1- Natural Language Processing (NLP) Fundamentals

A- Text preprocessing (tokenization, stemming, lemmatization)
B- Feature extraction (bag-of-words, TF-IDF, word embeddings)
C- NLP tasks (sentiment analysis, named entity recognition, text classification)

2- Deep Learning for NLP

A- Feedforward Neural Networks
B- Recurrent Neural Networks (RNNs, LSTMs, GRUs)
C- Convolutional Neural Networks for NLP
D- Attention Mechanisms
E- Transformer Architecture

3- Large Language Model Architectures

A- Generative Pre-trained Transformer (GPT)
B- Bidirectional Encoder Representations from Transformers (BERT)
C- T5 (Text-to-Text Transfer Transformer)
D- GPT-3 and its variants (GPT-J, GPT-Neo)
E- LLaMA, Anthropic’s models, and other open-source LLMs

4- LLM Libraries and Frameworks

A- Hugging Face Transformers
B- Anthropic’s models and APIs
C- TensorFlow and PyTorch for LLMs

5- LLM Training and Fine-tuning

A- Pre-training techniques (self-supervised learning, masked language modeling)
B- Fine-tuning LLMs for specific tasks (text generation, summarization, question answering)
C- Prompt engineering and few-shot learning

6- LLM Deployment and Optimization

A- Model compression and quantization
B- Serving LLMs in production environments
C- Load balancing and scaling LLM systems

7- LLM Applications

A- Chatbots and conversational AI
B- Text generation and summarization
C- Question answering and information retrieval
D- Content creation and creative writing
E- Code generation and programming assistance

8- Responsible AI and Ethics

A- Bias and fairness in LLMs
B- Privacy and security considerations
C- AI governance and ethical frameworks

9- Evaluation and Benchmarking

A- Automatic evaluation metrics (BLEU, ROUGE, METEOR)
B- Human evaluation and user studies
C- LLM benchmarks and leaderboards