Sequence Modeling, Transformers, and Transfer Learning

This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. This course provides a comprehensive journey into sequence modeling, transformers, and transfer learning, equipping you with the skills to build powerful models for natural language processing (NLP) and other sequential data tasks. You'll begin by mastering Recurrent Neural Networks (RNNs), including their architecture, training techniques like backpropagation through time (BPTT), and specialized models such as Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRUs). The course then moves into sequence-to-sequence models, which are critical for tasks like translation, summarization, and text generation. The next phase of the course explores the groundbreaking transformer architecture, the backbone of modern NLP models like BERT and GPT. You will dive into attention mechanisms, self-attention, and multi-head attention, understanding how these components capture contextual relationships in text. You'll also gain hands-on experience with pre-trained transformer models and learn how to apply them to real-world NLP tasks such as text summarization and translation. In the final section, you'll focus on transfer learning, a technique that enables the reuse of pre-trained models to solve new tasks with fewer resources. This course teaches you how to fine-tune models for both computer vision and NLP applications, including domain adaptation strategies and challenges. With a hands-on project at the end of the course, you’ll apply transfer learning to fine-tune a model for a custom task, demonstrating your ability to adapt state-of-the-art models to real-world problems. This course is ideal for learners with a foundational understanding of machine learning who want to advance their knowledge in deep learning, sequence modeling, and transfer learning. Prior knowledge of Python and basic machine learning concepts is recommended. The course is suitable for intermediate learners looking to deepen their understanding and practical skills in AI and deep learning. By the end of the course, you will be able to implement sequence models like RNNs, build transformers using attention mechanisms, apply transfer learning to fine-tune pre-trained models, and solve complex NLP tasks such as translation, summarization, and text generation.