Overview

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.

Syllabus

Recurrent Neural Networks (RNNs) and Sequence Modeling

In this module, we will explore the world of sequence modeling with Recurrent Neural Networks (RNNs). You'll learn about the architecture of RNNs, including how backpropagation through time works. We also cover advanced models like LSTMs and GRUs, and teach you how to preprocess text data and apply RNNs to sequence-to-sequence tasks. The module concludes with a hands-on project to implement RNNs for text generation or sentiment analysis.

Transformers and Attention Mechanisms

In this module, we introduce you to the transformative power of attention mechanisms in deep learning models. You’ll explore the architecture of transformers, learning about self-attention, multi-head attention, and positional encoding. With hands-on demonstrations of pre-trained transformer models like BERT and GPT, this section equips you to apply advanced NLP techniques to real-world projects like text summarization and translation.

Transfer Learning and Fine-Tuning

In this module, we dive into the concept of transfer learning, a powerful technique that leverages pre-trained models for a wide range of applications. You will learn how to use transfer learning for both computer vision and natural language processing (NLP), including fine-tuning strategies and domain adaptation. The section concludes with a project where you will fine-tune a model for a custom task, helping you apply these techniques to solve real-world problems.