In this folder, I present LLMs which are based on encoder models. The aim is to discuss examples which are suitable for problems with no labeled outputs. This implies that we categorize models based on inputs and associated learning methods.
- Identify different data collection techniques
- Data types, preparation and analysis.
- Apply use cases to clustering, principal component analysis (PCA)
- Introduce methods to deal with existing algorithms.
- Apply unsupervised learning algorithms for the preparation of various data sets in different formats prior to training.
- Contains code, examples and explanations on how to apply different types of ML methods to several problems.