Machine Learning in a Year: From Total Noob to Effective Practitioner
Read how the author went from self-described total machine learning noob to being able to effectively use machine learning effectively on real world projects at work... all within a year.
Failing neural networks
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.
Lesson learned: Find an thriving environment to surround yourself with when doing these kinds of learning stunts.
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 thecrappiest 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 like backpropagation 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 fromnon-vectorized to vectorized implementations of neural networks, which involved repeating linear algebra from university.
By the end of the year I wrote an article as a summary of how I learned this:
Testing out Kaggle Contests
During the christmas vacation of 2015, I got a motivational boost again and decided try out Kaggle. So I spent quite some time experimenting with various algorithms for their Homesite Quote Conversion, Otto Group Product Classification and Bike Sharing Demand contests.
The main takeaway from this was the experience of iteratively improving the results by experimenting with the algorithms and the data.
I learned to trust my logic when doing machine learning.
If tweaking a parameter or engineering a new feature seems like a good idea logically, it’s quite likely that it actually will help.
Setting up a learning routine at work
Back at work in January 2016 I wanted to continue in the flow I’d gotten into during Christmas. So I asked my manager if I could spend some time learning stuff during my work hours as well, which he happily approved.
Having gotten a basic understanding of neural networks at this point, I wanted to move on to deep learning.
Udacity’s Deep Learning
My first attempt was Udacity’s Deep Learning course, which ended up as a big disappointment. The contents of the video lectures are good, but they are too short and shallow to me.
And the IPython Notebook assignments ended up being too frustrating, as I spent most of my time debugging code errors, which is the most effective way to kill motivation. So after doing that for a couple of sessions at work, I simply gave up.
To their defense, I’m a total noob when it comes to IPython Notebooks, so it might not be as bad for you as it was for me. So it might be that I simply wasn’t ready for the course.
Stanford — Deep Learning for NLP
Luckily, I then discovered Stanford’s CS224D and decided to give it a shot. It is a fantastic course. And though it’s difficult, I never end up debugging when doing the problem sets.
Secondly, they actually give you the solution code as well, which I often look at when I’m stuck, so that I can work my way backwards to understand the steps needed to reach a solution.
Though I’ve haven’t finished it yet, it has significantly boosted my knowledge in nlp and neural networks so far.
However it’s been tough. Really tough. At one point, I realized I needed help from someone better than me, so I came in touch with a Ph.D student who was willing to help me out for 40 USD per hour, both with the problem sets as well as the overall understanding. This has been critical for me in order to move on, as he has uncovered a lot of black holes in my knowledge.
Lesson learned: It’s possible to get a good machine learning teacher for around 50 USD per hour. If you can afford it, it’s definitely worth it.
In addition to this, Xeneta also hired a data scientist recently. He’s got a masters degree in math, so I often ask him for help when I’m stuck with various linear algebra an calculus tasks, or ml in general. So be sure to check out which resources you have internally in your company as well.
Boosting Sales at Xeneta
After doing all this, I finally felt ready to do a ml project at work. It basically involved training an algorithm to qualify sales leads by reading company descriptions, and has actually proven to be a big time saver for the sales guys using the tool.
Check out out article I wrote about it below or head over to GitHub to dive straight into the code.
Getting to this point has surely been a long journey. But also a fast one; when I started my machine learning in a week project, I certainly didn’t have any hopes of actually using it professionally within a year.
But it’s 100 percent possible. And if I can do it, so can anybody else.
Bio: Per Harald Borgen is a developer at Xeneta. He mostly writes about learning new stuff.
Original. Reposted with permission.
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