someone's holding your hand someone's holding your hand and going come on this


Are courses enough for a job? | Machine learning engineer livestream Q&A

is how you do these things it's not until you try it for yourself that


you're really going to submit that knowledge


and do you need CUDA you have no CUDA in your MacBook Pro I use GPUs when I need


CUDA I use like Nvidia GPUs I don't use my I don't train machine learning models


on my Mac very much Driss says how would you approach machine learning a software


engineering at the same time I build full stack applications so just get


something deployed get people being able to interact with it because it's one


thing to build a machine learning model in Jupyter notebook is another thing to


be able to build something that someone can actually interact with so that'll


teach you a lot of just even if it's if it's just a crappy job but just the fact


that someone can interact with the code that you've worked with I'm talking like


go to a domain and use your code that's that's what I'm talking about when I


talk about software engineering of course you can go into more higher


level stuff than that but to get started that's how that's how I'm learning at


the moment is just building stuff that people can interact with all right I


think that that might be enough Jack Arnold says do I have a discord server


yes there is a discord server for the zero to mastery students so if you if


you send me a message I can probably send you link but usually it's just for


people who like I'm going through the course because I answer a lot of


questions there I don't have a personal discord server so good to see you here


Jack thank you so much everyone for tuning in I've had a lot of fun and as


always remember you can ask me a question anytime leave a question on


one of my videos or I prefer actually if you send me an email if I forget to


reply to your question that you've sent me send me a reminder all right because


email is like a blessing and a curse for me my email is Daniel at mrdberg.com


you can find it on my website you smart enough to find me online and send me a


message and whatever and but keep learning keep creating and I'll see you


next time thank you so much Dries Akash Sethwick Gav Cyril Josh Cummins everyone


I really appreciate this I hope you've had a lot of fun and if you're new to


Fix your tight hips and shoulders in 10-minutes (ball joint mobility session)

Here we go, we're stirring the pot, we're getting ready, we're getting excited,


we're getting ready to move because today is day, what is it, day 23 of


Restorona, thanks for the reminder there cameraman. Now I woke up this morning and


listened to my body and it said to me you know what, Daniel, the last few days


have been pretty, pretty full-on, pretty pumping style workouts like just hitting


it really hard and the important thing is, is yesterday's theme was rest when


needed and so today I figured, I asked myself what should we do and I thought


you know what, go for a long walk and do some stretching, mobility, so that's what


today's session is, I went for a long walk this morning just, just, just after


the sun came up with my dad and dogs, it's great to get out of nature, of course


staying away from other people at social distancing, but today you're going to


need a stick, we're doing a mobility workout, so you need a stick or a broom


stick and some sort of wall, I'm going to be using this wall and what we're going


to be doing is a 10-minute full body mobility session mainly focusing on the


two biggest joints which is the hips, so the ball joint, each hip and the


shoulders, so they're both ball joints, you take care of the ball joints of your


body, you'll take care of your other joints, so that's where it all starts.


Let's get started, we've got 10 minutes on the clock but we're doing one full


minute of each movement, so we've got about 10, maybe I think 10 plus a bonus


I've got written down here, so we'll hold them for a minute each and then swap to


the next one and so when we do a single leg we'll go left and then right etc etc


so let's get started. The first one is as always a nice and deep lunge, so we


won't need the, we don't need the stick until the later exercises but this is,


we're just holding a deep lunge. Now we're going for a full minute, now I want


you to just keep moving around and figuring out where you need some


movement. I woke up this morning and I was just tight you know, I was like oh I've


been pumping, I've been doing heaps of reps, heaps of body weight, even though


we're only doing body weight right, it's body weight is easiest to over train and


that means over train it means doing too much without resting enough and that's


where injuries happen and since we're not, we don't want any injuries we just


want to get moving every day a little bit, ten minutes every day. Today we're


we're gonna listen to our bodies and go you know what, sometimes I should do


nothing, in that case today's nothing is just just being mobile, just stretching


around, just going for a big walk if you can, if you can get outside. Beautiful so


that's the, we'll do the right leg forward now, so we just did the left leg


forward with a deep lunge, we'll do it the same here, again same thing just moving


around. Oh by the way that's today's theme is sometimes do nothing. You know


those scenarios in life where it just, it would almost be beneficial for you to do


nothing instead of something, I mean humans we have a tendency and I fall


victim to this fallacy all the time right, is that the easy answer is the


default to more to always wanting to be doing at least something. Now just keep


moving around here, lean forward you can come down onto your elbows if you want,


really spread this leg, get up onto the side of the foot, I've got some dirt on


my foot here because I was walking outside with no shoes on, just getting my


my feet in touch with the ground. Wow I'm feeling better already, I mean you loosen


up your hip and you'll loosen up your entire body. So now we're going for the


wall, deep lunge on the wall, the way to set this up is kneel next to the wall


and get your leg up like this and so your foot should be pressed against the


wall, in my case this is a glass door, same thing any wall will do and then what


we're going to do is just lunge forward like this. So remember to start with your


knee as close to the wall as you can and then just push back, maybe you can only


come up to about here, if you can hold yourself up on your on your knee that's


elevated and then push up like this. You should feel it right through here, leave a


comment below if you're feeling it through here because I definitely am,


especially right in there. Now I don't know the full muscle anatomy of


what it's actually called but it might be, you might be able to correct me if


you know muscle anatomy better than I do, might be the psoas muscle and if that's


tight, now we're going again, sit up, knee really close to the wall and then bring


it up, rotate it, there we go. Now my left leg is forward and my right leg is on the


wall, it might be the psoas muscle through here, I just refer to it as the


hip but I've heard somewhere that if you if your psoas muscle is tight, well then


and it makes sense right, you don't need a to like a full physiology degree to


understand this is if that muscle is tight it's going to send tightness


throughout your whole body, can lead to things like headaches, can lead to


stress, that's not what you need, especially in this time. So that's what


we're focusing on today is just just stretching out what we need, staying


mobile, moving around, sometimes doing nothing you know.


BEA U-to-Fall. So now we'll come off the wall, we'll come back to the ground. So


this one we're going to, it's I call this one the leg or the shin underbody, I


don't know the full, the correct term, there probably is something for it but


essentially what we're doing is just again another sort of hip but this time


it's also a glute exercise, we come around this side, I'm also feeling it


through here and so the goal here is again you can stay static, I like to hold


my right hand on my ankle here just to hold my leg horizontal. Now for example


you might also start out like this with your leg here because you can't you


can't fully get it out here and I've started like that before when I first


started doing this stretch I could barely even get down in there so adjust


to where you need to and I can definitely improve this even more if you


want to go lean out like this you can. You swap over to the right leg, I'm


feeling better already now that I've loosened up it's amazing how much just


five minutes of simple movements or mobility can do for you. Imagine going


around like your whole life just having some sort of muscle tightness or or


whatnot and that's causing you pain and then that pain is just negatively


affecting whatever you do right because wherever, wherever I said I said this to


my little brother and my one of my mates yesterday on the phone, I spend a lot of


my energy avoiding negative energy so you can you can take that for what it


means or whatever you want negative energy can come from other people I mean


I remember being in the workplace previous workplace and we'd had a hundred


and fifty odd staff or something like that and as soon as as soon as one


person was negative it basically sucked down the entire and only took one


negative person to suck down the entire hundred team. So now we have we have leg


spread and we lean forward. This is another great one if you have a band


this is probably even better so we're gonna spread our legs here in an angle


if you have a band you can put this in attach a band in front of a pole in


front of you then wrap the band around you here I don't have that on me and


then use the band to pull you forward but for now we're just gonna reach


forward go side to side reach over to one leg reach over to the other leg just


keep moving I mean we've only got ten minutes on the clock but I could really


go for far longer with this session that's it so yeah


I could hold that for a lot longer than a minute maybe I should have put


these all at two minutes and so now we have the stick so grab the stick wants


you to go just a little bit wider than shoulder width apart or as wide as you


can to rotate back and forth so I'll just do it from a couple of different


angles so this is all we're doing we're going back and forth with the shoulder


make sure you got some room to do this you're not knocking anything out it's


amazing how I haven't hit anything just yet and so I was saying oh there we go


you can get your arms vertical focusing on the ball joint here I really like to


think of it as you imagine a rusty gear on a on a bike chain or something like


that and that these sort of movements that we're doing for the ball joint for


the shoulder joint are like getting that gear moving for the first time in a


while and it's a getting rid of all that extra rust and so now we're going to go


with the right so I'm going to grab the stick here and I'm going to pull it up


with the left so you see here grabbing it with the right pulling out with the


left this is going to stretch out the front of the deltoid here so we go like


this we're controlling it pulling up watch as I pull up and pulling forward


at the same time and now you can move it around figure out where you want to go


this is a great one I really love this one and now what we're doing here is


we're just controlling the stretch on the right shoulder with the left arm so


the left arm is guiding here whichever arm is in the sky is guiding the shoulder


pull it forward see how it's going to come right up there this actually feels


great I could hold this for a lot longer than a minute keep it going now we're


going to swap shake it out shake it out I'm going to get our left arm grabbing


the stick somewhere midway and then pulling it up shooting it up with the


right you can do this with a regular broomstick like this is just a


broomstick from a hardware store without the head on it one of the best tools


you can ever buy you use it for mobility we've done a stick workout I'm not sure


that might have been day 12 or something like that no equipment needed right


that's it stretch it around and how many times in the theme of sometimes do


nothing how many times have you made a mess of a scenario when it would have


been better just to sit back it's like right right now what we're all doing if


you're watching this in the future


sometimes the answer to fix the the pandemic that's going on is for a lot of


people to just sit back and do nothing oh victory that's it you know what I'm a


little bit disappointed I could keep going with that I probably will but we


might end the video there and I'll leave you with those actually there was one


bonus that I did promise and so that is that is a downward dog into Cobra so


another one I like doing is just a bit of a if I was to wake up in the morning


and wanted to do a little bit of a flow like we just did I might come up do some


walking movements come up like this bend up go up you see this in a lot of yoga


practices and so come down you could do this for another minute and then


like that so come up into a downward dog and then all the way up into a Cobra


position like that beautiful I'm feeling my whole entire spine become fully


lubricated there and then of course you could do the squat hold move around but


we started with ten exercises there we held each of them for a minute because


we woke up what least I did woke up listen to my body thought you know what


doing a lot of a lot of repetitions a lot of pumping exercises it's actually


time for a rest so you prevent injury or recover today and then get back into it


another time so hope you enjoyed this mobility session leave a comment below


if you have any suggestions for a future video a future workout or future


stretches which one was your favorite did you feel that on the wall lunge but


What AI courses should you do? and What are my plans after finishing my studies? | Livestream Q&A

Yo, yo, yo, we are live. We're


celebrating 777 subscribers


and I know this this may be


that this may be that it's the


first proper live stream but


thank you so much. Why 777?


