you don't want to be the guy who's got all the gear and no idea it's how you use those tools


It's going to be a big (and fast) Summer of learning | Machine Learning Monthly August 2020

so check out the videos new gpus and also check out where's tim's post uh which gpu for deep


learning but all those links will be in the description as well as if you want to get this


this article machine learning monthly um make sure you go to the the link in the top of the


description and subscribe here that'll just go straight to your inbox if you don't want that you


can just check out the zero to mastery blog it'll come up there it's usually the first or second day


of every month um but yeah this video will usually come out three to four days after this article


comes out but it's late this month because I was moving place anyway it's been a massive month on


tour and machine learning as usual um let's switch back to the overall view as I said I'm still


working out the new studio but um as always keep learning and keep creating and if you what was


your favorite leave a leave a comment below if you have any resources you'd like me to put in a


future episode it can be yours I'd actually really like it if if you sent me something


um you can leave a comment below or email me danielatmrdberk.com


The 100 Days is Over! | 100 Days of Code 17

What's going on y'all you know what the hundred days of code has actually finished about two days ago


But I was taking a break from from learning last week


Just to do some writing to do some planning for the future to do some workouts to do some other things


But nonetheless I'm gonna keep keep coding


I guess the hundred-day series wasn't really sort of I know coding every day anyway


But what have I been up to I've been learning today?


It was week 9 of CS50 and completed all that which was on SQL flask and MVC which is a


model view controller type of web programming a way to


To sort of make sure your web is secure and still has website sorry is secure and still has functionality


But what can you expect in this series going forward?


Well, this will probably be the last video of the hundred days of code series and then after that we will go


I'm gonna still be learning deep learning artificial intelligence


I've got the AI nano degree coming up that's on the 1st of September and you can probably


Expect from me a few more blog posts as well


detailing how I'm learning it what my plans are for the future and


Why I'm sort of choosing my own path of study rather than going to university


But I'll see you tomorrow


My goal this week is to finish week 10 of CS50 as well as the intro to data science course in Udacity


Or at least get that 75% done. I know yesterday


I said I was gonna work on the intro to data science course on Udacity today


But I've decided to finish off these last two weeks of of CS50 by what is it by the 25th?


That's the due date I've given but I'm probably gonna finish them by either. What is it Tuesday the 22nd? I


Want to try and finish CS50 by either tomorrow or the day after and then I'll work through Udacity's intro to data science


And then I think that'll be that'll put me in a good place to start off the artificial intelligence nano degree


So today what have I been learning about?


well


JavaScript that's week 10 of


CS50 I've got some videos here to watch I gotta watch


JavaScript Dom


Ajax I just finished the lecture here


This guy David Mallon


One of the best lectures I've ever had I'm not even properly doing the course. I'm just doing it online. It's amazing


Highly recommend this course by the way CS50 what I've just learned in JavaScript is sort of blowing my mind


I learned about what an actual JSON is what an API is an API is essentially just a piece of code


Someone else has written that you can use


using various keys for example, like if you were to make them a


Website and you wanted to use Google Maps


You can use the Google Maps API and all the code Google have written for their maps


You can inject straight into your website and start using it straight away


And they've got guides on how to implement this and so it's making me really excited thinking about the things that I can build it


Build up like for example. I've got an idea


Just because my government at the moment is doing some sort of electoral vote. That's costing a hundred and twenty two million


I live in Australia to vote whether or not and it's by post like literally by post


They're gonna post the votes out. That's why it's costing so much


I've got an idea of sort of why not build a


By the way, the votes on equal marriage or gay rights marriage or what is it called?


Marriage marriage equality, right? And of course, it's gonna be yes


Why not everyone's loved the same in my opinion for everyone, but my views don't worry about my views


But the idea I sort of thought of is why not build some sort of application that allows people to vote


securely and uses I know facial recognition to scan their license and then take a photo of their face to make sure it's them and


That allows them one vote


Rather than having I have to sign up for this postal vote have it sent out to me and then return my vote or something


Like that that just to me. That's just too many ways that the


The votes can get miscounted but nonetheless I'm loving this course


Going back to foundations


Which is what this course is an introduction to computer science has really given me a good overall


Idea of what the possibilities are and what different languages can be used for. There's my product Pomodoro timer going off


But yeah tomorrow I'm gonna be helping out my brother working on some projects with him and I might do a little bit more


Of CS 50. We'll see how it goes. Hello world back into some coding today. I'm on the CS 50 week 11


Which is the last week? I'm not entirely sure what this is on


It just says the title of the lecture just says the end


But I'm excited for the shorts that it'll come so I'll tell you I'll tell you what this lecture is about once I finish it


So we've got an introduction to get in github. That's definitely something I need to work on


Automated analysis of music and audio. Oh, I guess that's like machine learning


Chrome extensions. I use a fair few of those


What do you use? I got a


Toggle which is a timing app pocket Evernote one password buffer


I don't really use these much anymore, but they're sort of just hidden in the background. My favorite one actually is


momentum


Have you guys seen this before if not check it out momentum Chrome extension? That's my main goal for today finish CS 50


Yeah, beautiful that is where's that San Francisco?


Awesome check out momentum. What else we have data science. Yes data visualization


Well, there's a fair few. There's like ten videos here. I'm not gonna be able to do all this today


There's like seven hours where the videos there. I might just do the ones that are


That I think are relevant to what I'm learning so the data ones and the getting github and whatever


They're probably all relevant in some way


But hopefully I can finish at least I know let's say half half for today


And then I'll let you know what what I learned and then in the next clip


I'll talk about sort of what the plans are after CS 50 and my review on the whole course as a whole or


Maybe that would be for another clip until I finish the the whole thing


I don't like leaving things undone like the sunken cost fallacy is in full effect


But I'm gonna get in some coding and then I'll catch you in a sec the week 11 class video was about VR


What's next in CS 50 and then just a whole bunch of summarization?


Or sorry what a summarization of what we've learned in CS 50 as well as what you can expect to use with these or make


With the technologies that we've gone over in the past, but I've been going through some of these shorts here


I just went through that one which was really awesome using data science with pandas


I'm watching them on two times speed as well, so I'm getting through them pretty quickly


I watched this one which is amazing and I'll go over that in a sec and


Get in github I've skipped over these two Chrome extensions and whatever I think when I watch this one next the data visualization with d3 and


React native the JavaScript one because I'm really interested in what react has to offer and potentially


Smart cities, I'm not sure what P versus MP is maybe prime versus non-prime


but the one on


The one on react and media or JS is really cool. I've got some notes here


I've got plenty of notes actually and just went through an example of how you can use use react media JS MongoDB


Jupiter flask and AWS all together the lecture who gave it


I think his name was Alan who's a student or something a fellow of CS 50


gave an example of how he built a data analytics app to go over you enter a movie stars name and it can go over their


their previous history of


I don't know movies they've been in and how much money each of those movies made so that people from the future can sort of predict


Where where to invest their money in terms of if they wanted to make a film with certain actors and this is really cool


I'm actually really enjoying going over this stuff because it's


Giving me a more broader overview of what I can do with the stuff that I'm learning


So previously I've been sort of sandboxed in the deep learning world of Python and data science


But now I'm learning how to connect all these technologies together and I'd highly recommend like CS 50 for anyone sort of


Introduction to computer science. I had an advantage that I sort of knew some things about it in the part in the well to start off


with not computer science as a whole but


Just programming in general like I've been doing the deep learning course in the machine learning course and the Python course for the last few


Months, so I had a bit of an advantage there


But it's definitely a great foundation for anyone who's wanting to sort of I know learn what the different technologies are in computer science


And how to combine them because in the combination of these technologies where the real value is is where you can create something awesome


So I'm gonna finish off these videos and then actually I'm gonna go make some food now and then I'm gonna finish off these videos


So I just learned a hell of a lot for some of the shorts


I just watched I learned about d3 with JavaScript for data visualization and react and react native and the power of that


Which is absolutely incredible. I didn't know how much react native could do and


What else?


Exponent I'm gonna or expo. I'm gonna show you a little highlight reel of all of them


So for example, this is what you can do with


J or d3 data visualization


So this is a basketball court and these are all like all these data points all shots that people have taken and so you see


with


d3 you can


Highlight the shots. So you've got players names there. So if you choose someone's name, it'll show you their shots


And imagine doing that with other kinds of data set. So the beauty of it is is that


Not only can you just have numbers on a page to sort of?


