This is the first of a series of posts documenting my experience and thoughts in undertaking the fast.ai machine learning course. And hence, the title: Fast AI ML Experience (FAME).
Learning Approach
The following influential excerpt is plucked from the first topic of the fast.ai machine learning course. I was really humbled and inspired by how succinct they describe an underrated, informal (at least for me) approach to learning or mastering a skill. I always used to think that if I'm gonna be the best, I need to do things by the books. Every fine details need to be covered. That takes the soul (fun) out of the learning. I will add more description when I find more words to express this monumental feeling.
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I'll be using a top-down teaching method, which is different from how most math courses operate. Typically, in a bottom-up approach, you first learn all the separate components you will be using, and then you gradually build them up into more complex structures. The problems with this are that students often lose motivation, don't have a sense of the "big picture", and don't know what they'll need.
If you took the fast.ai deep learning course, that is what we used. You can hear more about my teaching philosophy in this blog post or in this talk.
Harvard Professor David Perkins has a book, Making Learning Whole in which he uses baseball as an analogy. We don't require kids to memorize all the rules of baseball and understand all the technical details before we let them play the game. Rather, they start playing with a just general sense of it, and then gradually learn more rules/details as time goes on.
All that to say, don't worry if you don't understand everything at first! You're not supposed to. We will start using some "black boxes" such as random forests that haven't yet been explained in detail, and then we'll dig into the lower level details later.
To start, focus on what things DO, not what they ARE.
Your practice
People learn by:
- doing (coding and building)
- explaining what they've learned (by writing or helping others)
Therefore, we suggest that you practice these skills on Kaggle by:
- Entering competitions (doing)
- Creating Kaggle kernels (explaining)
It's OK if you don't get good competition ranks or any kernel votes at first - that's totally normal! Just try to keep improving every day, and you'll see the results over time.
To get better at technical writing, study the top ranked Kaggle kernels from past competitions, and read posts from well-regarded technical bloggers. Some good role models include:
- Peter Norvig (more here)
- Stephen Merity
- Julia Evans (more here)
- Julia Ferraioli
- Edwin Chen
- Slav Ivanov (fast.ai student)
- Brad Kenstler (fast.ai and USF MSAN student)
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