Well, I know it's a couple of


weeks late because we've hit


900. Well, we hit 900 today. I


should do another one for 900.


Alright, so if you got any


questions, feel free to ask them


for kicking off. We're kicking


off the first first official


subscriber celebration Q and A


and I might just be me sitting


here for for half an hour but


I'm happy to to answer any


question that you may have


about fitness, AI, health,


life, studying online, building


videos, making videos, meeting


people, talking to people,


talking to people, applying for


internships. I'm happy to to


try and do my best. Let me let


me get into the chat. What's


up, man? How cool is this? We


can do a live stream and go


live all around the world.


invite people. How do I invite


people?


Thank you for joining. If


you've just joined in, we're


going to have a new one called


77 subscribers. I'm going to


tweet this out actually. That


would have been a good idea.


Tweet it out. I should have


given people some warning to


know rather than just just


starting off randomly. Hey,


let's go. Let me get Twitter


up.


Some that are that people have


been asking me, hey, what's


going on?


700. I'm just going to tweet


this out and then we'll we'll


get some actual content going.


777 subscriber


live stream.


I can't even type right now.


What's up?


What's going on? Whoever's


just joined us or the number go


from two to three. We've got


some, we've got some people


now. now joining in. Ask me a


question but really, this is


for you guys. So, um so I'm


really, really, really stoked


that that we've managed to even


get to 700. I mean, I remember


when I first started this


channel, I was like, what am I


even going to make videos about


this? It was amazing. I could


never have even imagined that


that you, this would happen and


I get emails daily. I get


comments. Uh it's it's


incredible and studying online


is is is really I don't know.


Can be really lonely at times


because I'm just I'm just in my


room most of the time but um


having you guys uh having this


community that that we're all


part of that we're all building


together. How you doing, man?


We'd love to stay. Oh yeah. K


Parks. Sorry, man. I know it's


2 AM. This is sort of out of


the blue. I just decided to


test out the live stream but


we'll do more in the future.


Thanks for tuning in and do it


on Instagram with a thumbnail


of the live stream. Oh, I don't


know how to save. I don't know


how to save the thumbnail that


that YouTube just took but I'll


I'll put up a story. I'll put


up a story on Instagram so


people can check it out. Here


we go. We'll do a live stream


of the live stream. Whoever's


just joining, nice to meet you.


Nice to nice to see you here.


Thank you for joining in. If


you want to ask me a question


about anything, that's what


we're doing. We're celebrating


777 subscribers. Leave a


question in the chat. Otherwise,


I'll start answering some of


the ones I get asked most often


uh via Email or via uh YouTube


comment and whatnot. What's up


everyone? We're doing a live


stream. Can you even see that?


A live stream of a live stream


of a live stream in my bio but


yeah, we'll be here for the


next half an hour or so


answering some questions.


What's up? You even see that a


live stream of a buzzology.


Thank you. in a second. Let me


um put this story up onto


Instagram and get it in my bio.


So, if anyone wants to join


from there, they can. What am I


doing? I'm doing a live stream.


Um so, what I'm planning to do


is I really want to get into to


the AI commercial space and in


health. I don't exactly know


where or how to do that yet but


um my first plan is to finish


the AI masters curriculum that


I've set for myself. Uh I'm


currently almost finished the


AI nanodegree on Udacity. I've


got one more project left. I'm


onto the voice user interfaces


and I'll put that into the


future video um for learning


intelligence twenty-seven or


something like that but once


I'm done with the the AI


masters, it's kind of already


happening now. I'm going to um


start applying for jobs of


course uh whether it be I don't


mind relocating uh the US um is


a big place that I'd like to


get to um otherwise


So, yeah, I'll I'll answer that


in a second. I get too


excited. Um the work is is up


and coming in the AI space.


It's it's really good at the


moment. There's there's uh um a


lot of a lot of work happening


in in the data science world


all over the world. So, I mean,


that's that's my first port of


goal is is getting some


commercial experience because


right now, I've just a lot of


time learning in my room making


these videos talking to you


guys which is is amazing. Um


and I'm really excited that I


my skills that I've been


learning can transfer into


actually helping people through


through different products and


whatnot. Building some stuff on


these guys. So, so uh we can


help out help the world move


more and eat better. That's my


mission and I want to use AI to


do it. Not exactly sure why but


we'll figure it out, hey? Uh so


yeah, oh you can't find the NLP


project on my git. Okay, GitHub.


I'll just go for a second. Uh


where's my GitHub? Mr. D Burke,


if you want to check out any of


my stuff on on GitHub guys, I've


I really need to step up my


GitHub game. Does anyone have a


good guide on GitHub? Um I kind


of like every time I try to


post something on GitHub, I


kind of have to look up a guide


to to teach me. Uh that's one


thing I really need to to step


up. Um but let me go


repositories. Mr. D Burke,


can you do that? Can you do


that? Can I? Is that work for


you guys? Thank you so much.


Thank you so much.


Awesome. I will do that. Um is


it best to through YouTube or


something like that? I don't


know. If YouTube have a chat


function, yeah, the I find the


lazy fix to command line. I


thought ah the desktop app.


That's a good idea. I mean, I


I think that was that was a few


months ago at least. I'll have


to check it out again. That's


that's definitely something I'm


going to need to work on in the


future. Is a GitHub GitHub


interface because I mean, it's


so wonderful. It's such a treasure


trail of of of different


repositories, different code.


I've used it so many more times.


GitHub GitHub cheat sheet.


Thanks, Durant. Thank you so


much. And what's going on? If


you're just joining, um I see


you're here. We're celebrating


777 subscribers. Why 777? It's


because I have a dog called


seven and she's actually asleep


over here. I wonder if you can


you can see there. Yeah. So, um


and seven is also my lucky


number. It just appears


everything. Thank you, man.


Thank you for tuning in. All


the best, my friend. I'll I'll


talk to you soon. It looks like


it's going to be a long day.


But let me uh let me start


answering a question that I got


on uh Instagram today and if


you want to check out on on


Instagram, message me if you if


you like, you can message me


anywhere. It's at mister D


Burke. I'll I'll do my best to


answer your questions but it's


from Affidavit. Hey, buddy.


Just saw your comment on


Udacity's post. Um a recent


college graduate who has no


idea what to do with my own


diploma. So, my friend


introduced me to Udacity


because he used himself to


get me into the program.


Um


There's really a merit to the


program. Thank you uh for the


question. I can't exactly


answer um from from a career


outlooks perspective. I haven't


got a career out of this yet


but I have um started to get


some opportunities in the space


like for example, I have an


internship starting next next


year. So, I'm really excited.


Is our Udacity's courses worth


a merit? Well, I'm I'm biased


because I love Udacity's


courses and I've my whole AI


master's degree is pretty much


structured of Udacity's courses


but so far, man, I think I


think the world is is is moving


slowly. I mean, college is is


still amazing but in my opinion,


if I was to hire someone, I


would, my honest opinion is if


I was like in a CEO position, I


wouldn't really mind if you had


a a degree or not from college


in terms of computer science.


I'd like to see what you've


done. So, have you got projects


on your GitHub of you actually


coding things or have you


written blog post telling


people how they can uh build


their own projects or have you


made YouTube videos or


something like that. Um but as


you say, right? Because it is


still a new platform like


Udacity doesn't have the name


uh that Harvard has or that


other big universities have but


it's certainly uh in my opinion,


one of the leaders in terms of


online coursework. I mean, I've


I've done a fair few now um


through Coursera, through Udacity


and Coursera and Udacity are


both incredible platforms to


learn things from and we have


a lot of people who are


doing that. So, what I would


suggest you do is fill up the


gaps in unit. Yes, that yeah,


that's that's that's a


perfectly great point as well.


So, what you can do is if you're


learning learning something in


in university, say for example,


I had uh someone reach out to


me the other day. So, they're


learning computer science at


university but they don't cover


uh topics such as deep learning


and machine learning yet.


That's where the nanodegree can


come in handy as well. So, you


can do that. You can do that.


So, that's that's what I think


is that's kind of why I did the


the AI master's degree as well


or I created my own curriculum


so that I wasn't just getting


exposed to um one one one


online platforms um way of


teaching or way of methods or


whatnot. So, I I combined


combining different courses


across different platforms


and I found that in the


Udacity AI course, they both uh


are very good but they both


miss out on a few things and


where they miss out on they


they cross over. So, that's


what I'm finding uh doing


different courses from


different platforms. We have


profile here. Don't you think


courses at Udacity and Coursera


take up a lot of time compared


with books and blogs? Yes, that


is true. Um I mean, whatever


you're going to learn, it's


going to take a lot of time.


Welcome um empiricist


Academy. Nice to see you, man.


