Welcome to the FAQ page for Infermatic.ai! Here, you can find answers to your questions about large language models and the AI industry. Whether you’re curious about how to use our tools or want to learn more about AI, this page is a great place to start.
Ask Svak
Have questions about LLMs, AI, or machine learning models?
Related Questions
- What is the theoretical background behind the Minkowski distance formula?
- How does the value of p in the Minkowski distance formula (dp = (|x_i - y_i|^p)^(1/p)) affect clustering results compared to Euclidean distance (p=2)?
- Under what scenarios does Minkowski distance with p ≠ 2 (non-Euclidean metric) perform better than Euclidean distance in clustering?
- Can you provide a simple example of a dataset where using Minkowski distance (p=3) instead of Euclidean distance reveals meaningful clusters that are otherwise overlooked?
- In what situations is Euclidean distance preferred over Minkowski distance in clustering algorithms, and vice versa?
- What are the implications of using Minkowski distance (p < 2) versus p > 2 in terms of the types of features or data types that benefit from each distance metric?
- Is there any way to incorporate both Minkowski distance and Euclidean distance as features to further enhance the clustering outcome, or do they tend to cancel out each other?
You’re just a few clicks away from unlocking the full power of Infermatic.ai! With our easy-to-use platform, you can explore top-tier large language models, create powerful AI solutions, and take your projects to the next level.
Get Started Now