Outline

Configure text preprocessing pipelines to handle noise, normalization, and feature extraction • Implement tokenization techniques, including word-level, subword-level, and character-level tokenization • Design feature engineering strategies to enhance model performance and generalizability

Outline

Implement Hidden Markov Models (HMMs) and Conditional Random Fields (CRFs) for sequence labeling tasks • Analyze the strengths and limitations of classical NLP models, including n-gram models and decision trees • Develop a deep understanding of statistical methods, including maximum likelihood estimation and Bayesian inference

Outline

Design and implement Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks for sequence modeling • Configure Convolutional Neural Networks (CNNs) and Transformers for text classification and language modeling tasks • Evaluate the performance of deep learning architectures on various NLP tasks and datasets

Outline

Implement self-attention mechanisms and Transformer architectures for machine translation and text generation • Analyze the role of Large Language Models (LLMs) in NLP, including their applications and limitations • Develop a comprehensive understanding of attention mechanisms, including multi-head attention and hierarchical attention

Outline

Evaluate NLP models using metrics such as accuracy, F1-score, and perplexity • Implement fine-tuning techniques, including transfer learning and domain adaptation • Optimize NLP models using hyperparameter tuning, regularization, and early stopping

Outline

Design and deploy production-ready NLP systems using containerization and orchestration tools • Implement RESTful APIs for NLP models using frameworks such as Flask and Django • Configure and manage NLP pipelines using workflow management tools such as Apache Airflow