Speaker 2: (00:00) Every time I've tried to be like someone else. I've failed terribly whenever I've tried to be myself. I've been the best in the world at it. Surprise, surprise. I think this is a forgotten point, is that the most important relationship that you have in life is the one you have with yourself. Did you have something you want to get after you get after it? You don't need permission. Speaker 3: (00:20) [inaudible] Speaker 1: (00:31) what's up everyone? Thank you so much for tuning in to the artists of data science podcast. My goal with this podcast is to share the stories and journeys of the thought leaders in data science, the artists who are creating value for our field, to the content they're creating, the work they're doing and the positive impact they're having within their organizations, industries, society, and the art of data science as a whole. I can't even begin to express how excited I am that you're joining me today. My name is harpreet Sahota and I'll be your host as we talk to some of the most amazing people in data science. Today's episode is brought to you by data science dream job. If you're wondering what it takes to break into the field of data science, checkout dst.co /artists with anS or an invitation to a free webinar where we'll give you tips on how to land your first job in Davis science. Speaker 1: (01:23) I've also got a free open mastermind, a Slack community called the artists of data science loft that I encourage every woman listening to join. I'll make myself available to you for questions on all things, data science things keep you posted on the biweekly open office hours that I'll be hosting for our community. Check that out@artofdatascienceloftdotslack.com community is super important and I'm hoping you guys will join the community where we can keep each other motivated, keep each other in the loop on what's going on with our own journeys so that we can learn, grow and get better together. Let's ride this beat out into another awesome episode and don't forget to subscribe, follow like love rate and review the show. Speaker 3: (02:06) [inaudible]. Speaker 1: (02:24) Our guest today is a machine learning engineer who plays at the intersection of health, technology, Speaker 1: (02:28) [inaudible] and artificial intelligence. His vision to merge in love for health and technology has led to the development of innovative ways to leverage the power of artificial intelligence to help people move more and eat better. He describes himself as a heavily skewed optimist and as well known in our community as someone who is curious about everything and truly lives his core philosophy of always putting people first and technology second. If you're one of his 24,000 subscribers or 1.1 million viewers and you may recognize him for YouTube where he creates videos to help people learn better and healthier, or if you're active on medium where he has nearly 10,000 followers and over 6 million views, you may recognize him for his musings on the crossroads of technology, health, science, philosophy and life. True to his eeky guy of learning to create and creating to learn. He's crafted one of the most successful courses for machine learning on you. To me, complete machine learning and data science zero to mastery, which has a 4.6 star rating with nearly 14,000 students enrolled in over 120 countries. So please help me in welcoming our guest today, the ever podcast. Mr. Daniel, Bert. Daniel, thank you so much for taking time out of your schedule. Speaker 2: (03:38) It's an absolute pleasure that far out. I feel like a rock star. That was a, that was an inch and a half. So much. That's so kind. I really, I really appreciate it and like a lot of those things I didn't even know to be honest, like the, the views and whatnot, but no, you're too kind. You're too kind. I kind of, I see myself as a dude in the room who just likes to do things like go to hear that Speaker 1: (04:05) you are pretty much a rock star in our community. You've done so much for the data community that it was hard for me to figure out what to leave in and what to lead out from that introduction. Uh, so, so talk to me, talk to me a bit about how you initially got interested in artificial intelligence and data science and walk me down the path that led to the discovery of your achy guy. Speaker 2: (04:27) Oh man. IKIGAI So, uh, I was learning Japanese for a while. So that's where that word comes from. Um, I think they have Japanese and German as well. They have some beautiful where like for example, a German word that's probably a pretty convenient right now is zeitgeists, which is just like the general feeling of everything. Because at the moment while we're recording this, the, there is a pandemic going on. So the zeitgeist is, it feels like the whole world is banning together to work together towards a single problem, which I've never felt that energy before. But IKIGAI is a Japanese word. It's loosely translated in English. A lot of Japanese words, if you translate them to English, my Japanese teacher used to say, no, loses meaning loses meaning in English, but it's a, it's reason for being. So it's like just, just what do you do? Speaker 2: (05:17) Why, why do you exist? And my IKIGAI learning to create, creating to learn. And so it's a, it's kind of like just a circle. One fulfills the other. They both couldn't exist without each other. But that was the second half of the question. So I'll address the first part first. How did I get interested in artificial intelligence? I suppose if you're a nerd like me and you go on the internet at any point in time and you're trying to learn anything about technology, you're gonna stumble across artificial intelligence at some point. I remember, I always remember being distinctly like a single digit age. Yeah, I've run has these like cool stories when they were wearing, they were a child. You don't know if they're made up or not. This one might be made up, I'm not sure, but it's, it's pretty, it's pretty, uh, it's pretty deep in my memory. Speaker 2: (06:05) I remember my mom telling me a story when I was like maybe six or seven in the car and I think it was, I've subsequently done some research and found out that the movie actually does exist. It's, I haven't seen it, but I probably should. She told me about this movie called robotic man and robotic woman or something like that. And what happened was the man got injured at some point or something like that and they replaced all of his injured body parts with robotic limbs and something to do with these brain. And I just remember being like six or something like that, playing with Legos and building robots and just hearing the story and going far out that that is amazing. So did nothing about it for like the next 15 or 16 years until I started to learn to code online. Long story short, I wanted to learn to code. Speaker 2: (06:49) Uh, I was working at the Apple store, I was servicing and selling technology. I didn't really, I was a genius right there. The genius behind the in behind the genius bar. I didn't really like doing the repairs like on the computers, but I did like talking to people and I so I would often say to my colleagues, I'm like, Hey do you want to, do you