Get that uploaded tomorrow.


Completing Andrew Ng's Machine Learning Course on Coursera | 100 Days of Code 12

Remember every Friday I release new V-logs.


Oh and I'm gonna do some notes on,


finish off the notes I was doing on Ray Dalio's


principles book and get a podcast out


and get a blog post out for you guys as well.


So I'll link them in the description.


I think this video should be out.


No, all that stuff I just said should be out


before this video is.


But stay tuned.


I'm gonna do a blog post later this week as well


detailing what I'm doing to learn


or how I've been learning,


deep learning and machine learning so far in 2017.


And check out my medium series guys.


I always update that every day


of what I've learned during the day.


And so you can follow along and whatever.


I'll put that in the description as well.


But that's it, that's a wrap for day 56 of 100 Days of Code.


We'll catch you tomorrow.


Day 57, what's going on?


Okay, so wait, I just realized that I say


what's going on every clip.


So I gotta sort of cut that out and work out


how to transcend better from clip to clip.


But I'll work that out.


So it's epically raining today where I live.


Took this out, oh look at that ibis.


I got distracted.


So it's wet outside.


Perfect day for study if you ask me.


I'm doing some work here on Anki.


I got Python, I added some new cards in there yesterday.


I got about 15 new cards due.


My trackpad is dead.


I need new batteries.


There's no batteries in the house.


And I'm also working on some podcast notes here


for episode 107.


So what I've learned from speaking to people,


not the topic itself,


like not gonna be the title of the show.


Maybe it will, I'm not sure.


But it inspired me from,


I got inspired actually from last night.


I went to a meetup in my hometown


called the Brisbane Artificial Intelligence Meetup.


So shout out to everyone that goes to that.


And I went by myself and I was trying to convince


my brothers to come along.


I don't know, because I was like,


joy is, these moments are best experienced


when they're shared, right?


And so I'm like, that's all right, I'll go there


and I'll meet some people who are interested


in the same stuff I am.


And I was walking around in the crowd


and I couldn't believe it.


Like I was there by myself,


about 100 people there eating some food.


And the one person I start talking to,


like he was standing by himself,


I decided to talk to him,


was the host of the talk.


So shout out to David, if you're watching this, unlikely,


but doing some amazing stuff with evolutionary algorithms,


which I didn't even have any idea about before I went.


I should have maybe done some research before I went,


but the talk was amazing.


And I don't know, I really had a good time.


And I got David's card and he said to reach out to me


if I was interested in potentially some casual project work


in the future, nothing may come of it,


but that's all right, like that's the thing, right?


You never know, you go to these things


and you speak to someone


and you never know who you might talk to.


So I think they're valuable in small doses.


That being said, like when you're starting out like me,


say yes to almost all of these events,


but if you're sort of working on something


and you know what you wanna work on,


say no to all those events


and just work on what you wanna work on.


But I think the major key for me,


like going to these sort of things,


is finding people, making relationships


with the people that are interested


in the same thing as you are,


because no doubt I'm weak in so many areas


and you'll learn an incredible amount


from people who are, I don't know, different to you,


just different, that's all you gotta do.


They're an expert in something you're not an expert in.


So find what that is, learn from them


and make yourself better.


But I'm gonna go back to learning some Python on Anki


and then do a podcast


and then plan out what I'm doing for the rest of the day.


So we'll see you in the next clip.


I've also gotta work out the outro of the clip.


I don't know, I'll work this out yet.


I'm still new to vlogging, I've done like 10.


And yeah.


What's up, yo?


Day 60 of the 100 Days of Code series.


I haven't filmed for the last couple of days


because Saturday I took the day off technology


or most of the day off technology


and then Sunday I was doing some writing


so I didn't film any coding or whatnot.


But today I was back into Udacity.


I also started writing blog post three,


part three, sorry, of the how I'm learning


deep learning in 2017 series that I'm doing.


I'll link the other two in the description


and probably by the time this video is up,


the third one will be out as well.


So check it out.


It's just literally me documenting the steps


I'm going through and learning deep learning


such as what I'm doing to do it,


like where I'm studying it, what I'm using with it,


Khan Academy, books, Treehouse, Python resources,


all that sort of stuff.


I'm just gonna document it all


so if anyone wants to learn about it,


how exactly I'm doing it,


that'll be alongside the medium series I'm doing


which is blogging every day, writing what I've learned.


Today I learned about DC GANs in Udacity.


So deep convolutional generative adversarial networks.


That's a mouthful.


So yeah, GANs, this revolution in deep learning


which is a massive thing that's happened


only in the last couple of years actually.


It involves training two neural networks


and essentially getting them to work against each other


to generate some output and it's really cool.


What stuff can you use GANs for?


Video generation.


You can generate entirely new videos


that never used to work.


The way you can think of GANs is there's two networks,


a generator and another one that starts with D,


not a decoder.


I can't remember.


G and D, just remember one and two.


So one you can imagine is a magician


and the other one is the audience.


And so the generator which is a magician


gets better and better at creating fake data


which the audience has to sort of decipher


and they have to try and guess whether it's real or not.


And then the output of that is the new stuff, right?


So I'll link a video in the description of Siraj


explaining deep, sorry, GANs,


generative adversarial networks


because he'll be able to do it a lot more better than I am.


But I've done enough learning coding today.


I'm gonna go for a run.


I've got dodgeball later tonight


but we'll see you tomorrow.


I'm back into Udacity again.


I'm gonna keep learning about this


and hopefully tomorrow I'll have a better explanation


for you of what exactly a GAN is.


So, see you soon.


Check this out.


It's kind of creepy, right?


This is using DC GANs or deep convolutional GANs


to transform this face facing right


to this face facing left


using a whole bunch of other faces.


So it goes slowly, slowly, slowly


from facing right to facing left.


And now the iPhone I'm filming this on


is picking all of these up as faces.


So that's really cool.


And the crazy thing is


these are all generated by a computer.


What's up guys?


What you just saw was just a paper that I was reading


and it's the first academic paper I've written in a while


and it took me a fairly long time, at least 35 minutes.


And it's only about 10 pages or so long


but I was just trying to wrap my head around


all the new concepts that are in there.


And it's on DC GANs


which is deep convolutional generative adversarial networks.


And I learned more about GANs today


through the Udacity Deep Learning Foundation's nanodegree.


I know yesterday I said something about


that I would come back with a bit more knowledge about GANs.


Good news is I do.


I remembered what the D stands for


in the GAN architecture.


It's a discriminator.


So you've got, in a GAN,


you've got two networks working against each other,


a generator and a discriminator.


So the generator will take in a random noise sample of data


and create fake data that the discriminator


will have to decipher between real samples


and the fake samples that the generator produces


and that will decide what the output is.


But I'm not gonna go too much into depth on it,


otherwise this vlog would be way too long.


But I will link an awesome article in the description


that uses SpongeBob SquarePants


as an analogy to describe GANs.


And I think it's really good.


So I'll link that in the description.


Make sure you check it out.


I'll also link this academic paper


that I'm reading on archive.


I can't remember who it was by.


Let me just find that out for you.


There we go.


Unsupervised Representation Learning


with Deep Convolutional Generative Adversarial Networks.


That's a mouthful.


By some researchers at Indico and Facebook AI Research.


So really cool.


I've got some more diagrams here on GANs as well.


So as you can see, you've got the input random noise


into the generator network


and the generator creates these fake images


and you've got the real samples here


that go, the fake and the real samples


go into the discriminator network


and then the discriminator chooses


whether or not it's a real image.


And that's the output.


And so what are some outputs from a generator network?


So it could create an entirely new image.


So say for example, you typed in the description of a bird.


The generator network could create an entirely new image


of that bird.


Or say for example, I drew the outlines of a mountain.


GANs have been used to sort of by Adobe


and I believe it is called iGAN and Pix2Pix.


I'll put a link in the description to that as well.


Just drawing the outline of a picture


and then using a generator network


to bring up an example of what an actual real picture was.


So say I drew a mountain, the outline of a mountain,


like a triangle and some water.


A GAN has been used to transform that


into an actual real picture.


So that's cool.


As well as edges to cats.


So you could draw like the outline of a cat


and it'll transfer that into a photo of a real cat.


I think there was a hashtag going called edges to cats.


I'll link some examples in the description as well.


But that's it for learning.


I'm gonna go do some jujitsu.


It's my first time doing it so I'm really excited.


And tomorrow is gonna be all about Python


and same with the next day.


And I think I've got about three or so weeks left


of this deep learning course.


So we'll keep doing that and then I'll do my planning


for the next six months and I'll be sure


to hit you up about it.


But catch you tomorrow.


