NLP with Sequence Models: RNNs, LSTMs, and GRUs

Processing sequential data like text requires specialized neural networks that understand context and order. This text-based course guides you through the core concepts and practical implementations of sequence models in Natural Language Processing (NLP). You will start with the foundational definitions of sequential text processing and progress to building neural architectures that handle real-world language tasks. By studying structured code explanations and step-by-step breakdowns, you will learn how to represent text as dense vectors, model dependencies over time, and compare semantic meaning. What you'll learn: - Understand the mathematical foundations of recurrent neural networks (RNNs) and how they process sequential text data. - Build sentiment analysis models using word embeddings and recurrent architectures to classify text. - Generate synthetic text by training Gated Recurrent Units (GRUs) to predict the next token in a sequence. - Implement Named Entity Recognition (NER) systems using Long Short-Term Memory (LSTM) networks to locate and classify key entities. - Create Siamese LSTM architectures to compare semantic similarity between different sentences. - Apply modern tokenization techniques and sequence-handling strategies used in contemporary deep learning workflows. The course begins with essential terminology, covering tokenization, vocabulary building, and embedding layers. You will then explore simple recurrent networks before advancing to gated architectures like LSTMs and GRUs for complex text processing tasks. This course is designed for beginners in deep learning and NLP who have a basic understanding of Python and neural network fundamentals. No prior experience with sequence models is required. Start reading to build your own sequence-based NLP models today.