Um but yes, I think profile,


you're right. It does take up a


lot of time but if you're going


to learn anything, it's going


to take up a lot of time no


matter what no matter what


resource you use, right? It's


like learning is not there's no


eight-week shortcut to to to


learning these skills. It's a


it's a lifelong journey, right?


How fast does our field move?


And later, the the state of the


art um is is not no longer the


state of the art like in 2016,


there's state of the art for


image recognition that was


beaten in 2017. No doubt, it's


going to be beaten in 2018 and


that's just going to continue,


continue, continue as computing


power and algorithms get


better. So, back to back to


your question, Do I think,


don't you think courses take up


a lot of time? I mean, you


could spend, I bought a book


the other day and you could


spend weeks going through this.


Look how many pages it is.


Weeks, weeks, and months going


through this. You could spend


as long as you want on on the


the courses you have online but


uh the why I like the courses


because they give you um some


sort of little little dopamine


hit when you achieve a goal or


something like that. So, the


courses are four months long or


six months depending on what


you're doing and if you don't


complete it in that time, well


then, you have to start again


whereas a book I kind of have


this forever now. So, I can


keep going uh on this for as


long as I like. So, having that


timeline with the courses I


think is very valuable but then


again, combining them all using


books, using blogs, using


podcasts, using YouTube. How do


you best learn? So, if you're


going to learn something, it's


going to be something you can in


my opinion. Here we have um


Sanyem. Any college course or


online course we would take the


same amount exactly. Yeah.


Online can learn from her. I


learned from from my room. Um


empiricists, do you feel


confident enough yet to apply


to data science positions? Um


yes and no. Okay. So, II kind


of started going into learning,


I started applying for data


science roles. So, from a data


science perspective, I know


that I'm weak. My my weakness


would be getting a raw data set


and breaking it down, cleaning


the data, getting it ready for


a deep learning model. So, that


was, that would be my weakness.


That's something I'd have to


work on before I went into the


the, I started applying


properly for data science roles.


I do have an internship


starting at an AI company next


to me. They're engineers. So,


I'm going to see how exactly


they they operate. So, they


take a raw data set and then


merge it or or work it into


something usable. So, do I feel


confident applying for roles?


Yes, I do because I'm a


confident person and I think I


can learn fast or or learn what


I need to do. Um but do I think


that again, you'll never be 100%


ready? So, I think it's


important when you do think


about applying for roles to


always back yourself and have


faith in in the fact that you


can learn what you need to


learn and you'll never be 100%


ready for for whatever you do.


If you wait to 100% ready,


you'll never, you'll never


start but me personally, yes, I


feel confident to to start


applying but don't get me


wrong. I've only spent uh a few


months in in my room learning.


So, once I get to the real


world, it's it's going to be a


whole different experience and


what we have here Me too. How


to be more hyped. Oh, well,


shake my man. How how do we be


more high? Don't get, don't get


me wrong. I wake up sometimes


in the morning. I'm like, oh


man, this sucks. Then I


wake up and I'm like, oh, my


fear is in alive. So, it's


like, I might as well be


hyped. Well, because what else


is there to be, right? I could


be tired but who likes to do


that, right? Um so, I just, I


think that what is happening


here? Okay, I don't know what


just happened there. I press


play on my keyboard but how do


I be up? Energy is a choice. If


you're tired, I was kind of


tired before doing this live


stream. I'm not. I'm not


tired. Right? So, how do you be


tired? Just, sorry, how do you


be hyped? Choose to be hyped.


That's that's literally my my


my opinion on that. It's it's


it's I mean, to me, it's


binary. It's either you're


you're hyped or you're not


hyped and I I like being


hyped a lot more and other


things you can do like simple


things, right? Get enough


sleep. Uh eat well, exercise,


work out, do that sort of stuff


and then um talking to you guys


makes me hyped. So, I think um


you just have to figure figure


things out. Figure yourself


out. What gets you excited and


just keep chasing after that


and realize that if you are


sort of feeling tired, take a


step back before you take a


step forward. So, figure out


why, why it is. Are you doing


something you don't really want


to be doing? Do you have to do


that thing that you're you


really want to be doing? Would


you rather do that other than


that and just just ask yourself


a few questions. So, that's how


I get hyped. I'm thankful for


being alive and uh I use the


what's the other option as the


what's the other option? Not be


hot. I don't want to be not


hyped. I like being excited.


Who else? Oh, by the way, I'm


seeing the numbers go up.


Welcome. This is kind of an


off the cuff live stream. We're


going to have to go backwards


for you. Um but I want to thank


you guys so much for for your


support for joining in. This


learning journey has been


amazing and of course, we're


still at a ground floor level


guys. This is this is just


starting. We're going to we're


going to raise the roof on it


this year. 2018 is a massive


year. So, what do we have here?


Shran, learn, practice, apply,


fill, fill the gap, reiterate,


drive. You're all set to land


on the wall. That's what


happened to me and I totally


agree. That's the best way to


do it, guys. Learn, practice,


apply, fill in the gap,


reiterate. That's that should


be a plaque on a wall. What


Shran just said, write that


down if you if you haven't. So,


learn the things, practice


them, apply them, fill the gap,


reiterate. That's that can that


can be applied to anything, not


just what we're learning with


with AI and deep learning. That


can be applied to food and to


finance, to relationships, to


everything. Uh can I get a link


to your GitHub profile? Yes.


GitHub.com, Mr. D. Burke. If


you see something that you


think could be improved on


there, let me know because I'm


really, I'm a GitHub novice. I


need to improve on that and


then, what do we have here?


Have you ever learned any web


developments yet? That seems


like a popular field. Yes, I


have. I learned mainly


JavaScript, HTML, CSS, all the


entry level stuff on free code


camp. That's that's one


example. Otherwise, just a bit


making websites and whatnot. I


had a startup that failed


mostly my not but really it was


my fault because I just didn't


have my I shut down. Oh, it's


still live actually. II stopped


working on that because I


wanted to work on other things.


That's I'm not going to make


it. It wasn't that much web


development. We built it using


WordPress and a whole bunch of


plugins, maybe a little bit of


custom code but not too much.


My website, my personal website


is built on Squarespace. So,


that that is one area I need to


to improve on in the future. I


think is is web development


because the web is still one of


the main ways people access


your products. So, even if you


build a deep learning product,


you're still going to have to


develop it. So, um empiricists


if I directly as my language


want to redevelopment some of


it more of course that changes


from person to person.


Personally, I started with


Python. Um mainly Python


because uh that's just the


language of AI, right? The


language of machine learning.


So, um I can only comment from


the chat. Anyone in the chat


knows um uh can help out Lewis


with this question on Java. Um


I think if you want to get into


Android development, Java is


where you want to be at and but


otherwise, I'm sorry, I can't


really help you. If you want to


learn programming, any language


is fine. Um empiricist Academy,


I see you have 900 subscribers.


Uh seven is my favorite number


and I was meant to do this a


couple of weeks ago. I just uh


no excuses. I just didn't do it


because I was I was doing other


things but yeah, we will do we


will do these more often. I


think the next one will be at


1000 or whenever you guys want


really. I mean, um if you have


questions, I'll do my best to


end your channel for Java.


Thanks, Jorav. Udacity provides


a lot of support. Start with


Java. There we go. Perfect.


Kotlin is a good bet for


Android too. Oh, this is


awesome. We're all helping each


other out. Okay, first start by


learning OOP concepts and then


you can go learn any object


oriented programming language.


Yes, exactly. So, the basic


principles, that's what I'm


finding with once I've I've been


studying Python for about a


year now. Um the the principles


are language. Not not entirely


but definitely otherwise, we


might we might spend the next


or I think we can keep this


string going probably for about


another five or 10 minutes


before I've got to go drive


some Uber because I need to


make some some cash next week


and actually next week, I'm


really excited. I'm teaching


some some code camp. Uh I'm


teaching some code to kids at


at Code Camp which is awesome


because it's a way to look at


the future. No matter what


coding language you're


learning. It's it's not just


it's the ability to look at a


problem, break it down into


steps is what I think is so


valuable. Um about learning to


code is because you see


something and you think how


could that be built or how can


I turn that problem? How could


I create a solution for that


problem and then using the


code to solve that problem.


Okay, I can break this down. I


can start here. Make an input.


Do a couple of steps and then


we have an output but we have


the Clever Program.com to learn


Python. Yes. Um we saw that


live stream the other day. Hey,


that was he's he's a very smart


guy. I think that's Quasi. I


think that's his name but let


me get some let me get some


more questions on here so we


can we can answer. Let's go


dive into the the email. By


the way, if you're not


reading anything, um my email


is Daniel at Mr. D Burke. Does


anyone have opinion on Pine


Flask or Django Web


Development? I know they're


both. They're both still pretty


common, aren't they? Um I saw a


job actually uh in my local


government being hired for for


Django but that's that's just


my local government. So, I


mean, let's check it out. Who's


got some questions?


If you're just joining by the


way, we are celebrating 777


subscribers. Again, this is my


first actual Q and a live


stream. So, uh I'm not entirely


sure um if I'm doing this


right. So, feel free to give me


feedback whenever you want. We


have a question from LinkedIn


from Arjun. Hey, Daniel. Are


the Udacity courses classroom


based? You have a bunch of


other folks at the same time or


you proceed individually like


you're doing it with me uh uh


John uh all all classroom not


not classroom based. Sorry, all


video lecture based. So, you


can get on at any time and


watch whatever you like and


then you complete the projects


through your own time. You do


have a start and finish date


though and if you don't finish


by the finish date, then then


you're out. Welcome. I saw you


a few a few more people


joining. Hey, nice to see you.


