Foundations of NLP Sequence Models

Sequential data is all around us, from the text we type to the speech we hear. Understanding how to model and analyze this data is key to building modern language technologies. This text-based course guides you from the absolute basics of sequential data to the core concepts behind modern language models. You will gain a solid intuitive and practical understanding of how computers process sequences, translate languages, and generate text. What you'll learn: Understand the core concepts of sequential data and basic text preprocessing techniques; Explore the mechanics of Recurrent Neural Networks (RNNs), GRUs, and LSTMs; Implement sequence-to-sequence models for translation and text generation; Learn the foundations of attention mechanisms and transformer architectures that power modern AI; Apply modern tokenization and word embedding techniques to represent language numerically; Practice building and training basic sequence models using code-based exercises. Starting with fundamental definitions and sequence representation, you will progress step-by-step from simple recurrent networks to modern transformer concepts, reading through detailed explanations and studying clean code implementations. This course is designed for beginners interested in natural language processing and deep learning. No prior experience with sequence models is required, though a basic understanding of programming concepts is helpful. Begin reading today to unlock the potential of sequence modeling in your own projects.