this one session, nice and hard, nice


and short, nice and simple.


These barl ropes are a killer.


They're nice and heavy, great for grip strength.


And see Sam, he's doing the get ups there.


There is never a point in life where it's not like you won't need to be able to get


up off the ground really quickly.


If you're old and fall over, you're going to need to be able to get up.


So, we're about that big.


Next exercise, DMPs.


Big dog.


Back, back, back, back, back.


Let's get this finish ring.


Fittest first, then.


What was that, Justin?


Fittest first, mate?


You reckon?


Fittest first, yeah.


Oh, well if you say so, mate, how kind of you.


But I've hurt my leg, so maybe you should go.


Yeah, how kind, mate.


Mate, he can happily go first.


Yeah, absolutely.


And second, and third.


Yeah, he can just do it all for us.


Bet he's a thirsty cow, so.


Applying for my dairy farm apprenticeship with this video.


I can carry milk.


I'm gonna go and get some milk.


Merry Christmas.


You're done.


Let's do three laps.


What?


Yeah.


Bring it on.


Last set, best set.


Nothing will get you more motivated in the morning


than a big morning.


We're gonna make it.


Pass the torch over.


Let's go, brother.


You horrible bastard.


Last one, mate, come on.


Easy money.


That's it, mate.


Oh, yes.


How you feeling, cameraman?


10 out of 10.


And that's it.


Massive effort, brother.


Well done, bro.


Oh.


We need to do that again.


Three, two.


That's no good.


There we go.


I mess that up every time, Neil.


How you feeling?


Mate, unbelievable, unbelievable.


I think that was the first time we've done it


as a five-minute round,


and you know by the fourth round,


you still just gotta find some extra room in the tank


and dig in, so.


Well, that's exactly what we're doing.


We're training for the jujitsu sessions,


and that's at wipeout level,


which we are at the moment,


is five-minute rounds.


So this is like auxiliary training


to what we're doing, technique and class and whatnot.


So this is a bit of fitness that you don't get as batted.


You still get batted,


but you don't have someone trying to choke you out


on the other end and give you a sore neck.


But yeah, first time, three rounds, five minutes,


nice and simple exercises.


Again, we're just trying to mimic the stuff


that happens in jujitsu.


So like the ropes are for grips, the farmer's walks


at the end, they're for grips as well.


We've got the slams there for like,


as if you were to hold onto someone,


try to slam them down, kettlebell swings, technical stand.


I mean, when in life do you not need to be able


to get off the ground pretty quickly?


But, totally agree.


And mate, you know, like we've been doing this


for probably about four, five weeks, maybe more.


You and I do in those sessions,


and one thing I'm really noticing is I've got that tank


on the gap, on the bottom of the tank.


On the gap, on the mat, right?


So that like when I'm like four minutes in,


normally I'm just blowing, I just want to stop for a while.


I'm ready to go, you know, and I need a minute's rest


and I'm back in with the next person.


So it is definitely working.


Yeah, beautiful.


And it's all over in half an hour.


Give or take 10 minutes plus the warmup.


Remember, warmup, really important.


But time to get on with the day.


Thanks for the session, Ra.


We got it right this time.


Thank you, cameraman.


Udacity AI Nanodegree Progress and Bayes' Rule Explained | Learning Intelligence 11

Welcome back learners. It's learning intelligence episode 11. Pretend that's two ones not not the peace sign


Oh, actually, yeah the peace sign not the number two peace to you


Let's start this episode off with a little challenge


So this is at beginning of the probability class of the Udacity artificial intelligence nanodegree


And I got this question wrong and now I don't expect you to read it now


I'll put a screenshot up and so you can pause it and try to answer the question for yourself


Otherwise, I'll leave the I'll leave the actual answer in the description


So this episode I'm going back over the classes I did last week the probability Bayes-Netz


Bayes-Netz inference and hidden Markov models. I'm going back over them again


So they cement in my brain for the final project of term one, which is using hidden Markov models to detect


American sign language so different symbols of sign language and now I I like this sort of project


I like these sort of projects in in artificial intelligence because to me I I don't have a need at the moment to learn


American sign language, but if I can build an intelligent agent to recognize it for me


I would happily use that intelligent agent to help me out much like things like Google Translate


It's getting so good now that I know there's there's plenty of benefits to learning another language and frankly if I'm honest


I'm too lazy to try and learn it for myself at the moment


I don't have a fundamental need to learn another language


But if I can use Google


Translate to help me communicate with people from from other parts of the world that don't speak English or don't speak any any medium


That we can communicate on I would happily use that and I think that's another reason why I like artificial intelligence


Because we only have limited bandwidth as human beings, right?


