This is a follow up to an article Per wrote last year, Machine Learning in a Week, on how he kickstarted his way into machine learning (ml) by devoting five days to the subject. Follow him on Medium and check out his archive.
My interest in ml stems back to 2014 when I started reading articles about it on Hacker News. I simply found the idea of teaching machines stuff by looking at data appealing. At the time I wasn’t even a professional developer, but a hobby coder who’d done a couple of small projects.
So I began watching the first few chapters of Udacity’s Supervised Learning course, while also reading all articles I came across on the subject.
This gave me a little bit of conceptual understanding, though no practical skills. I also didn’t finish it, as I rarely do with MOOC’s.
In January 2015 I joined the Founders and Coders (FAC) bootcamp in London in order to become a developer. A few weeks in, I wanted to learn how to actually code machine learning algorithms, so I started a study group with a few of my peers. Every Tuesday evening, we’d watch lectures from Coursera’s Machine Learning course.
It’s a fantastic course, and I learned a hell of a lot. But it’s tough for a beginner. I had to watch the lectures over and over again before grasping the concepts. The Octave coding task are challenging as well, especially if you don’t know Octave. As a result of the difficulty, one by one fell off the study group as the weeks passed. Eventually, I fell off it myself as well.
I hindsight, I should have started with a course that either used ml libraries for the coding tasks — as opposed to building the algorithms from scratch — or at least used a programming language I knew.
If I could go back in time, I’d choose Udacity’s Intro to Machine Learning, as it’s easier and uses Python and Scikit Learn. This way, we would have gotten our hands dirty as soon as possible, gained confidence, and had more fun.
One of the last things I did at FAC was the ml week stunt. My goal was to be able to apply machine learning to actual problems at the end of the week, which I managed to do.
Throughout the week I did the following:
It’s by far the steepest ml learning curve I’ve ever experienced. Go ahead andread the articleif you want a more detailed overview.
After I finished FAC in London and moved back to Norway, I tried to repeat the success from the ml week, but for neural networks instead.
There were simply too many distractions to spend 10 hours of coding and learning every day. I had underestimated how important it was to be surrounded by peers at FAC.
However, I got started with neural nets at least, and slowly started to grasp the concept. By July I managed to code my first net. It’s probably the crappiest implementation ever created, and I actually find it embarrassing to show off. But it did the trick; I proved to myself that I understood concepts likebackpropagation and gradient descent.
In the second half of the year, my progression slowed down, as I started a new job. The most important takeaway from this period was the leap from non-vectorized to vectorized implementations of neural networks, which involved repeating linear algebra from university.
By the end of the yearI wrote an article as a summary of how I learned this.
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