5 Simple Movements for Leg Strength and Mobility *follow along*

Good Friday and yes it is good good good Friday so wherever you are in the world


happy Good Friday if you don't celebrate that sort of Good Friday I believe it's some part of the


Christian tradition of Easter and whatnot it's a Good Friday anyway at least it's Friday when


I'm recording this video but it is day 25 Rept Faronah and today we're doing a little bit of


well the theme is por que no los dos and thank you Wham for the for the shout there and now


my Spanish is pretty pretty terrible at best so that might not even mean what the theme is in


English in English it's why not both so in Australia there's a little ad for for tacos


and the family are deciding whether they want hard shell or soft shell tacos and the little


girl says por que no los dos and then the whole family starts cheering and it's just a it's a


really it's a really fun ad and if you speak to anyone who's watched any sort of TV in Australia


they probably know the same but where does that relate back to movement well that's today's


movement session we're doing strength and mobility with a lower body theme so we have five exercises


30 seconds each we're going to be doing and when I say strength and mobility it's going to both


build strength these exercises for the lower body as well as work on mobility which is often forgotten


about aren't we do a lot of sitting I've said this I'll sound like a broken record but I know


I finished the day after sitting down and my hips are tight so this is what this workout this session


aims to combat as well as building lower body strength so let's get started we'll get 10 minutes


on the clock open up the Tabata app on the App Store starting your Tabata workout get ready so


the first movement is a duck walk now this is actually quite difficult if you've never done it


but what you're going to do is just come down nice and low and walk on your legs like this now if you


can't walk like that your alternative is to come down to it fully on your knee and then lean forward


bracing yourself with your hand as you go down but if you can't you want to be able to sit all


the way down and we're walking like a duck as if we're waddling to the pond and then you can try


go backwards which is even harder time it just went off to rest but we're going to keep practicing


now we don't have much room in the in the bedroom so it'd be great if you had a little bit more


space but we're making the most of what we have this is because we're recording during the


coronavirus self-isolation sessions or self-isolation advice we're making the most of


working out indoors you could definitely do this outdoors though so now we have kneel to standing


come down kneel down if you can't sit in this position it is quite a bit to work up to so you


could just chill out here for a bit but I'm going to come up on my knees raise my toes behind sit


down and then practice coming all the way up to standing so we'll go back down so sit down on your


feet we'll go back feet on the floor toes up now it probably would have been a better idea to maybe


do each of these for a minute or so so that's what we'll do it's the time is telling us to rest but


we'll just practice going up and back down that's it all the way down now if you can't do this maybe


practice just trying to sit like this and just really working it out or come up practice being


on your tippy toes squatting down knees to the ground feet up finish this one oh see it takes


some balance there's the porcano los dos strength and mobility this time we have glute raises so


we'll lean through like this I actually don't know what the proper name is for these now you're both


feet in front of you and what we're going to do is both to the side and come up squeeze those glutes


at the top that's it now for this one I've got my left my left leg curve my right leg up through and


thrust maybe these could be hip thrusts we go up like that beautiful I'm gonna keep going with the


left for about a minute feel free to just swap back and forth if you want again these these


movement sessions aren't aren't strictly structured I've got a timer on yes but the goal is not really


to pay attention to the timer that's feeling great on the hips this will be great for a hip


loosen up and now if you can't go up with just your legs feel free to brace and then push up


like that I can already feel that my right glutes and now my right leg is bent I can already feel


that my right especially my lower back is a lot tighter on the right side then on the left so keep


coming up switch if you want that's it this is a real eye opener for me start to feel that I need


to do some more work on the right side keep it up and keep going another minute or so really


squeezing those glutes at the top whoo feel that hip release now if you're on some sort of solid


floor you may want to put a towel underneath you now we have one more which is the squat reach so


come down you could just hold a bodyweight squat like this right if this is if this is a challenge


just practice holding it like this but what I'm gonna do I'm gonna grab my ankle and reach up so


again this is almost a full body mobility and strength exercise not only are we holding a


full body squat but we are loosening our upper bodies I just felt a whole bunch of cracks go


through my vertebrae keep it up we'll keep going to the end oh you come just sit like this chill


out if you can again if you can't go that deep just practice holding it like this sitting there


for a minute wonderful now we're back to duck walks so come go forward again if you can't do


a full duck walk maybe just practice with a lunge you can always put your hand down to guide you to


the next one now just walk this way we're focusing on strength and mobility here walking like a duck


waddling we're gonna walk across the street stand up shake it out give those legs a little little


kick little jump you know what it is also today apart from being Good Friday a great Friday


actually it's my best friend Don's birthday so he got engaged yesterday so we gave him a shout


out for that yesterday congratulations Don and a big big big happy birthday to one of my best


friends and brothers Don turns 27 today that means I'm gonna turn 27 this year and now we


have kneel to standing so we come down all the way down to our knees and then up on our feet


this is really great for the feet and toes really great for the for the balance as well come down


nice and slow remember if this is hard on your your knees you might want to put a mat down oh


balance come down here we'll go for the full minute for this one as well if you're wondering


the timer I've got it set on for the 30 seconds of movement 30 seconds of rest but you could


easily just as well do 10 repetitions of this 10 repetitions of all the other movements do them


twice or three times I'm just keeping the timer on to uh make sure the videos don't get too long


keep it up this is feeling great already building strength strength and mobility I might start with


the right leg curl this time so again just coming up squeezing through yeah that's definitely


actually what we might do is change it this time so I can feel it with the left it's a lot looser


so now I'm alternating going back and forth come back through up like that wonderful we'll keep


going through the rest oh that's excellent we might even throw in another one to mix it up


you know keep it up okay my right hip is starting to loosen up now that's what it takes sometimes


it's just a little a little bit of loosening up a little bit of grease in the groove now what we


might do just to mix it up might do a glute bridge to finish off with a tabletop so come up up reach


over up reach over so this is going to be another one like our squat hold that we're about to do is


a whole body whole body mobility session the thing is why not both right so why not have strength and


mobility why not have let's get up why not have both why not have lower body and upper body but


the focus of this session is lower body strength and mobility I'm just shaking it out that's what


we want to do during our rest times of our movement sessions sometimes you want to keep


active but because today's session is a little bit more of a chill out I want to take as much


rest as you need get your body mobile get your body moving what do we have squat hold this is


the last exercise or squat reach if you can come here now you might not be able to hold it so I


want to give you one more alternative is find something solid and then just you can even just


hold on like this and practice your squat I often just go to the fence in the backyard and grab onto


the railing and just sit like this for a bit a whole minute and just keep moving around but


for the last 30 seconds we're gonna just practice our reaches it's one of my favorite ways to not


only build strength for lower body but get it mobile often the two the two don't happen the


two imply each other they don't have to be separate why not both right we can just sit here chill out


eat our food off the ground great workout you spent 10 minutes seven seconds exercise now if


you wanted to of course you could keep going that's a that's a good way to just make that


session a little bit harder but that was nice and simple right it's five exercises five movements


we did have a bonus one there with the glute bridge now my hips feeling far looser than when


they started you could do that as a wake-up session you could do that as a end-of-day session


to go you know what finish work it's time for me to get mobile go for a walk outdoors but thanks


for tuning in hopefully your lower body is a little bit stronger and a little bit more mobile


have a great great great great Friday and once again have birthday Don love you brother we'll


Data leakage during data preparation? | Using AntiPatterns to avoid MLOps Mistakes

Hi, this morning I caught Ms. Coffee Bean watching the PDF of this paper entitled Using


Anti-Patterns to Avoid MLOps Mistakes.


Ms. Coffee Bean, what were you thinking?


I guess you read it because you have little to no idea about machine learning in the real


world.


No wonder since you are in academia, where all the work you do ends in a paper.


But you know, there is life to machine learning methods even after they have been presented


in a paper, life you would like to know more about.


Ok Ms. Coffee Bean, since I do not know what else to do with you, what about this?


I found the data leakage anti-pattern to be the most interesting one.


But the write-up was quite dense, since the paper is made for experts.


Let's make it accessible to anyone by explaining this together.


Alright Ms. Coffee Bean, this paper is sharing lessons about MLOps, which is all about putting


machine learning models into production, but also about maintaining the whole operation.


However, the lessons of this paper are not about what to do and good strategies and patterns,


but more about what not to do, so about the anti-patterns.


Clearly the authors summarise their experience here, which means that the lessons are more


tailored for the financial sector.


But don't worry though, their applicability is much wider than this.


The paper guides us through anti-patterns ranging from technical errors to cases of


missing knowledge about the context in which machine learning is deployed.


Data leakage is one of nine anti-patterns in this paper.


Yeah, each one with catchy names, but data leakage is the most interesting one to me,


because these days I have been interested in the bigger question of model generalisation.


Since the train dev test split is one very weak test for model generalisation, data leakage


is the one responsible for not letting us perform even this simple baseline for generalisation.


Data leakage is a violation of one of the first axioms that we were taught in every


machine learning course.


Do not let samples from the test set leak into your training set and vice versa, otherwise


you will overestimate your performance since your model is already familiar with the data


you are testing on.


So how can this be an anti-pattern if everybody knows about it?


And it's quite easy to implement, right?


Well, no, it's not that easy to ensure total separation between train and test data.


The authors report four ways of accidentally enabling data leakage.


Each one is sneakier than the other.


Okay, great, so we are ready to see the data leakage anti-patterns.


Shoot!


The peekaboo anti-pattern is specific to time series data.


It is described in this little cryptic paragraph.


Now here is what Ms. Coffee Bean understood from it.


Let's suppose we have a time series, and for splitting the data between train and test


set, we could say we have all the data up to a certain time point t to be the training


data and all the data after time point t to be test data.


The splitting pattern could be also more complicated, but the principle of the peekaboo anti-pattern


works the same.


The problem arises when time series datasets have at least two dates.


The date of the actual timestamp of the data point, but also for example the date when


the data becomes available, like in job salary data, which becomes available in March but


speaks about February, so about the last month.


This time lag and the exact timing of things might be unknown to the machine learning engineer,


and messing around timestamps can really mess up the train-test separation by accidentally


including data from the train set, from the past into the test set from the future.


Combine this with a feature that is highly predictive of another feature and we will


have completely overestimated our abilities of predicting the future through this lag.


So what is the real lesson here, apart from train-test separation?


Know the data you are trying to model.


Know what every tiny feature means.


The next data leakage anti-pattern, called temporal leakage, is also related to time


series data.


With an ideal test set, we would estimate the model's true performance when deployed


into the real world.


Ideally, the test set would be independent from the training set to estimate how well


the model performs in unexpected situations.


But this paper warns us that if we take a time series data and sample train and test


sets from it in a non-sequential manner, then the two are not independent anymore, because


we have technically seen the future during training already.