want to take my repair shift which was down underground and like a dungeon and I'll deal with the customers coming in. Which was sort of people thought I was joking at the time cause they're all like are you sure? Like cause cause the repair shifts were held as the Holy ground because you could sit there and fix computers while watching YouTube and not have to talk anyone. But I was all set. I was like, I want to talk to people. Speaker 2: (07:30) And then I got to a point where I'm like, you know what? I've seen people come in with all these, that building all these amazing things are using this technology for different things. Like a lot of people would use that iPhone just to browse our Facebook and Instagram. But I was talking to creatives, everyone, people from all walks of life. And I'm like, you know what? I want to start to use these things that I'm servicing, selling to, to build things, to build technology on it rather than rather than just selling and servicing them. And so me and my friends, we would, that we worked together at the Apple store cause there was like 120 of us, 20 somethings really good fun time. And we would go to gym together, but we all went to separate gyms and so we had a problem. We were like, we would try to work out with each other, but the gym is like, Oh you're not a member here, you're not a member here, so you need design this foam and just a whole bunch of crap before you can even enter the door. Speaker 2: (08:17) So we were like, well let's build a service called any gym, which was like the Airbnb of gyms. So basically if I wanted to go work out at gym X, but my friend was a member of gym Y and we wanted to train together, we could just go, Oh yeah, any gym suite, sign up, get a pass form, walk in the door, scan it, whatever we're on. And so just simplifying that, right. That was just out of our own problem and we're like, yes we can just link up every gym in the whole world and you could have one membership and then go everywhere. So we started building that and this is, I had no previous like any coding experience, I'd done six or something exercises maybe four years prior to this in terms of building stuff online but then gave up hope really quickly. Um, and so I was building this not just with WordPress and and different plugins. Speaker 2: (09:06) Long story short, that failed after a few months cause we just lost interest. But I left my job at Apple in February, 2017 cause it just got to the point where I was like, I was loving it there but I was like building this thing on the side and I'm like no, I want to just, I was like I want to build it full time. I've got some savings. I'm going to just chill out and work on this and try to learn to code. Anyway, a couple of months after I left my job at Apple, we gave up on it because we were just really, if we're honest, we were in it for the wrong thing. Like we just wanted to, we saw the great success stories of these startups coming. Billionaires, we will know it in by that, right? It's like, Oh yeah, give me that big carrot of money. Speaker 2: (09:41) And so I was like, that motivation didn't hang around very long, you know, I don't know. Somehow I got annoyed at like trying to write all these different rules for like if someone came on and this happened and that happened and whatever, and because I had no programming experience my phone out, there's a lot of things to think about here. What scenario happens here? And then I actually don't know how I stumbled across it, but I think it was, I was, I was going to you Udacity to, to like one of their courses I like stumbled across like, how do I learn when development came across your Udacity, all of a sudden they're like, big promo at the time was, um, uh, a deep learning. Now that agree with Saraj revile, like he was the front guy like, and I just watched it on that far. Speaker 2: (10:25) Yeah, let's do, it reminds me of me like he's like a bit of a lunatic and talking about technology, I'm like, okay, okay, what's this? I had no idea what deep learning was at this point, but I was like, I saw like the infographic of what it could do. And it's like, okay, you have your, your data here, you pass it through some sort of computer program. Again, this is me not knowing anything about it. And so this is all myriad minima yes, colors, circles connected with dots, which was subsequently a neural network. And then it came out and it's like, uh, this is a photo of a dog or a cat or whatever. Like you know, this simple like demo that you always see the first one, I'm like, Holly, like you're telling me that all you do is you just go, here's the stuff I want you to learn computer and put it through in some way and then it comes out and the computer learns it for you. Speaker 2: (11:13) And so he has me who is like trying to code all this logic for, for our web application and just been like, I've got no idea here, but I could choose. Instead I could just use these deep learning techniques and have the computer learn for me. And so that fascinated the hell out of me. And so I'm like, you know what, what's the prerequisites for this thing? I was, I had no job at the time somewhere. I got plenty of time to learn. Um, I was living off savings and um, at the time it was a pretty big investment. Like I think I had maybe $7,000 in savings. So my living expenses are pretty low. Um, I like to keep it that way so that, that was enough for like six months of just living how I was. And so this Nanodegree was pretty expensive. Speaker 2: (11:54) It was like a thousand Australian. Again, I'm not sure of the exact figures, but a significant amount. And then it was like, I looked at the prerequisites, it's like Python code. I'd never written Python before and, but the signup date was in two weeks. And so I'm like, you know what, what the hell? I'm going to just try it out. This is exciting me. I've never really signed up to one of these things for myself before. I'd gone to university for five years and done a nutrition degree, failed two years of madness and whatever. But I kind of learned how to learn on my own. So I'm like, you know what, what the hell? And so it was like started in two weeks. So I went on to Treehouse, learn Python for two weeks. I'm in the Slack channel for [inaudible] great st all these people. Now I am a software engineer from Google. Speaker 2: (12:39) I'm just like, what? I don't know what I'm doing here. But anyway, signed up, went through that, got started, got fascinated while it caught the artificial intelligence bug as you will hand it in all my assignments a few days late because I was, it took me way too long to do them cause I just kind of had to learn everything as I went, as anyone has to do when they start a new course. That is. And so that was the long-winded tale of how I got into it. And then I just kept going. I figured out once I finished the deep learning course, I figured out I need to learn more things, put them all together in my master's degree, master's degree and then kept going. Speaker 1: (13:16) That's, that is freaking awesome man. Like the way you just transformed yourself from, from Apple genius to like machine learning genius. Like that's freaking awesome man. Speaker 1 WhatÕs