general interest Flashcards
What are the last machine learning papers you’ve read?
Keeping up with the latest scientific literature on machine learning is a must if you want to demonstrate an interest in a machine learning position. This overview of deep learning in Nature by the scions of deep learning themselves (from Hinton to Bengio to LeCun) can be a good reference paper and an overview of what’s happening in deep learning — and the kind of paper you might want to cite.
Do you have research experience in machine learning?
Have an explanation for your background and lack of formal research experience if you don’t.
What are your favourite use cases of machine learning models?
see article
How would you approach the “Netflix Prize” competition?
The Netflix Prize was a famed competition where Netflix offered $1,000,000 for a better collaborative filtering algorithm. The team that won called BellKor had a 10% improvement and used an ensemble of different methods to win. Some familiarity with the case and its solution will help demonstrate you’ve paid attention to machine learning for a while.
The winners describe using gradient boosted decision trees to combine over 500 models; previous solutions used instead a linear regression to combine the predictors.
Briefly, gradient boosted decision trees work by sequentially fitting a series of decision trees to the data; each tree is asked to predict the error made by the previous trees, and is often trained on slightly perturbed versions of the data.
Where do you usually source datasets?
Somebody who is truly passionate about machine learning will have gone off and done side projects on their own, and have a good idea of what great datasets are out there. If you’re missing any, check out Quandl for economic and financial data, and Kaggle’s Datasets collection for another great list.
How do you think Google is training data for self-driving cars?
Machine learning interview questions like this one really test your knowledge of different machine learning methods, and your inventiveness if you don’t know the answer. Google is currently using recaptcha to source labeled data on storefronts and traffic signs. They are also building on training data collected by Sebastian Thrun at GoogleX—some of which was obtained by his grad students driving buggies on desert dunes!
How would you simulate the approach AlphaGo took to beat Lee Sedol at Go?
AlphaGo beating Lee Sedol, the best human player at Go, in a best-of-five series was a truly seminal event in the history of machine learning and deep learning. The Nature paper above describes how this was accomplished with “Monte-Carlo tree search with deep neural networks that have been trained by supervised learning, from human expert games, and by reinforcement learning from games of self-play.”
What are your thoughts on GPT-3 and OpenAI’s model?
GPT-3 is a new language generation model developed by OpenAI. It was marked as exciting because with very little change in architecture, and a ton more data, GPT-3 could generate what seemed to be human-like conversational pieces, up to and including novel-size works and the ability to create code from natural language. There are many perspectives on GPT-3 throughout the Internet — if it comes up in an interview setting, be prepared to address this topic (and trending topics like it) intelligently to demonstrate that you follow the latest advances in machine learning.
What models do you train for fun, and what GPU/hardware do you use?
This question tests whether you’ve worked on machine learning projects outside of a corporate role and whether you understand the basics of how to resource projects and allocate GPU-time efficiently. Expect questions like this to come from hiring managers that are interested in getting a greater sense behind your portfolio, and what you’ve done independently.
What are some of your favorite APIs to explore?
If you’ve worked with external data sources, it’s likely you’ll have a few favorite APIs that you’ve gone through. You can be thoughtful here about the kinds of experiments and pipelines you’ve run in the past, along with how you think about the APIs you’ve used before.
How do you think quantum computing will affect machine learning?
With the recent announcement of more breakthroughs in quantum computing, the question of how this new format and way of thinking through hardware serves as a useful proxy to explain classical computing and machine learning, and some of the hardware nuances that might make some algorithms much easier to do on a quantum machine. Demonstrating some knowledge in this area helps show that you’re interested in machine learning at a much higher level than just implementation details.