Show something that someone something out of a data set you can use this this JavaScript framework to have a website dedicated to


showing someone a visual visually


Some patterns and data and I think that's really powerful and that's what's interests me a lot is is not only just


finding patterns and data but


Communicating them effectively because if you can find patterns and data, but you kind of communicate them effectively you most will have not found them


Right. It's it's the communication piece is just as important as finding those patterns. I'll show you one more thing, which is react native


So react native is a JavaScript or built on react, which is another JavaScript framework which allows you to


Right cross platform apps in just react native. Well, that's that's what exponent does and I'll link this in description by the way


Let me show you XBO.io. So this is like an application that you can build applications with


so rather than writing


an application in Swift for iOS an application in Java for Android an application in


JavaScript for the web you can write them all in


React or react native and then it'll be compatible cross-platform with Android iOS and the web which is amazingly powerful like


Apps major app companies like Airbnb Facebook are both using this technology to write their apps


And it's sort of really sparking ideas in my head because previously I've been


Driven away from from developing apps because you have to develop it in three three languages


Which is a lot for me to learn but if I can do it in just one and that's sort of


I can get the ball rolling and start building it myself using these frameworks like XBO and I'll link them in the description by the


way XBO.io


To to really build something that's instantly scalable instantly usable across platform


I think that's that's a good way to start but check it out D3 for data visualization or d3.js and


react native as well as XBO for


Using react native to build apps. You can publish them straight to the store from there from the XBO app. That's awesome


But I'm gonna finish maybe one more video on this


Week 11 and then I'm gonna wrap it up for study for today. We're finishing CS50


done. Oh


That was cool actually


How everything changed to green just there of that to do bar. Let me show you this is react by the way, boom boom


Check this out react so Trello uses react


That's one of the reasons why I'm also interested in react because I love I know I geek out on really cool cool made apps


Like and I think Trello is a really cool cool web app


So I just learned something awesome about smart cities data. There's so much to learn


There's so much like so many problems and I think that's the that was the overall goal of CS50


Is this I think it can be summed up in this one diagram that I drew


So you got inputs you got a box here outputs and that's essentially this box is essentially problem-solving


So your inputs could be some sort of problem that you have and your outputs could be of course a solution


and the problem-solving here would be


Code or whatever and working out some sort of solution to generate an output you like


On the whole CS50 amazing course. I highly recommend it


I'll put all the links in the description as well as well as the Trello board where I'm tracking my own


artificial intelligence slash data science master's degree


That's enough code for me today. I'm about to do some yoga


I think I might live stream it on Twitch just to see how it goes. I know just for some fun


Why not my twitch link will be in the description. You can check out all my past videos there


I might start live streaming some of my workouts some of my cooking videos and maybe maybe other stuff in the future


I don't know. I'm still working out this whole


Whole Internet of things and I'll getting myself online and whatnot and putting myself out there to the world


But what's my goal for tomorrow? I'm gonna learn beyond Udacity's intro to data science


And then we'll see where we go from there


I'll finish that course and then I think the next step will be the artificial intelligence nanodegree


which I'm very much looking forward to and


Probably a whole bunch of other project work that of course share with you guys fun fact if you're born in the year


1980 you have a total of 43 more solar eclipses in your lifetime. There's actually one the other day


2017 eclipse and why am I showing you this? Why am I showing pictures like this?


Check this out. By the way a human to do the New York City Marathon


Two hours 11 minutes. That's pretty quick


Look at these graphics. They're cool. Hey, how long will it take Galactica to get?


from Earth to Alpha


Center


Uri


Center Uri Center Uri 4.4 light-years


54 trillion miles per hour 29 minutes. That's pretty quick


But reason why I'm showing you this is because I'm learning about data visualization


I'm doing Udacity's intro to data science course up to part 4


We'll actually just finish part 4 next part is map reduce and not entirely sure what that is


But I'll check in once I've learned about it


But this part really excites me creating visualizations out of data


Because you can be the best data analyst there is and sort of find patterns in the data


But if you can't effectively communicate it to someone who doesn't understand the math and the underlying


Programming and how you went about finding all these findings then the data is pretty much useless, right?


It's like if you if you have the secret to


I don't know the answer to what's the meaning of life and you tell no one what's the point of having it?


So my I don't know I've in the past 18 months or so


Really sort of had a switch in my life about communicating or sort of had a formative moment


Let's just say that and I've been trying to communicate myself better through


YouTube through podcasting through writing through whatever so data


Visualization and sort of pulling patterns out of data and communicating them with others really really speaks to me and I really like this


I know this concept of data science. Don't get me wrong. I love the programming side of things


But I also I want to create some cool visualizations and show people


Key messages that I've I've derived or someone else's derived from data


Using visualizations that so the masses can understand it because no one likes to sort of have just giant


Formulas on a blackboard and just expect people to understand it. I'm gonna go find out more about map reduce


After I take my dad out so figure it out what map reduces. It's a programming method that sort of disperses


computation over a whole bunch of computers rather than sort of using one computer to do large computations and it's usually used with


Large datasets that's why sort of we're going over this because of the world of big data is just exploding, right?


and you probably wouldn't use it for anything under five terabytes worth of data because


Under that you can use it on a relatively small amount of computers


but say for example your Google and you're trying to analyze the


Map the mapping of the entire world is started with Google Maps


You might implement map reduce to run sort of data analysis on that across a whole bunch of computers


so you can do it more efficiently quicker and


Reduce power consumption. I'm not entirely sure about that last one


But that's the whole idea of map reduce spreading


Spreading your computation across a whole bunch of computers. So I just finished the Udacity intro to data science course


Where is it? Oh, there we go intro to data science


Really good overview of what data science encapsulates


And the final thing I worked on was map reduce which is a programming sort of a way of programming to analyze large datasets


I think I talked about in the last clip


But I'm going to spend the next couple of days now that I've sort of got no courses on the go


I finished the s50 I finished the intro data science


I've got about a week before I start the artificial intelligence nanodegree from Udacity


So I'm gonna spend a few days


cleaning up this trolley board of what I want to do and what I want to learn over the next six or so months because


That's how long the AI nanodegree runs for it's got a period two terms


So three three months for the first term then a two-week break and then another three months


So we're gonna work out what I want to learn over at least that next six months period


alongside the artificial intelligence nanodegree so


You can see here. I've got a few things in the to-do column on the trolley board


I'll link this in the description by the way. I want to finish get rid of this out of here out of the doing


And then I've got a few courses I want to do


So probably what you can expect from me in the next couple of weeks or so is


In-depth blog post of what my plans are over the next six or so months


But this will be the last clip of the hundred days of code series


And I'll work out what the next series is going to be but I'll have something to do with


The AI nanodegree as well as what I'm learning on


On the side with that so thanks for the hundred days of code series you can check out all the links


I wrote about what I did literally every day in a medium series


I'll put that in description as well as I've got an article series running about how I'm learning deep learning in 2017


Which you probably heard before if not it'll be in description as well


10-minute Bodyweight Back & Shoulders Bedroom Workout (follow along)

You know in the coming months, leadership at every level is going to be like the audition,


the acting audition, the leadership audition for the coming years.


And I mean, I'm being serious about at every level.


The question or the theme of this Reps for Rona session this day nine is leadership.


You know, I read a story of a doctor in New York City.


It's kind of like a day by day of what he would be doing.


So wake up at 6am, large pot of coffee because of course all the coffee shops are closed


and as you could imagine, big day ahead.


And then it goes through detailing himself walking through the ward.


Basically it's all set up for coronavirus.


He has to deal with patients.


Every patient he goes to, he's basically assuming that it's something to do with coronavirus.


And it paints a really graphic picture of what happens.


And so by midday, he's forgotten to even drink water because he's wearing a mask the whole


day.


And he says, I can see one more patient before he even has a drink of water by midday.


How many times have you been working on something that you've forgotten to even drink water?


By late afternoon, he realized he hasn't eaten all day because he's been dealing with different


patients.


And then by 6.30pm in the evening, he's going home, he makes sure to scrub everything, to


scrub his phone, to scrub his clothes, to scrub his badge, to scrub his pens, every


single thing.


It's because he's going home to visit his family, to see his family.


When he gets home, his wife meets him in the hallway of his apartment, strips off all these


clothes, she puts them in a secure bag, takes them elsewhere before he even walks into the


apartment.


His neighbors know that he's going to be walking through the hallway naked because of the situation.


He goes into the apartment, fully unclothed, fully naked, goes, washes down before he even


has any interaction with his family.


To me, that is leadership.


I mean, leadership comes in many different forms.


You might look at it as someone who runs a big successful company, someone who's really


rich, someone who's got a lot of followers on social media, gets a lot of likes on their


photos or something like that.


But to me, that's just the things that can be measured.


To me, leadership is also the things that can't be measured, like love, kindness, empathy,


those things, those important things that cannot be measured.


So to that doctor, thank you for sharing your story.


And when I see things like that, that's why I'm so excited to make this short little video.


I know that was a kind of a ramble to the preset to begin this video, but I believe


it's important.


I figured I can't just sit back and do nothing during this time.


But then again, the other side of that is leadership sometimes is also sitting back


and doing nothing whilst the people who are capable do their thing, essentially you getting


out of the way.


But let's get started.


We have shoulders and back body weight workout.


Now someone requested this, they said they were having trouble in their office working


out their back and shoulders, which is correct, right?


It's pretty easy to do pushing movements, but hard to do pulling movements, which works


the back, pulling movements work the back in a body weight scenario.


So I did some research and I found some exercises that are going to work our shoulders and back.


So let's try them out.


We've got push backups, Superman holds, aka reverse bananas.


We have dead stop to Superman.


We'll see what that is in a second.


Then we have Superman again, a lot of supermans in here, W, Y and T variations of Superman.


And then we're going to finish with a T push up.


Remember five exercises.


We're going to do that for two rounds for 10 minutes.


You know the drill, 30 seconds on, 30 seconds off.


So the first one is a push back up.


So come down, start in a push up position, go down, but this time we're pushing back


to our feet.


Come back, down.


Instead of pushing up, we're pushing back.


Keep those toes activated.


Come down, push back, come up, down, push back, up, down, push back.


That's it.


Keep it loaded.


Come up, come down, now push back.


You should be trying to push back through your shoulders, through your arms.


Down, push back, up.


There we go.


So I figured in this time it's almost like, I've said this a few times now, it's almost


like the whole world has been given a little bit of a reset to go, okay, here's a test.


It's like God or the universe or whatever spirit you believe in, whatever higher power,


artificial general intelligence has said, here's a big challenge to humanity.


See what you can do to fix it.


So now we have Superman hold, which is aka reverse banana or reverse hollow hold.


Down on your stomach, arms out, feet out.


So as if we're like Superman, you can pretend you're flying through the sky.


Who are you going to save?


We could really do with some laser vision right now.


Hold it, tense those glutes, tense that back.


