fast.ai 见闻
搜集在fast.ai世界里看到的值得关注的动态和见闻
insights from fastai team
interviews with DL heros
interview with Sylvain by Sanyam Bhutani thanks to @init_27
I simply copied the following Q&As from @init_27 's post above
How Sylvain got started with fastai?
I kind of forgot about it (neural net) until October 2017… I was curious to see how the field had progressed — of course, I had heard all the hype around it — so I followed the MOOC version 1…I instantly loved the top-down approach… I have a strong background in Math, but it’s my love for coding practical things that kept me going.
What is it like to work with Jeremy Howard?
We never sleep, but that’s mostly because we both have toddlers!..I’ve improved a lot as a coder and I keep on learning new things from him. Just seeing how he iterates through your code to refactor it in a simpler or more elegant way is always fascinating. And I really love how he is never satisfied with anything short of perfect, always pushing to polish this bit of code or this particular API until it’s as easy to use as possible.
Could you tell us more about your role at fast.ai and how does a day at fast.ai look like?
Since I am based in New York City, we mostly work in parallel. We chat a lot on Skype to coordinate and the rest of the time is spent coding or reviewing code, whether it’s to make the library better or try a new research idea.
As for my role, it’s a mix of reviewing the latest papers and see what we could use, as well as help Jeremy develop new functionality in the library and prepare the next course.
What more can we expect next from the awesome library?
we’ll try to make it easier to put fastai models into production, we’ll focus on the applications we didn’t have time to finalize during the first part of the course (object detection, translation, sequence labeling), we’ll find some way to deal with very big datasets that don’t always fit in RAM, and also play with some research ideas we didn’t get to investigate (training on rectangular images for instance).
How do you discover these ideas, what is the methodology of experimentation at fast.ai?
The methodology could be summarized into: “try blah!”, as Jeremy said in one of the courses. We try to have an intuitive understanding of what happens when training a given model, then we experiment all the ideas we think of to see if they work empirically.
Very often, research papers focus on the Math first and come with this one new theory that is going to revolutionize everything. When you try to apply it though, you often don’t get any good results. We’re more interested in things that work in practice.
How do you stay up to date with the cutting edge?
By experimenting a lot! The fastai library isn’t just a great tool for the beginner, its high flexibility makes it super easy when I want to implement a research article to see if its suggestion results in a significant improvement. The callbacks system or the data block API allow you to do pretty much anything with just a few lines of code.
any advice for the beginners?
Start a blog, where you explain what you have learned. Explaining things is often the best way to realize you hadn’t fully understood them; you may discover there were tons of small details you hadn’t dug enough into.
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