(Weeks 16–18 | Lecture 9 Hrs / Lab 27 Hrs / Ext 0 Hrs | 36 Total Contact Hrs | 1.2 Semester
Credits)
Students will:
- Develop Generative Adversarial Networks (GANs) for synthetic data creation.
- Build Variational Autoencoders (VAEs) for data generation tasks.
- Understand and apply the fundamentals of diffusion models and LLMs.
Prerequisite: MLDL 107 – Transfer Learning and Fine-Tuning
Tools: TensorFlow, Hugging Face, OpenAI API
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