This is what we're focusing on.


Tense them back.


So what that one was doing is because we have to hold our arms back up like this, we're


tensing our back, holding our arms extended.


So there's a lot of, what you'll see is a lot of movements are dynamic, but there's


also a lot of value in movements that are static, aka the holds, the plank hold, the


Superman hold, the hollow hold.


So if you can hold a position for a certain length of time, that's also another variation


of strength.


Now we have dead stop to Superman.


So come back down on the ground.


So we're going to go out, up, back, up, down, out, up, back.


So this is kind of like a push up into a reverse hollow hold hold.


So we've got up, down, extension, up, down, up, stop, out, up, down.


Up, wonderful.


So this is a little bit of a mix up, right?


We're being creative here.


Remember yesterday's theme was constraints.


Today our constraint is a bedroom floor and we want to figure out how we can use just


our body weight to work our shoulders and back.


Next up, we have another variation of Superman.


This time the W, so arms like this for W, arms like this for Y, and arms like this for


T.


So this is called Superman W, Y, T.


So we're going to do W, up, we're going to do Y, up, we're going to do T, up, that's


it.


Now back to W, up, out to Y, squeeze that back, that's it.


Out to T, squeeze that back, yes, that's it.


Now back to W, squeeze, out to Y, squeeze.


How are you feeling Matt?


Oh, that was a good one.


A lot of these, see this is where the fun part comes in.


I get to do these every day and every time I move, I always feel 110% better after.


Once I get a little bit of a sweat cracking, which is now, I feel amazing.


Once I get the blood flowing, and so another fun part is aside from getting moving, it's


trying out different kinds of movement like today.


Now we have the T push up.


So we're going down, up, into a T position.


That's it.


And now as we go up, we're trying to squeeze our back much like we did in the Superman


T. So down, up, squeeze the back, hold it there, squeezing.


And we go down, up, squeeze, how's your balance?


Hold it, nice.


Down, up, squeeze, wonderful.


Ladies and gentlemen, we are halfway through, beautiful.


You feeling like Superman yet?


So now we have push backups again, then we'll go back into Superman hold, dead stop to Superman,


Superman WYT and T push ups.


You know, another form of leadership is what do you do when people aren't watching?


The small little actions that aren't going to get recognised.


So we're back, push back up.


So let's go down here, push back to our toes, come back up.


Down, push back to your toes, come back up.


Push down, back to your toes, come back up.


Down, push back up, back to your toes.


Oh, I mean back to the top, to your toes, top, down, toes.


That's it.


That's really getting the mind going there, really getting the mind going.


And so we just said, what do you do when people aren't watching?


The things that you aren't necessarily going to get recognition for.


It could be something as big as running a hospital while there's a pandemic going on,


or it could be as small as like I saw, for example, my little brother who's filming made


his bed this morning.


So now we've got Superman hold.


Let's go here.


Up, reverse hold.


Now nice squeeze, point those toes, point those hands.


Pretend like we're Superman.


Super, super, Superman.


Who's your favorite superhero?


Leave a comment below.


Mine, probably Batman.


What about you, Sam?


Iron Man.


Actually, Captain America, because don't you think I look like Captain America?


Captain Australia, actually.


So we have now next is dead stop to Superman.


And as I said, leadership doesn't have to be some extraordinarily large action.


It can be the small things.


I said, for example, I want to shout this out again.


My little brother made his bed.


I saw him do that this morning.


I wouldn't usually see him do that, but it made me so, so proud.


Just a small little thing.


Exercise.


Superman, dead stop to Superman.


Let's do it.


So dead stop, Superman, up, back, back up, back to a dead stop.


Now out for Superman, squeeze, come back, push up, down to a dead stop, out for Superman,


fly through the air, come back, up, down, push out, out for Superman.


That's it.


Hold it.


And back up.


Finish.


Oh, yes, yes, yes.


Team, keep it up.


We have two more exercises.


Are you having fun?


You should be because I'm having fun.


Now leadership is also to me those people you would never hear about.


The people who take care of the elderly.


My mom is probably one of the best leaders.


I got lucky enough to have the mom that I do.


I look at her and she's one of the best leaders I know.


So let's go.


W, Y, T. I don't know what that stands for.


W, Y, T. Let's go.


W squeeze, Y squeeze, T squeeze.


Let's go.


Let's wrangle some yellow targets.


There we go.


W, Y, T. Nice.


One more.


W, Y, T. I was having too much fun with that.


We broke into the rest time with some movement, but that is all right.


Sometimes you have to disrupt the routine, you know.


Now we're going to finish with a T push up.


So again, I haven't done this exercise.


I've probably done it at some point in the past, but that's the fun part about moving


every day.


And it's also a creative challenge.


Come down.


Down, push up, up into a T, squeeze that back.


Excellent.


Come down, up, squeeze that back.


Very nice.


Ready?


Down, up, squeeze that back.


Breathing.


Come down, come up.


That's it.


Squeeze it.


Now, up.


Nice work.


Keep it up.


Rest.


You know what?


Is that done?


That is.


That's 10 sets.


So let's have an action replay of what we did do.


We started with the push back ups.


Again, if you want to make this a little bit more challenging for yourself, we only went


through that for 10 minutes, but I've got a little bit of a sweat up here.


You could go through that for 20 minutes.


Do each exercise four times.


Remember, don't overexert yourself though.


This is just to get moving and give your immune system that little bit of a boost.


This is a way.


Moving every day can be your way, your small act of leadership during this time.


Push back ups, Superman hold, aka reverse banana, dead stop to Superman, Superman WYT,


wrestling yellow tigers, and then we finished off with a T push up.


So the question I'm going to finish off with this is what is leadership to you and who's


a leader in your life?


As I said before, one of my little brother did a small act of leadership this morning.


Got up, made his bed.


Did he have to?


Of course not, but he did it anyway.


No one might've been watching except I was this time.


My mom got really lucky.


As I said, I hit the lottery ticket of life with her.


My dad is sick.


She did full time care of my dad.


Expects no recognition from it.


So these words, they're only words, but they're meant.


Mom, I love you.


Sam, I love you.


If you're watching this, I love you and keep moving.


Don't forget who's a leader in your life and what does leadership mean to you?


Leave a comment below.


Keep moving.


Wash your hands.


Who said robots can't dance? | Machine Learning Monthly December 2020

Hello and welcome to Machine Learning Monthly for December 2020.


A show where we go through the latest and greatest of machine learning over the past month.


Well, that's actually a little bit of a lie because sometimes the things we go through are older than a month.


But the reason we go through them is because they're worth going through.


So, of course, this originated as a newsletter and a blog post.


Let me just show you it here. There we go.


So if you'd like to read this without listening to me ramble on about it all, the link will be below.


You can also sign up. It will be delivered beautifully to your inbox at the start of every month.


And as you'll notice, the video comes a little bit after.


But, Happy New Year and let's get into it.


I'm a little bit nervous about this one because it contains some things that I don't quite fully understand.


So I'm going to have to try and explain them.


I'll just tell you why I find them important. How about we do that?


And if you want to find your own explanation, the resource will be there.


Let's get into it. December. Woohoo!


Now, what you missed in December as a machine learning engineer.


So my work, I am working on a new course with TensorFlow.


So TensorFlow code first, deep learning. Let's have a look at the GitHub repo.


You can go check this out. I'm going to be updating this fairly often as I start going through it.


There was a little update a couple of days ago. Here's what's happening.


The biggest update so far of the ReadMe has been all of the exercises and extra curriculum. Exciting.


To go along with the notebooks. So how it's structured.


I make these notebooks that have a whole bunch of code and annotations.


And then I record videos talking through all the code and annotations.


And then I set exercises and extra curriculum. But, enough about that.


You can find the link for that where you find links.


So check that out if you're interested in learning TensorFlow for deep learning.


You will require a beginner machine learning course. Such as the one I also teach.


There's a couple of plugs for me to begin this video.


But, let's go in here. Another thing that I did.


How good is Apple's new M1 chip for machine learning?


So in the last issue of Machine Learning Monthly, I said that.


Or we looked at the fact that TensorFlow had just released a version of TensorFlow Mac OS.


Which is TensorFlow to run on Apple Macs.


Because if you've ever used TensorFlow on a Mac before in the past, it hasn't been the greatest.


So we did a little bit of a speed test. Where we go?


We'll go to the blog post. There's also a video to go along with it.


But, I just did a speed test comparing the new M1 MacBooks to my 16 inch MacBook.


Let me zoom in a little bit for that.


And let's just say this. Without going through all of it, you can read the title.


Apple's new M1 is a machine learning beast.


It definitely is. Like it beat my 16 inch.


And it also, it's just fast to run in general. So read that if you want to have a look at the full test and what not.


Let's go here.


Now, let's get into the best from the community.


I'm already sweating. See this is Australian summer, you know.


It's 8am. It's barely just past 8am.


And it's hot. Hot.


But I'm also sweating because I'm excited to go through all this amazing machine learning resources.


The best from the community. So this one is from Yogesh.


So thank you for the submission Yogesh.


Of course, if you want to submit something to machine learning monthly, you can find my email.


It's danielatmrdberke.com. Send me it. Put machine learning monthly submission in the subject.


And if it's good enough, it will come into the machine learning monthly issue.


Now, you've seen pre-built machine learning models such as with TensorFlow Hub or something like that.


But how about pre-built machine learning pipelines?


Now, I was pretty amazed at this. Hopefully it's still live.


This is from jrextraingenerators.github.


So here we go here. I'll give this one a little bit of a star. Amazing work.


What's your name? What's your first name? Oh, there we go.


Johannes. So amazing work Johannes. And thank you to your guests for submitting this.


