### Q1: What is the basic structure of a neural network, and how do its components function together?  
### Q2: How does machine learning differ from deep learning in terms of data processing and model complexity?  
### Q3: Explain the concept of overfitting in machine learning and discuss its implications on model performance.  

### Q4: How can the understanding of generative models, such as GANs, enhance the development of AI-driven products?  
### Q5: In what ways can training data influence the effectiveness of a machine learning model in real-world applications?  
### Q6: Describe a scenario where inference is critical for user interaction with a Generative AI product.  

### Q7: How would you approach explaining the significance of precise terminology in Generative AI to a non-technical team member?  
### Q8: Can you share an example of how misunderstanding a term related to Generative AI could impact a project?  
### Q9: Why is it important for professionals to keep abreast of new terminologies in the rapidly evolving field of Generative AI?