up artists check out our free open mastermind Slack channel, the artists of data science loft at art of data science, loft.slack.com I'll keep you posted on the biweekly open office hours that I'll be hosting and it's a great environment and community for all of us to talk all things, data science, look forward to seeing you there. Speaker 1 It's such a, it's such a transformation kind of in your own career and your own path over the last couple of years. Uh, where do you see the field of data science and AI kind of heading in the next two to five years? Speaker 2: (14:09) Well that is a good one. Um, I don't like to make predictions because it just, if y'all look at my track record, I think human beings just in general are terrible at making predictions. I think it's just becoming more and more of a tool. So my bias is towards using machine learning as a tool. Like I would, so I've spent the last couple of years learning like just pure machine learning and data science, steep learning, all that sort of stuff. But now I see myself transitioning more into sort of combining that with something like another developer skills such as web development, mobile application development, and then using what I've learned as machine learning as a tool to build a product of some sort. That's where I see it more and more going. Like for example, you've got tools such as auto ML on Google cloud and all this sort of stuff. Speaker 2: (14:55) So what it's going to be more, more like is once we sort of figured out, okay, we've got the main problems in machine learning, regression, classification, recommendation systems, et cetera. If we know these from the forefront, which we kind of do by now and we can design systems and databases to store data in a way that we collect from whatever services we develop that is suited towards those problems and then rather than the kind of the try and error sort of mode that that deep learning is at the moment, it's like adjusting hyper parameters manually. All that sort of stuff is that once your data is just once you build your product or service, once your data is in that format because that's the [inaudible] takes most of the time, right, is getting the data ready into a format that can be used with machine learning and then you just Called fit and everything works out for you. Speaker 2: (15:43) I think going forward over the next couple of years, it'll just become part of the software stack as in uh, you're developing a web application or mobile application of sorts and machine learning or just be one of those things. I, it's getting to that point now I think with like frameworks, like TensorFlow, JS and teachable machine, and you'll just be able to plug it in at some point and it'll solve whatever order. It'll give you a different way of looking at whatever problem you're working with. That's fascinating man. Yeah,that's a fascinating, yeah So that's, so that's just where I'm thinking from a research, I don't know cause that's not where, where my interests lie. But in terms of developing, I think it'll be, it'll just become more and more as something you can just plug in and you're off to the races. Speaker 1 : That's awesome man. That's really, really like, I never thought of it that way. It's really great observation. So in this vision of your future, what do you think is going to separate the great data scientists from just the merely good ones? Speaker 2: (16:35) I like this one as well. That's a great question. So what I've been thinking is with that being said, uh, so I've got a disease, right? And I think every engineer has, has this kind of disease is that engine is like learning tools for tools sake. And then the problem with that is like for example, something new gets launched and you're like, yes, I need to learn that tool. And it's like, yes, I need to learn that framework. I need to tend to flow all of them. All these new features. Yep. I'm going to learn every single one. And so I have that tools, right? I see. See, see new things come out of the tunnel. Speaker 2: (17:18) I want to learn that, want to learn that. But then what happens is that instead of becoming like an artist in workshop that you have in your backyard where you have like a dozen tools that you use to build things like chairs and build boats, things that that you can actually use. You become a hardware store where you've got every single possible tool that you could imagine, but you don't know how to use all of, well, you don't know how to use all of them, any of them in debt or you've got too many choices for when it comes to actually trying to make something. And so I say this right, I saying this, but it's important to remember that I'm still, I'm still a, I still see myself as a beginner in this field. Um, what's important to note is that I'm just a, a talker in that sense. Speaker 2: (18:04) So my engineering skills don't match what, what I'm capable of communicating yet. Like I build my communication skills through dealing with thousands of customers at Apple. So in my opinion is going to sort of separate people from, from average Joe to someone who's, who's seen as great is someone who's able to take the small collective amount of tools they have but applies to constraints to themselves. You see, that's what professionals do. And amateur says, give me everything, give me more tools. A professional goes, I've got enough tools now, a couple of half thousand, whatever it is. Now I'm going to use these tools to build something thing and then deliver it out in the world. Because again, once, uh, once something's done in the real world, then that's when it gets tested. That's, that's the real battle testing. So that's what I see is going to separate. It's like if you, what can, and this is what I always sort of like criteria is like if I look at someone's like, what have you delivered? Like that is, that is like my sort of, for lack of a better word, of judgment, of, of skill is okay. It's one thing to, to be able to like show, okay, you've, you've worked on this, but it's like how much of it is in public and how much of it has been interacted with someone who's not you. Speaker 1 (19:22) Oh dude, I love that was fucking awesome man. I love that you said something in there that, that really, uh, is a great, uh, set up for the next question I had. Right. So an artisan being an artist and with a bunch of different tools at your disposal, right? So would you say data science and machine learning, would you consider that to be a art or purely a Art science? Speaker 2: (19:42) A lot of things are the same thing at the same time, right? Where it's like all of more than one thing at the same time. So you could bucket, I read a great article the other day, I've actually recorded sort of a video on it, but that's going out later, which is a don't learn machine learning. And as the author, uh, builds, builds tools. And so the context is important. The author builds tools to, uh, deploy machine learning models. So he, the article is in the sense of don't learn it if you're not going to build anything with it. And so, okay, that's, that's one way of looking at things. And this is where, but I like that he had a strong opinion on it because that's if you get