So what Johannes' web app does, it's a web app to generate template code for machine learning.


Because machine learning is getting pretty, how would you say, pretty mature.


So in terms of a lot of the frameworks and things that you do, see look at this little beautiful graphic.


Aren't GIFs just amazing for sharing work?


Let's have a look. Try it out.


So what this is, is a code generator built with Streamlit.


Jumpstart your machine learning code.


If we specify the model in the sidebar, click on it closed, training code will be generated, download and do magic.


Okay, let's see what we should do.


Task, image classification, PyTorch, yes, why not.


Which model? I would love to see efficient net here.


Let's go ResNet. Yep, ResNet.


50. 50 is a good number.


Input, yep, I'm working with NumPy arrays.


Use GPU, cross-entry pre-loss, beautiful.


Atom, trusty Atom, always trust Atom.


And oh wow.


Download.py.


Look at this.


Pre-processing.


Oh my goodness.


Model, ResNet 50, training.


See this is really cool.


So if you wanted to learn what some PyTorch boilerplate looked like for machine learning,


you could come in here, generate it and then copy it out yourself, like write it out by hand rather than downloading the whole thing.


That way you would understand what goes into a machine learning pipeline.


So amazing work here from Johannes.


A really cool project to share what you can do with machine learning and of course, Streamlit is just phenomenal.


That was in a previous episode of machine learning.


Monthly.


Streamlit.io, if you want to create some machine learning based apps or data based apps, check out Streamlit.


So another shout out to Streamlit.


Anyway, let's get into the best from the internet.


So this is one I don't quite yet fully understand.


I mean I've read the blog post and I've watched a video or two, but that doesn't mean you fully understand it.


So I wonder if the paper's out yet.


But anyway, it's what was making headlines all throughout the machine learning and artificial intelligence world last month.


Alpha fold 2.


And now they kind of confuse me when they put alpha fold here, but really alpha fold 2 was the one that got the really good results in biology.


Anyway, it's a marketing thing.


So long story short, how do we describe this?


There was a hard problem in biology.


We have been stuck on this one problem.


How do proteins fold up for nearly 50 years?


How can we see DeepMind produce a solution like this, having worked personally on this problem for so long and after so many stops and starts, wondering if we'd ever get there?


This is a very special moment.


So this is Professor John Malt, who's a professor at the University of Maryland.


Very smart gentleman, saying that basically, have they got a one liner of what it does?


No, they don't.


Is the paper out yet?


Publish the paper.


Ah, okay. So I don't think the paper's out yet.


So until we've published a paper on this work, so I haven't fully read the paper.


If there is a paper, let me know, please, in the comments.


But essentially what DeepMind did, and this is the amazing thing, if I recall back to my biology classes, if you imagine an amino acid sequence is made up of letters.


I actually wrote this down.


Like this.


A, C, T, C, T, T.


Right?


So that's a sequence.


Now, if you put those sequences together, they code for some sort of protein.


And if that protein folds in a certain shape, let's go back to the blog post, folds in a certain shape, let's say, where is it?


Really complex shape, like this.


Look how complex that is.


So if we start from just simple letters, such as A, C, G, T, etc., etc., times that by a lot.


And they combine to fold up into some sort of different shape.


The really interesting thing about these shapes is that they tie closely to what the protein actually does.


So it's roll.


So where alpha fold is sort of what alpha fold can do is predict the shape of what a protein is going to turn into, to fold into.


Now, why is that important?


Because then you can start to predict what role a certain protein is going to have.


Now, again, what are the use cases for this?


Well, this is groundbreaking research.


We're not quite sure what they are, but I imagine things.


Now, this is where my explanation is probably still in thought space.


It's not confirmed.


I imagine if you could predict the sequence of a protein and the shape it goes into and subsequently its role in the body,


you could potentially develop some sort of treatment for some sort of illness.


Again, big caveat on this.


I'm not entirely sure.


I've only read the blog post.


I don't fully understand what's going on here.


But my favorite thing was this.


They used to make this was an attention based neural network system trained end to end.


So why is that exciting?


Well, when I first got into machine learning and I sort of knew a little bit about biology, so ACGT, etc, etc.


I was like, wow, and then I learned about sorry, LSTMs and sequence to sequence models.


I was like, why can't we apply sequence to sequence models to proteins?


And of course, I was young and naive.


But it turns out the attention based neural network system, if you're not sure what an attention based neural network system is,


it's look up transformers.


They're an architecture which are basically blazing the field of NLP,


which is essentially finding patterns and sequences of information.


And they're doing the same thing here.


Of course, that's very simplified version.


But it makes me want to dive deeper into learning about attention based neural network systems.


So go check this out.


This is groundbreaking work.


We'll probably look at this in machine learning monthly episode 355 and go, remember Alpha Fold 2?


Well, now Alpha Fold 7 is coding up full humans.


I don't know.


Don't don't quote me on that.


I'm not a fortune teller.


Phenomenal work from DeepMind there.


I need to take a quick break and get a towel. I'll be back.


We will soldier on.


We've got more machine learning.


Beautiful things to go through.


Another one from DeepMind.


Breaking records with no rules.


This is Muse Zero.


Another thing I'm phenomenally excited about.


It was a massive back end of the year for DeepMind and OpenAI.


So let's look into Muse Zero.


Again, this is one of those things I don't fully quite understand yet.


I've only read the blog post.


I need to go through the paper myself.


But nonetheless, it is phenomenally exciting.


So Muse Zero mastering Go, Chess, Shogi and Atari without rules.


Now, why is this one exciting?


So if you remember all the cool things.


Why don't I move my head?


Move me over here.


Yeah.


If you remember all the cool things that Alpha Go did.


You still see that?


I was probably better up there, wasn't I?


There we go.


Working things out on the fly.


Now, if you remember all the cool things that Alpha Go did.


It became the first program to master Go using neural networks and tree search.


But it had some knowledge.


It had human data, domain knowledge, known rules.


And then Alpha Go Zero was basically the same thing as Alpha Go.


But this time, it didn't have any human data.


It didn't have any domain knowledge.


It only knew the rules of the game.


And so learns to play completely on its own without human knowledge.


And then Alpha Zero comes out.


And it's able to do Go, Chess, and Shogi.


So three games.


And it only knew the rules of the games.


And then come down to Mu Zero.


Well, it can do Go, Chess, Shogi, Atari, without anything.


I actually, yeah, how did it do this?


I'm not quite sure I read it.


And it's like, well, I need to reread this about three or four times to understand.


Mu Zero learns the rules of the game, allowing it to also master environments with unknown dynamics.


So if you imagine Go, Chess, and Shogi, I believe, are perfect rule-based systems.


So they have like a set, like chess has a set number of rules that you can do.


But of course, there are many different combinations you can do those rules.


But the rules are set.


Whereas where Mu Zero kind of shines is that not only can it do Go, Chess, and Shogi,


so these like fixed rule games, it can do Atari games and get superhuman results.


And so Atari games, of course, they have some rules.


But the way you can apply them, the space of the rule sets is just so phenomenally large.


It's incredible.


So where is...


So Mu Zero, it models.


So in reinforcement learning, it doesn't often learn a model, it learns a policy.


So it learns, the saying goes that something like supervised models learn a model from the data,


whereas a reinforcement learning model learns data from the model.


Someone smart said that, and it rung true in my head, but I haven't fully quite grasped why.


Anyway, Mu Zero models three elements of the model, of the environment, sorry,


that are critical to planning.


The value, how good is its current position, where it is in the game, the policy,


which action should it takes next, and the reward,


how good was the last action.


So these are all learned using a deep learning neural network,


and all that is needed for Mu Zero to understand what happens


when it takes a certain action and plan accordingly.


So that's phenomenal.


Now, I believe where the combination is...


Well, here's the Mu Zero performance.


I'm not going to go into that.


All you've got to understand is that Mu Zero just wipes the board


with basically everything that came before it.


What it does is it combines deep...


Where is it?


Deep neural networks and search.


Yeah, there we go.


So it combines a model, a deep learning neural network model,


with Alpha Zero's powerful lookahead tree search.


So they're the two combinations.


Now, there is a paper available, and you know what?


I actually did even read through the paper, but it's one of those things,


and this is important to know.


Like with complex topics and new things,


this is groundbreaking research from multiple scientists.


When you first read it, it's probably going to sound like,


what's going on?


But then as you keep going over it, it starts to make sense.


And then probably in a few years' time, we'll look back at Mu Zero and go,


yeah, of course we do things that way.


So what is the applications for this?


Where is it?


The funnest thing?


Yeah, here we go.


Mu Zero's ability to learn a model from its environment and use it to


successfully plan demonstrates a significant advance in reinforcement


learning and the pursuit of general purpose algorithms.


So that's so exciting that it can apply itself to three different rule-based


games and then sort of an open space game like Atari.


The ideas behind Mu Zero's powerful learning and planning algorithms may pave


the way towards tackling new challenges in robotics, industrial systems,


and other messy real-world environments where the rules of the game are not known.


So one of my sort of things that I'm really excited to see if it can be


applied to is self-driving because the rules of driving are not really known.


You kind of, well, there are some basic rules to driving, but the way that


how you apply them is such an open space.


So really cool.


Check out that from DeepMind.


Now, let's get into the next one.


New year, new TensorFlow upgrades.


So TensorFlow 2.4 is out with a bunch of quality of life and performance improvements.


My personal favorite, I'm not going to go through each one, is where is it?


Updates in Keras.


So mixed precision.


Let's have a read.


In TensorFlow 2.4, the Keras mixed precision API has moved out of experimental


and is now a stable API.


So most TensorFlow models use the float 32 deep type.


