- This question or questions like it really try to test you on two dimensions. The first is your knowledge of the business and the industry itself, as well as your understanding of the business model. The second is whether you can pick how correlated data is to business outcomes in general, and then how you apply that thinking to your context about the company. You’ll want to research the business model and ask good questions to your recruiter—and start thinking about what business problems they probably want to solve most with their data.
2. Who do you follow in the ML/Data Science community? 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.
- Related to the last point, most organizations hiring for machine learning positions will look for your formal experience in the field. Research papers, co-authored or supervised by leaders in the field, can make the difference between you being hired and not. Make sure you have a summary of your research experience and papers ready—and an explanation for your background and lack of formal research experience if you don’t.
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.
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. See more info here
6. What are your thoughts on GPT-3 and OpenAI’s model?
Answer: 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.
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.
More reading: Quantum Machine Learning
CUDA stands for Compute Unified Device Architecture. It is a parallel computing platform and application programming interface (API) model created by Nvidia. It allows software engineers to use a CUDA-enabled graphics processing unit (GPU) for general purpose processing a parallel computing platform and programming model developed by Nvidia for general computing on its own GPUs (graphics processing units). CUDA enables developers to speed up compute-intensive applications by harnessing the power of GPUs for the parallelizable part of the computation.
CUDA in Deeplearning Deep learning has an outsized need for computing speed. For example, to train the models for Google Translate in 2016, the Google Brain and Google Translate teams did hundreds of one-week TensorFlow runs using GPUs; they had bought 2,000 server-grade GPUs from Nvidia for the purpose. Without GPUs, those training runs would have taken months rather than a week to converge. For production deployment of those TensorFlow translation models, Google used a new custom processing chip, the TPU (tensor processing unit).
In addition to TensorFlow, many other DL frameworks rely on CUDA for their GPU support, including Caffe2, CNTK, Databricks, H2O.ai, Keras, MXNet, PyTorch, Theano, and Torch. In most cases they use the cuDNN library for the deep neural network computations.