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Course Outline
Introduction
- What constitutes generative AI?
- Generative AI compared with 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.
Creating Images with Generative AI
- Generating images from textual descriptions.
- Utilising GANs to create realistic and diverse images.
- Utilising VAEs to create images with latent variables.
- Employing style transfer to apply artistic styles to images.
Creating Text with Generative AI
- Generating text from textual prompts.
- Utilising transformer-based models to create text with context and coherence.
- Utilising text summarisation to create concise summaries of lengthy texts.
- Utilising text paraphrasing to create alternative 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.
- Utilising metrics such as inception score, Fréchet inception distance, and BLEU score.
- Utilising 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 between humans and AI.
Summary and Next Steps
Requirements
- A solid understanding of fundamental 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
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)
