List of topics included in this learning:

1. Introduction to ChatGPT:
   • Understanding what ChatGPT is and its capabilities.
2. Natural Language Processing (NLP) Fundamentals:
   • Basics of NLP, including tokenization, text preprocessing, and text classification.
3. Recurrent Neural Networks (RNNs):
   • Understanding the architecture that underlies many NLP models, including GPT.
4. Transformer Architecture:
   • The architecture behind models like GPT, including self-attention mechanisms.
5. Attention Mechanisms:
   • How attention works and its role in NLP.
6. Pre-training and Fine-tuning:
   • The process of pre-training models on large text corpora and fine-tuning them for specific tasks.
7. Language Modeling:
   • How language models like GPT generate text and predict the next word.
8. Training Data and Datasets:
   • The data used to train and fine-tune models like ChatGPT.
9. Model Evaluation:
   • How to evaluate the performance of language models, including metrics like BLEU and ROUGE.
10. Ethical Considerations in AI:
    • Exploring ethical issues, biases, and guidelines in AI, especially in language models.
11. Bias and Fairness in AI:
    • Understanding the challenges of bias in NLP models and strategies to mitigate it.
12. Conversational AI:
    • Techniques and challenges in developing conversational agents like ChatGPT.
13. Chatbot Development:
    • Building chatbots using NLP models and APIs like ChatGPT.
14. Fine-tuning ChatGPT:
    • The process of fine-tuning ChatGPT for specific applications or industries.
15. ChatGPT Use Cases:
    • Exploring practical applications of ChatGPT in customer support, content generation, and more.
16. Model Deployment:
    • Deploying ChatGPT in real-world scenarios, including web and mobile applications.
17. Hyperparameter Tuning:
    • Optimizing the performance of language models through hyperparameter adjustments.
18. Continuous Learning and Model Updates:
    • Strategies for keeping models like ChatGPT updated with new data.
19. Interpretable AI:
    • Techniques for understanding and explaining the decisions made by AI models.
20. Future Developments in NLP:
    • Staying updated with the latest research and trends in NLP and language models.
21. ChatGPT Alternatives:
    • Exploring other NLP models and chatbot frameworks like BERT, Dialogflow, Rasa, etc.
22. Building Conversational User Interfaces:
    • Design principles for creating user-friendly interactions with chatbots.
23. NLP APIs and Tools:
    • Leveraging NLP APIs and tools for natural language understanding and generation.
24. System Level Instruction
25. User Message
26. Prompts (commands) List