Generative AI & Transformers: Master LLMs, Diffusion Models, PyTorch Implementation, and Certification Preparation.
Sub Category
- Other IT & Software
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Objectives
- Comprehend the fundamental architecture of the Transformer model, including the self-attention mechanism and positional encoding.
- Design and implement custom Large Language Models (LLMs) using modern deep learning frameworks like PyTorch or TensorFlow.
- Effectively apply advanced prompt engineering techniques to maximize performance and safety of pre-trained LLMs.
- Master fine-tuning strategies, including parameter-efficient methods (LoRA, QLoRA), for adapting LLMs to specific tasks efficiently.
- Build and deploy Retrieval-Augmented Generation (RAG) systems for enhanced factual grounding and enterprise AI applications.
Pre Requisites
- Solid understanding of Python programming (intermediate level or higher)
- Familiarity with foundational Machine Learning concepts (training, validation, metrics)
- Basic experience with deep learning libraries such as PyTorch or TensorFlow
- Experience working with PyTorch or TensorFlow/Keras framework basics.
FAQ
- Q. How long do I have access to the course materials?
- A. You can view and review the lecture materials indefinitely, like an on-demand channel.
- Q. Can I take my courses with me wherever I go?
- A. Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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