Um thank you so much for tuning


in to the stream celebrating


777 subscribers. If you want to


ask me anything at all, ask me


anything at all. Um or do you


just what do you proceed


through the classes individually


like in Coursera? They are very


similar. Udacity and Coursera


Coursera are very similar. They


both have uh video style


lectures. They have code


samples. They have uh guided


semi-guided projects and they


have forums which are are very


similar. Um I don't I don't


think so. It won't let me. Hey,


are you a university student?


No, I study study online. I


used to be a university


student. I graduated in 2015


with a um Bachelor of Science.


I should have it on my wall


here somewhere but I don't.


It's still I'm pretty sure my


degree is my official university


certificate. My certificate is


still in tube which I got it.


Um because it's it's a paper


certificate. Uh I don't I don't


carry paper around anymore.


So, I'm on a challenge path


doing the self driving and


flying car nanodegree but I


definitely agree that the


deadlines are yes. Yes, that's


that is a very challenging


path. Uh how is the the when


does the flying car has the


time to be a challenge? Because


that looks incredible. I mean,


flying, we're working on flying


cars that II can't believe that.


I mean, I drive Uber uh just to


get some money at the moment


but flying cars. Oh, my number


I mean, any what time now, I


like, I like having deeper


knowledge. I know there's other


software. What? How, how is


that? No, I think I know


there's other software like uh


OBS and stuff like that but if


there's not a one-click button


or made by YouTube, why do I


have to jump through all these


steps? Lewis, I'm through all


these steps. Lewis, Thank you


so much, Lewis. I really, I


really appreciate you coming


out. Thank you for coming out.


Django Floss, pretty, pretty,


pretty substitute here. I saw


with your designed curriculum


for learning AI with interest.


Guys, I'm so lucky to to have


you, have you all here. I


really, I really am. I really


am and as we, as we journey


through this, we're all going


to, I can't wait one day to to


work together with you guys.


I hope that one of you will


eventually start a company or


if not, I will start a company


and we'll bring, we'll bring


some awesome things into the


world and if not, if not, we


can work for something like


that. I think we'll do it on


because those guys if we are


on because those guys a job


somewhere. Are you working


towards building your own


business? I'm going to start


with with healthcare.


You into it. I'm trying to talk


up you. things here and before


I I start to see us to another


project. I think I said I yet


a bit too early to AI now to


agree before I but that'll be


within 2 weeks. We start


teaching fast.ai and satellites.


I'm an absolute wizard at web


development. I'm not sure about


web development guys. I'm still


an absolute rookie when it comes


to developing stuff even though


I've been studying this stuff


for a year. Once you find out


that you learn stuff even though


for a year, find out that you


learn more or the more you you


more you dig up, the more you


realize you don't know.


No, I'm not. Well, you're


you're you're you're helping me


out. I'll I'll call upon I'll


call upon you before when I


need need some help for web


development. Who do we have


here? It's a helpful web


here? Oh, guys. Exciting news


actually. next month, I'm going


to Melbourne to do an internship


at realestate.com.au which is a


real estate company in in my


home. It's it's where you can


go to buy a house, rent a house


or yeah, I think that's it. Buy


a house or rent a house. They're


the two main things you can do


in real estate. That's it. Buy


in real estate, right? The next


month, going to Melbourne and I


made friends with the things


because making these videos


have kind of slipped into a a


much better engineer than I am


but I think where I will be is


how how we can be. I was sort of


the small


when I was the web like, okay,


and we built this. It's


amazing.


I've got a challenge inspired


from yours. I want to document


my flying car and email me


digest. Um a car. I think it's


it's bookmarked on my medium.


So, it's on medium. It's such a


good resource but yes, of


course, you can. Um I'm still


working this out as well. So,


um I I don't know. I don't know


what the formula is for for


documenting my stuff but all I


know is if you're starting it,


you're going to have a lot of


fun and plus, it's a mention


knowledge AI developer.slack.com


Slack.com Oh, that's that's


that's that's the Slack channel


you're talking about. My boy


Sheik. So, if anyone wants to


join that, this is the Slack


channel you're talking about.


My boy Sheik. So, if anyone


wants to join that, jump in,


jump in. Sheik's created a


Slack channel. I'm going to join


that for for this. I'm going to


go out. It's oh, by the way,


happy Easter. I mean, if you


celebrate Easter or if you


don't but regardless, it's a I


consider myself a citizen of


the world. So, I mean, a lot of


people celebrate Easter. A lot


of people don't and happy


Easter. I know it's Easter


somewhere around the world.


So, yeah, I want to dive into


data science. There we go. I


want to dive into data science


as to now. I'm looking for


various MOOCs. Do you recommend


any paid Coursera or Udacity


course? I am complete beginner.


Yeah, Sheik, you got me with the


April fool. So, if you're a


complete beginner, where would


you recommend guys? If you're a


you start? In the chat, we're


going to help Sue Sudeepta out


and man, I'm bad with names.


Sorry but thank you. Thank you


for having a look on Udacity


for the introduction to to


Python courses. It depends what


you want to learn. If it's data


science, I definitely have a


look into the intro for Python


courses on Udacity. Coursera


have a lot of have a lot of


free free learning resources.


Hey, actually, I think


Coursera are moving to a paid


model for most of their staff.


Um Treehouse, another great


resource for Python. That's


actually a program.com.


YouTube channel. Actually,


Siraj. How have I not mentioned


Siraj? Siraj, Rival, Hugh,


Siraj, Rival on YouTube. There


we go. How much time does it


take to complete machine


learning course on Coursera? Uh


Rijdeep, Rijdeep, it it took me


about 9 weeks and that was


while doing other things. So,


if you're doing that course all


on its own as well as working


with Coursera, the courses, it


does last for about 13 weeks


but you can certainly complete


a lot faster if you if you just


keep doing going, going, going.


Yes, Sanyan, you're completely


right. So, back to what you


were saying, Sudipta, the


nanodegree gets you more


recognition and non-adopted


path. If you want to start for


free, there's plenty of


information on the pathway


that's already been made and


already been tested by


thousands of people. Uh the


nanodegree program for for data


analyst or or a bunch of other


things is is tried and true. I


I'm doing one of the nanodegrees


right now and yes, they they


cost more than than other


places but the you you get what


you pay for essentially. I can


really back that up and now we


now we're at a cool place.


We're at a place where we're


celebrating 777 likes for


celebrating 777 subscribers and


I think that is about time to


tap out unless anyone has any


more questions, I'm going to do


another one of these. Maybe we


could almost get them started


once a week or something like


that just to have a catch up


with with whoever wants to join


in but I will think about in


the that in the future. So, if


anyone doesn't have any more


questions, I'll probably wait


until the end and then we'll tap


out but again, thank you all so


much for joining in. Um I'll


see if I can upload this video


to my channel. What do you


think? Do you think this is


channel worthy or should I just


leave this as as going into


cyber dust? Um because I think


we answered some good questions


on here. We had some great


discussions. So, uh Rich, isn't


it? Isn't it almost similar to


ML and Coursera but on Python?


Yeah, so the course on Coursera


is in math lab. The deep


learning.ai course on Coursera


is all in Python. Now, the


machine learning course on


Coursera may have been updated


uh to be in Python but when I


did it, it was all in MATLAB.


You could almost, you could


actually do it in Python if you


wanted but the the programming


course is in Python. So, if you


have um uh knowledge of Python,


try uh the deep learning.ai


course because there are some


machine learning principles in


there as well. What will be


working on in your new


internship? That is a great


question. So, the new


internship um starts on


Thursday. I will, I'll be


recording what I can. I don't


know if I can record it in in


the uh actual um workplace


because of course, it's going


to be a long time. Uh they


already know. They kind of kind


of said to come in because they


they watch my YouTube videos


and they said they love them.


So, um they they have a number


of projects. They don't just


work on a specific field. So,


I'm going to be partnering with


the ML engineers and just


seeing what their workflow is


like. Uh I'm not entirely sure


what the exact project is and


then we have uh so thank you


so much for tuning in. edit it


to important questions for your


channel. No, it is still


MATLAB. Thank you, Shaik. Um


and then we have here. Do you


have light light Yagami? Do I


have any plans for an


individual project? Yes. Um one


of them, an entry level


project. I'm actually building


my first Alexa skill and I know


it's part of the um AI nano


project and I think that has a


foundation to to go into my own


uh project. So, the project I


want to work on is an Alexa


skill but for morning workout


routines. So, I love working


out, right? I'm a gym junkie.


I'm a I'm a fitness geek and I


would like to, well, that's my


mission, right? It's to use AI


to help the world move more and


eat better. So, I want to build


an Alexa skill that is a


morning workout routine. You


can get up in the morning, say,


you know, you're going to get


up in the morning and she will


run you through a workout of


sorts. And I think my Alexa is


is running off at the moment.


So, that's that's going to be


my first project. Otherwise, I


would like to work into use AI


for personalized medicine. What


does this look like? Well, at


the moment, our current health


care system kind of works off a


cookie cutter diagnosis. So, um


I would like to use AI to get


to the point where the difference


isn't completely different to


how my body operates. Um there


are some similarities like a lot


of similarities. Don't get me


wrong but find little um


differences can make all the


difference when it comes to


medical treatment uh or health


or general life health. So,


that's that's what I'd like to


do. I'd like to use AI somehow


in the in the case of


personalized medicine. I


haven't completely worked out


how to use it yet. Um so,


Dipta, how much statistics


knowledge do I need to go into


uh go to advance? I got


financial aid and enrolled in


University of Michigan


introduction to science of


course Sarah. It covers


comprised. I think that's a


great that's a great um course


to start with. I was actually


thinking of of going into that


one. That University of


Michigan one. Um so, how much


statistics knowledge do you


need to know? I've only ever


gone to university in my life.