So we have turned this into an easier interpolation problem, what was previously an extrapolation


problem of predicting the future, which is harder.


The next type of leakage is oversampling.


It talks specifically about oversampling techniques that are usually applied if one class is very


underrepresented in the data.


Strategies like SMOTE or similar methods basically interpolate between the new few existing points


to create new points.


So in a sense, the data has been smeared out to have a larger population in the feature


space.


But you see the problem.


The new interpolated points would be informed about all points, from train and test alike.


Therefore, if we split only after the oversampling strategy, we would have contaminated the test


set with train set information, and also the other way round, of course.


So from anti-patterns 2 and 3 we can remember.


The order of the data pre-processing steps and splitting into train and test splits matters.


The last mentioned anti-pattern responsible for data leakage is called Metrics from Beyond.


To Ms. Coffee Bean, this is very similar to the oversampling strategy.


This talks about other operations that, if performed before train-test splitting, could


smear out information from one set to the other.


One example could be data normalization by subtracting the mean and dividing by the variance,


for example.


If we do this for train and test together, the normalization operation informs the future


train set of the mean and variance of the whole set, including the test set.


And vice versa, of course.


Which data leakage anti-pattern is the one that you hate the most?


And if you're honest, which one of these traps have you fallen into at least once so


that you have learned the lesson forever?


What are other data leakage examples that you would like to share with us?


If you're interested in what you have seen so far, read the whole paper to find out all


about other anti-patterns.


Until Ms. Coffee Bean will see you again, repeat after her.


Always separate your train and test data like your life depends on it.


Simple (but very difficult) Morning Home Workout

Morning! What's happening everyone? My name is Daniel Burke, I'm a writer disguised as


a machine learning engineer and today it's workout time! Yeah! So it's nice and early,


just before 6am. Check out that, glasses all frosted. And so we've got like a little bit


of a garage session today. You know what my trick is when I wake up early? Is as soon


as I roll out of bed, I tell myself, I feel fucking amazing! And it works. Then I have


some water, go to the bathroom, do stuff. But I've been doing some training with my


mate Justin, that's what we're doing this morning. Going to his garage and we're going


to do a little workout which is kind of like a BJJ training system. If you're not sure


what BJJ is, it's Brazilian Jiu Jitsu. But this workout could be adapted to anything.


And if you wanted to know the style, it's like a HIIT style, so high intensity interval


training. But without any further ado, let's go to Justin's. What is going on here? Mate!


Justin's dog. She's so cute. Hey Lilo! Hi puppy! How are you? You excited? Yeah, come


on! I open the car door and this little monster, come on. No, Justin, I mean Lilo. She traversed


me mate! There, she got out. Little puppy. There we go. How are you brother? Good morning!


Namaste! Sam would be joining us but he's sick, so he's going to rest up. We'll set


up and then take you for a little tour. So we've got four stations, medicine ball throws.


We're going to plant them up on that wall there. We've got some battle ropes and then


we've got burpees over here and then pull ups. One thing I'm going to be sorry to interrupt


your video. There's a really thick bar at the back of that. It's actually quite good


to alternate to with a bit of like really thick grips. Oh, thick grip. Yes. So that'll


get those forearms going as well. Yes, maybe we can do some thick grip pull ups. Thick


grip. That'll be great gripping mate. Someone's bowled it. He's chilling out. He's chilling


out. So we've got four stations. We're going to do one minute each and then three rounds


of that. Trying to replicate the workout is called fight gone bad. So it's like styled


off a MMA fight that's gone bad. So one minute each, a little bit of rest in between and


then one minute in between rounds. Most important thing, get warmed up. All righty. So we got


started with about five minutes of dynamic warming up. And so the goal here was to mobilize


the ball joints, particularly the hips and the shoulders. So I started off just going


back and forth in like a squat position with a kettlebell and then did a few rotations


around the head. So 10 each side to really warm up those shoulder joints. And we went


through this for about five or so minutes. That's a really nice stand. Was that like


big money? My biggest thing with the warm up is to make sure you've got a little bit


of a sweat before you start. So here I'm doing some hip thrusters to get the glutes active.


And then because we just woke up, I was doing some rolling exercises to just mobilize my


lower back. Again, just trying to mobilize the entire body before getting started. I


started on the battle ropes. Justin was on the burpees. You can see with the battle ropes,


I'm kind of going from double hand to single hand. And then to kind of like these twists.


Now I really like these because it's using your entire core. Then we switched over. So


we do each of these exercises for about a minute or so and then swap to the next one.


So while I'm outside, Justin's inside and vice versa. And I'll tell you, getting started


like the first set, the body's not really propelling oxygen yet. So it's hard to get


into it. And I believe the shirt came off for the second round. And don't you worry.


As you can see here, I took some rest. So even though the rounds were only a minute


long, it was to do those movements for a solid minute back to back to back for four minutes


was tough. And it's important at the end of each round, make sure you high five your partner.


And Justin, I think he wouldn't have thought that I saw this. Yeah, here's him taking a


break mid pull ups. And don't worry, I had to take a break too. In these pull up sessions,


I was only getting about five or so in.


Four sets, or three sets, four rounds. How you feeling bro?


So broken. That's the hardest one.


That's the hardest one so far. Yeah. Woke up this morning saying I feel fucking amazing.


I feel in bed now.


Finished the fuck on by it, the session. Now we got a finisher. These jugs are full of


water. It's going to help with the balance. Just going to walk up around that pole and


back.


We're coming in grips baby. She's slipping. We're never gonna let go Jack. Never let him


go Jack. If there's anything you need in life, it's strong grip strength. Absolutely. Let's


do it man. See we're bringing it home. Check out this. You don't get that in the gym. Wild


bite. Talking about the turkey. Oh and big worm. Another set of wild bite. The native


animal of the Northern Beaches. What you or the turkey? The turkey mate. Really they're


the same thing?


How was that brother? Mase. That was the hardest one we've done so far. Mate I'm a bit sore


from had training last night. My knee, I bumped knees with someone, Dino. Far out. You get


turned into a weapon man. But that finisher, that finisher is great. It is. So that's what


that training session is for is to kind of, because we're into Jiu Jitsu, that's kind


of our addiction at the moment is kind of replicate that. But even if you're not into


Jiu Jitsu, you can still do that type of workout. You're done in what under 20 minutes? Yeah


15, 20 minutes. Yeah. Even if you don't have all the equipment, ropes, you can substitute


for something like pushups, burpees, you can do anywhere. Pull ups, find a bar. If not,


you don't have a medicine ball, you can just do squat jumps or something like that.


Garage Fitness baby. Thank you mate. No problem at all. And look, I think it's that piece


of just putting your body under continuous strain, right? So like four minutes of just


really hard work and then you'll earn that minutes rest, reset yourself and get back


into it. Well, each of those exercises on their own aren't incredibly difficult. Absolutely


right. But when you put them back to back to back, that's when you get, that's when


they get hard. That's when they get hard. And that's when you build that engine. A minute


of burpees man. What kind of savage likes a minute of burpees? Anyway, we'll see you


in the next video. Probably another Garage Fitness session. If you'd like to see any


Ask a Machine Learning Engineer Anything (live) | April 2019

5, 4, 3, 2, 1.


Alright, how we doing?


Alright, we're live!


What's happening team? Happy Sunday!


It is Sunday the 28th of April 2019.


I'm excited. I'm doing this live stream to answer some of the questions that I get most often.


So if you want to leave a question in the chat, go ahead. I'll get to it as soon as I can.


Otherwise, I've got a little question bank saved in some notes here.


I'll probably tackle a few of those in between answering questions from the chat.


Yo, what's happening all? How are we?


Leon, Prabhu, how are we?


Yes, Leon, I read your telegrams earlier today, but I haven't checked it probably in the last 3 or 4 hours.


Did you send me any more messages?


We'll get back to those messages and set up a proper setup another time.


I've got a few questions I'm going to answer on this stream.


But we'll get a chat organised.


Robert, how are you my friend? Hello from Brisbane.


Brisbane's an awesome place. It's my favourite city in the world.


I'm pretty biased though because I've grown up and lived here all my life.


But April's been a fun month.


At the start of the month, I was in New Zealand. I had a Jiu Jitsu tournament. I lost both my fights.


That was a great experience.


I went bungee jumping. Don't tell my mum.


That was amazing too.


Then I got back and I've been hard into study.


I've been looking at some maths courses on Coursera.


Otherwise, just trying to improve my Python skills or data science skills.


That's where I'm at at the moment.


It's just the same as always.


Health, studying every day if I can, writing every day, moving every day in terms of working out.


Just trying to make things.


I like building things.


Alright, everyone's jumping in.


Welcome to the channel if you're new.


My name is Daniel Burke. I live in Brisbane.


I'm a machine learning engineer.


I work at Max Kelson which is a tech company in the Fortitude Valley which is close to the Brisbane city.


It's an amazing area.


We work on problems from all different kinds of industries.


So if you have any questions about machine learning, about health,


that's what I'm also really interested in.


About making content online, about getting a job, about life.


I'll do my best to answer it.


It's 9pm here so I'll probably hang around until about 10pm.


Maybe do a half an hour, 45 minute session.


I've got to drop my brother to the airport early tomorrow morning so we'll get started.


Alright, let's jump straight into questions from Prabhu.


I'm a complete beginner in ML.


How should I start learning data science in ML?


How should I start learning and build projects?


Prabhu, what worked really well for me was just picking something and...


Okay, I think...


Is the audio working?


My headphones just told me that they died.


Audio still working?


What just happened to my headphones?


This is great. This is the perils of doing live streams.


Can you all still hear me?


