1.Handwrite Transformers
2.Machine Learning Design
3.Understanding AI and Its Application in Verifying False Information
4.BCN Loss Formula Explanation
5.VIT/Transformer Fundamentals
6.Technical Details of VAE vs. Other Models
7.Designing a Loss Function
8.NLP and Machine Learning Basics
9.Tricks in Pre-training Large Models
10.Principles of Batch Normalization and the Role of Gamma and Beta
11.Describe YOLO Model Architecture
12.Explain Multi-Head Attention and the Time Complexity of Transformer
13.Designing a Library for Predicting Optimal Insertion Points in an Information Stream
14.GBTI and XGBoost Difference
15.Differentiate between various LLM attention mechanisms and position embedding techniques.
16.Discuss fine-tuning methods for Large Language Models (LLMs) and share any relevant experience.
17.Compare GRU and LSTM in terms of their structure and performance.
18.Explain the attention mechanism and the advantages of a decoder model in transformer-related architectures.
19.Implementing Contrastive Loss Function
20.Incorporating General AI into Data
21.Deep Learning Fundamentals and Two Layer DNN Model
22.CUDA Experience
23.Deep Neural Network Layers
24.Common Recommendation Algorithms
25.Describe the process of designing a recommendation system combined with NLP, focusing on how features are obtained and designed.
26.Explain all the models used in your chatbot project and detail the mathematical reasoning process within the NLP model.
27.Describe the model used in the Ranker of a recommendation system, explain the model and its complexity.
28.Describe the model used in the Candidate Generator of a recommendation system and explain the model complexity.
29.Machine Learning Algorithm Implementation
30.Position Encoding in Transformers
31.Transformer Model
32.Differentiate between various BERT models.
33.Explain the structure and principles of the transformer model.
1. Handwrite Transformers
You will be asked to handwrite the code for Transformers, ensuring that you understand the concept and can implement it.
2. Machine Learning Design
In the interview, you were asked about machine learning design related to your resume. Can you discuss how you approached these questions and the types of machine learning models you discussed?
3. Understanding AI and Its Application in Verifying False Information
Do you understand AI, and how would you use AI to assist in verifying false information in your work?
4. BCN Loss Formula Explanation
Explain the BCN loss formula and provide a code example to demonstrate your understanding.
5. VIT/Transformer Fundamentals
Explain the design choices in VIT/transformer models, such as padding mask and position embedding. Additionally, discuss the loss function used in multimodal models like CLIP and the impact of transformer model size on performance.
解锁更多真题，请点此 登录 篱笆帮
