Several technical questions re: ML/AI.
Applied Research Interview Questions
152 applied research interview questions shared by candidates
details of your previous projects, experience, tools that you are familiar? differences between some SOTA methods and conventional algorithms? explain your stages to complete some specific task,
A data set that should be solved using Linear Regression - Understand that it is a regression problem, describe the loss, the algorithms that can solve it.
String manipulation. How to design random forest.
They ask me if I have failed in any project and how I deal with it
1. What projects are you working on? 2. Explain one of your ongoing projects and your contribution to it. 3. How many lines of code have you written in various programming languages like C++, Python, etc.
How would your friends describe you?
Tell me about a time when you had a disagreement in a group?
NVIDIA final interview If the training loss curve goes up, what does it mean? How will you fix it? What are the possible causes of the training curve being flat? How does tanh activation contribute to this? How can you fix it, especially the tanh problem? Any different activation functions and why them ? What can you do to prevent overfitting? Also which overfitting technique will you use to specifically bring down test loss (and not validation loss) How does randomization in the initialization of weights help in training ? Ascii art programming question NVIDIA second interview Maximum likelihood - give equation, why can you use log and why do you use it in the first place RL - on. Vs off. Policy Overfitting - why do you normalize C++ technical questions - Pointers, Memory NVIDIA first interview No technical questions NVIDIA previous group second interview Stacks/Queues Programming question NVIDIA previous group first interview Basic LSTM technical questions (discussed in detail in the general questions)
SLAM pose graph relocalisation details
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