Background and Fundamentals: Review of RNNs, LSTM, GRU, Backpropagation through time, Automatic Differentiation, Log-linear modeling, NLP Feature Engineering, Softmax, MLE estimation, Probabilistic models of NLP. NLP Representation Learning: Word and Sentence Representations- Encoding Words, Pooling and n-grams, Word Embeddings, Unsupervised Word Repr, Skip-Gram, BERT, Contextualized Representation. Structured Prediction and Language Modeling: Neural n-gram models, Part-of-speech tagging, conditional language modeling, Neural probabilistic language models. Machine Translation and Language Generation: Translation task, Seq-to-Seq models, Attention mechanism, Self-Attention, Transformers, Decoding, Graph search, common strategies, real world application. Generative AI: Introduction to Generative AI Models, Explainable AI, Prompt Engineering, Fine-tuning GPT, develop a deep understanding of GPT, including its mechanisms, features, and limitations, Ethical Considerations in Generative AI Models & GPT, the Future of Generative AI, Security and Privacy Considerations.