I'm going to get to questions as soon as I can.


I'm just having some issues with my headphones.


Alright, let's go to the next question.


Alright, I'm going to keep going.


Okay, beautiful. Thank you all.


Thank you all.


I'm a complete beginner in ML. How should I start?


How would I start going back?


You've done the Andrew Ong course on machine learning. You're right. It is mostly theoretical.


What I would start as soon as possible is jumping into working on some sort of code.


That way, instead of learning all the theory, here's my approach to learning.


Overall, I like to do things as soon as possible.


Aka, there's a saying that my friend said once and I've replayed it in my head over and over again.


Boil the ocean.


If you tried to boil the ocean, how would you go?


It would take a lot of energy and you probably wouldn't get very far.


So, here's my take to learning.


Take that analogy of boiling the ocean and apply it to learning.


If you try to learn everything about one particular thing before you actually try to do something with that knowledge,


it's not really going to work out, right? It's going to be like trying to boil the ocean.


Instead, what do people do?


They boil kettles of water, so small amounts at a time,


for what they need to use it for, aka making a cup of coffee, making a cup of tea.


Now, how does this relate back to learning? How does this relate back to machine learning?


Well, this is my advice and I try to do it my best myself,


is that when you're starting to learn something new, do it like a phase, like an athlete, right?


An athlete trains for something and then tests their skills.


Train, test, rest. Train, test, rest.


That's what you can do with any type of learning, any type of skill.


And the same goes for machine learning.


Now, how can you do that?


Well, what you can do is, I'm a big fan of fast.ai.


So, fast.ai is a free resource.


You can just go to fast.ai.


If you also Google fast.ai machine learning course,


they have a great series of videos by Jeremy Howard, who's one of the world leaders in machine learning and AI,


which is a very practical approach to getting started in machine learning.


It's what I recommend anyone start.


And I've done the course, I regularly revisit anything that Jeremy teaches,


anything that he says because of his approach to teaching, because of his approach to learning.


It's very hands-on, practical.


So, Prabhu, that's a bit of a long-winded answer to your question.


I would start jumping in, coding things, building things as soon as you can,


and revisiting those skills by making things with what you've learned.


And now, here's the thing.


I had a question from, let me see what their name is.


This is the thing with projects.


A lot of people sort of stray away from projects.


I get a lot of questions recently, how do I start my own project?


This is from Aditya.


Aditya sent me an email, and you can send me an email whenever you want to.


So this is, how do you work on your own projects?


Aditya says, I have also completed the deep learning specialization on Coursera,


but I'm not able to start a project, even though I can understand what is happening in many other projects.


I want to ask you, is this normal?


How do you start ML projects from scratch, and how to be confident about them?


Did you have any of these problems, and if so, can you help me?


Okay, so this is on the topic of projects, which is very important in my opinion.


Now, what happens with projects, and why people don't start them,


is because the way education has been set up, at least in my country,


at least from what I know, is to, they give you a guideline of what to follow.


So this is all of education to start with.


It's like, here's what you should do.


If you follow this list, you will get good marks.


That's how projects work to begin with.


However, what holds a lot of people back, including myself, is not even using one of these.


Starting a project that doesn't really have a guideline.


It can be anything, and that's what's hard about it, is that it can really be anything.


So the way I talk about it is use the crossroad analogy.


What does that mean?


So maybe you're learning machine learning, right?


And maybe you're interested in something else.


For me, it's health.


How can I apply machine learning to health and create my own project?


And now here's the biggest thing.


It's projects don't really have a criteria, especially if they're your own.


What the main goal is, is for you to use the skills you've been learning in some environment,


in another environment that isn't necessarily, someone hasn't laid out for you, right?


In a course, someone has gone ahead and gone, here's what you should do for this project, right?


But in your own project, you don't have that, right?


You're creating the next rule.


If you need to do something, you might need to learn it.


You might not know what to do next, and that's important, right?


Because that's what happens.


I face that almost every day on the job.


I don't know what to do next.


So I have to research what's going on.


And now here's the big thing.


A project doesn't necessarily have to come with an outcome that is completely world changing.


Don't hold yourself back from starting your own thing just because it won't be amazingly,


incredibly world changing.


Awesome if it is, but you have to start somewhere.


You never know where it's going to lead.


So maybe take a brainstorm, sit down for 10 minutes, 30 minutes, an hour,


write down things that you're interested in, see how machine learning can be applied to that,


go to Google, search for data sets related to that problem.


Can you apply machine learning to that?


And then what do you do after that?


Well, it's totally up to you, but in my opinion, you should document your findings


and then communicate it to someone else.


So that's what I try to do with my projects.


I did that for the bioinformatics one.


Wasn't biologically sound, wasn't scientifically sound,


but I learned a whole lot about genes.


I learned a whole lot about DNA.


I learned a whole lot about Python programming, and I have a great story to share.


So that's my advice for you to work on your own projects.


Thank you for the great question.


Aditya, we'll jump back to the chat now.


It's a bit of a long-winded one.


I'm going to keep, I'm going to try to answer as many questions as I can, right?


So bear with me.


I'll get to it.


And thank you all for joining in.


I really appreciate it, guys.


I really, really appreciate the questions, really appreciate the comments and whatnot.


Okay, everyone saying, hey.


Prasanna, hey.


AJ, what's up, man?


Hey, Daniel, can you give me some ideas on some projects people starting out in ML


could attempt to further their career?


AJ, I think I answered that one a little bit there,


but essentially if you want to work on your own projects,


create almost whatever you want, I'm being honest,


but document what you do, right?


That way you can share with someone.


And big point here, it doesn't necessarily have to work out, right?


And a future employer, here's a scenario.


Imagine this.


You're like, I worked on this project.


It didn't completely work out, but I know where I went wrong,


and here's what I would do on the next one,


and here's what I'm doing on the next one.


That's even better, right?


It's like here's what I've actually, here's the thing I've done.


This didn't work out.


Scrap that.


Brought up another one.


Knew what didn't work.


Did this one better.


Still working on it.


Going to keep going on this.


That's my advice for projects.


And that's what I try to do myself.


Sam, best ML courses?


I've written about this before.


Anything in my AI master's degree.


If you Google Daniel Burke AI master's degree,


they're all my favorite machine learning courses.


There's a lot out there, but I can only really speak about the ones that I've done.


Sam, you do so many courses.


Yeah, you're right, because I'm hungry for knowledge, man.


I'm hungry to learn.


Plus, there's so much in this field that you kind of have to just keep learning,


right, because it's just so much.


Prasanna is saying, I am planning to do Udacity career path for AI.


Is it worth taking?


It's expensive, but people have positive feedback as a kind of those aspects.


What is your opinion?


So essentially, is Udacity's AI course worth it, and what are my opinions?


I found it incredibly worth it.


If you put it this way, here's how I put any type of investment.


Do I have a piece of paper?


No, I'll just explain it, right?


I'll use words.


So any type of learning investment, say Udacity, $1,000, easy number, right?


And it's about that anyway.


$1,000, yes, it is a big upfront expense.


When I first paid for Udacity, I was driving Uber on weekends,


earning like $400 cash, so that's almost a month worth of salary to pay for this course, right?


So $1,000 expense, however, what you learn with that skills, right,


what you learn with that $1,000 expense has the potential to earn you far more.


You can't even explain it.


Maybe if you get a job that's $50,000 a year, that's a low estimate after doing a Udacity course, right?


Then you've spent $1,000 to earn $50,000 a year.


Maybe that $1,000 you took you a month to earn, and now $50,000,


you're earning whatever that is over a 12-month period.


$5,000 a month, something like that.


Maybe $4,000 a month, $1,000 a week now.


So that's what I think about in terms of investment.


And now, do you have to spend $1,000 on Udacity?


Could you get the materials for free sourced elsewhere?


Yes, potentially.


However, the benefit of doing a course is that someone has structured the information for you, right?


So that's the value add.


Plus you have world-class instructors.


Plus you have the potential for career services.


Plus you have the potential to meet other people who are studying what you're studying.


So there's a lot more benefits of a course rather than just the certificate at the end, right?


You've got to think about what am I actually paying for here?


It's not just to have that shiny certificate.


Where is it?


This one, like this is what I got the other day, and I got this hoodie as well.


It's not just to have that, right?


There's a few more things to it.


And that goes for any other education investment.


Like if you could spend, I don't have a book, oh here we go.


Here's one of the books I'm reading now.


Absolutely loving it.


I'm going to have a review on my channel when it's done.


It's about how artificial intelligence can be used in healthcare, right?


That's something I really want to work on.


So that book I think, I don't know, on Amazon, like $30 or something.


But that has given me ideas and inspiration and information that I will hold on to a lifetime, right?


For $30.


Investing in your education, guys, like if you can afford it, it's always worth it.


As long as you use the knowledge and the skills that you obtain, right?


I'm a big fan of investing in education and health.


So tie back to Udacity.


I've definitely found it worth it.


It helped me towards getting a career.


Was it the only thing that helped me getting a career?


No.


It was a multitude of courses and also the sharing of my work online, right?


So Udacity, worth it.


Will it definitely get you a career?


No one single course will definitely get you a career.


You have to use the knowledge you get from the courses, apply that, share it online or share it somewhere.


Show people your work.


That's sort of the trifecta effect.


Superfox.


Is there a difference between data scientist and big data?


Superfox, data scientist is someone who gets information out of data.


That's how I see a data scientist.


If you take a pile of data here, I'm a data scientist, I'll look at that and I'll be able to go,


hey, there's some information here.


I should tell these people about this stuff.


So big data, you can imagine as a, let's say a box and a data scientist looks into that box and goes,


is there anything useful in this that I can show to other people that can bring value to them?