stuck in sort of the gray matter of not having an opinion on different things. Speaker 3: (20:23) Um, so you said machine learning or data science, art or science. So this is, I think you could, you could take it from both angles. Um, one of the points in the articles was, are you wanting to build products or research? And I think, yeah, okay. That's a good decision to make. But you could also go the, the products that you're building, um, trigger the research to happen. So you're like, you run into a problem, I can't solve this with certain product, so I have to research and find new ways or you do research and that implies eventually that you found something, okay, I'm going to turn this into a product. And so, uh, Western philosophy like to see things put things in buckets, whereas Chinese sort of last fee is everything is the same thing at the same time. And so machine learning, art or science, I like to see it, uh, as both. Speaker 2: (21:11) Um, so that's how I keep it fun for myself is that I own an income through my formal things in terms of machine learning, I create machine learning courses, basically just taking complex documentation and communicating in a way that people can understand. Um, and that is, so that would be my, uh, science sort of thing. Making a work of it. But my art and using it is, is writing about it and, and making articles and whatnot and putting narratives around it or making YouTube videos about how, um, I'm learning different concepts. So I see it from both aspects and one this is coming back to the learning to create, creating to learn one fuels the other. When I get tired of seeing machine learning as a, as a science type, say I can recharge myself like by coming at it from an artist's point of view and then vice versa when I've had enough of of sitting and watching myself code on a YouTube video, well I edited it and like far out man, I spent eight hours just watching myself talk. Speaker 2: (22:16) I can come back to it from a science aspect and go, you know what happened? I use this to create value for, for someone else in terms of a way that we can exchange in a financial sense because that's, that's one of the ways I make a living. So they feel, I think that's a really not just machine learning. I think you can do that and sort of almost in any aspect as you have. Um, some of the, the coolest people I've met, right? We'll have like some way of formal way of formal, again in inverted comments of, of earning a living. But then they have an art on the side that they pursue out of pure love. And so my formal way of of of making a living is through courses and education resources. But my art is writing articles and making YouTube videos. Speaker 1: (23:04) I love that. I love that. It's just love that viewpoint because I share very, very similar viewpoint to that. I kind of see it as both an art and a science. It becomes an art when you put that emotional labor behind it, right? When you're delivering something to the world as a gift. And I think you do that very well with your content man. And speaking of, speaking of, of applying it as an art, um, at least to me, I'm pretty sure you one of the OGs of the a hundred days of code, because I remember very vividly when I first came across that hashtag it was one of your posts. So talk to me a bit about what got you started on that. Speaker 2: (23:38) Why can't, I honestly can't remember how I got started, but I do remember seeing it as much like you said, and then just going, you know what, that's a pretty simple way to do things. And it wasn't, it wasn't really over-thought too much other than that. And the reason being is I kind of, I made a podcast a day for a hundred days before in the start of 2017 I know, it was just me, me talking. I wanted to improve my conversational skills in front of a camera and with a microphone listening. And it wasn't, it wasn't, I had a few guests on it, like for example, just friends and my dad and other people. And it was really just, I wanted to improve my ability to speak cause I hadn't done it in a while. So I was just like, well I'm going to make a podcast today for a hundred days. And that turned out that the beautiful thing about it was, is that it was, it made the decision very easy. It's like, have I made a podcast today? No. Okay. Make a podcast today. Um, and so the same thing. I just applied that methodology with learning to code. I'm like, well, I want to learn to code and we're going to take this seriously. It's like, yes, I want to have fun doing it, but I, if you want to learn anything it requires, if you want to build any skill requires discipline and so simple. And it was just, have I practiced code today? No. Okay, well I have to do it. Speaker 1: (25:00) I love that man. I love that wasn't her favorite, favorite day out of that a hundred days of code, did you, do you have one that's sticks Speaker 2: (25:06) I did. I did read that question, but I don't have a right answer. Like I, I, as I said, we, we forget it. Memories, memories. A funny thing, right? We forget the specifics. I do remember the feeling I, so I remember there'd be days like, let's go day one yet feeling massive amount of energy day 27. It's like, well, I spent all day bailing my head up against a wall day 26 on some problem and now I don't really want to go back and do that because it sucked. Um, yesterday and so day 27, look, I'm making these days up, but you have those days where it's like I don't really, yesterday it was pretty shitty. So I don't want to go back and have to face that problem again. Speaker 1: (25:54) So I want to jump into, talk a little bit about uh, some of the content that you've got on your blog. He has some very amazing blog post, especially the one that really sticks out to me amongst many and we'll get into all of them, but you've got some content on there about learning how to learn. Especially you have an article there about six techniques to study machine learning every day. So do you have some tips for our listeners that they can implement today to help them along in their upskilling process? Speaker 2: (26:23) One of the best one, one of the ones I've implemented from myself over the past couple of years, or whatever it is, is being your own biggest fan and harshest critique at the same time, because no one else is going to do it for you. Um, and so it's very easy.I think people, it's find it, well, at least me personally, I think it's very easy for people to be hard on themselves because all, all that the internet kind of is, is a big comparison machine. So what you'll see is, I know this happens to me too, so I know exactly how it feels. Uh, you'll see someone put their work out there and you'll look at it and you'll compare your current skill level to do their skill level. Even though you might be completely different chapters of your life, don't compare your chapter 1 to their chapter 10. Speaker 2: (26:53) And so very easy to be hard on yourself and going, well, I'm not gonna put anything out there because it pales in comparison to what this person is doing. And don't worry, I do that too. So it's very easy to do. But what is even harder to build? What's what I've sort of been working on is become your own biggest