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Course Outline
Introduction
- Defining generative AI?
- Generative AI compared to other AI types
- Overview of primary techniques and models in generative AI
- Applications and use cases for generative AI
- Challenges and limitations inherent to generative AI
Creating Images with Generative AI
- Producing images from text descriptions
- Leveraging GANs to generate realistic and diverse imagery
- Utilizing VAEs for image creation with latent variables
- Applying artistic styles via style transfer techniques
Creating Text with Generative AI
- Generating text from prompts
- Employing transformer-based models to produce contextual and coherent text
- Utilizing text summarization for concise overviews of lengthy documents
- Using text paraphrasing to express identical meanings in different ways
Creating Audio with Generative AI
- Synthesizing speech from text
- Transcribing speech to text
- Composing music from text or audio inputs
- Generating speech with distinct voice characteristics
Creating Other Content with Generative AI
- Producing code from natural language descriptions
- Drafting product sketches from text
- Generating video content from text or images
- Constructing 3D models from text or image inputs
Evaluating Generative AI
- Assessing content quality and diversity within generative AI
- Applying metrics such as Inception Score, Fréchet Inception Distance, and BLEU Score
- Conducting human evaluation via crowdsourcing and surveys
- Implementing adversarial evaluation methods like Turing tests and discriminators
Understanding Ethical and Social Implications of Generative AI
- Ensuring fairness and accountability
- Preventing misuse and abuse
- Protecting the rights and privacy of content creators and consumers
- Promoting creativity and collaboration between humans and AI
Summary and Next Steps
Requirements
- A foundational understanding of core AI concepts and terminology.
- Practical experience with Python programming and data analysis.
- Familiarity with deep learning frameworks such as TensorFlow or PyTorch.
Target Audience
- Data scientists
- AI developers
- AI enthusiasts
14 Hours
Testimonials (2)
The interactive style, the exercises
Tamas Tutuntzisz
Course - Introduction to Prompt Engineering
A great repository of resources for future use, instructor's style (full of good sense of humor, great level of detail)