All the rest I caught up on


Khan Academy or various other


blog posts around the the


internet. So, I think don't


worry too much about the


statistics. Most of it is uh


built in to the programming


languages. So, as long as you


you improve your skills in


Python and and uh NumPy and


Pandas and whatnot. Uh most of


the statistics will be built in


to that but that being said,


having some knowledge of the


code is very helpful for you


and run it. I meant to say that


sometimes I feel that the


course teaches deep learning


concepts. We're still


implementing the hot. Yes,


definitely. I agree. Um it is


concepts. Uh the code by


yourself is hard. I find that


hard as well. That's why I got


the whiteboard so I can start


practicing uh building models


from scratch on that. Um but


that is something I still find


hard as well and I think it's


it comes down to uh just you


know, the hard part is you just


have to practice implementing


them from scratch. Practice


getting a data set, cleaning


the data, getting it ready for


a deep learning model and just


doing that from scratch. So, I


think I totally agree with you


with the um with the building


from scratch being hard and I


mean, the projects are hard on


themselves sometimes. So, um


yeah, it's just about of uh a


half matter of practicing


starting with a blank page and


then you can do it when you


can. But anyway, I'm so sorry


to the people who are just


tuning in. I just saw the


number going up but we were


here for the last 45 minutes.


So, my dog is going crazy for


um we were here for 777


subscriber live stream which


was a couple of weeks late but


thank you so much for everyone


who tuned in. I had a great


time. We'll do more of these in


the future. We definitely will.


I'll be more structured in the


future. Um but we will be more


structured. I will get some


questions together. I will tell


you when it's going to come out


in the future and we will all


learn together. but thank you.


Thank you. Thank you. Interesting


idea by the way. I have a agree


in medicine. If yes, it would


be good to integrate


knowledge is very few do that.


Wonderful. I'll be reading


about you in a few years in the


newspaper. Who knows? You'll


be definitely be seeing more of


me on YouTube. So, you won't


have to read about me in the


newspaper. You can go to my


YouTube channel. Um I have a


degree in nutrition and food


science not in medicine. So,


I'm not officially a doctor but


thank you all so much and we'll


Deep Learning for Symbolic Mathematics!? | Paper EXPLAINED

Hello!


Ms. Coffee Bean and her team of archaeologists discovered an ancient machine learning paper,


and the finding is interesting for three reasons.


Firstly, the paper solves a part of symbolic mathematics with deep neural networks, surpassing


even the mighty Mathematica on the test set.


Secondly, it is not really self-supervised learning, but yet the amount of training data


for this task is virtually infinite.


Stick around to see why, and thirdly, it is one of Ms. Coffee Bean's favorite papers.


I think she has a weak spot for mathematics.


So welcome to this AI Coffee Break, where you will find out how artificial intelligence


tackles symbolic mathematics.


Deep learning can do symbolic mathematics, or at least a part of it, as far as this paper


goes, but why should anybody care about this?


Well, you should be hyped because of at least two reasons.


First, mathematics follows rigorous rules and is exact.


Perhaps you remember that e to the power of x integrated is again e to the power of x


plus a constant, and nothing else would be the answer here.


But deep learning with its neural networks is clearly statistical, which can be seen


as an opposite to rule-based systems where you could prove that you arrive at the right


answer 100% of the time.


One more reason to care about neural networks solving symbolic mathematics is exactly the


symbolic nature of this problem.


Neural networks had huge success on domains where representations are continuous in the


first place, like in computer vision where pixels take continuous values, dark spots


take low values, while bright pixels have large values.


But while neural networks perform well on continuous problems, they are still struggling


with symbolic reasoning.


So with exactly the mathematical equation this paper deals with, which are in forms


like x plus y, which could stand for any two numbers being added up, while the numerical


expression is continuous, as there are several other numbers between 2 and 3, x and y are


the opposite of continuous.


They are symbols that can stand for numbers or even for other symbols.


And there's nothing in between the symbol x and the symbol y, so to say, the only relationship


defined here is stated in the equation, but that's about it.


And neural networks so far have been applied on numeric tasks, where the network gets as


inputs two numbers and the output is again a number, like adding or multiplying two numbers.


And there, they perform quite poorly.


But this paper considers the symbolic type of calculations, where the input is an equation


describing the relationship of symbols and the output is again in symbolic form, but


this time representing the solution.


If you remember from mathematics classes, depicted here is integration.


Another set of symbolic mathematics problems are first and second order ordinary differential


equations, or short ODEs.


Ms. Coffee Bean thinks that it is here in the problem choice, where the genius of the


paper is condensed.


For this video, we will stick with the integration, since we want to stay at a beginner level


and ODEs are renowned to scare people.


For everyone interested in ODEs, check out the paper linked in the video description.


Disclaimer, it works very similar to integration.


So we think we agreed that both integration and ODEs are hard for humans and now we want


to solve symbolic integration with neural networks.


But neural networks require training data, so pairs of functions and their integrals.


Where to get that from?


We could generate some functions randomly and take systems like Matlab and Mathematica


to predict the integral.


But these systems are not very fast and sometimes fail, even for integrable functions, so forward


generation is a good start for some training data, but won't get us far and we won't


be better than Mathematica.


Therefore a little out of the box thinking is required.


Integration is hard, but the backward operation to it is differentiation, which is fast, easy


to compute, following simple rules.


So let's use this lesson and additionally to forward generation use something else.


By generating random functions on the output side instead of the input side and then computing


their derivatives.


These function pairs can be then added to the training pool.


This is cool and it's called backward generation and it can generate a lot of hard examples,


but has the following problem.


It is very unlikely to randomly generate very complicated examples in the first place and


even more unlikely is to have them simplify to a very simple function in the derivative.


It is important to have these long-short combinations too, otherwise the network could become biased


to long equations.


To address this, the authors use another clever trick.


Two random functions f and g are generated, their derivatives f and g are computed and


these pairs are added to the training set.


But now, with the rule of integration by parts, one can also add the training set combinations


of these f and g functions like this.


f times g is already known because it is already in the training set, only that the second


term is unknown.


We know small f and the integral of g, but we do not know the integral of f times g.


But with enough luck, it might already be in the training set, if this already contains


a lot of stuff generated with forward and backward generation beforehand.


So every time the authors discover something like this in the training set, they generate


a new data point by backward generation with integration by parts.


But Ms. Coffee Bean, wait a little.


The authors have now three means of generating as much training data as they want, with forward


and backward generation, also using integration by parts.


But all this relies on randomly generating functions, which are then either integrated


or differentiated.


So three questions remain.


How to generate functions randomly?


And how to feed them into a neural network?


And what is this mysterious neural network anyway?


Okay, we will answer these questions one at a time.


To randomly generate functions, we first need a way to represent mathematical expressions.


The choice here falls to trees, where operators and functions are internal nodes, the operands


are children, and constants and variables are leaves.


The choice falls to trees because of lots of reasons.


Trees represent the order in which operations are executed.


Associativity is clear and parentheses become obsolete.


And because of this, they are very easy to grow.


On the other hand, expanding expressions in the sequential representation is very clear


at both ends, but adding something in between might get a little messy.


So yeah, if one wants to grow an expression randomly, one just has to randomly select


functions, operators and operands randomly and just check that unary operands, like sinuses


or exponentials, get only one child, while binary operands, like addition or multiplication,


get two children.


Then on to the second question.


How to process mathematical expressions, now in form of trees, by a neural network?


Well, we don't process the trees.


Wait, what?


The authors linearize the trees to a sequence again by using prefix notation, where each


node is written before its children from left to right, like in this example.


The advantage over the common way of writing mathematical expressions is that the prefix


notation does not require any parentheses, which would make the life of the neural network


harder by having to remember to close them after they have been opened.


So no trees?


Neural networks working on trees were very unvogue in natural language processing, see


tree LSTMs, but now NLP is dominated by transformers.


And guess what?


The neural network employed to solve symbolic integration and ODEs is a transformer.


You did not expect that to happen, right?


Transformers are now everywhere, yeah.


So the authors use a transformer architecture to translate from the input sequence to the


output sequence.


And with the awesome data generation procedure that can generate virtually infinite amounts


of it, there is enough data for the transformer to learn the problem.


It does not really matter that symbolic integration is hard, I think we all agree here, because


there is enough data, which proves to be in this case 20 million examples.


Yeah, they generated 20 million data samples, just like that.


This is why Ms. Coffee Bean thinks that this is genius.


Take this equation.


You want to maximize research results.


You know transformers can do anything, so of course you use them.


You have your problem, but you realize it's limited data is too little for the transformer


to learn something meaningful, but you still want to do good research.


So you choose an application where you have virtually infinite amounts of data and the


recipe to maximizing success is set.


Well, don't take Ms. Coffee Bean so seriously, but it seems like this is kind of the recipe


for success nowadays.


And this is what partially happens in self-supervised learning, because there you have almost infinite


amounts of training data.


So yeah, in any case, the 20 million samples that the authors here have seem to be very


effective, because their transformer network is able to solve the integration examples


with around 99% accuracy in less than a second per example, while Mathematica, given 30 seconds


of compute time, can only solve 84%, Maple 67%, and MATLAB only 65%.


But what are the remaining 1% in integration performance of the transformer?


Well, we remember that we are in the statistical world of neural networks.


So until we meet you next time, watch out for outliers if you wander out alone in this


world.


And hey, do not forget to like and subscribe.


Building a Deep Learning Image Classifier | Learning Intelligence 43

Shhh! Even if you hear me quiet, there's a wild animal behind the camera.


Hey! How are you?


Look at you, this is so friendly!


What's going on team? Welcome to Learning Intelligence 43!


Shhh! There's another wild animal behind the camera.


Uh oh! It's a battle!


Hey hey, it's okay, it's okay.


What's going on Josh?


What's up brother? You having some food?


Oh, everyone's asleep in this house. What do you want to do today?


I'll eat this soup first.


You want that soup? And you want to build a model?


Yeah. Can you give me some soup?


Banana not banana?