fan. And now this is, it's, people might say, Oh, well that sounds like you're fully yourself, but it's no, it's, it's, it's a, it's the same lesson that you get taught in the airplane every time you get on, it's put your oxygen mask on first, help yourself and then help others. So if you become your own biggest fan, you're putting work out there, you're putting your soul into your own own, whatever it is, your own articles, your own videos, people are going to feel that in some sense. Speaker 2: 28:02) Yeah. Because you cannot describe that. They will, can, we have this energy about us if you can't put into terms you can't analyze with, with data and all that sort of stuff. And so that's what I try to do. Whatever it is. I'm trying to learn whatever it is and trying to create, of course, I take it seriously. I'm hard on myself and like I go back and I go, well, this could be better, right? But at the same time ago, I tried to make things that I would personally like to, uh, like to have read or like to like to have watched. And so in that sense, I'm, I'm a fan of my own work. Speaker 1: (28:38) I dig it, man. I dig it. That's kind of the main, the main reason I'm creating this podcast was that, you know, there are a lot of data science podcasts out there that are, you know, doing tremendous work with respect to, uh, talking about technical concepts and technical stuff, right? But there's no podcasts out there that talk about data scientists in their journey and their journey, the struggles they faced. And so kind of, I don't, I don't see that out in the market. So like why not me? Why, why can't I create it? Speaker 2: (29:07) That's so good. I totally agree. I like to, as mad as it is, I prefer the story of, see, it's, uh, I think like you can get advice and mentors of all ages, right? But I think I really underlooked one is someone who's like one to two years ahead of you. Um, so I was exempt because the cruise, if you, if you look at someone who's, who's been in the game, say for example, a CEO of a company, let's use Jeff Bezos, CEO of Amazon or something, just cause everyone sort of knows, jeopardize us, right? Amazon has been running for a couple of decades. If you get advice from him on how to start your own business, it's sort of, cause he's so many degrees away from, from the actual start, it's going to be a little bit like muddled right in there. That's just, that's just, you try to remember what things are like 20 years ago. Speaker 2: (29:58) I can't, so okay. There may be some principles that that continue over time, but in terms of specific details and technicalities and whatnot, a great person to look up to is someone who's just a couple of years ahead of you. So they know it's still kind of fresh in their mind. And so that's, that's what a lot of my work is, is, is talking about, it's, it's what would I have liked to known when I was getting started or the me, uh, two to three years ago or one to three years ago or something like that. Um, because those lessons are still still fresh in your mind. Um, but yeah, exactly what you said. I think I, I would love to listen to, to something like this. I love hearing the story. People like to see how the sausage is made. Like how someone is, is, is learning things like imagine being a fly on the wall while Zach Newton was figuring out calculus or something like that. So that'd be, you know, funny, fun fact actually good timing is that in 1665 when Isaac Newton sort of had his breakthrough year and whatever, um, Cambridge university was closed down due to a plague. So that's like, so he was at home and had nothing to do except play around and figure things out and he kind of, he worked out the laws of motion and then calculus while he was on, um, locked to half basically. So little bit of inspiration of if people are locked in. Speaker 1: (31:27) Oh man. Hey, I like, I like, I like what you're saying about finding a mentor that's, you know, maybe just a couple of steps ahead of you as somebody like you. Like, you know, I'm, I'm a mentor for up and coming data scientists and I found your article on being your own mentor. Really insightful. Um, do you think that you can share a couple of actionable tips for our listeners so that they can implement that kind of philosophy and become their own mentor? Speaker 2: (31:53) Yeah. Well, you're going to find hates, but it's, I think it's a little bit of skin in the game. I think the tips or techniques that you can, it's, I find the best tips and techniques are when someone says what they personally do. So of course there's a, there's a list of things of, of, of what you can do to be your own mentor. Some might be better than the ones I do, I don't know. But I find the best is, is when you ask someone, what do you do? Like personally, like for example, if you are investing, it's like where is your bank account? Like that is the best advice because just like you are not, it's not, it's the truth, right? So here is what I personally do. So I write, I journal a lot. And this, you'll hear this technique a lot. Speaker 2: (32:41) Um, I about a thousand words per day. I don't count. I just, uh, just let things ramble. And what it is, is it's a conversation with myself. Um, you might see this and I'm keeping notes at the moment for one of the projects I'm working on, a lot of them are conversation with myself cause that's how I understand things. And when you first start, um, so I didn't do this before 2017. Um, so at the time I'd just, a reason why I started is I'd broken up with my girlfriend. I was like, heartbroken, sad. These things happen. That's life. And then I read like a one way to figure things out, these two to write them down, have a conversation with you It's just, it's just me and me and me. Um, and so practicing that every sort of day and I was like, what's wrong? Why? Why am I feeling like this? What's, what's holding me back? Cause that's, that's the role of a mentor, right? Is to not necessarily give you the right answer because that's, it's a, it's the horse to water or you can teach a man how to teach a man how to fish. He eats for a lifetime kind of thing. A mentor is not a good mentor, won't give you the direct answer. And this is what I tried to do in my machine learning course as well as like when people ask questions, I don't give them the answer. I kind of asked them, what have you tried so far? And some people might get pissed off and that it's like, just tell me the answer there. Speaker 2: (34:09) Um, but I believe a good mentor kind of seed you with questions to get you to think about your own roadblocks and how to remove them. Because in the, at the end of the day, you don't learn anything from getting answers. You learn things from asking more questions. And so that's what I'm doing when, when I'm trying to be my mentor, when I'm writing things down because you get lost in thought but found in words because the words make it concrete. So I write down questions like, what's what's holding me back? How would, how would I like to spend tomorrow? How would I like to spend next week? What's, uh, what's the main from three to five years? How do they think, um, what happened last week that that could be improved on this week? So just, just