Natural Language Generation (NLG)

This program offers a comprehensive exploration of NLG techniques, teaching participants how AI can automatically generate coherent and contextually accurate human language. The program covers language models, neural architectures, ethical considerations, and hands-on projects focused on implementing NLG in real-world applications like automated writing, chatbots, and content creation.

• Master the fundamentals of NLG and its various architectures.
• Build and train models for text generation using state-of-the-art techniques.
• Understand the ethical challenges and biases in NLG.
• Gain hands-on experience with transformer-based models for NLG tasks.
• Explore real-world applications of NLG in content automation and communication.

1. Introduction to Natural Language Generation
   • Overview of NLG
   • Applications and Trends in NLG (e.g., Chatbots, Content Generation)
   • Key Challenges in NLG
2. Fundamentals of Natural Language Processing (NLP)
   • Tokenization, Lemmatization, and Stemming
   • Word Embeddings (Word2Vec, GloVe)
   • Sequence Modeling in NLP (Bag of Words, TF-IDF)
3. Recurrent Neural Networks (RNNs) for NLG
   • RNNs and Sequence-to-Sequence Models
   • LSTMs and GRUs for Text Generation
   • Encoder-Decoder Architectures
4. Transformers for Language Modeling
   • Attention Mechanism and Self-Attention
   • Introduction to Transformers
   • BERT, GPT, and Their Role in NLG
5. Advanced Language Models
   • GPT-2, GPT-3, and GPT-4 Architectures
   • Pretraining and Fine-tuning Techniques
   • Comparison of Pretrained Language Models (e.g., T5, BART)
6. Conditional NLG
   • Text Generation with Conditional Inputs (e.g., Text Summarization, Translation)
   • Seq2Seq with Attention
   • Applications in Machine Translation (MT) and Summarization
7. Controlling Text Generation
   • Controlling Style and Tone in NLG
   • Beam Search, Greedy Search, and Sampling Methods
   • Top-k and Top-p Sampling
8. Evaluating NLG Models
   • Evaluation Metrics for NLG (BLEU, ROUGE, METEOR)
   • Human Evaluation vs. Automated Evaluation
   • Challenges in Evaluating Generated Text
9. Ethics in NLG
   • Bias and Fairness in Language Models
   • Ethical Considerations in Text Generation
   • Misinformation and Abuse of NLG Systems
10. Fine-Tuning and Deploying NLG Models

• Fine-Tuning Large Language Models for Specific Domains
• Model Deployment in Real-World Applications
• Scaling and Optimizing NLG Models

11. Case Studies in NLG
    • Hands-on Applications (Chatbots, Automated Report Writing)
    • Industry Use Cases (Marketing, Healthcare, Journalism)
12. Final Project
    • Build and deploy an NLG model for a specific task (e.g., text summarization, chatbot, or creative writing generator)