I was asked to code a simple Python task to test my codability. We also discussed some of my repos on GitHub. In addition, there was a few general knowledge questions about the web, networking, and Linux. The interview was real, down to the important stuff. I loved it, and it turned out to be one of my most exciting experiences I have ever had.
Projects, ML basics, Skill set and tools
Describe your projects and explain how you go about solving problems
Phase 2: 1) You talk about your previous ML project and the interviewer asks you accompanying questions: e.g. what is mAP, difference between model pruning and quantization, etc.
Coding test was based on data structures and I would recommend to practise before attempting the test.
have multiple rounds of interviews, while others may combine different stages or use alternative methods like assessment centers.
Given dataset and a pretrain model, do fine-tunning and show results.
List, tuple , slicing and loop problems
what is gain? what is the difference between CNN and RNN
Presentation part of the third interview involved technical questions about past projects. Focus was given on the in-depth knowledge of the methodologies and approaches.
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