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 compared to other AI types
- Overview of key 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 text descriptions
- Using GANs to produce realistic and diverse images
- Using VAEs to generate images with latent variables
- Applying artistic styles to images through style transfer
Creating Text with Generative AI
- Generating text from text prompts
- Using transformer-based models to create contextually coherent text
- Using text summarization to condense long texts
- Using text paraphrasing to express the same meaning in different ways
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 profile
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
- 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 between humans and AI
Summary and Next Steps
Requirements
- A foundational understanding of AI concepts and terminology.
- Experience with Python programming and data analysis.
- Familiarity with deep learning frameworks like 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)