Encoder-Decoder Architecture: Sequence-to-Sequence Learning

This course offers an in-depth exploration of the encoder-decoder architecture, a fundamental concept in modern natural language processing (NLP) and machine learning. The curriculum focuses on understanding and implementing this powerful architecture for sequence-to-sequence tasks such as machine translation, text summarization, and question answering. Students will learn about the main components of the encoder-decoder model, including attention mechanisms, and how these elements work together to process and generate sequential data. The course combines theoretical knowledge with practical application, featuring a hands-on lab where learners implement a poetry generation model using TensorFlow and Keras. By the end of the course, participants will have a solid understanding of how to build, train, and deploy encoder-decoder models for various NLP tasks.