Manaha asks, hey Daniel, can you share a machine learning schedule, a math learning schedule for machine learning?


Manaha, I don't have a great one at the moment.


I'm trying to improve my math machine learning.


My schedule for studying, I try to do a little bit every day.


And what does a little bit mean?


Sometimes it's half an hour of reading.


Sometimes it's right up to a full day of six to eight hours of just studying whatever it is I'm learning.


So why a little bit every day is because it keeps the brain flowing.


It's like the body, a simple analogy.


If you don't move your body well enough, it's going to become stagnant.


You're going to become unfit.


So it's the same with the brain.


I find if I take too long off a break in between things, my brain sort of goes to mush.


And I like thinking about things a lot anyway.


So studying a little bit every day is fun for me.


But math learning, I've been looking into the math machine learning course by Coursera


and also the computational linear algebra by Rachel Thomas.


There are some great resources there.


Anubrata asks, can you give comprehensive interview idea for a data scientist?


A comprehensive interview idea.


Anubrata, I can't really.


I'm not the best person for that.


And here's another thing, guys.


I'll be straight out with you about things I don't know and I can't answer.


I'll do my best to answer the stuff that I can.


And I'll do the best.


I'll just say, I'll tell you straight up if I can't.


I'm still in the first machine learning role I've ever had.


And my interview process wasn't the traditional one where you sort of go in and have a coding session on the whiteboard


or have to solve some algorithm challenge or something that you've heard of.


It was more of a, hey, do you want to come in and start working on some data problems?


And I did.


I worked on a data frame with pandas for a day and they said, do you want to come back next week?


And I said, yes, did the similar thing.


And then they said, do you want to come back again the next week?


Yes, said yes.


And then on that day they said, do you want to roll?


And then I said, yes.


So I was more sort of doing an internship that went to a role.


Mert asked, hey, Daniel, what did you do to take care of the math and statistics foundations for machine learning?


So the math foundations, I use Khan Academy and statistics, a book, I'm actually rereading it at the moment.


It's called Think Stats for Python.


Think Stats and Khan Academy are both free.


So Mert, if you Google Think Stats by Alan Downey, if someone wants to write that in the comments section, that'd be great.


I can't really type at the moment.


I'm just going to keep talking.


So yeah, Khan Academy for math, linear algebra, matrix, multiple matrix, manipulation, calculus, all that sort of stuff.


And statistics, Think Stats.


It's a beautiful book and it's got Python code examples.


Leon shared an article.


Make sure you check that out.


For some reason, one password keeps opening up.


Holy gosh, it just jumped straight to the bottom of the chat.


I'm so lost.


All right, we're back to it.


Mert says, did I complete Imperial College London mathematics for machine learning specialisation?


Mert, I haven't done that, but I believe that is the one I've been looking at doing.


As I said, I've got a... well, I haven't said that actually.


I've got a few things going on that are preventing me from signing up and pulling the trigger,


namely just projects, other things that I want to work on.


But yeah, that is definitely the one I've been looking at.


Super Fox.


Yes.


What is the easily way to learn math for machine learning that actually makes sense?


Not like school where you learn math.


To be honest, I don't know.


I don't know the most easily way to learn math for machine learning.


Math, what changed the idea of math for me was viewing it as a language.


So math, math is like the language of nature.


So much like you would talk English to someone else or talk something or Japanese or Chinese or something like that.


Mathematics is a language of everything around us.


Right.


So I was actually emailing back and forth someone with a 20 year old from Sydney in my home country,


asking what are my thoughts on learning online versus learning at university.


And I said to him, I was actually kind of thinking of going to university to learn math, right.


To do well, here's my boil of the ocean type thing.


I'm like, this is just a thought that went through my head.


I'm going to think it out loud.


Potentially going to university to learn mathematics.


Now, why? Why?


Because mathematics can be applied to almost anything.


Same with code. Same with media.


I like learning skills that are applicable to any field.


So math you can use almost anywhere.


If you know about math, you can converse with mathematicians.


You can you can implement code.


You can back it up with math, et cetera, et cetera.


Now, why learn it at university?


Because in 2016, I was learning Chinese and Japanese.


I did it for a year. Right.


But now,


私の日本語上手じゃないです。


My Japanese is not that good anymore.


And 中文不法。


Chinese, no good. No good anymore.


Why? Because I stopped using it.


Now, the quickest way I was learning languages was talking to talking to other people who spoke the languages,


namely people in my class.


And so when I view because I view mathematics as a language now,


the quickest way to learn it, like I was talking with one of my colleagues about


Monte Carlo dropout. Right.


And he was showing me some of the math behind it.


And if you're not sure what Monte Carlo dropout is, don't worry.


You can I'll do an article or something on that in the future.


But in the meantime, you can Google it.


Long story short,


I was understanding a lot better because I was conversing with someone else about math right about another language.


Again, my headphones too.


Is my audio working?


It should be working.


I was thinking about going to university to learn mathematics because you'd be in an environment with other people who are also speaking that language.


Right. It's the same with code.


It's like if you're learning code with someone else, you're partnering up with them.


It's it's if you're speaking the same language to them.


Right. Then it's a lot easier to practice.


Whereas when I was learning Japanese and Chinese, as soon as I stopped speaking the language, I lost all the skills.


Right. So that's why I can't really speak it that much that that very well anymore.


OK, that's a bit of a long winded story to my thoughts on mathematics.


Now, let me get back into the chat.


I've got some new headphones. You're right, Super Fox.


I don't know why my headphones keep dying.


They say that I'm full battery.


Forte, a bit of a personal question.


Do you see yourself being with someone and maybe raising a family in five years?


Forte, great question.


Do I see myself being with someone in five years?


Who knows? My last girlfriend was when we broke up in 2016.


So three years ago, three years ago now or so.


So a lot can happen. A lot can happen in five years.


A lot can happen in one year.


To be honest, I'll be 30 years old.


I potentially see myself with someone, but not necessarily raising a family by that stage.


I'm who knows though, right? Things can change.


A lot can happen in five years.


But at the moment, I'm not dating or not looking to go on dates or anything.


I'll go on dates, whatever.


But at the moment, I've got too many things that I'm focusing on and working on to sort of invest time in that aspect of life.


But it is definitely something I'll be interested in pursuing in the future.


Rusty says, what do I think about downloading good courses from Torrent if they're too expensive?


Are video lectures enough?


And where can I exercise?


Rusty, I don't condone stealing anyone's content.


I would say rather than sort of downloading stuff from Torrent, do the best you can.


There's a lot of great free resources on there.


I'm not an advocate for stealing other people's work.


If you can't afford it, I would potentially reach out and tell them your scenario.


Maybe they'll be able to help you out.


Where can you exercise?


You can take the knowledge you've been learning from those lectures that you've watched and try and apply it through your own project that I've talked about at some point at the start of this video.


Saurabh Medeyo, how are you, man?


Good to see you guys here.


For getting ML job, do we need to understand derivations of equations or is it okay just to know how those equations work in background?


Saurabh, I'll be honest, I don't know full derivations of the equations.


I know how to take code and apply it to problems and research different ways of doing things and apply it to different things.


That is essentially what I do in a nutshell.


But I am looking into ways of upskilling myself on the math behind it.


Right. So it's not just I'm applying code.


That is my particular role.


I'm in machine learning engineer in a consulting role.


So I partner with businesses and take their existing data and then build machine learning pipelines with that data.


Now, if you want to be a machine learning researcher or someone at university who's studying machine learning,


potentially the math may be something that you really want to dig into.


My main the main demand of my role is being able to use Python code, especially and knowing knowing the ins and outs of data science,


not necessarily the pure math behind the code.


Xballer23, do you think you could earn 200k a year as a machine learning engineer?


Yes, I plan on earning a lot more than that in the future.


But 200k a year will likely for me be within the next year if I if I play my cards right.


Super Fox, what diet is best for fat loss? That will depend completely on the individual.


Nutrition is a funny science, right? It's it's very different per individual.


I want to try to put some data behind it. I haven't quite got that yet, but that's going to be a future project of mine is trying to work out that.


Otherwise, fat loss. One of the easiest ways is to be in a calorie deficit.


So what does that mean? Eat less calories than you burn.


It's kind of hard to measure. What you can do is track the amount of calories you eat every single day, right?


Using an app like MyFitnessPal, track your weight every every morning at the same time.


Now, even though the metrics may be slightly wrong, if you do this enough, you'll get the consistency.


So what that means is maybe for a week straight, you're weighing 75 kilos and you're eating 3000 calories and then you drop it to 2800 calories.


And your your weight starts to go down. Maybe you're 69 kilos the next week.


If you keep following that trend, then then you continually lose weight.


But otherwise, send me an email if if you want to know more about that.


And I just read your your intermittent fasting one. And yeah, that's that's a good set up.


Intermittent fasting, skip breakfast, small snack for lunch and eat a healthy dinner.


That's also a good set up. But otherwise, send me an email if you are if you want to talk more about health and we can go into more depth there.


I'm just trying to get through get through a bulk of these questions as fast as I can.


Let me know if you're if I'm doing a good job answering them. Like, do you want me to go deeper?


Leave a thumb up in the chat. Like if everything is going great or are you happy with how it's going?


If you could if you have any advice or improvements for future questions, like let me know.


That doesn't mean I'm stopping. I'm just just seeing how we're going at the moment.


Muhammad Ali Habib, I like your videos, Daniel. Keep it up. Thanks, Muhammad. Really appreciate that, man.


I've done several online ML courses, projects and ranks first in an ML competition. That is amazing.


Have you seen people getting remote ML position with no prior job experience?