So tonight Josh has been learning iOS. What is it? CoreML? CoreML and ARKit?


Look at that! A little dog!


And so today we want to build a... Have you seen the app Hot Dog Not Hot Dog?


Let's start with a hot dog.


We want to build a similar one but banana not banana.


Because we want to build a project to do with nutrition education and use an iPhone with CoreML and whatnot.


So we're going to start... Actually we don't even have any bananas in the house.


We'll figure it out. I think we're going to get some images from Google Images.


I'm going to build the image classification model and Josh is going to work on integrating that into iOS.


So that's today's project.


It's going to be fun!


You excited bro? How's that soup?


Yeah, it's pretty hot.


I'm going to eat two now.


I only need one dish.


OK Google, 45 minute timer.


OK, 45 minutes. Starting now.


Oh boy am I ready! Same meal as always. Nice and simple. Eggs and greens.


Going to eat this while the chicken cooks and then get into some study.


And I prepare all my food in big batches so that way I'm not always cooking.


I love to cook but it does take up a fair bit of time.


So I want to make sure if I want to study and I want to eat, I can come up, heat up a meal.


It's going to be a good high quality nutritious meal to fuel me for the rest of the knowledge gains.


That was delicious. Let's do some studying.


Time to build us a model. The chicken is still on.


OK Google, stop the timer.


I'm so excited to get back into studying. I almost forgot about the chicken I had in the oven.


Good thing we've got Uncle Goog on the help. Uncle Goog? Aunty Goog? What do you call them? Whatever.


Oh yeah!


Yeah! Foggy.


Alright, food's done. Breakfast's done. Meal's done.


It's time to build a food model. Or a food classify model.


Josh, what are you doing? You're gaming man! Playing some games!


Alright, I'll get our model done. First things first I like to do with any problem is spend a little bit of time planning it out.


So I'm going to do at least one Pomodoro, aka 25 minutes of uninterrupted time of working out how best to approach it.


And then I'll report back. Alexa, 25 minute timer.


25 minutes. Starting now.


Alexa, stop the timer. Alright, so I found some pretty good guides online.


Especially this video. Using Google Images to gather deep learning training data.


So essentially that's what I need to do first to create banana not banana. I'm looping through Google. Have a look at this.


To get all of these images of bananas to train my model.


Ah! We got a bunch of errors. I need to fix this. Check this out. All these errors. Error, error, error, error, error.


So what I'm trying to do is run a script to download me images from Google. And right now it's not working. But we will fix it.


Do you hear that? My puppy's gone crazy. Better get him. Hey, hey, hey, hey. What are you two doing? Hey, hey, hey. Inside, inside. Come on. Good girls.


Good girls. Good girls. Ah, ah, ah, ah. They've gone rogue. What have you been up to? Absolutely drifting. Drifting? Drifting? Come on. Come on. Plough. Inside. Inside. Inside.


No. Absolutely no control. I've lost it. Back to code.


It's working. Look at all these images of bananas coming up. It's just filling up my desktop with images of bananas. Well, not my desktop, but a folder on my desktop.


See? Just slowly populating with images of bananas. This is going to be our training data set. This guide is actually very, very cool.


If you want to learn how to do some deep learning with computer vision, check out this website. It's in the description, by the way. Back to it. Check it out. Check it out. Check it out. Check it out.


What's happening here? Our model's training. Oh, yeah. We followed through the tutorial here. It's actually one of the best tutorials I've ever seen. Check it out. I can't stop speaking highly enough of it.


Next is to write a test network. This is training the network here. We're going to test the network to see how good it is, but the ultimate test will be taking a photo of a banana, a random one that we find, and then running it against the network and seeing if it's a banana or not a banana.


We might compare it to Zenith. Are you a banana? No. No? See? Hopefully the model predicts Zenith as not being a banana.


All right. We've got a trained model on banana, not banana. We've got a test network, but we need some images of bananas in the ultimate showdown. Zenith's not here.


As you can see, my co-founder's working very hard. We're going to go to the gym, get a workout in, and then come home for the ultimate showdown.


There's a supermarket near the gym, so we'll get some bananas and really test this model to see if it actually works with real-life banana pictures, not just Google images.


Workout's finished. Feeling real good. Now it's time to get some bananas. Let's find them. Here we go. I wonder if these will work. These will do. I like bananas, so I'm going to get heaps.


Actually, I think I need a few more things, so I'm going to do that whole thing in front of your face.


Bananas acquired. All right. Now the ultimate challenge begins. When we get home, test the model. Banana versus Zenith.


Oh, Zenith. Oh, wait. Zenith isn't here, so we're going to have some dinner. I'm going to make a banana sandwich. I'm going to make a banana sandwich.


Oh, Zenith. Oh, wait. Zenith isn't here, so we're going to have some dinner. I'm going to make sure he's coming home, because I really want to test this banana versus Zenith.


Hey, Siri, text Zenith. Are you coming home tonight? Question mark. Your message to Zenith Rappley says, are you coming home tonight? I'll send it.


He better be. Now, we're going to go heat up one of the meals I cooked today, because it's all prepped and ready to go. Get these for safekeeping.


What are those? Mum, did you get some bananas today? Yes. I got plenty of bananas now. Stocked up. Keep those safe. Meal's on.


You know what, guys? It's nearly midnight. I know if Zenith's getting in here. The door. Is that you? Can we do Zenith versus banana? We've got to do it.


You ready to train the model? Yeah. You got me excited. All right, we need to get a photo of you, and we need to go get a banana. We've got the banana. See the resemblance?


Look at that. Ring, ring, ring, and the banana phone. All right, we need to get a photo of you and a photo of this. Just for the audience.


We're not cheating here. We've got Zenith versus banana. First, we'll do banana.jpg. All right, we're running. This is banana. How's the model going to go? Using TensorFlow back-end.


Not banana! Daniel. 99.96.98% not banana. Are you kidding me? I swear to God if it says I'm a banana.


All right, that's one fail for today. Let's get in Zenith. What are you hoping for, Zenith? That I'm not a banana.


This is the one that counts. This is the moment of truth. We can fix that banana image, but we need to make sure that Zenith is not a banana.


Alexa, play some dramatic music. I couldn't find any dramatic songs. One job, Alexa! One job! Alexa, play a dramatic song playlist.


I can't see the playlist. Hopefully our model works a bit better than Alexa. All right, this is the moment of truth. We're going in. Zenith.jpg.


Alexa, play Temperature by Sean Paul. Temperature. He's not a banana! In a higher percent.


He thinks I'm more like a banana than a banana is. That's right. You're not a banana.


Alexa, stop. Alexa, stop. Everything's going wrong. When Alexa playing a banger like Sean Paul's Temperature, she ain't stopping for nothing.


Apparently this is not a banana, but I did get a backup after many trials.


Many trials. Banana! You got it! With only a 78.75% accuracy. But there we go, ladies and gentlemen. Zenith is not a banana, and that is a banana.


Leave a comment below on how we can improve our design.


Because it's not that good at the moment, but we got there. Positive results all around. Zenith, sign us out. Thank you. You're not a banana.


This is it from the Daniel Burke Show, and keep learning!


UMAP explained | The best dimensionality reduction?

Hey, we are back with the Dimensionality Reduction series.


In our last video of the series, we talked about one way to escape the curse of dimensionality


through an older algorithm called PCA.


Today we will talk about a newer and very popular dimensionality reduction algorithm


called UMAP.


PCA and UMAP are very different.


PCA factorizes a matrix characterizing the data, which puts it into company with algorithms


like NMF or SVD.


But UMAP like TSSNE, if you know it, builds a neighbor graph in the original space of


the data and tries to find a similar graph in lower dimensions.


But how does it do it?


UMAP stands for uniform manifold approximation and projection.


This sounds intimidating.


And the paper behind UMAP can be even more intimidating.


But do not worry, because we break it down for you.


The two steps of UMAP are high-dimensional graph constructions and its mapping to a lower


dimensional graph.


The construction of this high-dimensional graph is what makes UMAP so special compared


to its competitors, since it's hard to do it right and fast.


And the cool part about UMAP is that its steps are mathematically proven to work.


So first there was the data in the high dimensions and we want to approximate its shape or topology.


Each data point is a so-called zero simplex.


And a certain theorem ensures that the shape of the data can be approximated when we connect


these zero simplices, which are our data points, with their neighboring data points forming


one or two or higher dimensional simplices.


And with this we can approximate the topology.


So all what we need to do is to make these connections.


For this, the UMAP algorithm extends a radius around each point and makes a connection between


each point and its neighbors with intersecting radii.


So far the radii are equal.


But remember, we want to approximate the shape of the data, so we want a connected graph


containing all our data points.


But this wish of ours brings in two problems.


Firstly, it often happens that in the data there are larger gaps, where there is no next


point to connect to in the graph.


This happens usually in low density regions.


Secondly, there are often high density regions, where there are a lot of neighbors in the


given radius and everything is way too connected.


This second problem gets even worse with the curse of dimensionality, where in high dimensional


spaces the distances between points become more and more similar.


Okay then, so if we have these two problems with a fixed radius, then let's use a variable


radius instead.


This choice is also mathematically supported by the definition of a Riemannian metric on


the manifold, but do not worry about that.


Just keep in mind that there is math proving that the choice of a variable radius does


not cause any trouble.


So now the radius is greater in low density regions and smaller in high density regions.


But UMAP does not estimate density directly as a number, but uses a proxy.


The density is estimated to be higher when the kth nearest neighbor is close and lower


when the kth nearest neighbor is far away.


Notice that this k in kth nearest neighbor is a hyperparameter that we need to choose,


because with its help UMAP makes a density estimation to find the right local radius.


If k is big, then more global structure is preserved, if k is small, then the radius


decreases and the local structure is more preserved.


So the right k could give the perfect balance between local and global structure preservation,


but there are rarely any recipes for finding the optimum automatically.