things like that. A lot of people, I don't know why, but have lost every time I've tried to be like someone else. Speaker 2: (34:58) I've failed terribly. So whenever I've tried to be myself, I've been the best in the world at it. Surprise, surprise. So another technique that I do is making eye contact with yourself in the mirror and just looking and just talking to yourself. And it sounds, of course it sounds crazy, but this is, I think this is a forgotten point, is that the most important relationship that you have in life is the one you have with yourself. So if you're being completely honest with yourself, you're loving yourself, you're staying true to yourself like that. It's not, it's not going to happen overnight. It takes practice. And so like learning anything, like building a skill. It takes practice to develop over time. Same with becoming your own mentor. It's something that you have to practice. Speaker 1: (35:50) I love it, man. Especially the part about looking at yourself in the mirror. I don't know if you're familiar with that, David Goggins, but he has this, this concept of the accountability narrow where he just stands in front of a mirror and just stares himself in the eyes and just gives himself this type of mental talk. Um, so Adam Grant, who's a tremendous Arthur, one of my favorite authors, disgust, has this excellent quote about mentors. And it's like the best way to learn from mentors is not to absorb what they know, but to internalize how they think. Collecting their knowledge helps you address the challenges of the day. Understanding their thought process helps you navigate the challenges of a lifetime, which I think is awesome, right? That's the difference between giving somebody a step-by-step map and a compass. I'd rather take the fucking compass any day and just find my own way out. Speaker 2: (36:39) You're right totally agree. That's Adam Grant. I'll look him up. Speaker 1: (36:42) Yeah, man, he's awesome. He's written. Um, he's written give and take and originals, which are both excellent books, man, highly recommend them. Um, he had this other post that I really liked. It was called sucking at something new that really resonated with me. Um, especially when you're breaking into the field, you're pretty much going to suck at everything until you don't. Uh, so there is a line in there that where you kind of have to remind yourself, I'm new here. It's okay to suck. That really like, like gave me chills man. Um, can you, can you speak to that? Yeah. It was powerful man cause it really resonated with me. Right. Like really, really Speaker 2: (37:17) That's a, that's a, that's a, that's a the treasurer at the end of the rainbow for a writer is to to hear that their writing has invoked a physical reaction. That's that thing that I appreciate that. Speaker 1: (37:30) So, so can you, can you speak a little bit to that and maybe provide, you know, some tips to help people that are going through that suck phase? Speaker 2: (37:37) I'm going to be honest. I still see it as, it's almost every day. Is is day one, right? It's like it's okay. It's, it's uh, uh, the ancients had this, this saying, it's something like the craft is long, but life is short as in any craft that you take up, whether be machine learning or data science or writing or whatever it is some sort of form. But if you're a true, if you want to be a trained to true mastery of it, it's going to take an entire lifetime. And even then the drew master knows his eye or he or she once they reach the end of their life, whatever. But it's like there was always something more to do. Like Einstein, the story is he was on his death bed trying to solve the last theory that he had or something like that. Speaker 2: (38:21) And so I think the sucking phase is always going to be there if you continually challenging yourself. And now it's only prevalent really a lot at the beginning. Well, no, not only prevalent at the beginning, it's prevalent wherever, whatever stage you're at, if you're trying to improve yourself. And the important thing is to remember that progress disappoints in the short term. But surprises in the long term. So for example, day to day, you're not going to see very much change at all. You might actually even think that you're getting worse. They did that. But that's, that's not a way to compare any sort of worthwhile, worthwhile skill because as you know, um, as we all know, building worthwhile skill, not just pseudo skill, like something that like you just hear people who are aloud about things kind of like me, um, takes a long, long time to build. Speaker 2: (39:18) We're talking a couple of decades here. If you're comparing yourself day to day, week to week, month to month even, you're going to always be disappointed. It's going to suck majorly broil sock. However, compare yourself on the longer term, six month on six months or a year on year, and you'll start to see if you've been in our, I'm not going to say that it's not going to be hard. It's takes diligence and discipline to build any kind of skill where I happen to build skill and machine learning over the past couple of years. You might be the same. You'll see at the end of your first year, you're like, wow, I've leaps and bounds where I was from the start of the first year. As long as you put in the, the discipline and the effort to build the skill, the same thing we'll go from year two is like from year two to the start of year one, your, you'll be leaps and bounds to where you weren't there. Speaker 2: (40:12) Same thing with year three. And then subsequently, however, day to day terrible progress that's going to suck. But that's um, if you're too cushy on yourself and that's where it's going to suck even more because, and this is what I said, it's going to take discipline and whatever it is, if you're too to on yourself, it's going to suck even more in the longterm because you'll start to realize like, wow, I didn't, I didn't really improve too much cause I just coasted through. Um, and so that, that, that longer term feeling of disappointment sucks way more than the, than the term, uh, day to day effort that you put in. Speaker 1: (40:56) Yeah. I like that man. Small, just small incremental efforts everyday. Small incremental changes aren't noticeable until you reflect back on it. Long enough time point to be like, Oh shit. That's where I was before. Look at me now. I dig it man. That's a great philosophy. Speaker 2: Yeah, exactly. Longterm . Speaker 1: (41:19) Are you an aspiring data scientist struggling to break into the field or then check out DSD J. Dot. Co forward slash artists to reserve your spot for a free informational webinar on how you can break into the field that's going to be filled with amazing tips that are specifically designed to help you land your first job. Check it out. DSDJ.Co/Artists. Speaker 1 Uh, so you mentioned something earlier that I wanted to get into. Um, so you know, there's a lot of noise out there in terms of resources for learning. Um, tell me more about your self created master's degree. How did you come up with that idea? Where did you find the right resources and how did you design that curriculum? Speaker 2: (42:03) Yeah, so I think it might've even