Natural Language Generation Course

Module 1: NLP Foundations, Linguistics, and NLG Fundamentals

• Analyze the fundamental concepts of linguistics and their application to natural language processing and generation
• Develop a comprehensive understanding of the NLP pipeline, including text processing, tokenization, and feature extraction
• Evaluate the strengths and limitations of rule-based and machine learning approaches to natural language generation

Module 2: Text Preprocessing, Tokenization, and Feature Engineering

• Implement text preprocessing techniques, including tokenization, stemming, and lemmatization, to prepare text data for NLP tasks
• Design and develop feature extraction methods, such as bag-of-words and term frequency-inverse document frequency, to represent text data in a numerical format
• Configure and optimize text preprocessing pipelines using popular NLP libraries and frameworks

Module 3: Classical NLP Models and Statistical Methods

• Develop and apply statistical models, such as n-gram and hidden Markov models, to natural language processing tasks
• Analyze and evaluate the performance of classical NLP models, including their strengths and limitations
• Implement and optimize statistical methods, such as maximum likelihood estimation and Bayesian inference, for NLP tasks

Module 4: Deep Learning Architectures for NLG

• Design and develop deep learning architectures, including recurrent neural networks and long short-term memory networks, for natural language generation tasks
• Implement and optimize deep learning models using popular frameworks, such as TensorFlow and PyTorch
• Evaluate the performance of deep learning models for NLG tasks, including their ability to generate coherent and contextually relevant text

Module 5: Transformers, LLMs, and Attention Mechanisms

• Implement and optimize transformer-based architectures, including BERT and RoBERTa, for natural language generation tasks
• Develop and apply attention mechanisms, including self-attention and cross-attention, to improve the performance of NLG models
• Analyze and evaluate the performance of large language models, including their ability to generate coherent and contextually relevant text

Module 6: Model Evaluation, Fine-Tuning, and Optimization

• Develop and apply evaluation metrics, including perplexity and BLEU score, to assess the performance of NLG models
• Implement and optimize fine-tuning techniques, including transfer learning and domain adaptation, to improve the performance of pre-trained NLG models
• Configure and optimize hyperparameters, including learning rate and batch size, to improve the performance of NLG models

Module 7: Production NLP Systems, APIs, and Deployment

• Design and develop production-ready NLP systems, including APIs and microservices, for natural language generation tasks
• Implement and optimize deployment strategies, including containerization and cloud deployment, for NLP systems
• Evaluate and ensure the scalability, reliability, and security of production NLP systems