Some trial and error is required, since k depends on each dataset individually.


But not all k nearest neighbors are equal, since each have different distances from the


point we are looking at.


And the connections between each point and their neighbors get a weight, a connection


probability where points which are far away are weighted less and lower connection probability.


Now that this high dimensional graph is constructed, it is ready to be projected to lower dimensions.


This graph projection algorithm is too much for Ms. Coffee Bean to explain in detail in


this video, but you can imagine this projection as taking the high dimensional graph with


their edges as being springs, where each spring is stronger as the edge probability increases.


Which means that points connected by high weighted edges are more likely to stay together


in the lower dimensional space, because the spring holds these points together.


And perhaps interesting to notice is that these spring forces are rotationally symmetric,


which leads to clusters sometimes landing on one side after one UMAP run and on the


other side after another projection.


So UMAP has two main strengths over the famous graph-based dimensionality reduction technique


called T-SNE.


It is faster due to its optimizations and strong mathematical foundations, and it has


also a better balance between locality and globality in clustering.


Take for example this visualization from the awesome blog from Google Pair linked below.


We have this mammoth in 3D on the left and we can see side by side how UMAP and T-SNE


map this 3D mammoth into two dimensions.


We can play around with the number of neighbors taken into account when constructing the high


dimensional graph and we can clearly see how low numbers focus on the local structure,


while higher numbers more on the global structure.


The minimum distance parameter allows to specify how tightly the algorithm will map points


into the target low dimensional space.


A high minimum distance will spread the points more.


But it is important to notice that a stepwise change of these two parameters continuously


changes the UMAP result.


T-SNE on the other side is not that great in this aspect, because when changing the


parameter of T-SNE, T-SNE's result completely changes.


We really recommend you to play around yourself with all examples in this blog post.


So far we have seen examples where UMAP maps from 3D to 2D, but the visualizations we have


seen so far are toy examples.


They're just for us to get an intuition about the inner workings of the UMAP dimensionality


reduction algorithm.


What UMAP excels at is reducing from a lot of dimensions.


Here is a real world example of 764 dimensional MNIST data containing handwritten digits.


It could be nice if we could reduce their dimensions to two or three dimensions so we


can visualize this pixel space the digits are living in.


For this we can write a little python code to load the MNIST data, to load the UMAP package


for dimensionality reduction and a visualization package of your liking.


We like baby plots and you will see why.


We read in the data and we see we have 60,000 training instances of 28x28 pixels, which


are together the 784 dimensions we plan to reduce from.


For reducing we fit and apply the UMAP algorithm and we do it once for two dimensions and again


for three dimensions.


We reduced to 2D and 3D to show you what a cool thing baby plots can do.


It takes both the 3D and 2D embedding and can animate a transition between the two.


How cool is that?


Hereby we can see that UMAP could already cluster almost all handwritten digits together,


meaning that UMAP here worked as an unsupervised clustering algorithm.


Also we can see how useful a 3D visualization can be over just 2D, where more complicated


structures and relations can be visualized.


If you want to visualize these things in 3D yourself, in either R, JavaScript or Python,


and load your interactive 3D plots into a PowerPoint presentation to show to everybody,


check out the baby plots website.


This was it from Ms. Coffee Bean.


Read the paper if you're interested in the mathematical theory and proofs behind UMAP,


find it linked in the description below or watch the first author of the UMAP paper presenting


his UMAP invention linked below.


Now go and reduce your dimensions with UMAP.


Setup Apple Silicon Mac for Machine Learning in 11 minutes (PyTorch edition)

Hello, I've got a brand new M1 MacBook Pro here, 14 inch.


You can see it's got that little clippy MagSafe charger.


And I've just literally set it up,


taken it out of the box.


And in today's video, we're going to set it up


for data science and machine learning


with a Pytorch flavor, because very excitingly,


Pytorch just released accelerated training on Apple Silicon.


And mind you, at the time of recording,


this is May 22nd, 2022, Pytorch for Apple Silicon is in beta.


So we are going to be installing beta software,


but the good news is that it will improve over time.


What we're going to do in this video


is set up a base environment for data science


and machine learning with Pytorch, of course,


but we're gonna have libraries such as Jupiter,


NumPy, Pandas, Matplotlib and TQDM, I think.


Just a handful of helpful data science libraries.


So without any further ado, I'm going to open up Terminal.


And I've got these steps all on GitHub.


So I'm just gonna zoom in here,


put that over to the left, one there.


I'm gonna open up Safari.


And I'm going to go to the website,


the link will be in the description, by the way,


mrdbergk.pytorch.apple.silicon.


And so we've got some steps here.


Here we go.


The requirements are you need an Apple Silicon Mac.


So this should work on any Apple Silicon Mac M1, M1 Pro.


This I've got an M1 Pro here,


but you can see that I'm running Mac OS Monterey version 12.4.


For Pytorch to work on Apple Silicon,


you do need Mac OS 12.3 plus,


otherwise you won't get accelerated Pytorch training.


So what we're gonna finish up with here is an environment


ready to code with various data science


and machine learning packages,


including a version of Pytorch, this is very exciting,


that is able to use our Mac's GPU


for hopefully faster computing.


But just keep in mind, it is in beta at the moment,


it will be improved later on.


I'm just gonna hide the dock as well.


So we get a bit of full screen action here.


There we go.


I like to use that setting on my Macs anyway.


So the first step we're going to do is download


and install Homebrew from brew.sh.


This isn't really specific to what we're doing,


but Homebrew is a package manager,


which is a package manager helps you install other software.


It just gives you a bunch of access


to a whole heap of things that should have come


with your Mac, but didn't.


So I'm just gonna copy that link from brew.sh.


And by the way, all the steps here, you can follow along.


Just gonna enter that in the terminal,


and I'm gonna enter my password here.


Oh, got it wrong.


Hopefully I'll remember it, I just said it.


Third time's a charm.


There we go.


Okay, I'm gonna hit enter.


This is going to download a whole bunch of software


from the internet.


And I'm just gonna fast forward the video


and let this run,


because it may take about five minutes or so.


So I'll see you once this is finished downloading.


All right, so that took about five minutes or so


on my internet connection to download and install.


So there's a few steps that we have to run here


to fully complete it.


And this is run these two commands in your terminal


to add Homebrew to your path.


That just means we'll be able to use the command brew.


So something like this brew install wget.


So I'm just gonna copy these commands here,


and I'm just going to paste that in here.


And then I'm gonna run this command,


and I'm gonna run this command,


and I'm just gonna paste them in the terminal,


hit enter and right clear.


So we have some white space.


Beautiful, that's step one done.


We've installed brew.


That's gonna be very helpful.


Now we're going to download MiniForge 3,


which is a conda installer.


This is going to help us create our environment.


So I'm just gonna click allow.


It's gonna download here, MiniForge Mac OS.


If we look at what that is, MiniForge 3.


If we go there on GitHub.


So this repository holds a minimal installer for conda,


specifically to conda forge.


So MiniForge is really helpful.


Does it have Apple?


Yeah, Apple M1.


So it's got an emphasis on supporting conda for Apple M1,


which is exactly what we're after.


So that's in my downloads folder now,


which is also over here, downloads.


And I've got a little helpful command here


to help us install that.


So now we've got it in our downloads.


We're gonna run these three commands here,


chmod to change the permissions on the file


so we can execute it, which is what this X stands for.


Then we have sh, it's gonna allow us to run the sh file.


And then we're going to activate MiniForge.


So I'm gonna copy all three of these,


paste them in the terminal, hit enter.


Terminal would like to access the downloads folder.


Yes, because that's where this little file lives.


And enter, do we agree?


Enter, enter, enter.


This is just saying,


do we agree to all of the terms and conditions?


I'm not actually sure what's actually written here,


but it's pretty trustworthy open source software.


Do we accept the license?


Yes.


It will be added to,


this is where MiniForge 3 is going to live.


So users, this is my user account,


Daniel, MiniForge 3, it will live there.


Okay, and now it's gonna install


all of these helpful packages.


And namely, most helpful will be the conda command,


which is what we're after.


So we can create an environment.


Do we wanna initialize conda?


Yes, we do.


So you'll notice a little change in my terminal path here.


Beautiful, thank you for installing MiniForge 3.


So now I have a base environment.


So I've got base in front of my name here.


And then if I go conda env list,


this is going to tell us all of the software environments


that we have.


Beautiful, there's base.


So I'm gonna clear that again.


Now what we're going to do is create,


oh, missed this step.


So we need to restart terminal just to make sure


all of those changes came into effect.


I'll quit terminal, open it back up, zoom back in.


So we've restarted terminal, now we've got base there.


So that means we've got access to conda.


I'm gonna create a directory to set up PyTorch.


You can name this whatever you want.


I'm just creating one called PyTorch test.


PyTorch test there, clear, ls,


there's nothing in this directory.


So let's change that.


We're gonna create a conda environment here.


Now Python 3.8 is the most stable version of Python


I've found to work.


I know Python's up to about 3.10 at the time of this video,


but I'm just gonna stick with 3.8.


If you need a different version of Python,


feel free to change it there.


All we're doing is we're creating an environment


inside this PyTorch test directory.


Going to hit yes.


It's going to install Python for us


and a few bunch of other things.


There's pip, setup tools.


I'll let this run.


Wonderful.


So to activate that environment,


we could use this full command,


but I've already done that using conda activate.env.


If I clear the screen here again,


then if I go ls, we've got a file in here called env,


which is short for environment.


That's just how I like to create my environments.


One environment per project.


I'm gonna clear this again.


So we've just done that step.


Now we're going to install


the PyTorch nightly version for Mac.


Now this is something to note.


At the time of recording this video,


we are installing the nightly version.


However, that is because PyTorch for Mac


only just came out.