To be honest, Muhammad, I I I haven't just because I haven't looked into it.


But I know there would definitely be people out there who are doing remote machine learning positions with no prior job experience would probably be a little bit harder.


Yeah, a little bit harder than if you did have experience.


The same way applies, I think, for remote than an in-person job as well is if you have skills and you can communicate to someone well enough that you have those skills,


particularly like a client or an employer, then then you give yourself the best chance of getting a job.


So that's that's that's the two things, right? Is all three things is have the skills to begin with.


Communicate those skills in a way people can understand and especially the right people, right?


So maybe maybe a client, maybe a future employer and three find the right people, right?


To show you show those skills. So that's a three three parts to getting a job.


Preform, have you read any of the theoretical books like Introduction to Statistical Learning or Deep Learning Book by Ian Goodfellow?


I haven't read either of those books, but I would be interested in reading Deep Learning Book by Ian Goodfellow.


A colleague of mine has the hard copy. It looks it looks great.


And I've read a few pages here and there, but I I've certainly not read much of it.


Now, here's the thing. I've tried to take a different approach to learning.


What I found is that if I like digest a book cover to cover, I can know something pretty well.


So what I'm thinking is in the future, well, going forward, I'll try this out right now.


I'm just a perpetual experiment is I'll read something cover to cover, right? Like this. Just digest it as quickly as I can.


And then take that knowledge and spread it out somewhere using it as quickly as possible and then iterating over that knowledge.


So that's that's the approach I'm sort of trying at the moment is read a book cover to cover and then take that knowledge straight away and apply it somewhere.


Anirudh Jawalla asks how to stay focused during offline book boot camps during offline boot camps.


I'm not entirely sure what you mean by offline boot camps, but stay focused.


It's similar. It's similar to to any kind of learning. This is this is focus is a very valuable skill.


One of the things I do is remove. It's a simple one, right? Remove all distractions.


Right. I put my phone in my phone. I can't even my phone is not within reach. Put it that way. I think it's in the other room in a drawer.


Right. Phone distraction gone. I have permanent do not disturb on all of my devices, as in on my Mac, on my iPad, on my phone.


Do not disturb is always on so I can focus on the one thing that I'm doing. I'm very deep into single tasking.


Right. So right now I'm I'm I'm present here. I'm working on live streaming and answer these questions as best I can.


Now, that's that's the definition of focus, right? It's just one thing at a time.


And what's another way that I do that? I work on that every morning. This is what follows me around every day.


I don't have my today one on. I actually do. Here we go. There's a list, right?


Put the date up the top point. You can see that maybe date up the top and then a bunch of things here.


And then as I go through the day, I take notes and I scribble it out. Right. That's that's how I stay focused.


I write down every morning a list of five to six things. I don't try to make it too big.


Right. Because really, there's there's only a certain number of things that you can do during the day that are that are productive.


So I keep it I keep it nice and concise and I work through those things and I set I've got this little app called Stay Focused.


And it's essentially a Pomodoro timer on on your computer and it goes for twenty five minutes at a time.


And if my day is sort of broken up into multiple parts, often it is because it just that's the work nature that I'm doing at the moment.


I prefer to have long like four hour blocks of non interrupted time, but just with a few things that I've got going on, I can really only get maybe an hour hour or two.


So so I I set a timer and I do nothing but what I've written down for that timer.


And then I write about it like, hey, I'm like, for example, a project that I'm working on right down.


OK, first step I ran I ran this model. These are the results I got next step.


I need to I need to fix something up. Right. So I need to I need to do this.


And I'll literally have a conversation with myself on the paper. And so I know the next time I come back, even if I take a break, I've got the next steps of what to do.


So that's that's a that's a few things that I do to stay focused. And then, of course, good sleep, good food, moving every day.


Those three things should be default. And here we go. Muhammad Muhammad, thank you for the question.


If you're interested, check out health and psychology data. Oh, every year there's a Kaggle challenge for the psych physiologic data.


Oh, sweet. I'll save that link. Thank you, my friend. Come back to the chat. Skip brain.


SKT Skitterbrain says, Can you please check your mail? I sent you a mail regarding DL. I will get to my email as best I can.


I try to I try to get back to everyone as soon as possible. If I haven't got back to you fast enough, send me another email.


Rafael, hello. Good morning from Brazil. Good evening, my friend. It's it's nice to see you.


I hope it's a I hope it's a beautiful sunny day in Brazil. And if it's not, I hope you have a great day. Rafael, cheers for tuning in.


Praful, which approach would you suggest for learning Python data science stack, non-PySciKids, Pandas, PyTorch TF course or documentation?


For me, it was courses. Documentation is helpful to read. But courses I like. Here's how I learn.


I like to see how things have been done. And then I go, can I do that myself? That's that's generally my approach to every kind of learning.


I see a little example. I try to copy it. And then I try to inject my own flavor into that and then build something off that.


So, yeah, courses and then inject your own flavor. That's a that's a trademark. Inject your own flavor.


Rusty Kick. Do I have to have deep understanding of all mathematical concepts or do I just need to learn that this method doing that without understanding why and how?


Oh, wow. Am I back? Do we just copy and paste that question?


I think I've already answered something along the lines of that. We'll jump into the next one.


Layla, what the difference between deep learning machine learning and which is better for AI?


OK, so Layla, the way I understand it is this a very simple, simple analogy.


You've got some data here, right? This is your input. So we're going to start over here and it's going to go this way.


Right. So machine learning, maybe you have an algorithm that's like only a couple of steps.


Right. And this is very high level analogy, right? A couple of steps and you've got this input goes through those couple of steps and then you've got the output and it's like a shiny little object here.


Deep learning. Now you've got those little steps of machine learning.


Now, back in the day, that's really computationally expensive. Now it can be done in seconds.


Deep learning adds what if we just added lots of these little steps to try and find out more patterns.


So instead of just finding out this one step where the patterns, let's find out a hundred.


OK, so back in the day to do one step took long enough, but a hundred steps that would take forever.


Now we have a lot of compute power. Right. We can do a hundred steps. It still takes a little while.


But you can go, OK, all the way over here, shoot this input and out comes all this magic from deep learning.


Right. So deep learning, machine learning are quite related, except that deep learning, you could think of it.


If you imagine these are steps in terms of finding information out from an input, finding finding different patterns within a data set.


Right. Deep learning, multiple steps, machine learning, one step.


And now that's again very, very high level overview. But that's that's a that's a basic understanding is as much as you'll almost need.


Because now we're sort of getting to a point where if you can deep learning and machine learning, you can apply them very similarly.


Right. Similarly, similarly. Thanks to the beautiful frameworks that we have available.


Hey, dude, can't you give us a heads up when you go to the next chat instead of last minute dot com?


You're right. I need to get better at this. I'm sitting on an inner city train.


P.S. started Brazilian Jiu Jitsu. Super Fox. That is amazing. Brazilian Jiu Jitsu is very fun and it's probably the best thing I've ever done in terms of physical activity.


I love it so much. Apart from like body weight and animal movements, they're also amazing.


But keep getting after it and I'll do better and I'll try to do better. I always say this.


I try to do better in terms of scheduling live streams, but just they kind of just pop up.


Right. I like to make it a spontaneous button.


What that was, you can ignore that. Shree Navas V. So what curriculum do I need to follow to become an expert in machine learning engineer?


There is no real way to define that. Everyone will be different.


This is sort of a cop out answer. But if you Google Daniel Burke, I master's degree.


That's what I followed to get started. And I am by no means an expert yet.


I see myself as a perpetual student. Eventually, you could consider it.


I consider experts as someone who knows more about the topic of what they're talking about than someone else.


And I'm sure a lot more of you know more than I do about machine learning.


But that's that's my definition of an expert. As long as you know more about the person you're talking to, you could potentially be called an expert.


But I do not consider myself an expert machine learning engineer.


I'm a machine learning engineer in training constantly because there's still so much to learn.


But yet all the stuff that I followed in Daniel Burke, I master's degree. That's that's what I got started with.


How do you stay focused while watching offline courses and stay motivated?


That's that's what I mentioned before. I use a couple of techniques to stay focused and just embrace the suck.


Right. Learning is hard. Being focused is hard. But that's that's why you're doing it.


You're not doing it to do easy things. You're doing it because it's it's hard.


Hari, what's up, my brother? Good to see you, my friend.


Clinic Clinic Clinus says, How long journey is it to go experience from front end node JS dev to be hired as a data scientist?


Already started second degree as ML specialisation and Andrew NG course at the same time.


OK, I can't answer this question. Anytime that it's like, how long till I get hired or how long till I become experienced.


Right. All these. And this goes for almost anything, right, guys?


It's like everyone's pathway is going to be different. But with a lot of I'll just share what I did.


I from I had never written a line of Python code. I started with a deep learning course, studied every day for nine months,


and then was sharing my work online relentlessly and then got messaged on LinkedIn.


And a couple of weeks later was hired as a machine learning engineer. So did I get lucky?


Am I an abnormality? Maybe. I don't know. But all I know is that I was working very hard to upskill myself and I still do.


So in terms of exact timeline, I can never give that answer.


But all I know is that it's possible if you dedicate yourself to learning, to trying new things,


to improving yourself, sharing your work, communicating with others, you can you can definitely you can definitely transition from being a node JS dev to a to a data scientist.


Shree Shree Navas asked how to read a book in a smart way to get the maximum out of it.


My favourite way is to read the book and then write an article about it or something like that, or apply what you've learned straight away to a problem, right?


A work on a project or something like that. So read, write, apply. That's my way of reading books.


Forte, hey, you also mentioned about investing on education, which got me curious.


What made you choose to study computer science rather than going to a university to study it?