However, there are lower precision types such as float 16 that use less memory.


So float 32, how I understand, is a number stored in 32 bits of memory on your computer.


But say your computer only had, let's say, a really easy number, 100 bits of memory.


It has probably a lot more.


But if it stores it in 32 bits of memory, it can only really store three numbers.


So what mixed precision does is why don't we just store those numbers as 16 bits of memory


and take up half the space.


So we can store six numbers in that 100 bits of memory.


So mixed precision is the use of 16 and 32 bit floating point types in the same model


for faster training.


This API, this is the most important point, this API can improve model performance by


three times on GPUs.


That's amazing.


More than two times.


Three times.


And 60% on TPUs.


So check out the mixed precision tutorial if you want to check that out.


I'm personally trying to figure out how I can best use that going forward as well to


use up less space and utilize my hardware as best possible.


So massive updates from the TensorFlow team.


Thank you so much.


Again, we missed out on a whole bunch more.


We only covered my favorite one.


So check those out.


Now, if there's one thing I'm searching in life, it's for beautiful explanations.


So if we come in here, this is the kind of blog post I'd like to write.


That's when I know something's good.


If I read something, I'm like, wow, I wish I did that.


That's how I know it's good.


Same with videos, et cetera, et cetera.


That's my one rule actually for making things is like, would I like this to exist in the world?


So backpropagation 101, the algorithm that powers many of the neural networks and machine


learning models that we build.


How to trick yourself into understanding backprop without even trying.


This is from the team at think.ai.


So I believe think powers spaCy, which is there we go.


Think's redesign is brand new.


But previous versions have been powering spaCy, which is an industrial strength natural language


processing framework.


So if we go into here, this is a phenomenal blog post.


If you want to understand backpropagation from a high level overview, you could go through


this blog post and you will be tricked into understanding backprop.


Otherwise, this is like a phenomenal way of like explaining something.


So it's only with code and words.


So backprop as you know, if you've been familiar with it, it has a lot of math behind it.


So of course, that's important to know the math behind backpropagation.


Where is it?


Yes, you should know backprop.


This is a great post from Andre Kapathing, head of AI at Tesla.


So that's a bonus point for machine learning monthly, this episode.


But anyway, this is a phenomenal explanation from the think team of what backpropagation does.


So I'll let you read through that.


It uses the example of three work colleagues all working together to try and improve some


system and how they receive feedback, et cetera, et cetera.


And it tricks you into understanding what's going on in backprop when you're writing it


for neural networks.


CS50 plus AI.


Now CS50 have a new 2021.


See, this is confusing too, right?


Because I went on to CS50.


This is artificial intelligence.


Let's just dive in here.


There we go.


Some great diagrams.


And it's published on New Year's Eve.


However, if you look at the CS50 website, CS50, oh, it's even changed colour.


Now, where is, hmm, announcements?


They said something about 2021, spring 2021.


Look, I was on some CS50 pages, a fair few of them actually.


So that's why I got confused.


But they have a new artificial intelligence module which goes through.


So neural networks have kind of taken over the field of artificial intelligence, or at


least they are the ones you hear the most about.


There's actually a lot of different other components such as decision making, decision


trees, which are very important, minimax, depth limited minimax, depth first search,


breadth first search, heuristics, greedy base first search, et cetera, et cetera.


Genetic algorithms.


Finally, neural networks.


So if you want a very good overview of most of the working methods and popular methods


in artificial intelligence, definitely check out CS50's artificial intelligence course.


I believe they also have, it's free, it's on YouTube.


If you can find it, please leave a link in the comments because the CS50 website, although


it has phenomenal information, it's quite convoluted to try and find things.


So this is like their 2021 announcement.


And I know it says 2020, but I'm pretty sure this is the 2021 version.


Correct me if I'm wrong.


But anyway, CS50, phenomenal to learn the fundamentals of computer science, and now


they're getting into AI.


So exciting.


Anyway, I say anyway too much.


I need to fix my speech.


This is the benefit of recording videos.


You get to listen to yourself and see, hey, you say, um, and you say, hmm, and you look


down and you, you don't pay attention to what you're saying.


Pay attention to what you're saying, Daniel.


Adaptive models, models which update themselves based on unlabeled test data.


So this is a key point here.


Many machine learning models are built off the assumption that training and test data


are from the same distribution.


So say you're building a food detection app and it detects 10 different classes of foods.


Your assumption is, and you're evaluating your machine learning model on the training


or you're training it on the training data set and you're evaluating it on the test data


set.


The assumption is that if your evaluation metrics are really good on the test data set,


is that those pictures that it's been evaluated on are quite similar.


This is what it means by from the same distribution to what the images in the training set are.


However, if you ever deployed a machine learning model into the real world, you know that that


assumption quickly breaks.


So what adaptive models is, is again, this is brand new research.


I'm not quite fully around it yet.


So this is why I linked the blog post and the papers for you to read.


Adapting on the fly to test time distribution shift, so that means test time distribution


shift is when your model goes out into the real world, that's test time.


And suddenly the data that it's being used on in the field is not exactly the same as


the data that it's been trained on.


In turn, your model is performing poorly.


So where we go here?


Imagine that you're building a next generation machine learning model for handwriting transcription


based on previous iterations of your product, you have identified a key challenge for this


rollout.


After deployment, new end users often have different and unseen handwriting styles leading


to distribution shifts.


Yeah, so that's a challenge.


So if we come down here, what does it do?


Where is it called?


This is it.


Adaptive risk minimization.


So the work here from the Berkeley AI research team is our work proposes adaptive risk minimization


or ARM, which is a problem setting an objective that makes use of both groups at training


time and batches at test time.


This synthesis provides a general and principled answer through the lens of meta learning,


the question of how to train for test time adaptation.


In particular, we metatrain the model using simulated distribution shifts, which is enabled


by training by the training groups, such that it exhibits strong post adaption performance


on each shift.


So what they do, they metatrain a model using simulated distribution shifts.


So while the this is how I understand it, you have a training data of some sort.


So it's over here.


And while it's training, you you create some sort of simulation as if that training data


was changed.


So you simulate the fact that that the training data has changed as if it would be to be used


on unseen data.


And you train the model on both the training data and the simulated change at the same


time.


So that hopefully your resulting model is more robust to test time distribution shift.


Again, this is take that with a grain of salt.


This is something that I've just gotten into and just found.


So I'm not completely around it.


That's how I understand it.


Where is the results?


Here we go.


We found that our methods empirically resulted in both better worst case and average performance


across groups compared to prior methods, indicating both better robustness and performance from


the final train model.


So some phenomenal research there for making your machine learning models better in the


real world.


Let's go back.


Software engineering cross AI a systematic review.


So I had a question the other day from my email saying, Daniel, how do I, excuse me,


had to burp.


And I had a question, Daniel, how do I get into software engineering?


That was a great question because I'm personally trying to figure that out myself.


And software engineering for artificial intelligence and machine learning software review is probably


the biggest piece of literature out there at the moment.


Again, caveat to this one, I haven't read through all of this, but if you want a massive


resource on how software engineering can partner up with AI, look, we're still scrolling.


Let's just keep going.


Then you go through here.


So I'd probably look at this like the table of contents.


We're still going by the way.


Look at the table of contents of this and see what sticks out to you the most.


We're into the references.


So what is it actually?


It's about 40 pages, 40 pages of how ML and software engineering and AI combined together


from a whole bunch of different resources.


So this is some phenomenal work to whoever put this together.


Thank you so much to, oh my goodness, I got four names there who I'm going to do a terrible


job of pronouncing, but thank you to these people.


I'm going to leave it at that.


Let's come back here.


So yeah, if you want to look into software engineering for AI and machine learning software,


this is a great literature review.


I've skimmed it.


I haven't read it all, but yeah, that's probably what you want to do too.


We go into here.


Board, just replicate yourself with AI and who said robots can't dance?


So this is a fun part.


An AI powered news anchor just made its or her, what do I say?


I'm not sure here.


It's 2021.


In South Korea and artificial intelligence powered news anchor makes its first on screen


appearance in South Korea.


So if you're bored of reading the news, why don't you just replicate yourself with AI?


You know what?


I'm done.


I'm done with machine learning monthly.


I'm going to insert my AI here and he's going to take over the next one.


So Jeeves.


No, I'm kidding.


I don't keep Jeeves in the cupboard.


That would be mean.


Keep them under the stairs.


And so apparently the news anchor can actually read a thousand words per minute.


So not only can your news anchor read your news, can read a lot faster than you can as well.


So this is a really, how do I even describe it?


Like a thought provoking you use case for what AI can be used in the future.


So there's a video.


Can I play this on my channel without getting banned?


Who knows?


That is wild.


I can't speak Korean, but it's just like the mannerisms of this.


If that's AI generated, that's.


Wow.


I'm not sure what's he saying.


Maybe a sunny in Korea, but that's just, who knows what's going to be created in the future


if we're at this stage already.


And then finally, of course, we've got to finish this off with a bit of dancing.


We didn't start it off with a bit of dancing.


I know.


Thank you for pulling me up on that.


This is a phenomenal video by Boston Dynamics of a robot dancing.


I can't play this one now.


If you need, if you need moves for your next party, watch this video and copy these robots.


Oh, I can't keep playing that.


YouTube is going to ban me.


Dammit YouTube.


But I watched this video.


It warmed my heart.


Such a great, I don't even know how to explain it.


I know these are robots, but it just, it gave me this warm fuzzy feeling inside and it made


me want to dance.


That's how I know something's good.


If it makes me want to dance, check out that video, copy the dance, bring it out at your


next party.


And I have a question.


If a robot does a particular dance move, is it always called the robot?


