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Course Outline
Introduction
- What is generative AI?
- Generative AI vs other types of AI
- Overview of main techniques and models in generative AI
- Applications and use cases of generative AI
- Challenges and limitations of generative AI
Creating Images with Generative AI
- Generating images from text descriptions
- Using GANs to create realistic and diverse images
- Using VAEs to create images with latent variables
- Using style transfer to apply artistic styles to images
Creating Text with Generative AI
- Generating text from text prompts
- Using transformer-based models to create text with context and coherence
- Using text summarization to create concise summaries of long texts
- Using text paraphrasing to create different ways of expressing the same meaning
Creating Audio with Generative AI
- Generating speech from text
- Generating text from speech
- Generating music from text or audio
- Generating speech with a specific voice
Creating Other Content with Generative AI
- Generating code from natural language
- Generating product sketches from text
- Generating video from text or images
- Generating 3D models from text or images
Evaluating Generative AI
- Assessing content quality and diversity in generative AI
- Using metrics like inception score, Fréchet inception distance, and BLEU score
- Utilizing human evaluation through crowdsourcing and surveys
- Applying adversarial evaluation methods such as Turing tests and discriminators
Understanding Ethical and Social Implications of Generative AI
- Ensuring fairness and accountability
- Avoiding misuse and abuse
- Respecting the rights and privacy of content creators and consumers
- Fostering creativity and collaboration of human and AI
Summary and Next Steps
Requirements
- An understanding of basic AI concepts and terminology
- Experience with Python programming and data analysis
- Familiarity with deep learning frameworks such as TensorFlow or PyTorch
Audience
- Data scientists
- AI developers
- AI enthusiasts
14 Hours
