Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Introduction
- What is generative AI?
- Generative AI versus other types of AI
- 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
- Utilizing GANs to generate realistic and diverse images
- Employing VAEs to create images with latent variables
- Applying style transfer to impose artistic styles onto images
Generating Text with Generative AI
- Producing text from text prompts
- Leveraging 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
Generating Audio with Generative AI
- Generating speech from text
- Generating text from speech
- Generating music from text or audio
- Generating speech with a specific voice
Generating 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
- A foundational understanding of basic AI concepts and terminology
- Experience in 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)