So I there's no way you or I could learn everything that possibly is but with the help of intelligent agents


We can get superhuman intelligence in fields that we wouldn't even think of learning about


That's one thing that fascinates me about that AI. One major thing. There's plenty more and


Anti-distraction tip my iPhone is in my drawer


It's not even on the desk. I keep it in here so


I can't see it. So I'm not distracted


So I'm not trying to get into to use Facebook user Instagram use snapchat, although I love those services


They do distract you or distract me certainly from for more important work at this stage


I do believe they have a place though. They do have a place that was long introductory clip


I'm ahead off to gym give my brain a blade get some blood into my body come back finish chapter 13 of


artificial intelligence and modern approach textbook


I've only got a few pages left so learning lots of cool things about Bayes rule actually and a lot of probabilistic AI


Is based off the simple rule of Bayes rule. Let's check that out. So what's so cool about Bayes rule?


well Bayes rule is used in a lot of


Probabilistic AI and I've done up a few slides here that will go through and hopefully give you an entry-level


Understanding of where Bayes rule can be used. So let's jump in slide


Number one Bayes rule is useful for determining an unknown probability of an event occurring when you have good probability


estimates for related events now that


Was a in my opinion when I first read it a lot of jargon


But I understood it much better when I went through an example


So let's look at this the way I understand Bayes rule the way the textbook sort of lays it out in a and


What they call a simple case is Bayes rule is the probability of a cause happening


given the effect


So to work this this probability of the cause out you can take the probability of the effect given the cause


times the probability of the cause


divided by the effect


So again fair amount of jargon there, but let's go. Let's do a real-life example. Where could this actually be used?


So let's say pretend you're a medical doctor for a second and you want to work out


The chances of someone having a certain disease based on their symptoms so the disease would be the cause and the symptoms


Would be the effects we have


people with meningitis


Have a remember you're a doctor now people with meningitis have a stick stiff neck 70% of the time so a doctor knows that and


150,000 people have meningitis and


1% of patients come to the doctor with stiff necks so knowing this knowing these factors here


What is the chance of a patient with a stick neck having meningitis and so using Bayes rule and?


The equation we just went through we can adjust it for our scenario to work out the probability of the disease


Given the symptoms, so what's the probability of having meningitis?


given the fact that they have patient has a stiff neck by taking the probability of


The patient's symptoms given their disease the times the probability of that having the disease


Over the probability of symptoms so again


Let's see this in action so remember people with meningitis have a stiff neck 70% of the time so this means


If we let s equal stiff neck and M equal meningitis the probability that someone has a stiff neck


Given the fact that they have meningitis is 0.7 so 70%


Probability is 0.7 and now 1 in 50,000 people have meningitis so let M


Equal meningitis so that means probability of M equals 1 in 50,000


1 divided by 50,000 and the probability of people having a stiff neck is


0.01 so 1% of patients come to the doctor with stiff necks, so we want to work out


What is the probability of a person having meningitis so M equals meningitis given the fact that they have a stiff neck?


So we can use these prior details to work out the probability of this occurring


So we just substitute into the equation we go back up here


The cause is meningitis the effect is the stiff neck and I'll let you fill in the other details here


But we sub in the numbers here so


Probability of a stiff neck given the fact they have meningitis 70% is 0.7 times the probability of


Meningitis which is 1 in 50,000


divided by


the probability of someone coming in with a stiff neck is


1% of patients so divide that by 0.01 and that gives us a probability of


Chance that a patient coming in with a stiff neck has meningitis


So in other words 1 in 700 patients who come in with a stiff neck will have meningitis and so


That's just one example of where or a simple example of where Bayes rule can be used to work out the probability of one


Event occurring given the known or using the known probabilities of related events occurring


So we worked out the probability of a patient having meningitis given the fact they have a stiff neck by using


the known probabilities of the fact that 70% of


patients with meningitis have a stiff neck one of 50,000 people have meningitis and


1% of patients come to the doctor with stiffness and now of course in the real world


You won't always be given all of these all of these probabilities


They won't be known you have to work them out


The process can be a lot more involved than what we've just gone through but that was just one simple example


I've been reading for about an hour or so and I'm starting to to almost fall asleep on the book


It would probably be a comfy book because it's so thick


I know if you can see but I'm reading about up to the chapter


Hidden Markov models which is aligned with with what I'm learning at the moment in the in the Udacity classroom where I'm revising the classes


Before project 4 so I've gone I've gone back through probability gone back through Bayes nets


I've gone back through inference and Bayes nets and now up to Hidden Markov models


But I'm reading the reading the required readings first, but I'm almost about fall asleep


I'm gonna have 10 to 15 minute nap and see if that recharges my brain


Maybe go for a little walk and then get back into it because I figured there's no point me pushing on with reading


If I'm not gonna be retaining the knowledge


I'm gonna let the subconscious do the work and let all this info about Hidden Markov models


