Tip 1 : Know the profile, prepare for that profile.
Tip 2 : Don't just prepare ML algorithms, know some classical algorithms as well.
Tip 3 : Should have at least 2 Projects for that profile.
Tip 4 : Know some deployment as well.
Tip 1 : Simple and short resume with details, projects, and education
Tip 2 : Prepare resume based on the profile
The current company had no such interview process, The founder and I have worked previously together and were contributing. We had worked previously on the ML domain, and I had also worked on Speech processing which is the core tech of the startup.
So here I'm describing my interview experience with Precily.ai for the Data Science Internship (this experience is aligned with most of the ML profiles be it CV, Speech, NLP, or data science)
The profile was based on Deep Learning and NLP.
I was asked about Simple DL architectures, Loss Functions, some stats concepts, and mathematics of backpropagation.
these were fairly easy if you are in the ML/DL domain.
Some of the hard questions were based on advanced models/concepts like attention, Bert, Seq2Seq, and YOLO (surprisingly).
The timing of the interview was around 2pm and it was like a normal call and the interviewer also helped me wherever I was stuck.
Why is attention needed? how do we evaluate attention for Seq2Seq models? Is the attention evaluated at once?
What is the Loss function of YOLO?



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‘*’ – Matches any sequence of characters(sequence can be of length 0 or more)

Here's your problem of the day
Solving this problem will increase your chance to get selected in this company
What is recursion?