Technical Assessment (20 minutes) LeetCode Question: Longest Palindromic Substring Coding live where the interviewer can see your code on a shared editor. Discussion and Questions (40 minutes) Self-Introduction Project Discussion: Explain a project related to LLM LLM Creation: How is a Large Language Model created? Model Architectures: Explain the different models and their architectures (e.g., GPT, LLAMA, Falcon, BLOOM) Positional Embeddings: What is the purpose of positional embeddings? Normalization Techniques: Difference between BatchNorm and LayerNorm Retrieval-Augmented Generation (RAG): - What is RAG? Explain its purpose. - Even if you use RAG, there are hallucinations occurring. Why is this so and what can you do to mitigate? - What other current academic advancements in RAG? Explain about some frameworks that are trending. Scenario Question: If we have large-scale data (billions of records) from the web, how can I efficiently select math-related data? Discuss using distributed computing frameworks if possible.
Algorithm Interview Questions
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What are the relevant projects that you have done?
Describe how the transformer architecture works
RLS, LMS difference
5)write program to read data from text file.
Most about skills & experiences you have.
Technical questions were targeting basic understanding of neural networks, general problem solving ability and there were also some questions about object-oriented design.
Technical questions about previous deep learning projects in the CV
Q: what is your technical background?
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