Generative AI using Python
Description
This course introduces the concepts and practical implementation of generative AI models using Python. Participants gain hands-on experience building and deploying models that create text, images, or other media, leveraging state-of-the-art libraries.
Key Objectives
By the end of this module, participants will be able to:
- Understand the fundamental architectures behind generative AI, including GANs, VAEs, and transformers.
- Use Python libraries such as TensorFlow, PyTorch, and Hugging Face Transformers to build generative models.
- Train and fine-tune generative models for tasks like text generation, image synthesis, and audio production.
- Evaluate model outputs and optimize for quality and diversity.
- Integrate generative AI models into applications and workflows effectively.
Target Audience
- Aspiring AI Engineers, Data Scientists, ML Developers, and Technical Researchers
- Seniority Level: Intermediate to advanced professionals or graduate-level learners seeking hands-on expertise in generative AI
Prerequisite Knowledge
- Working knowledge of Python programming
- Basic understanding of machine learning principles and data structures
- Prior exposure to ML frameworks (TensorFlow or PyTorch) is beneficial but not required
Classroom
Sessions can be delivered:
- Live online via video conferencing platforms, with recording available for later review
- Interactive workshops with practical exercises, real-time demonstrations, and collaborative activities
- Hybrid approach combining live online delivery with on-site support if needed
The teaching methodology combines presentations, live demonstrations, hands-on exercises, and interactive discussions to ensure participants actively practice AI in realistic work scenarios.
