Large Language Models

Large Language Models are advanced natural language processing models that are trained on massive amounts of text data to understand and generate human-like language.

This competency area includes an understanding of the concepts of natural language processing (NLP), machine learning, deep learning, programming languages, TensorFlow or PyTorch, transfer learning, data preprocessing, model fine-tuning, evaluation metrics, debugging and troubleshooting.
 

Key Competencies:

1. Natural Language Processing (NLP) - Understanding of core concepts in NLP, such as tokenization, part-of-speech tagging, named entity recognition, and syntactic parsing.
2. Machine Learning - Understanding foundational concepts in machine learning, including supervised and unsupervised learning.
3. Deep Learning - Knowledge of deep learning architectures, especially transformer architectures commonly used in LLMs like GPT-3.
4. Programming Languages - Proficiency in Python as many popular libraries and frameworks for NLP and machine learning are implemented in Python.
5. TensorFlow or PyTorch - Knowledge of either TensorFlow or PyTorch, for implementing and fine-tuning LLMs.
6. Transfer Learning - Understanding how to transfer knowledge from pre-trained models to new tasks.
7. Data Preprocessing - Ability to clean and preprocess textual data for preparing datasets for training LLMs.
8. Model Fine-tuning - Understanding of fine-tuning pre-trained models for specific tasks or domains for customizing LLMs to meet specific requirements.
9. Evaluation Metrics - Knowledge of how to evaluate model performance using relevant metrics for assessing the effectiveness of LLMs.
10. Debugging and Troubleshooting - Ability to debug and troubleshoot issues that may arise during the implementation or deployment of LLMs for efficient development.