However, it will probably be


in the stable version going forward.


So just be aware, this command here,


I may change in the future on this website.


So don't pay too attention too much


to the exact command here,


but just know we're currently installing


the nightly version of PyTorch.


If you want to find out the most stable version,


you can go start locally.


I'm just going to maximize this.


We have PyTorch build.


We're currently using the nightly build,


and we're using pip.


However, in the future after PyTorch 1.12,


you should be able to use the stable version,


install on Mac, pip, Python, CPU,


then this command here should work.


But right now, as of May 22,


we are using the nightly version.


So just keep that in mind.


Beautiful, we're installing PyTorch here.


I will update this command on the GitHub page here


when the non-nightly version comes out.


Wonderful, so there we go.


We got a whole bunch of things installed with that.


We have URL lib3, we have pillow,


which is an image library.


We have numpy, we have torch, exactly what we wanted.


The request library for downloading things,


torch vision and torch audio.


All of the nice things that we want.


Now we're going to install


some common data science packages.


This is gonna be really helpful


for when we wanna work on data science projects.


PyTorch is more machine learning focused.


So let's install these.


We've got Jupyter, Pandas, numpy,


matplotlib, scikit-learn, and tqdm.


tqdm is not really data science related.


It's just helpful for progress bars in Python.


So I'm gonna hit yes.


I'm going to let this run.


May take a minute or so,


depending how fast your internet connection is.


Beautiful.


Look at all those packages installed, thanks to Conda.


But now, if everything went right,


we should be able to start a Jupyter server.


So we'll just run the command Jupyter notebook.


This should launch a Jupyter notebook server in our browser.


It may take a little while to run


the first time you've ever run these commands, by the way.


Excellent.


So we're gonna start a new Python 3 notebook.


I'll zoom in here so you can see.


And then I've got some test code here.


So this is going to import PyTorch, numpy, pandas, scikit-learn,


matplotlib, and just make sure we've got access


to all of them, and check a few other things,


which I'll show you as it runs.


So we've got a little check here


to see if PyTorch has access to MPS.


Now MPS stands for Metal Performance Shader,


which is Apple's GPU architecture.


So if you're using Apple Silicon here,


which I've got the M1 Pro chip,


this is what we want access to.


We want to enable PyTorch to be able to use the GPU on our Mac.


Beautiful, look at that.


And we've got PyTorch access.


You see here, we've got the dev version.


That kind of means we've got access to the nightly mode.


Just remember in the future, there may be a stable version,


or there probably will be a stable version of PyTorch


that you can use rather than the nightly version.


So just keep that in mind going forward.


Now, do we have access to Metal Performance Shader?


True.


Do we have it available?


So does PyTorch have access to it?


Yes, it does.


And what device are we using?


MPS, which stands for Metal Performance Shader,


instead of the CPU.


And finally, one last test.


We can go here.


Now, the last time I ran this,


this is just to check if we can send a tensor.


So a tensor of data to the target device, MPS.


We'll run this code here.


Yeah, there we go.


The last time I ran this, I got an error.


Just keep in mind that because we're using a nightly version,


there may be a few errors going forward,


but just wait for the stable version


if you don't wanna see these errors.


But because we're using beta software, we get some errors.


So check out the description for the links.


If you have any issues, leave a comment,


How to code neural networks without math*

Look at this, this is how I think of neural networks.


It's basically just a stack, especially like this is an LSTM but transformers are the same.


Transformers are like literally like a big dictionary. They create an embedding


and you do matrix multiplication on that embedding and there's your transformer model.


But this is how I think of coding neural networks. I never think about the math behind it. I just


stack the things on top. Of course I've looked into the formulas of a sigmoid and matrix


multiplication all that stuff and linear layers but I write neural networks like this.


So yes math is fundamentally important to it all but for me I prefer to write code and learn


Machine Learning INTERNSHIP Day 1 Takeaways | Learning Intelligence 29

On the way to the internship. A little bit scared but you know the same feelings that are scared cause all the same emotions that


The same hormones that cause you to be scared are the same ones that cause you to be excited


so that's what I'm gonna do. I'm gonna flip that scaredness on its head and


Start to get excited and go in and learn some things. Just caught the train here with my little brother


So thanks for the chat, bro


Yeah, I'll catch up with you guys either after the day or through the day if I can share what I've learned.


Oh my goodness. Oh my goodness


Guys, I had an amazing time at the internship today. It's about 7 p.m. Tonight. So I'm home a little late


I was up really early and now it's dark outside, but I'm pumped


You know why? Because when you're learning great things and you're around great people


It's hard not to be full of energy and I think that's that's where I get most of my energy from is from working on hard


things


Committing to it, doing it, seeing results and meeting great people


But I wrote down a whole bunch of notes at the internship


But I took away three main key points that I want to share with you that I think are important at at any stage


of of where you go, so


Number one for me is whenever I go to a place. It's always whenever I go to meet new people


It's actually the first thing is team. If you don't have a good team the foundation isn't solid


Whatever you build no matter what it is how good the product is it won't be it won't be that great and the place I


Went to today they have a great team everyone I met was excited about what they're working on


They're all incredibly smart in their domains and they were just fascinated by technology


Especially AI and machine learning which is of course what fascinates you and I so much


So wherever you go if you're starting a startup or if you're going for a job or you're doing an internship


Please please please sort out the number one thing is team


If you don't if you don't want to work with someone or if you can't work with someone else


It's going to be very hard going forward in the future so make sure first and foremost


You're going to work for a good team, or if you're building something you build a good team number two


Projects and now I can't tell you the exact details of what?


The the company I went to today what they're working on, but I can give you some idea around it


So say for example you're a law firm


And you want to work out how to use your data to make future settlements a more streamlined process


That's the sort of stuff that we were working on so we were taking case histories taking the data from that


Manipulating a bit and trying to work out what relationships are there over?


15 years worth of cases to make future cases more easily streamlined easily easy to go from


Start to finish and that's just one of the projects if you go into a place first of all is team


The second of all is projects you want to be make sure that people are working on some amazing projects and even in your own


World you have to make sure if you want to you want to be driven


You have to be excited about what you're working on so make sure the projects you're working on first of all excite you and


That's the main thing right if they excite you somewhere down the line. They'll excite someone else so number two


projects number three is


Not really relatable to anything outside of the tech world except for AI but brain computer interfaces


but the segue of this is going back into team and talking about people is


Be excited about the things that you're learning right and be excited about the possibilities of your field


I stayed back at the end of the day with with one of the ML engineers the machine learning engineers that I was partnered with


Through the day and we spoke for 45 minutes about brain computer interfaces


for everything from neural link to stuff open AI are working on to stuff he's been thinking about in terms of


Neuroscience and marrying that with AI I would say actually number three can be have an open mind


Right go to these things don't be don't be streamlined in what you want to do of course


Have your own opinion and and be strong on your own interest and whatnot


But beware this is there's heaps of other people around and they're gonna have


They might not be interested in things you are so a really good way to build some solid relationships


Is to be interested in what other people are interested, so there's my three major takeaways from the internship


Otherwise what I was doing is actually just on the laptop most of the day working in Jupiter lab


Manipulating data sets practicing using pandas and pythons


So if you're going into a data science role or an AI role make sure your python skills your pandas skills


Pandas in particularly because that's the major data data science language at the moment make sure they're down pat so


What do you think


One of the most important things you look for when you go on to work somewhere for me number one was team


Can you work with the team? What can they work with you?


Number two is projects. Are you excited to be working on what they're working on?


number three was


Have an open mind and of course the technical skills like python and pandas and whatnot are important


But what I think is more important is being able to take a problem and break it down into smaller steps and solving each step


Systematically, what do you think leave a comment below and and we'll sort it out


We'll reply to each other and we'll figure out a good list of what people should look for when they're going into an internship


And speaking of comments last week's comment of the week goes to Gemini ski


I love the way you applied stoicism wisdom to to going into a new situation by


Assuming the worst-case scenario you always come out on top and I'd like to give a big thank you to Jared Gore


Who's my latest patreon? Thank you so much for your support Jared


I really really appreciate it and guys don't forget to leave a like subscribe if you want to see more but


last but not least as


always


The Applied Data Science Course is epic! | Learning Intelligence 42

We're about to flip and send it dude.


You ready?


Good morning!


You know, everyday I want to get something done.


Today's Sunday, and I still want to do some things, right?


I've already spent the morning relaxing, went for a walk with the boys.


We've got some coffee, so I'm feeling good.


So I write down a list, I want to do some things today,


and the best way I stay organised is with this beautiful yellow book.


I write down whatever I want on here, random thoughts, diary notes,


but usually I start off every day.


Top of the day, well, no, date at the top of the page,


and then about six things, not too many things that I want to get done during the day.


If you can see, write and work out are two non-negotiables for me every day.


I write every day, following them core and medium in my blog, whatnot, if you already are.


I work out every day, and that can be just going for a walk or doing some weights or something like that.


But today I'm going to do DS course week four, as you saw in the last video, finished assignment three.


We're up to week four this time, beautiful, really excited for that.


Edit video, so edit the last video, you probably already see it's uploaded already.


Video clips, which reminds me to actually record these clips today because sometimes I forget.


Play, which is, you've got to have fun every day, right?


And there's seven things on here, so six is kind of a lie.


And the wild card at the bottom here is Panda's Book Part Two,


so I want to maybe read chapter two, but if I don't get to that, that's all right, I'll just put that in another day.


Welcome to Learning Intelligence 41.


Learning Intelligence 42. Let's get into some study.


Now you know what? I'm going to change it up today.


I'm going to study in the dining room because look at that beautiful light source.


Look at this, look at this environment. You can see the outdoors, got a little bird there for some background noise.


Yeah, instead of being in the room all the time, I'm going to sit out here.


Check out the lectures in week four.