Explain CNN, Transfer learning, etc.
Deep Learning Engineer Interview Questions
519 deep learning engineer interview questions shared by candidates
describe the project you did
The live coding challenge included building a classifier model and evaluating its performance on a provided dataset.
- Group Norm - Deep Learning using fusion of multi-modal data (RGB and LIDAR)
The importance of synthetic data, past projects and technologies.
Online Round - easy to solve in a day if you have 3-4 years of experience writing code. I did, so it was fairly simple for me. Face to face - He treats you with disrespect and copy pastes some LeetCode mediums and asks you to solve it. If you take time to solve it, he calls you a poor candidate without caring about anything else, and asks you to stop wasting his time.
Edge devices Transformers YOLO (feature extractors, if YOLO uses grids, how would it be able to capture spatial data, how to process all the bounding boxes to get a single one i.e. non max suppression ) Difference between YOLO and R-CNN (and its faster variants) in terms of performance and speed. GPU optimization for trained model (important one which was asked in both interviews) - If I have a model that has 30 FPS on GPU, how would I tune it to get 50 or 60 FPS? Image matting - since my project revolved around it Types of convolutions Skip connections Difference between AlexNet and ResNet How to extract key value pairs from text using NLP
Previous experience, machine learning theory, data management
Coding interview, level of difficulty: average question.
Coding interview, level of difficulty: average question.
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