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Course Outline
Introduction
- Defining generative AI
- Differentiating generative AI from other AI types
- Overview of primary techniques and models in generative AI
- Applications and use cases of generative AI
- Challenges and limitations of generative AI
Generating Images with Generative AI
- Producing images from text descriptions
- Leveraging GANs to create realistic and varied imagery
- Utilizing VAEs for image creation via latent variables
- Applying style transfer to impose artistic styles on images
Generating Text with Generative AI
- Creating text outputs from text prompts
- Employing transformer-based models to generate contextually coherent text
- Using text summarization to distill long texts into concise summaries
- Utilizing text paraphrasing to offer alternative expressions of the same meaning
Generating Audio with Generative AI
- Synthesizing speech from text
- Transcribing speech to text
- Composing music from text or audio inputs
- Generating speech mimicking a specific voice
Generating Other Content Types with Generative AI
- Producing code from natural language inputs
- Creating product sketches based on text descriptions
- Generating video content from text or images
- Constructing 3D models from text or images
Evaluating Generative AI Outputs
- 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
Exploring Ethical and Social Implications of Generative AI
- Ensuring fairness and accountability
- Preventing misuse and abuse
- Respecting the rights and privacy of both creators and consumers of content
- Promoting creativity and collaboration between humans and AI
Summary and Next Steps
Requirements
- A foundational understanding of AI concepts and terminology
- Practical 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
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)