Build its own neural pathways in my brain while I have a little nap having a break was the right idea


After having a lie down for about 10 to 15 minutes or so and then going for a walk doing some stretching doing some yoga


Taking my dogs for a walk having about a hour or so break just to wake up


I was almost falling asleep before in the last clip. I came back and finished the reading


I was doing in the textbook. I took Calman filters


So just finished the Hidden Markov models section in in the AI nanodegree


I'm gonna have to sorry in the artificial intelligence a modern approach textbook


I'm gonna have to go back over it because there was a lot of tough


Compto tough concepts. I can't even get the words out the tough concepts are tough words to say in there


They really have to go over like matrix multiplication and and vector multiplication what not just need to refresh those


I'll probably use Khan Academy for that but highly recommend if you're studying and you're falling asleep


Get up and move around or just have a nap have a 20-minute nap or something like that


It works for me every time when I try to push through the law of diminishing returns kicks in I get to a stage where?


I'm so tired, but I want to keep studying and I keep studying and I get more tired and the studies not not working because


I'm going I'm reading the same thing four times over again


It's not sticking if you're tired if all you can do it all you can think about is sleep


Bite the bullet take a 20-minute nap get back into it


It'll be far more effective than if you keep pushing through my brother graduated from high school last night and they gave him this plant


But I think I'm gonna try and claim it. I think it's a sunflower


I thought I was wanted something green in my room since I spent way too much time in here


No, that's a bad thing. I like it in here. This is my sanctuary


I've showed you this before there's my couch my nap bed or yoga mat standing desk


Got the set up here, and this is where reading writing gets done


I just finished reading actually I got emailed a couple of days ago a book proposal if if I could review a book proposal from


I think it's Manning publications one of the one of the editors reached out to me


I'm not sure how they got my contact details actually it must have been through through one of the online


Mediums that I published through and asked me to review a book proposal that they got on generative adversarial networks


Or GANs what we covered in the deep learning nandagiri or covered in a little bit a little bit of detail and asked if asked


for my opinion on


Whether or not the book proposal was up to scratch and whether or not the authors were the right


Authorities to be writing this kind of book and I'm completely blown away like these authors Jacob he graduated from Oxford University


And has worked worked with a company that's partnered with Reid Hoffman and Google's DeepMind and Vladimir


It's been in Y Combinator a Y Combinator startup amazing people


So I'm only new to this world and I'm getting getting reached out to via email with these sort of things and this is opportunity


I mean, it's not it's not a paid opportunity


But I mean this is where they start from so I what I'm trying to say is if you're trying to make it in the


In the world and the developed world in the creator world. I know I'm still a very entry-level entry-level creator


Let's just say that the value of putting yourself online is that you get to you get to be a part of a community if


You don't fit in in your local community and I to be honest


I don't fit in in very many of my my local community


So I'm sort of I'm trying to make my own and that's why I put a lot of these works out online


That's why I'm sharing my journey and whatnot. Trust me


It's it's daunting putting yourself out there the first time but once you get used to it


You you wouldn't have it any other way


But anyway, that's gonna wrap up learning intelligence episode 11 next week


I should be working towards project for the last project of term one of the artificial intelligence and a degree


So I'm really excited for that


You're gonna detect some American Sign Language using hidden markup models one more thing before you head off


Don't forget to subscribe to my channel


It would mean the world to me or if there's anything in particular you'd like to see in a future video


Or even if you have any advice for me, how can I make these things better for you? Leave a comment below


I'd love to read them. I look forward to hearing from you. We'll see you next week and as always


How the World's Biggest Companies Design Machine Learning Applications | ML Monthly April 2021

Friends, welcome back.


Or if you're new here, welcome.


It's that time of month again.


It's very, very exciting.


We're up to Machine Learning Monthly for April 2021.


Now, if you've never seen this before,


Machine Learning Monthly is a newsletter that I write


once a month that collects the latest and greatest


from machine learning, but not always the latest


because we want things that aren't just about hype.


We want things that are battle tested through time.


And this is actually a very special edition


of Machine Learning Monthly.


It's the design edition.


So not gonna do any more talking.


If you've been here before, you know the drill.


Let's jump into it.


So we have here Machine Learning Monthly, April 2021.


If you want the link to this article,


you don't wanna listen to me talk,


the link will be in the description.


You can see it there.


It's on the zerotomastery.io blog written by yours truly.


At the top, oh, by the way,


if you wanna get this in your email inbox,


you can subscribe here.


It'll send you this article at the start of every month.


Very, very exciting.


We start off with my work.


The Zero to Mastery TensorFlow


for Deep Learning course has launched.


Amazing.


Let's go to the GitHub.


If you've seen any of my other videos,


you probably well and truly know


that this is launched by now.


But if you haven't, here's where you can find


all of the course materials on the GitHub.