Oh, but that is it.


Machine Learning Monthly for December, 2020.


Happy new year.


It is December, I mean it is January, 2021.


I may do the next episode with my AI generated version of myself.


But as always, leave a comment below if you'd like to, or what was your favorite, but if


you'd like to submit something, also leave a comment below or send me an email to daniel


at mrdberg.com with the subject Machine Learning Monthly Submission.


And we might see it in a future one of these videos slash issue.


If you'd like to read the blog post or sign up and have it delivered directly to your


inbox, the link will be below.


But as always, keep learning, keep creating, and keep dancing.


It's not the same without music.


I'll just put some music over the top of this so it looks like I'm actually dancing.


AI makes you an artist + Tesla's self-driving car updates | Machine Learning Monthly August 2021

Friends, welcome back. It is that time of month again, your favorite time of month,


Machine Learning Monthly. The video, the article, the newsletter, we cover the latest and greatest


of machine learning, but not always the latest because some things have stood the test in time.


It is August 2021. Let's get started. If you'd like to get this article, the link will be in


the description below. That means you don't have to listen to me talk. You can just click the links


and all that sort of stuff. We start off with my work. I'm super, super, super excited about this.


My first book, my first novel, a fictional story of Charlie Walks. Charlie is a machine


learning engineer working at the largest technology company in the world, writing code at night,


working on his secret project, XK1. Oh, sorry. During the day, he writes code. At night,


writes letters to his nephew, Paulie, because Charlie is a machine learning engineer, but wants


to be a writer. So you can check that out. Charliewalks.com. That's what I've been working


on in the past couple of years, and it is now out. The digital versions are out. The print versions


will come later in the year. And so we have submissions from the community. Now I love to see


this, especially this one, the artificialists. I hope I'm saying that correctly, because I believe


it is Latin for artificial. It's a medium publication and discord server created by


members of the Zero to Mastery Academy. So that means people who've done Zero to Mastery courses,


whatever they may be, have gone off and created their own community to share their learning


journey and works with the world. So there's articles on the medium publications, such as


Your Path to Become a Machine Learning Engineer by Alessandro Lamberti. Big shout out to Alessandro.


You might have seen him in the machine learning data science discord channel. He is everywhere.


He's amazing. And then, of course, Networks, Neural Networks 101 by Ashique Shafi. Again,


you might have seen him in the discord channels. Next, we have Writing Online. Now again, these are


community submissions. If you'd like to see your work in a future edition of Machine Learning


Monthly, please send me an email at danielatmrdberk.com with the subject title Machine Learning


Monthly Submission. And you might see it here. But as it says here, I am a huge advocate for


writing online. I would say that starting my own blog, mrdberk.com, writing on medium,


various other places online, is probably the best thing that I have done in the past four or five


years of creating things online. But we have last month's ML Monthly. We covered Gradio. So if you


look at Gradio, which is the easiest way to share your machine learning demos, highly, highly


recommend checking that out. Pierre Pablo Ippolotto, I hope I'm saying that name right, saw that and


said, hey, Daniel, I've got a medium publication where he writes online. And I've also got an


article on how to get started with Gradio. So check this out if you want to get started. Start


demoing your machine learning models, get them into hands into the hands of others with Gradio.


And this beautiful introduction to Gradio by Pierre. So thank you so much for the submission


there. And then we also have Nifosimi's blog. If we go here, Nifosimi reached out to me saying that


they're hoping to start a career as a technical writer. And so what are they doing? They're


starting the job before they have it, which means writing about technical things before they even


have a job as a technical writer. That is my one piece of advice always, if you want to get a job


in anything, whether it be starting your own business or a job at a tech company is start


the job before you have it. So that is enough from my work. That is also the community submissions.


Let's now look at some resources from the internet. Are you a data scientist, engineer,


analyst who does ML, or are you something else? So Eric Bernardsen, I think I'm saying that right


again. She's probably gonna be like, wow, can Daniel say a single name right? Had a beautiful


article in last month's Machine Learning Monthly, July 2021, which is a story of a data journey at


a startup midsize technology company. This time, there's a beautiful tweet here. I think this


specialization of data teams into 99 different roles, data scientist, data engineer, analytics


engineer, ML engineer, etc. is generally a bad thing driven by the fact that tools are bad and


too hard to use. So yeah, whenever someone asks me, should I be a data scientist, data engineer,


analytics engineer, ML engineer, my opinion on that is really, I'm very biased here. So keep that


in mind is I'm more of a generalist. So when you start to do one of these, you'll do a little bit


of all of them. However, as Eric says, specialization is a good thing. I'm not gonna reread


the whole article. You can read it here. But the main argument of the article that I picked out was,


are you a tools orientated person or a goals orientated person? And so the debate, a question


I get a lot is should you use TensorFlow or PyTorch? Should you use Python or R? And it's


like, you could spend all of this time trying to find the perfect hammer, or you could pick one


and start hammering nails. Can you get the job done with the tools that you currently have?


So go and read that article for some more rather than an argument for and against specialization,


just taking a step back and going, hey, is this the root problem we're trying to address?


Next up, we have want to become a data engineer. Here is a roadmap. Now this is a beautiful,


modern data engineer. Well, speaking of data engineers, by the way, roadmap 2021. Now this is


by the team at data stack TV. So thank you so much for creating this. If we come in here,


when you first get into the data industry or tech industry in general, there can be a lot of


different terms that just sort of throw you off, like CS fundamentals. In fact, I would have even


put computer science fundamentals because if this is for a beginner, they might not even know what


CS is. Computer science fundamentals, if you start off here, you want to know basic terminal usage.


Now what's a terminal? We go into here. How do you write commands in here? So if we go CD desktop,


what does that do? LS, what does that do? Terminal. Data structures and algorithms,


APIs, REST. Now all of these things, if you've never heard of them, are going to sound foreign


to you. But as you get into the world of computer science, you will figure them out. Other questions


like how does a computer work? How does the internet work? Git, what do you use GitHub for?


CLI, VIM, so on. Programming languages, Python. It's got a little heart here. It's close to my


heart too. I didn't make this. They just put a heart there. We come down, it goes all the way


down to different types of data processing and databases and messaging and networking. A lot of


things I don't even know what they are here, but I'm glad to have these steps here to sort of put a,


it's more of a, not necessarily a straightforward map, like as in you should do all of these. It's


more of a compass to sort of direct you in the right direction. Like, hey, here's what the


community is using. So you might want to check it out. It might not be for you. It might not.


So on and so on. There we go. TensorFlow, PyTorch, Keras, et cetera. Now this is an exciting one.


Number, what is this? Number three? Number three, if you can't beat them, join them.


The combination of CNNs plus transformers. So if we look in Facebook AI, this is some new research


that's come out of Facebook AI team, computer vision, better computer vision models by combining


transformers and convolutional neural networks. Now we've covered transformers a fair bit in


previous machine learning monthlies. We all know how good they are for NLP and they're starting to


make their way into vision. However, there are some sort of caveats between transformers and CNNs.


Like one has, one's very strong. If we go down, let's find it. Yeah. A CNN has a hard inductive


bias. What that means, if you imagine an image, it means that every pixel is treated as the same.


It gets the same weighting along the image. And they assume that pixels close together are closely


related. Whereas a transformer model sort of pays attention, thanks to the attention mechanism,


pays attention to what needs to be paid attention to. Now you can dig deeper into that. Oh, that was


a very, very high level overview. The benefit of a hard inductive bias from CNNs is that they


perform well with smaller amounts of data. However, they have a hard ceiling of how well they can


perform with lots of data. Whereas transformers kind of have really no ceiling of how good they


can perform. The more data you feed them, the better they get. So if you combine them, you get


the best of both worlds. So this is a beautiful, beautiful blog post describing what they use to


sort of combine them, which the main component is the gated positional self-attention. That's a


mouthful, GPSA. And I believe, yeah, Convict, we have here sample efficiency, the new Convict


architecture outperforms the data efficient transformer using much less data. However,


as the amount of data increases, the performance gains decrease. On to the next one, number four.


So you want to become an AI artist or an artist in general? I remember in year eight,


my art teacher, I drew the most beautiful Ninja Turtle ever. And my art teacher told me it was


a bad drawing and I thought I couldn't have a draw. But then VQGAN and CLIP came out and it


allowed me to create AI powered artwork. So for this example, I tried to create AI art. And if


you're wondering, VQGAN, let's have a look. VQGAN is a type of generative adversarial network.


There we go. We got this great article here, Generating AI Art with VQGAN plus CLIP.


There we go. Vector quantized generative adversarial network and contrast of language


image pre-training. Whoa. Okay. I prefer the acronym VQGAN plus CLIP. CLIP is another algorithm


from OpenAI, which connects texts and images. So go and read that blog post there. I'd highly


recommend that. We've covered that in a previous Machine Learning Monthly. Amazing, amazing


research. But what do you get when you combine these two? Right? Well, you get an algorithm


that is capable of generating some wild artwork. So for this example, I told it to start here with


this little tightrope walker, which is related to my book, if you want to read it. And I wanted it


to try and get to recreating the cover. And I fed it a prompt with some text. Remember,


CLIP connects texts and images. Man with a mohawk walking between nature and city landscape,


Unreal Engine. Now, Unreal Engine is a graphic design program. It's not my wheelhouse, but I


read in the blog post that's linked here. If you want to create real looking art, you can try using


tricks like using Unreal Engine. And it ended up creating something like this after a thousand


iterations with a Colab notebook. So sort of looks real, sort of looks funky, a little bit cyberpunky.


