DDS-8150

Rothman, D. (2022). Chapter 1: What Are Transformers. In Transformers for natural language processing. Packt Publishing.

Rothman, D. (2022). Chapter 3: "Emergent vs. Downstream Tasks: The Unseen Depths of Transformers. In Transformers for natural language processing. Packt Publishing.
These chapters introduce transformer models like BERT and explain their use in NLP tasks, providing key concepts for Week 5.

Tunstall, L., von Werra, L., & Wolf, T. (2022). Chapter 3: Transformer Anatomy. In Natural language processing with transformers, Revised Edition. O’Reilly Media.

Tunstall, L., von Werra, L., & Wolf, T. (2022). Chapter 6: Fine-tuning pretrained transformers for text classification. In Natural language processing with transformers, Revised Edition. O’Reilly Media.
These chapters provide hands-on experience with NLP using transformer models, making it a perfect fit for practical NLP tasks.

Author(s). (Year). Chapter Title. In Introduction to transformers: Architecture and applications. Apress.

Michelucci, U. (2022). Applied deep learning with TensorFlow 2: Implementing machine learning and deep learning applications with Python. Apress. https://doi.org/10.1007/978-1-4842-8672-8
This chapter covers the fundamentals of GANs, including how to build and train them using PyTorch, making it a core resource for Week 6.

Vasilev, I. (2023). Chapter 5: Generative Models. In Advanced deep learning with Python: Design and optimize neural network models. Packt Publishing.
This chapter explores more advanced GAN architectures, offering insights into the optimization and training of adversarial models.