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Course Outline
Introduction
- Defining generative AI.
- Distinguishing generative AI from other AI types.
- Overview of key techniques and models in generative AI.
- Applications and use cases for generative AI.
- Challenges and limitations associated with generative AI.
Generating Images with Generative AI
- Creating images from textual descriptions.
- Utilizing GANs to produce realistic and diverse images.
- Employing VAEs to generate images with latent variables.
- Applying artistic styles to images via style transfer.
Generating Text with Generative AI
- Producing text from textual prompts.
- Using transformer-based models to create contextually coherent text.
- Summarizing lengthy texts through text summarization techniques.
- Paraphrasing text to express the same meaning in different ways.
Generating Audio with Generative AI
- Synthesizing speech from text.
- Transcribing speech to text.
- Composing music from text or audio inputs.
- Generating speech with specific voice characteristics.
Generating Other Content Types with Generative AI
- Writing code from natural language descriptions.
- Creating product sketches from text.
- Producing video from text or images.
- Generating 3D models from text or images.
Evaluating Generative AI Outputs
- Assessing content quality and diversity in generative AI.
- Utilizing metrics such as Inception Score, Fréchet Inception Distance, and BLEU Score.
- Conducting human evaluation via crowdsourcing and surveys.
- Applying adversarial evaluation methods like Turing tests and discriminators.
Understanding the Ethical and Social Implications of Generative AI
- Ensuring fairness and accountability.
- Preventing misuse and abuse.
- Respecting the rights and privacy of content creators and consumers.
- Encouraging 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.
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)