But I did also try it with a random, like so no prompt, no starter image, no goal image. And it


created this psychedelic landscape. I mean, be careful if you're on mushrooms and looking at


this. I don't know what I'm seeing here. Maybe some floating cities that could be like a giant


shrimp dragon floating through space with a purple flowery background or purple tidal waves and


whatnot. Maybe that's like a little samurai of some sort. I don't know. What's your interpretation?


Leave a comment below. And if you want to generate your own AI powered artwork, RoboFlow have a great


blog post explaining their experience. And there is a Google Colab notebook where you can use Google


Colab to create, here we go. We'll get out of this. Use the VQGAN algorithm and create an image like


I've created with your own starter image, target image and computer text prompt. Now we have number


four, self-driving cars are always five years away, but it looks like they're getting closer.


So recently over the last month, Tesla, AI and Waymo, three of the largest self-driving car


companies had sort of either conventions or a blog post announcing their latest and greatest


of hardware and technology and techniques that they're using to move towards full-strips driving.


And they've come a long way. The biggest one was probably Tesla's Autonomy Day keynote,


which I highly, highly recommend checking out. There are some beautiful things in here. One of


my favorite was, let's jump into the auto labeling. Now I've got a little image here,


which is describing half a dozen or so of my favorite takeaways. There's a clip labeling,


how a clip goes from straight video collected from the Tesla car, and then they automatically


label it. And then if you wanted to find better examples, so you say you had a really rare example,


I think the example they use was two people running along a highway with a dog. If you wanted


to find rare examples, you can summon the fleet or ask the fleet, it's like, hey fleet, show me more


examples that look like this scenario. I think that's beautiful, as we all know in the world of


data, if you want better examples, or if you want a better model, you need to train it on examples


that are rare. And then automatic mapping. So some other car companies are using HD maps. So


they're crafting these maps by hand with LIDAR and other techniques and whatnot. But Tesla,


because they have so many cars out there, they can create automatic maps from vision only,


because say you had an intersection, a four way intersection, and over time, hundreds of cars or


thousands of cars even went through it. Over time, you'd have enough data to sort of go, okay, I'm


pretty confident that there's a lane here, lane there, a lane there, and there's a turn here,


etc, etc. And the beautiful thing about automatic mapping is that it updates over time. Then of


course, there's scenario generation, or Sengen for short, I like to call that Sengen. Sengen,


it just rolls off the tongue really nicely, you know, is that you can create with the data they


have, they've created photo realistic scenarios or simulations of actual driving. And then they


train their models on the simulated environments. Now, of course, the models that are deployed in


the cars are only ever evaluated and tested on real world data. The scenario generation is only


ever used for training, not testing. And then DOJO, which is Tesla's custom AI chip, the fastest by


their metrics, I'm not too up with the computer chip in terms of flops and whatnot, what those


speeds mean. But according to the graph that Tesla showed, it's the fastest out of everything


that's currently available. So for machine learning training, and the beautiful thing is,


if you want to use it on Pytorch, you can just type in torch.device and feed it the DOJO name.


And then finally was the Tesla bot. And I really liked what I've no idea what this is going. But I


really liked how on the slide, Elon said that the bot is friendly. And he said that you'll be able


to run away from it. We're setting it such that it is at a mechanical level at a physical level,


you can run away from it. And you should be able to overpower it, it can only lift up to 45 pounds,


which is about 20 kilos. So if you got some, if you got if you're rocking pythons while riding


python, you should be able to overpower the bot. But highly recommend checking out the Autonomy


Day video in full. Beautiful to see some of the latest and greatest in the self driving car space.


Then of course, there was Waymo's blog post, which was how they're moving towards full self driving


with the world's most experienced urban driver. And not gonna lie, Waymo produced some pretty cool


graphics in this one. I think the main trend that I noticed in Waymo's blog post compared to Tesla's


was the emphasis they used on different sensors. So let's see, sensors, our advanced sensors,


most advanced sensors, comprised of complimentary sensors, most powerful sensors, the powerful


combination of these sensors by our sensors, sensors, sensors, sensors. So they just, whereas


Tesla's going vision only, so cameras only, Waymo have LIDAR and radar and a whole bunch of sensors


that I'm not even I can't even talk to because I've never really even heard of them or read about


them. But their videos were very impressive. So check out Waymo's most experienced urban driver,


they call it the Waymo driver. And now it's good to have competition, you know. So finally,


we have CommaCon, which was Comma AIs. I introduced CommaCon at the end of last week's,


last month's ML Monthly. They're going vision only as well. And they're a much smaller team


than Tesla. They have 22 staff from what I last heard. They've just released the Comma 3,


which is a new device that you can retrofit in many of the most popular new car brands out there


now to add self driving features to your own car. So really, really respect the Comma team. I love


their work. So go and check that out. Self driving. It's always five years away, but it looks like


it's getting closer. Finally, we have I think this is number six. If not, I will correct it.


We have machine learning in the wild. I love this. This is a rapid fire round. Right. So we're


coming out. We're coming out the sixth round. We're throwing some quick jabs here. Applied ML.


Let's have a look at this. It's a beautiful GitHub repo collected by Eugene Yan, who's also


an amazing creator in the data science space. So check out Eugene's work. He has a great blog.


Applied ML collects curated papers, articles and blogs on data science and machine learning


in production. So all the way from data quality. So how do you keep your data in a quality


condition to data engineering to data discovery feature stores? What is a feature store?


Classification, regression, forecasting, recommendation, search and ranking, embeddings,


natural language processing, sequence modeling, everything you could imagine, best practices in


and most importantly, it's in production. So go and check it out. This is a repo. Look, I did not


go through all of these. You can't. There is so much here. It would take you months and months


and months. So best if you're working on something, you need something round of let's relate it to one


of these topics. There's 29 of them or something. So say you wanted to weekly supervise your your


data. You could check out how Snorkel Drybell do it or Osprey or Overton. Actually, we mentioned


Overton in a previous machine learning monthly from Apple, the big dogs. Speaking of Apple,


Apple put out a beautiful blog post on how they use machine learning for running on device photo


recognition. So you may have an iPhone, you might not. I believe Google Photos does this as well with


Android. But this is recognizing people in photos through private on device machine learning. So you


could argue that Apple is either one, two or three of the leading companies in terms of using machine


learning on device. Anyway, Photos has a feature that can recognize different people in an image


such as Anna, Luke, Laura, Sophie, Jane, Tom, which runs on your device. So the photos are never


uploaded to Apple servers. That means machine learning models go through your photos on your


phone. Keep it private machine learning models like it's not peeking at you unless you have some


nudes on there. It's probably going to see your nudes. But it's just turning your nudes into


numbers. Don't worry. It's going to detect your face, upper body, match those two, create a face


embedding, pass that face embedding to a cluster, pass the body embedding. So that's the beautiful


thing because the face can sometimes be excluded or shadow or different lighting. But if you try


to match the face with the upper body model, that's when you get better results. And then they


put it into your photos gallery and it all runs when you put your phone on charge. So it saves


power. And finally, we have Google's AI team substantially improves their polyp detection


algorithm. So way back in machine learning monthly one year ago to this episode or to this issue,


August 2020, we have, let's go polyp. We had number two was using machine learning to detect


deficient coverage in colonoscopy screenings. So what happens, you go for a colonoscopy,


a tube goes up your lower end and cameras take photos of your intestine to see if you have


polyps, which are sort of small little nodules that may turn into colon cancer. And what Google's


AI team do is they use computer vision to reconstruct your intestine into some gangly


looking thing like this. And they use computer vision again to detect whether those polyps are


worth looking at again, or whether they're present or not, because the polyps could turn into colon


cancer, which close to a million people per year die from. And so if you detect colon cancer earlier,


what can you do? You can start to treat it earlier. Well, good news is that they've updated


their research and they've improved from, we have 97%, the system detected 97% of polyps.


So 97% sensitivity. What that means is that out of a hundred, it detected 97 of them. So it would


miss three out of a hundred at 4.6 false alarms per procedure, which is a substantial improvement


over previous published results. Last time they got around 94%. So again, this is an incremental


thing, but this is amazing, right? And of the ones that it missed of the false alarms or the


false positives, follow-up review showed that some were in fact valid polyp detection. So this is a


beautiful thing about machine learning models is that can often discover things that annotators


or performing endoscopists have actually missed. So the combination of the human endoscopist and


the machine learning model performs better than either one on their own. So check that out. If


you're interested in machine learning and health, I would highly recommend reading up on Google AI's


research. They do a lot of things in the health space. That is it for machine learning monthly of


August, 2021. Of course, if you like this article, the link will be in the description below. You can


sign up, put your email in there and that'll be delivered to your inbox at the start of every


Data Engineering on Google Cloud Certification Progress | Learning Intelligence 48

Oh yeah! Check that out, that was very strange actually. My headphones died as I took them


off. So, good timing. Alright, we've passed part 4 of the Google Cloud ML Data Engineering


Certification. This one, the part we went through was all about how do you deploy a


machine learning model, right? How do you get it into production? That is what I'm focusing


on at the moment. It's not so much that I want to do it now, actually, I'm working on


it, we're doing it at Max Kelsen. Long story short, if you build a great machine learning


model but it lives in a Jupyter notebook, it's not necessarily helping anyone. What


I personally want to do and what probably a lot of you guys want to do as well, I hope,


you should want to do this, is get your code out there into the world and into production.


What do I mean by into production? That could be anything, right? But it means to get someone


using it. So, I don't have my iPhone on me because that's in my drawer because I've been


studying. So, when you use your iPhone and you use an app or something, chances are Facebook,


Instagram, you know the common ones, they all use machine learning, but how cool would


it be to have one of your own out there that use machine learning to do something incredible.