gotten worse, to be honest, since I created that. And what I mean is that I not worse as in the resources, the resources are phenomenal. All of them are great, but it's the paradox of choice. It's when you have too many options, you have no options. There's a great book by Barry swats on this, uh, I believe it's called Barossa choice. But anyway, so once I finished the deep learning honor degree back in start of 2017, maybe halfway through 2017, I kind of finished and I was like, well, I don't know, I know this deep learning thing but not very well. I need to learn some other stuff around machine learning and just coding in general. Like I didn't, I didn't really know how to write Python code very well at all except to just fill in gaps in deep learning projects. Speaker 2: (42:50) So I was like, well, I kind of need some sort of pathway to follow or I could just plan around at least to build a foundation. Once I had a foundation, kind of knew what was, what was what, probably still building that foundation to be honest. But, um, I was like, then I can build upon that and get really specific, uh, in terms of applying the skills that I'd built to, to something that I'm really interested in, whatever that was. And so I did some research. This is S it's not, it's not like some grandiose thing. I just went on the internet for a few days and started researching how to learn machine learning or what courses are best for machine learning or how to learn Python code. And so of course anyone does that search today. They'll get a mountain of stuff. And I was going through them like, wow, this is in Udacity. Speaker 2: (43:43) You got you to me, you got Coursera ego, everything, tree house, whatever. Um, and I'm like, this is too much. Like as, as much as it was triggering over the dopamine hits in my head going, yeah, yeah, yeah. Give me all these resources. If you don't choose something, you'll make progress in, in nothing. So I, I basically just collected the, the ones who people would mention, see this is, this is where we come back to. It's like, what did you actually do? Like the best, the top best advice. I'd like to hear what, what did you do? So I read stories of what people actually did to learn these things and I'm like, well, I'm just going to steal exactly what they said and add my own little twist to it based on my own curiosity and own interest and then go, you know what? Speaker 2: (44:25) I'm going to put these things together. I think this is the right order. Again, I didn't really know at the time that it would, um, what order I should burn things in. And really, um, I to think you, you almost don't have to have any sort of order. You can just bounce back and forth. But I had this collective amount of resources and I'm like, okay, I'm going to put them in an article. You got to make the article public that way if I fail, if I drop off the wagon, because again, I was in my bedroom alone, like I needed some sort of little bit of accountability. So I put it public and I'll, if I fail, it's going to be publicly. And that) They don't like other people knowing that they failed. And so that was a, that was one of the reasons I put it out in public is like, okay, well I've got this thing here and if I'm going to be creating things sort of around bitten based on it and people are inevitably going to stumble across and there'll be, someone might hit me up in like a year and go, Hey, what is your progress on this? And even if they wouldn't, I had it in my mind that someone might do that and so well I need to, if I actually, if I'm serious about building skill in this field, that was my compass. So that article, as you mentioned before, was my compass to sort of guide me if I, if I thought about, okay, what should I learn next? I would go back to my resources and go, well I'm going to take down this next. Speaker 2: (45:53) That actually turned out to be one of the best things I ever did because, and the important thing is here is that I practice learning on my own before. And so that was one form of using a technique when you're learning how to learn is creating some sort of, and I don't want to, I want to emphasize that the structure doesn't have to be rigid because there's a best learning happens sort of by accident. Uh, when you, you're sort of like the internet was discovered by accident. So yes, make some sort of rough outline, which was what my master's degree was. And then that was to build foundational skills in the field of, of AI and ML. I, so the important part was the knowledge, the best knowledge, the things I remember most, uh, when I've taken what I learned from that baseline curriculum and applied it in some, something of, of my own accord. Speaker 2: (46:50) So that is where I think the real knowledge happens is when you take some sort of foundation knowledge that you build through a course or some sort of online reason those, and then put it into your own project. So injected some of your own style. Speaker 1 I love it that, that segues really well into the next question I had. So you talked to me a bit about interview on certificates versus projects. You've got an awesome article on the weekend project principle. Can you briefly break that down for us? Speaker 2 Yeah, sure thing. Well, this one's, I've thought about this answer and it came to me pretty quickly. So I like the philosophy of our first thought. Best thought. So I could probably think this out, but really they want to sing is when I hear about someone, when I think about someone, I don't give a shit of what certificates they have. Speaker 2: (47:39) I look at what they've built. So let's another really public example, Elon Mask, how, what degrees does a Mask? I have no idea, but he has, I know he's built three, four or five companies of, of things that have brought value to others and inspired others and I, and he's just an easy one. Right. But the same thing goes with almost any, any one of any person. I look up to my own mom, I look up to my own mum. If she doesn't have any certificate to say that she's a great person, she, she just is. And so that's the same thing with, with a certificate shore. They're one great way to, um, to demonstrate I could just just keep showing up and just cruising by. Yeah, I'll, I'll pass the exams or whatever, get the bare minimum. And then I've got the same certificate as someone who, who put in a lot of effort and that sort of stuff. But where I think, what have you done on off your own accord? What have you, what have you seen in the world that you would do if nobody was watching? That is what I, that is what really impresses me. Rather than someone just showing, showing me this, this certificate, like it let's, let's face it an online course. I can sign up today right now and just leave all the lectures on autoplay and get that course. I'm not saying that people do that, but um, you can't really do that when you're building your own thing. Yeah. If you just coast through it, it's not going to exist. Speaker 1: (49:16) So one last question before you jump into a real quick lightning round. What's the one thing you want people to learn from your story? Speaker 2 Oh my gosh. I think it comes back to what I was, something I mentioned before is