Of course, the details to what you need on this page.


But if you want to learn TensorFlow,


if you want to learn the fundamentals of deep learning,


if you have some experience,


because there are some prerequisites for this course,


if you have some experience writing Python code,


a little bit of machine learning,


jump into this and try it out.


If you wanna give it a go before you wanna sign up


and pay for it, well, the first 14 hours are available


on two YouTube videos on my channel.


But that's enough about that.


You can see all the details here.


Let's go into machine learning monthly from the community.


Now, this is very important.


If you would like your own work in a future episode


of machine learning, something that you've worked on,


anything in machine learning can be beginner,


can be intermediate, can be advanced.


Doesn't matter what it is.


I'm here to promote your stuff.


There is nothing more than I love than seeing people


create things that they're interested in.


Today, we have a submission from Louis Francois.


I might be saying this wrong, Louis Francis.


But Louis has a YouTube channel called What's AI?


Let's jump into this.


And he has some amazing videos.


So infinite nature.


Oh, this was a paper that came out recently,


which is about using GANs to generate massive video scenes


from a 2D image, that's very exciting.


There was also another video that I watched the other day,


which was how to use transformers for computer vision.


As you may have seen in a previous issue


of machine learning monthly,


we covered the vision transformer architecture or the paper.


And it's using the transformer neural network architecture


for computer vision where CNNs have usually reigned supreme.


So I found that video very interesting.


But go check out Louis's channel, What's AI?


Big shout out to Louis, thank you for the, or Louis,


I may be saying this completely wrong, sorry.


And big shout out for the submission.


But now let's go into the internet.


So the best resources, the latest and greatest


from the internet, but not always the latest.


As I said, this is a special design edition


of machine learning monthly.


One of the most often questions I'm asked,


aside from I want to learn machine learning,


how do I start?


The answer to that one, by the way,


is you just Google it and you go down the rabbit hole


for six to nine months, and then come up


and see if you can breathe there,


because there's a lot out there.


But the next question after that is,


what kind of machine learning project should I work on?


And I usually answer with that is follow your curiosity,


because truly there are an unlimited amount of answers


I could give for that question.


But I'm not gonna read through the post exactly,


you can read through that.


Long story short, I say follow your curiosity,


because that's ultimately going to drive you


to where you are best suited to whatever you're gonna work on,


machine learning or not.


And now, the more and more I learn


about building machine learning full scale projects,


not just models in a Jupyter or a Colab notebook,


is that machine learning is an infrastructure problem,


which means the ML code is actually quite a small part


of any big large scale machine learning project.


So if you go to my blog, mrdberg.com,


I've got a beautiful little graphic that emphasizes this,


how the biggest companies in the world


design machine learning powered applications.


This is a repost of this article.


But this graphic here is just exemplifying


the pieces of the puzzle.


Of course, these are not all of the pieces of the puzzle


around your ML code, these are just an example.


But that's the theme of this series or this post


of this edition of Machine Learning Monthly,


is how do you design all of these things


around your machine learning code?


And what I've done is, as we'll see in a second,


is collected resources from the biggest companies


in the world that are building machine learning


powered systems and put them in one place


so you can see how the best do it


and then apply that to your own projects.


So let's keep diving in.


And what I mean by infrastructure is all of the pieces


around your machine learning code.


But big caveat here, none of these,


these are guidelines throughout here,


none of these will replace the knowledge you gain


from experimenting yourself.


So read these as a guideline to give you a little,


a picture in the clouds of what you might like to build,


but none of them will do it for you.


Let's jump in.


The first one is Apple's Human Interface Guidelines


for Machine Learning.


We'll just dive into the resource.


I'll let you read the post if you want.


And, but, so why is this important?


Let's discuss.


And I'm sweating already because first of all,


I'm very excited to share these resources with you.


Second of all, Australia, it's supposed to be nearly winter,


but it is like the sun's beaming through my window


and it is ridiculously hot.


Anyway, no complaints.


Let's jump into this.


So why is this important from Apple?


Well, I haven't got my phone on me.


It's in another room, very important.


If you're doing something important,


keep your phone away from you so it's not distracting.


This is running on a MacBook, but they have,


I think it's somewhere over a billion devices


around the world.


And every single one of them runs machine learning,


whether it be on the system OS itself,


so the systems operating system, or on an application.


And so this developer guidelines is if you wanted to build


an Apple application with machine learning in it somewhere,


what should you think about


when you're building that application?


How could you get that on the app store,


get that into the hands of others?


Because ultimately that's what we want to do,


or that's what I want to do,


is get machine learning powered applications


into the hands of others.


So if you want to do the same,


this is some great things to think about.


So how could you design the user interface


and user experience, often referred to as UX,


of a machine learning app?


One of the most important questions here


that they go through is defining the role