Introduction to Generative AI

• Overview of Generative AI and its significance in modern applications.

• Comparison between generative and discriminative models.

• Exploration of key generative models: GANs, VAEs, and autoregressive models.

• Discussion on ethical considerations, including biases and deepfakes.

•  

Fundamentals of Deep Learning

• Understanding neural networks and their architectures.

• Training techniques: backpropagation and optimization algorithms.

• Regularization methods to prevent overfitting.

• Introduction to frameworks like TensorFlow and PyTorch.​

Variational Autoencoders (VAEs)

• Concept and architecture of autoencoders and VAEs.

• Understanding latent space representations.

• Applications of VAEs in image generation and data compression.

• Hands-on project: Implementing a basic VAE model.​

•  

Generative Adversarial Networks (GANs)

• Introduction to GANs: generator and discriminator dynamics.

• Training challenges and solutions in GANs.

• Variants of GANs: DCGAN, StyleGAN, and CycleGAN.

• Hands-on project: Creating images using GANs

•  

Natural Language Processing and Text Generation

• Basics of NLP: tokenization, embedding, and language models.

• Exploring transformer architectures like BERT and GPT.

• Techniques in text generation and summarization.

• Hands-on project: Building a text-generating chatbot.

Creative Applications of Generative AI

• Music generation using models like MuseNet and Jukebox.

• Video synthesis and deepfake technologies.

• 3D object generation and design applications.

• Hands-on project: Developing a creative AI application.

Deployment and Real-World Integration

• Integrating generative models into web applications.

• Deployment strategies using cloud platforms (AWS, Azure).

• API development for AI models.

• Best practices for maintaining and updating AI models.

•  

Future Trends and Ethical Considerations

• Emerging trends in generative AI research.

• Ethical implications and societal impact of generative technologies.

• Regulatory frameworks and responsible AI development.

• Final project presentation and course wrap-up.

•