In this vlog, Daniel Bourke introduces his new video diary series and recounts his first hackathon experience at the Brisbane AngelHack event. He describes the hackathon format, his solo arrival, the small‑scale nature of the event (about 38 participants), and his team's two‑minute pitch and demo, noting positive feedback on his public‑speaking and identifying areas for improvement such as voicing ideas and contributing to code. Daniel then shifts to his personal coding journey, announcing the start of his "100 Days of Code" challenge (day 4‑5). He details his daily routine: working on Udacity’s deep‑learning assignments, training a neural network for image classification, receiving feedback, resubmitting, and simultaneously doing a workout while the model trains. He celebrates a successful submission, outlines his next steps—studying machine learning on Coursera, using Octave/MATLAB alongside Python and R, and learning linear regression. Daniel also mentions launching a daily Medium series to document his learning, and shares his longer‑term career goals of becoming a data scientist and either joining or founding a startup. The vlog blends casual, reflective narration with concrete updates on his technical learning, project work, and personal development.
Chapters
What's good, YouTube? It's your boy Daniel, aka Bourke, aka Bourke, aka Bourke. It's May 15th, 2017. I'm gonna start doing this vlog thing. I've been thinking about it for a while. I did a podcast experiment over the last 100 days or so. I did one episode of podcasting a day, so I'm thinking maybe I could do 3 vlogs a week or I don't know, I haven't worked it out yet. So I'm just going to try this one, upload it, maybe it's a few minutes long, see how it goes. And yeah, so what have I been up to? Well, over the weekend I did a hackathon, my first ever hackathon. It was so much fun. So what's a hackathon? It's where you go, you meet some people, you form a team, or you go with your already made team, and the goal is you build something over a weekend and then you present, or however long the period of the hackathon is. For me, it was a weekend, so got there on the Saturday, uh, started coding on 12— at 12 noon on Saturday, and then everything would be frozen. Your code stops at 1 PM on the next day. So, so much fun. First ever time. I have barely any experience with coding, so I was going in as a novice. I had some design experience, some entrepreneur experience, But it was so much fun. Met some great people. I went by myself. There were a few teams already there. It was relatively small for a hackathon, I found out, because it was my first one. I didn't know really what to expect. So there's about 38 people at the Brisbane Angel Hack. Shout out to all you guys. And yeah, my team, I ended up pitching for my team. So we had to do a 2-minute presentation and a demo of what we'd built. The web developers on my team did an amazing job of building an actual pro— demo that I was able to present. I got some good feedback on my public speaking skills, so that was, that was thumbs up. What could I improve on? I think next time voicing my ideas and opinions and stuff like that, I think it can improve on that next time. And also being able to contribute to the developer side of things. I want to be able to build things next time. So that's what I've been started recently is 100 Days of Code. Today is day 4. I plan on coding for the next 96 days, at least a little bit each day. So what have I been up to today in particular? Well, this morning I started out with some Python and 100 Days of Code. I'm writing about it on Medium, so you can check that out. And now Udacity assignment, workout, machine learning. So I'm actually doing the Udacity assignment right now. I'm training my neural network here. Getting some live help because I'm struggling a lot. As you can see, I've spent like, I don't know, almost 4 hours total on it today. I submitted it once a couple of days ago, got, got returned, got some feedback on it, so I need to update it and fix it. I've been doing a lot of training, so training the neural network. I'll do some more videos in the future on what machine learning is, what deep learning is, and stuff like that. But while it's training, because it takes so long to train, I figured I'm going to do a workout. So I got trusty stick, workout mat. I'm gonna do some push-ups and pull-ups and whatever, and I don't know, I figured kill two birds with one stone. So multitasking right now, my neural network's training on the computer and I'm gonna train my body by working out. So yeah, I'll, uh, I'll hit you guys up if once I've submitted again. Hopefully I can get it done within the next few hours. So, mid-workout, my model finished training, but it's not as accurate as I want it to be. But nonetheless, I've been working on this for about 9 to 10 hours now, and I think it's time to, to upload a new submission for my assignment rather than keep going for— it could be— I could be here all night just continually training and whatnot. So here we go, image classification uploading. I confirm that it's mine. Submit. Woohoo! Should hear a response in 24 hours. Hopefully this one's a bit better than the last one, but I'm going to get back to my workout and I'll catch you in the next video. Day 5, 100 Days of Code. I'm really excited. I just got out of bed and I passed my second assignment on the Udacity deep learning course. Meets specifications. Not the best marks ever, but nonetheless, I put like 20+ hours of work into this project. Just because I'm such a novice, I don't know how to do everything properly and advanced yet, so I'll keep working towards that. Today I'm going to be studying machine learning on Coursera. I've got a lot of that to catch up to, and I've been reading a lot about becoming a data scientist because I figured That's, that's somewhere where it can be applied to almost any field, right? So if you're a data scientist, you can use those skills in almost any field, like public speaking, right? You can use public speaking in almost any field. So yeah, I don't know, I've been reading a lot about that, but I'm gonna go do some more study and see you next clip. I was at the library most of the day doing machine learning study. It's getting quite complicated. What did we work on today? I started using Octave and MATLAB, which are both machine learning— well, what the course asks for to use as libraries. I think Python and R are both the two of the main languages for machine learning these days. I'm even— actually, I want to tell you all I'm starting a like a Medium series. Every day I'll just do a few lines and a few words on medium.com/@mrdburke, M-R-D-B-U-R-K, of what I'm doing, how I'm learning code. So as you can see, writing in here, today was day 4. Actually, no, yesterday was day 4. Today's day 5. Course era. So I'm doing my first machine learning programming assignment, linear regression. There's MATLAB, and I've got Flux on my Mac, so hence why it's such an orange color. If you've never used it before, check out f.lux. But I'm about to go read, go to bed. I'll upload this video in the morning. And yeah, I'll start getting a schedule when I'm uploading these videos. I don't know yet. Um, and I was— I've done more research into learning about data science. I either want to start my own startup or work for a startup. So that's my plan in the next year or so. So I say so a lot. I've got to get rid of that.