Forte, my first, I went to university and did biomedical science. I failed the first two years, right?


Because I was 17 and didn't really know. I was just a lazy teenager and didn't really have a...


I was just more concentrated on girls at university than actual study and yeah, just a lazy teenager.


And then I switched to nutrition and food science after two years of failure.


And I got great marks. Why? Because I was interested in nutrition and food science outside of university, right?


Because I was an athlete. I was competing in different sports.


And I'm like, hey, I'm going to study this because I'm interested in it.


And then two years after graduating from university, I started machine learning, computer science and whatnot.


And I followed the same path. I'm like, you know what?


I don't feel like going back to university this decade, so I'm going to just see what I can learn online.


And it turned out to be turned out to be great. So that was literally it.


Otherwise, I looked at computer science at my local university, the one I went to for science degree, and it was forty two thousand dollars a year.


I have spent probably under eight thousand or maybe I would say under six thousand, under six thousand dollars online total in terms of studying everything.


And the computer science degree was two years. Am I saying which one's better?


No, I'm just saying what I prefer to choose. Cost, commute time, accessibility to exactly what I wanted to learn and following my interest.


Combination of those of those led to my decision to study online.


Sam, what's your goal job or start up in a job? Which position?


I don't have a goal job. I have a goal to build a health tech company and stay tuned with that.


I'll I'll make content on the future when that happens. Goal job. I've already got it.


Machine learning engineer. I don't really have a have a job in mind, but no,


I'd rather build my own company and see where that goes. But of course, I haven't yet.


But when I do, I will share share what I'm doing and share what I've learned.


If it fails, well, well, that's not an option. Put it that way.


Yami Yagami says Yami Yagami.


Well, I'm so behind in all of these, but we will keep going, right? We'll keep going for another 10 minutes or so.


If I haven't answered your questions, don't worry. We'll get to them in a future stream.


Otherwise, we'll send me an email. You can find my email pretty easily online or message me on Telegram.


T dot me. This is I'm just putting I'm not stopping. I'm just putting this here in case you want to send me a message. T dot me slash Mr.


Deberg. That will get you direct contact to me and I'll answer you as best I can.


Kronal, oh no, Yami Yagami. Can you describe your typical work day and specifics with a project example?


This might variable by vary by workplace, but how much flexibility do you have?


That's a great question. I literally shared a LinkedIn post about this the other day.


OK, here's my typical work day. 9 a.m. roll into work. Check Slack.


Let's say 15 minutes or so. Find a bunch of links because everyone has shared the most amazing research that everyone else is publishing in the field.


From 915 till 10 a.m. I will read as in read articles, read papers, read read email.


If I if I have an email I get to usually not because the luxury of being a machine learning engineer where I work is sort of most of our time is dedicated towards just working on a project rather than dealing back and forth email.


Of course, still get email. That's life. We communicate mostly with Slack, but so let's say nine to nine thirty maybe taking care of communications.


Nine thirty to ten will be reading and upskilling. So maybe I'll read a paper. Maybe I'll read an article or two that I've booked.


And then ten till ten till three four. If there's no meetings in between, of course, I'll have lunch usually from let's say let's say one to two or one to one thirty one till two.


But yet ten till four with lunch in between is usually working just entirely on a project trying to figure out like to build it to to research about it, to ask colleagues, what can we do here to to contact the right people, to look for data, to look for algorithms.


But the most recent one I've been working on is a natural language processing problem where it's it's it's quite good because another company has worked on this before.


So the data is already clean. However, the client that we're working with left that previous company and has chosen to work with us.


So now my goal is just improving the results the previous company got. And I've already done that using Flair, the natural language processing library.


If you Google Flair, natural language processing, it's amazing. And so what what we're looking to do is because we've already improved the results on there, it's binary classification of text.


Right. So imagine you're a big business and someone sends you a message, right?


You're a support message to help me. What binary classification problem is? What does that message go into? Needs help or doesn't need help?


Right. You can just imagine it like that. We've got two hundred thousand records.


How do we create a binary text classifier for that at 95 percent plus accuracy? Built that with Flair, natural language processing library.


And now what I'm going to look into next is a little bit of a little bit of cherry on top is trying to work out model uncertainty or data uncertainty.


Now, this is this is something that's been around a while. Right. But it's it's trying to rather than just using confidence interval to measure how well your results are, it's trying to to figure out how can we make these results better?


Is the model potentially the fault? Right. Model uncertainty or is the data at fault? And so how do we do that?


This is we're using a Monte Carlo dropout technique. And this is what I said before, but I'm not going to go into in-depth here. I'm probably going to write an article about it once I find out more.


So, yeah, in the meantime, if you want to look at Monte Carlo dropout, it's a way to figure out the uncertainty, the uncertainty behind your model's predictions.


That's how I understand it at the moment.


Oh, and then after we've done that, 10 to 4, 4 to 5 is kind of like wrapping up communications with the team.


So it's like, hey, I worked on this today. My code is in GitHub. Here's a little bit of a report. Might be like three or four bullet points just so everyone else is across it.


Then loop back to some of the other messages, slash emails, slash communications I have missed through the day and then set a plan for tomorrow.


That's what I usually do right at the end of the day is go, okay, here's what I worked on today.


Right. And this is what I'm going to do tomorrow. That's how I structure my day.


9 to 9.30 communications, 9.30 to 10 reading, 10 to 4 coding, working on projects with lunch in between, and then 4 to 5 committing code and updating communications.


So that's and flexibility is like in terms of flexibility is kind of like, here's a problem. Figure out any way you want to solve it within reason, of course, as long as it doesn't cost hundreds of thousands of dollars.


That's that's within reason. But otherwise, if I can find a way, I can use it.


So pretty flexible there. Kurnal says, have you looked faster at international fellowship for deep learning course?


Kurnal, I haven't done that. But if you want to send me a message about that, I'd love to hear more about it.


Super Fox has got the deep deep medicine book. That is amazing. I love the book. Alexis Le Chat, to get a career started.


Oh, my Alexa just went off. So I don't need you. To get a career started after courses, portfolio steps, it's usual to go to hackathons, conferences, workshops.


Yes, all of those are great. My personal favorite is meetups. But those all work as well.


Essentially, what you're trying to do is build relationships in the field and go to this person. Hey, here's what I've been working on.


I'm be upfront. I'm looking for for work like specifically in these this field. I've got I've got these three projects.


Maybe it's in finance. I've got these three projects in finance. I've done predictive analytics on here.


I'm trying to to get to get involved in the field. Is there anyone you think I should talk to?


You never know who who's going to who's going to be able to give you a hand. But it's just it's just putting yourself out there. Right. It's going to feel awkward.


It's going to feel weird at events. But hey, I get that too. Right. And it's just it is what it is.


And submit, how do you got your first internship? I've talked about that that before in this stream.


So I'm not going to jump back into that again. But there's an article that I've written as well is called Daniel Burke.


I want to get started in machine learning and artificial intelligence. How do I do that?


So if you Google that, you'll find it. Or if you go Daniel Burke, start I am now Google that and you'll find the story of how I got it.


Vikram, how crucial are online courses to getting a job as a data scientist? No course is as crucial as skills.


Do not just do not never, never mix up courses for skills. This is what I did when I first began.


Right. I would just keep completing more things rather than just building skills and sharing skills.


It's a very, very sort of tough lesson that I had to learn. Right. Because it's like I just want to keep doing more courses because they tell me what to do and I can follow the follow the guideline.


That that doesn't necessarily build the right skills. Right. So courses are great for setting a foundation.


Right. But what you need to do to really advance yourself is take that foundation and build upon it.


So courses are great for a foundation, not 100 percent necessary. You could you could almost learn all this stuff for free online if you're a dedicated courses.


Just go to the trouble of structuring that information for you. All right.


We'll probably do about five more minutes because I'm going to wrap this up. It's 10 p.m. and I need some sleep.


I got to get up early to drop my brother to the airport. Things you do for the.


Line of lies anyone UK that is looking to go to uni for machine learning. There is a course for computing.


Yeah, that's a great course.


Dude, get some air pods, air pods. I got them. Maybe I should maybe I should have used them.


How did you. OK, so I I asked on us. Hello. I'm not sure if.


If you have answered this question, I want to ask how about the fast.


How did you do the fast AI course? Did you do any homework every lesson or did you go through it the first time?


OK, for the faster course, I went through every lecture.


Just just watching the lecture and then went back and reviewed each each lecture by doing the project.


So I just watched them and then went back through and did the projects. So that was the fast.


I I've been meaning to do the 2019 version, but as I said, again, there's just an endless list of things to do.


Super Fox arriving at your destination, answer your questions.


I hope I did a good job to answer your question, Super Fox. Otherwise, you can message me anytime you want.


Anyone can do that, actually. Toby says, hey, do you know any good website or courses to get started?


Machine learning from a young age. Toby, one of my favorites is data camp.


They do a great the great way to start learning machine learning and coding straight away.


Otherwise, YouTube is is also phenomenal. If you do some if you do some searching.


Barrett, Barrett says, Barrett, Barrett says, I know a few things about machine learning now and I'd like to do a summer camp for two months.


I would like to have enough knowledge to participate in competitions this two months.


What do you suggest? Barrett, I'm not entirely sure what what your question is getting at, but if if you're concentrating on learning something for two months, that's great.


But don't sort of expect to know everything after two months.


These any good skill takes a long time to learn, not just two months.


But yeah, a summer camp for two months would definitely I can I can imagine helping to improve your your skill set.


But don't don't expect it to be the only time you or sorry, the only time you have to put into learning something.


Harini says, Please give an outline on a basic curriculum for being a machine learning engineer.


