In this module, you’ll explore how sequential models support text classification tasks in PyTorch. You’ll examine recurrent neural networks (RNNs), long short-term memory (LSTM) models, and sentiment analysis workflows. The hands-on labs enable you to train, evaluate, and refine these architectures while examining how regularization and optimizer choices affect convergence, generalization, and overall model performance. Finally, you will also compare RNN and LSTM results to identify performance trade-offs and justify the most effective architecture for the task.

In this module, you will explore how transformer architectures support modern NLP workflows. You will examine self-attention, positional encoding, tokenization, and transfer learning as the foundation for transformer-based text classification. You’ll use PyTorch to work with core transformer components and fine-tune a pretrained model using the Hugging Face ecosystem. Finally, you will also interpret evaluation results, compare tuning outcomes, and justify fine-tuning decisions using performance evidence.

In this module, you will complete a cumulative final project that integrates skills from across the specialization. You will submit your output in Jupyter notebooks that demonstrate your proficiency in PyTorch, neural network design, and NLP techniques. Finally, you will also consolidate your learning with a course wrap-up and assess your understanding with a final exam.