Generative AI is a rapidly evolving field that focuses on creating new, synthetic data similar to existing datasets. This course provides a structured approach to understanding fundamental concepts, techniques, and tools required to build and deploy generative models. Participants will explore various generative models, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Diffusion Models, and Transformers, and gain hands-on experience in applying them to real-world problems.
Pre-Requisites:
- Basic programming knowledge (preferably Python).
- Familiarity with Python libraries like NumPy, Pandas, Matplotlib, and TensorFlow/PyTorch.
- Some understanding of probability distributions and linear algebra.
- Exposure to deep learning concepts (helpful but not mandatory).
Key Learning Outcomes:
By the end of this course, participants will:
- Understand Generative AI and its applications across industries (image generation, text synthesis, data augmentation, etc.).
- Differentiate between generative models like GANs, VAEs, Diffusion Models, and Transformers.
- Gain hands-on experience in implementing and fine-tuning generative models using TensorFlow/PyTorch.
- Learn evaluation metrics like Frechet Inception Distance (FID), Inception Score (IS), and Perplexity.
- Explore ethical considerations, including bias, misinformation, adversarial attacks, and copyright issues.
- Deploy generative models using Hugging Face, Streamlit, or OpenAI API.
Target Audience:
- AI enthusiasts and professionals looking to expand their knowledge of Generative AI.
- Students and professionals from non-technical backgrounds who want to understand AI-generated content.
- Developers and data scientists interested in building real-world applications using Generative AI.
Test & Evaluation:
- Assignments: Regular hands-on tasks for skill reinforcement.
- Final Project: Developing and presenting a generative AI model.
- Assessment: MCQs + practical evaluation.
- Certification:
- Certificate of Completion upon successful course completion.
- Project Letter for those who complete the final project.
- No certification for participants who leave the course midway.
Delivery Mode & Duration:
- Mode: Online Live Sessions
- Total Duration: 120 Hours (60 Hours Live Sessions + 60 Hours Hands-on Assignments)