that every time I tried to be someone else, I failed terribly. Every time I tried to be me, I'm the best in the world at it. So if you have something that you want to get off to get after, and I think that's, that's the, that's the only message I have. And that's the one I want to. And really it's, it's of a, as I said, it's a self, it's a fulfilling message because that's the one I want to internalize myself. So I broadcast that. Uh, I try at least try to, if you have something you want to get after, get after it, you don't need permission. Speaker 1: (49:57) I love it, man. Yeah. Everybody else has already taken, right. So might as well be you. Speaker 2: (50:01) Yeah, man. That's it. It's, it's, it's a, it's a simple, it's cliches that have the truth in them. Um, so it's, yeah, you can get into the technicalities, but it's, it's a, or had to do things and that sort of stuff. Um, but yeah, it's, it's that energy that you get from pursuing something that you know, you know you're into. So that's, that's a good energy to feel. Speaker 1: (50:24) Awesome. So let's go ahead and jump into our lightning round. First question, Python or R? Speaker 2: (50:32) Uh, I'm biased. I only know Python. So, um, uh, I've heard a lot of great things about R but I have no, I can't talk with any experience. So Python. Speaker 1: (50:43) Awesome. So what's the number one book, fiction or nonfiction, that you would recommend to our audience here? And what was your most impactful takeaway from that? Speaker 2: (50:54) Uh, anti-fragile in the same to lab. So that is, it's basically my Bible, um, or the whole series actually insert. So, uh, so it's, it's for uncertainty. Uh, that's, I think it's Latin for uncertainty. So there's five books in the series. Um, I believe these books will be read in, in like a thousand years. So, uh, some of the best. So what you find is I think a lot of self help books these days. Uh, I just remixes of, of, um, and it's not a bad thing of remixes of ancient philosophy. So in Certeau, um, is the, I think you could say it's nonfiction and fiction cause it's got some stories in there. It's actually, I can't describe the one takeaway because it's kind of like a, a mindset of things. So that's the fiction slash nonfiction and then the, Oh, the nonfiction. I've got a lot of these, so fiction I, I like reading stories that are like real life. So my favorite, some of my favorite authors actually rather than one book, I'll just give some famous favorite authors. Uh, Charles Bukowski. I like his writing. Um, who else? James Frey white. I've got a book right here. Dostoyevsky which is a rash and I can't pronounce the name, but Dostoevsky. No, it's from the underground. That's really good. Saline saline is a French author, so who that but Koski, James Frey, saline Dostoevsky, John Fante, all of those, all of those authors. I think, I think it's really important for people to be uh, uh, literary knowledgeable. Speaker 1: (52:33) Yeah, I do. Definitely, definitely, definitely. Like I read a ton as well. Um, I, I've, I've heard antifragile come up a lot in a bunch of different books that I've read and a bunch of, you know, different people that I follow, talk about it. I think that what's the concept antifragile is something that if you put it under pressure, it doesn't crack or break, it actually emerges stronger. And I think that's something that, and I think with this whole COVID 19 situation, I'm hoping that humanity's going to demonstrate that, you know, what, as a society, if we all come together, we are anti-fragile. Speaker 2: (53:04) I totally agree man. I think there's a big opportunity. Speaker 1: (53:07) Yeah, man. So what's your morning routine like? Speaker 2: (53:10) Go up, make my bed, go to the bathroom and then get outside. So that's, and again, I like to mix it up a lot of days a day started with any kind of movement is a great day for me. So whether that'd be a walk or stretching and then, um, most days lately I've been having coffee on the back deck with my dad. So, um, I've really been liking that and then some writing. So it usually, every morning I do at least a thousand words of writing. Again, not really cam that just, just, just typing away. It might be journaling might be creating something for an article. So I keep it simple, wake up, make bed, get moving, like literally get moving, walking, dancing, something like that. And then, uh, straight into writing. Speaker 1: (53:56) I love it, man. Yeah. Part of my morning routine. The one of the very first things I do after, you know, going to the bathroom is getting some movement in. And your recent series of videos, uh, let's say reps for Rona has been Speaker 2: (54:07) Reps for Rona, yeah, I saw, I saw someone post that hashtag and like, Oh, I want to steal that. Speaker 1: (54:13) Yeah. So it's, it's been great for me because you know, having to work out indoors, um, it's been helpful to get some new ideas of some movements that I've never even seen before. So thank you for that man. Appreciate that. Speaker 2: (54:27) Oh man. Well thank you for watching. I'm glad you're enjoying it. I'm having fun doing them. Speaker 1: (54:32) That's awesome. So what's the best advice he ever received? Speaker 2: (54:36) Oh, far. Yeah, there is a lot. The best advice I ever received. It's a cliche, right? It's, it's love yourself. Like that's, that's it. It's, it's uh, there a lot in antifragile. I've got a lot of highlights from it, but it's the same. It's, I, I kind of, that I discovered this one, uh, through, through writing and it's, I think the best advice is from extern from someone else. Uh, it's hard for me to, there's, cause there's a lot. It's kind of, um, there's so much good advice that I've got from other people. It's, uh, it's hard to get the signal from the noise because there's just so much, but the ones that you discover through your own, uh, process, your own adventure, your own experience, they stick the best. And so mine was, I discovered in the last few years was just love yourself. I found that my self worth was too tied up in the, the opinions of others and, and pots relationships. And so that was a real tough place to be. And so I've figured out love yourself. I started doing that, started practicing that. And the energy it gives you is, is second to none. So that's, that's, that's from Ramon discovery. Speaker 1: (55:50) I love it. Then. So how can people connect with you? Where can they find you? Speaker 2: (55:55) Um, so my website's probably the best place to go. I've got a lot on there. I'm like a frozen yogurt machine when it comes to this stuff. All output. Um, mr dbourke dot com. So that's MRDBOURke.com or otherwise, like, um, if you search up to Daniel Burke, I should be, uh, somewhere there. Speaker 1: (56:18) Awesome, man. I courage everyone to go check out Daniel's work. He's got some amazing content on his blog. Uh, so much more we could have covered. But man, I appreciate you taking time out of your schedule, taking time out of your morning to be here with me and looking forward to seeing much more content from you in the future. And, uh, thanks again, Matt for being here. Speaker 2 Thank you harpreet . Great. I had a great time. Speaker 3: (56:45) [inaudible].