1.Evaluate recent research papers.
2.Discuss the implementation details of a project you worked on.
3.Explain the principle of softmax and how would you test it?
4.Technical Details in Detection for ML/CV
5.Machine Learning Detail Discussion
6.Implement a Specific Layer Using TensorFlow or PyTorch
7.Explain the forward and backward propagation in neural networks.
8.Implement SVD forward and backward propagation.
9.Explain distributed training, dataloader collector, and model definition in PyTorch.
10.Deep Learning and Computer Vision Questions
11.Machine Learning High-Level Understanding
12.Graph Convolutional Network and Spatial-Temporal Graph
13.Deep Learning Fundamentals
14.Implement KNN and Discuss Related Concepts
15.Deep Learning Knowledge Assessment
16.Design an ML System for Recommending Nearby Drivers
17.Machine Learning Concepts and Hardware Acceleration
18.Language Model Inference Architecture
19.Differences Between Adam and AdamW Optimizers
20.Differences Between BERT and GPT
21.Transformer Multi-Head Attention Complexity
22.Implement Dropout in Neural Networks
23.Transformer Architecture and Self-Attention
24.Deep Learning Basics and Image Denoising
25.ML Problem Solving in NLP
1. Evaluate recent research papers.
What is your opinion on some of the recent papers published in the field of text-to-speech or voice conversion?
2. Discuss the implementation details of a project you worked on.
Can you discuss the implementation details, data preparation, and evaluation methods used in your voice conversion project mentioned in your CV?
3. Explain the principle of softmax and how would you test it?
During a coding interview, you were asked about the principle of softmax. You are also required to identify potential issues with softmax and devise tests to verify its functionality.
4. Technical Details in Detection for ML/CV
Discuss technical details related to detection in machine learning and computer vision, based on your project experience.
5. Machine Learning Detail Discussion
Discuss various machine learning details such as regularization, babysitting neural networks, and batch normalization. Provide in-depth knowledge on these topics.
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