Harini, if you Google Daniel Burke, I masters degree that is the entire curriculum that I took to become a machine learning engineer.


Poldy says, Is your trolley board still updated? Yes, it is.


I haven't been doing as much learning as I had in the past just because of time.


Like literally time, like when I first made that trolley board, I was learning every single day.


I still learn every single day, but just not tracked in that trolley board.


I will I will get back to updating that though. Thank you, Poldy.


Daniel, you rock. I really appreciate that. Also, which is Toby says, which is important for machine learning, the mass knowledge of computer science.


They are all are important, right? It really depends on what what problem you are working on.


Every every skill is important. Don't don't underestimate anything in terms of skill sets you have for machine learning.


It really just depends on what you are working on.


Palf, hey, how you doing? Good to see you guys. Poldy, I really appreciate it. How much experience do you use as a machine learning engineer before you can start earning 200k plus yearly?


Toby, that's a tough question to answer, right? You could walk you could start a machine learning engineer job for 200k.


Yeah, if you're good enough.


I'm not I'm not at that yet, but I I plan on increasing that and I'll when I when I do, I'll I'll share you.


I'll share. I'll share how I did it. Do you have an accountability partner to keep you on track?


No, my accountability partner is all of you. I have I don't have one.


I have thousands, which is which is kind of why I started this channel right to build this community to share what I'm working on to hear what you're working on because that actually inspires me to keep going.


So it's a loop, right? Like I work on things and share them with you guys. You work on things, share them with me and I am loving it.


My best accountability partner is my diary, right? When I write down like what I'm thinking, what I want to work on, where I want to be and stuff like that.


So I think everyone can be their best accountability partner. You could use a mirror, right?


Simple. Look yourself in the eyes. Are you doing what you want to be doing?


Tough question answer, but you can pick a man up, girl up, whatever up.


Answer that question, right? Something we all have to work on. All right.


Well, cloud services, knowledge for ML engineers, which one's the best?


They are all amazing. I'm I'm using Google Cloud mostly at the moment. Again, it's like one of those things.


It's like you could choose to try and learn them all or you could just become really good at one.


I'm becoming really good at Google Cloud. AWS I know a bit about as well. But Google is where I'm where I'm at at the moment.


Yuvraj, hey, good. Good to see you, my friend. And otherwise, plans on leaving Australia.


I had plans to go to the US, but no, I'm staying in Australia for now. I really love it here.


So, guys, we're going to wrap this up. I'll probably answer one more question.


Thank you so much for joining in. As I said, email me if your question hasn't been answered.


Daniel at MrDberg.com or Telegram T dot me slash MrDberg or just Google my name.


You can contact me wherever you want. I try to get to messages as quickly as I can.


All right, one more question. I'll scroll right to the bottom to get a fresh one.


Kavish says, how do I manage exercise hours during working days?


Kavish, it is just something that goes on the list for me every day.


It's non-negotiable. Exercise is something that I have to do every day and not necessarily exercise movement like today.


I did yoga, I went for a bike ride, did a little walk. So, movement in general.


That is something I have to do every single day. Otherwise, I just don't feel great.


I feel better when I do do movement. Sometimes it's really intense workout.


Sometimes it's, as I said, yoga or just going for a long walk with my brothers or just by myself.


But it just goes into the schedule. Usually when I get home from work or sometimes in the morning if I do training.


And again, headphones are dying. But otherwise, thank you all so much for joining in.


This has been Ask a Machine Learning Engineer Anything for April 2019.


As I said, if your question hasn't been answered, please hit me up wherever you want.


But otherwise, yeah, have a great Sunday and have a great week.


We'll see you next month. Peace out.


Oh, and P.S. Thank you all so much for joining in. I really appreciate it.


I really appreciate the questions. I really appreciate the comments.


And as always, I need to put my video up for this one. I need to get it right down in deep.


So it looks it looks real fresh. Keep learning. See you later.


Well, this is great. I'm pressing end stream and it's not ending.


The most important article on programming you’ll ever read

One of my favorite articles of all time is


Teach Yourself Programming in 10 Years.


I think it's by Peter Norvig,


and I'll put the link below, whatever.


But the whole synopsis of the article is to learn slower.


And you know how you see all those like


speed learning or speed reading or do X and


get fit in six weeks and all that.


Like the modern world loves speed,


but speed is a marketing construct.


If you, I've been spending time on a farm recently,


and if you go out into nature,


nature has no concept of time, does not care.


Everything just has its own pace.


And so that's a real beautiful way.


If the modern world is used to or love speed,


a real good way to differentiate yourself


is to create your own timeline.


So to use your biology, to go back to nature,


use your biology as a clock.


So if you're trying to learn something,


and now school does a terrible job of making this,


making learning feel like you have to learn this,


you have to learn this, and it's not hard,


you're not doing it right,


and you need to be sweating on your brow,


and if you're not working hard,


you're not going to get that cookie that you,


you really want at the end of high school.


Oh, but you finish high school and then,


oh wait, there's another cookie,


it's at the end of university.


Oh, you finished your undergraduate?


Beautiful, well we have another cookie for you.


If you just did some postgraduate studies, some masters,


you'll get that beautiful cookie.


Oh wait, you finished your masters?


Well now we have a PhD for you.


Just a little more, a little more learning


that you have to do, and then you'll finally feel


like you've learned something.


And so, I failed my first two years of university,


so I'm probably not the best one


to talk about a concept like this,


but what I have noticed is that


every time I've learned something


because I've had to learn it,


as in I've been force fed it by some curriculum


that's over fluffed because,


especially in university these days,


they kind of have to pack a lot of coursework in


that isn't usually 100% necessary,


but you have to feel like, because you're paying so much,


you have to feel like you're getting your money's worth.


And that's why a lot of books these days,


300 pages of fluff, around 10 pages of good stuff,


what should have been just left as a blog post


instead now is turned into a New York Times bestseller.


And so, the argument, or my argument against


the speed reading, if something is,


or speed learning or whatever, doing something fast,


is of course you have nature as your argument,


when nature doesn't really care about time,


but if something is fun to learn,


if something is fun to read,


if I'm really enjoying a book, I don't want it to end.


I don't want to speed it up.


Have you ever been reading a really good book


and got to the last 10 chapters, or the last 10 pages,


and you've been prolonging it?


You've been prolonging it because


you don't want it to finish.


Maybe you just start reading one page a night.


You've got up in the morning, you're excited.


If you're learning something new, you're connecting the dots.


I remember learning to ride a bike as a child,


or learning almost anything as a child.


You would play.


You wouldn't try to hurry things up.


I don't remember trying to go, or asking anyone,


how long will it take for me to learn to ride a bike?


And so, I get a lot of questions,


like how do I learn coding faster?


How do I learn machine learning faster?


And I can understand where the questions are coming from,


because I too used to be of that mindset.


But what I've noticed is every time I try to rush something,


as in try to learn something faster


than what it could possibly be learned,


it's caused a great amount of unhappiness.


And maybe that's your style.


Maybe you like that.


That's your...


There's a thousand ways to...


The truth depends on how you look at it.


If you're asking for the correct orientation of Orion's Belt,


it depends on where you're looking at it from.


And so maybe that's your motivator.


You like that grind of being unhappy,


unless you sort of just put in the work,


put in the effort.


But that's not my style.


My style is to...


If something's worthwhile, I want to enjoy it.


I want to take worthwhile learning,


I want to enjoy the process of learning it,


because that is truly the funnest part.


The connecting the dots.


How good does it feel?


The hundred times that you fall over


when you're learning to ride a bike


is what makes that first ride so enjoyable,


that adventure.


So that's what I live for when I'm learning


or creating something,


is I don't want to speed up the process.


I want to magically stumble upon one of those moments


where one dot joins to the other


and it becomes this beautiful dance.


So I'm making this video,


it's going to be called Learn Slower


or something simple like that.


And I wanted to keep it simple


and to of course highlight the article,


Teach Yourself Programming in 10 Years,


is what's the rush?


If you are looking to do something worthwhile,


use your biology as a clock.


If something doesn't interest you,


if something's not fun,


well maybe you should either move to something else


or figure out a way to make it fun.


So you don't have to adhere.


The internet is such a beautiful place


is that you don't have to adhere


to the traditional style of learning anymore.


You can create your own path,


you can make it whatever you want.


Now of course, if you do make it whatever you want,


rest assured, if you have that freedom,


you now also have the responsibility


to take it upon yourself to follow that path


or create that path.


So in essence, learn slower, have fun with it.


Keep learning, keep creating, keep moving.


2020 Machine Learning Roadmap (95% valid for 2022)

If you can't tell, I'm so, so, so excited to be able to share this with you.


Now, I'm not going to hold you up for long.


I just wanted to take care of a few things before we get into the roadmap.


But the most important point is to make sure you have a play around with it yourself.


Check it out.


All the links that you need will be below.


There's the mind map itself or the roadmap itself.


There's the GitHub repo to go along with it, which has the full version image of it.


The most up-to-date link will be the map itself.


There's also the slides in the GitHub repo if you want to check them out.


And there's also timestamps.


So feel free to jump around.


You don't have to watch the whole thing.


Just jump around to points that suit you best.


And if there's anything else that's missing, if you have any questions, leave a comment


below and I'll get back to you.


And nonetheless, enjoy.


All right, all right, all right.


Welcome to Machine Learning Roadmap for 2020, aka a machine learning flavored visual interactive


living mind map slash compass.


Well, that was a bit of a mouthful, but let's not spend any more time talking about it.


Let's actually see this thing.


So if we come here, oh, this is interesting.


We've got some colors.


We've got some nice icons.


We've got a little white box with machine learning in it.


And so what we're going to do in this video is basically explore the field of machine