Like for me, I've got an idea of using machine learning to help people learn about nutrition


by taking photos of foods. So, I want to, me and my brother have worked on something


similar so that'll be a future project. But we've got a certificate on the way. By the


time this video goes up, it'll probably be here. So, high five for the certificate. We'll


get it back up. There we go. How shiny does that look? I'm going to take a break and then


we're going to get into part five. Let's do it. There we go. What's up brother? What's


happening? Not much. What game is this? It's like a knockoff version of Smash, but for


Xbox. You know how much I love movement. Been breaking up study recently with skipping.


I find if I do like a Pomodoro or something, so like 25 minutes of study and come out outdoors,


usually it's a bit sunny out, but it's late in the afternoon now. I don't have a watch


on. It's like 4.30, 5pm or something. 100 skips and then that I find gets everything


flowing, gets the brain going because you sit down for too long and get a bit stagnant.


So you move around, get the body jumping and then back into it. Now, I wonder if I can


get 100 straight. I'll probably just jinx myself. I can. I'm a little bit fast at the


end. I think that's 120. All right, heart rate's up. We'll go back in. We'll get into


part five. Do a couple of Pomodoros. I've got a few left for today. I think we'll do


eight or 10. We'll see how today goes. You see, the thing about machine learning models,


as you probably know, they love data. So the Titanic problem that we did had about a thousand


rows. Now, what if your data set has close to a billion rows? So a million times more.


Well, with the Pandas, that's what we use for the Titanic one or that's what gets commonly


used with smaller data sets. The data frame or the table you work with stores in memory.


So the RAM of your computer. Now, storing a billion rows in RAM might not be the best


option. So what can you do? That's when you need distributed systems. That's when you


need cloud platforms. That's when you need giant machines. I'm still puffing those skips.


If you haven't got a skipping rope, get a skipping rope. That point there, the more


data thing was, it was a simple one, but that's what I like about this course is it broke


it into... There was three steps to improve machine learning. I've got my little notepad


here. So number one was more data. That's the example I just had. What if you had a


billion rows of data, not just a thousand like our Titanic little baby sample. Number


two is feature engineering. So we didn't do any feature engineering in the Titanic example,


but what is feature engineering? I think the camera is slightly moving. That's all right.


I'll just hold it. Feature engineering, how could you combine two features into one? And


what do I mean by feature? Imagine a column. A column is a feature. So imagine Titanic.


It's an easy example. So you have a column for age, cabin, class. What if you combined


age and class? So you had people who are, let's say over or under 50, right? This is


a rudimentary example, over or under 50 and in first class or second class. So you've


combined the age column with the class column. Now that's just one example, but that's another


way to improve your machine learning models is if the features you get don't sort of give


you the best results, you can create your own. And then the final one is hyperparameter


tuning. So more data, feature engineering, and then hyperparameter tuning. So if you


do build a model and you want to test a whole bunch of different settings on that model,


if you imagine you've got a different combination of let's say learning range, let's say optimization


rate, let's say the number of batch size that you use. What if you wanted to test a whole


bunch of different combinations? And that is where Cloud AutoML comes in, or you can


do like Hyperopt, which is a library for hyperparameter tuning. I mentioned that in the Titanic video.


Grid search that searches a whole bunch of different parameters. Now that's enough talking,


that's three ways to blow out your machine learning, well to improve your machine learning


models, not blow them out, that's what we want to do, improve.


Final skipping. Oh yeah, I think it's starting to rain. We'd better make these quick. Skipping


in the rain. I think there's a song about that. Skipping and singing in the rain. Keeping


count. Messed myself up. And again. All right, one more Pomodoro. You notice how I stuffed


up and I lost concentration? Similar to when you study, focus on the one thing, that's


why I set the timer and don't do anything else, except just got really dark, really


dark. Back into the light and don't do anything except focus. So we're going to finish off


this part five and then I think that'll be it for today.


I forgot to turn the microphone on. So there's no audio, but we're going to just essentially


sum that up in a lot quicker and better fashion. Course five I've been doing on the Google


Cloud Platform Data Engineering Certification Mouthful has been on streaming data. Now you


have your regular data, which maybe say it's a data just in a box, it doesn't really change,


it stays in that box. Streaming data is like stuff going through a river. So imagine YouTube


or Spotify or whatever or something or Google, they always have data flowing through. It's


not just a box full of data, it's always getting updated like a river. It's like you say you


put your hand in the river, you put it in again, it's never the same river. Pretty sure


that's a saying somewhere. Anyway, what's important to know this? Well, it's as data


grows and grows and grows and senses become more prevalent, it's a problem that has to


be taken on. You're not always going to have just a box of data to work on to build great


models off, you're going to have constant input of data. Now all this stuff I've been


learning on here, like all different services like Data Flow and PubSub and Google BigQuery


and BigTable, they're all new to me. So I'm kind of like, still like, what do I do here?


So if someone was to ask me, could I build an end to end pipeline with it? Not yet, but


I'd probably know where to look, I'd know where to find out. And that segues into a


little question I got, I'll answer this to wrap up the video, I think that's enough study


today. Someone asked, reached out to me, when do you start to understand things? And he's


been coding Python for a couple of months, but struggling a bit with some of the concepts.


Well, I'll come straight with that, I'm in the same boat, right? I've been coding for


18 months, two years, or let's just say 18 months, whatever, the timeframe doesn't really


matter. Because I've spoken to people who coded for 10 years and still look up basic


things. You'll find blog posts of people who are experts in their field, some of the highest


quality developers and they'll say, the honest ones, the honest ones will say, they look


stuff up every day, they're on Stack Overflow trying to piece together things. And that's


what I do too, that's where I'm at with this Google Cloud Platform services, because it's


still all new, I mean, there's so much out there, right? There's always a new framework,


a new way of doing things. What I think is important is to get some foundation skills


and never lose that curiosity or that eagerness to try and figure something out, right? That's


the most important part, because when you first start out, it's going to be frustrating.


I go through it, I go through it every day, you'll have failures the other day, I failed


at committing some GitHub code or some code to GitHub and lost 500 lines of Python code.


And it's gone!


So, yeah, sorry Michael, that was my colleague we were working on that with. So we all have


failures, we all have struggles, but we learn and we figure out how to look for the right


questions, not necessarily the right answer, look for the right questions of where to go


next. So if you're struggling with coding or with data science or any of these concepts,


trust me, I'm in the same boat. That's why I'm here studying every day, I'm studying


along with you, trying the best we can to learn. But I'm going to go do a workout in


the backyard and have some dinner and then we'll get back into this tomorrow.


Keep learning team. See you soon.


See you.


I tried to say see you next time and see you soon at the same time.


My brain operates too fast from the math.


Keep learning team.


Completing Andrew Ng's Machine Learning Course on Coursera | 100 Days of Code 12

Check it out guys, day 55 of the 100 days of code series.


As you can probably tell, I was learning about Python today.


And if you're wondering why my screen's so orange,


it's probably because of flux.


Now what flux does is it takes out the blue light


of your screen.


So as you can see, it's 9, 9.54 p.m. where I am.


So I'm about to get ready for bed.


And so taking the blue light out of your screen


late at night helps to, I don't know, helps with sleep.


I don't know the science behind it.


Something to do with, I'm guessing with a fire maybe,


because humans as we evolved sort of,


we all spent time in tribes next to fires.


So maybe that's why having an orange screen is good.


But I don't know for sure.


What I learned today, I did a whole bunch of Python,


went over some notes I took in the past


from when I started Python,


and added to my flashcard system, which is called Anki.


I'll put a link in the description


for all of this stuff, by the way.


That's my Medium series.


You can check that out on Medium.


But yeah, there's Python, Anki, flashcard software,


amazingly good, helps with space repetition.


And yeah, I was learning about regular expressions in Python.


So regular expressions, from my understanding,


you can use them to sort through like a text document


and pull out important details.


So like contacts, emails, names, addresses, whatnot.


And I'm hoping in the future,


it can be something that you can use


for pulling data from websites,


because I think that'd be really handy.


I know you can use Python for that,


I just haven't learned about it yet.


And another thing to finish off the night,


I've been looking at Ray Dalio,


actually read his book, Principles, I've got it here.


I think you can find it online somewhere.


He made it available in 2011, I believe.


And so yeah, as you can see, I'm taking some notes here,


Ray Dalio, Principles, some awesome YouTube videos.


I'll link them in the description as well.


But just having an amazing time learning from the best.


He's worth $16 billion, has his own hedge fund,


one of the biggest in the world,


and all around amazing guy.


But I'm about to do some reading, go to bed.


Tomorrow is, I think, week 10 of machine learning.


So I'll see you then.


Check it out, y'all.


I just finished Machine Learning


by Stanford University and Andrew Ng on Coursera.


11 weeks of courses done in about, I don't know,


10, nine, I can't remember how long,


but finally finished.


And well, not finally finished, I'm excited.


I learned an incredible amount through that course.


But that being said, it's not the end.


I've still got my deep learning nanodegree to go.


I've still got the Treehouse Python track


that I'm working on.


And then I've got a whole bunch of planning to do


to create my own data science slash slash


artificial intelligence master's degree


over for the rest of this year at least.


But welcome back to day 56 of the 100 Days of Code.


Check out all this.


I just finished the last lecture.


So summary and thank you.


Andrew's an amazing teacher, by the way.


I highly recommend this course.


These are all my notes.


11 weeks of course notes.


Plus a massive Evernote document.


All the way back to week one.


I should have put the date there so I know when I started.


Oh well, question is what do I do for the rest of the day?


Because I feel like that's a major accomplishment.


I don't know, I think I'm gonna review some Python


that I've done.


Also got to, I'm gonna edit a V-log as well.