What are the different training methods for AI models?
There are several training methods for AI models, including supervised, unsupervised, and reinforcement learning. Would you like to know more about each?
Yes, please explain supervised and unsupervised learning.
Supervised learning involves training a model on labeled data, where the correct output is known. Unsupervised learning, on the other hand, deals with unlabeled data and aims to find patterns or groupings.
What are some examples of each method?
For supervised learning, examples include image classification and spam detection. Unsupervised learning examples include clustering and dimensionality reduction.
How do these methods impact model performance?
The choice of training method can significantly impact model performance. Supervised learning typically yields higher accuracy when labeled data is available, while unsupervised learning can reveal hidden structures in data.
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