Introduction to Generative AI

-     Overview of generative AI and its significance

-     Key differences between discriminative and generative models

-     Historical evolution and major milestones in generative AI

Fundamentals of Neural Networks

-     Basic structure and function of neural networks

-     Introduction to deep learning frameworks (TensorFlow, PyTorch)

-     Understanding layers, activation functions, and backpropagation

Generative Models: GANs and VAEs

-     Introduction to Generative Adversarial Networks (GANs)

-     Variational Autoencoders (VAEs) and their applications

-     Hands-on implementation of a simple GAN model

Text Generation and Natural Language Processing (NLP)

-     Overview of language models (e.g., GPT, BERT)

-     Techniques for text generation and summarization

-     Practical applications: chatbots, content creation, and translation

Generative AI in Computer Vision

-     Image generation using GANs and VAEs

-     Style transfer and super-resolution techniques

-     Applications in art, design, and media

Ethics and Responsible AI

-     Understanding bias in generative models

-     Ethical considerations in deploying generative AI

-     Best practices for responsible AI development and use

Industry Applications of Generative AI

-     Case studies: Generative AI in healthcare, finance, and entertainment

-     AI-driven innovation in product design and marketing

-     Future trends and emerging opportunities in generative AI

Final Project and Certification Exam

-     Capstone project: Building and deploying a generative AI model

-     Peer review and feedback sessions