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
- Defining generative AI.
- Comparing generative AI with 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.
- Utilizing GANs to produce realistic and diverse images.
- Employing 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 coherent and context-aware text.
- Employing text summarization to create concise summaries of lengthy texts.
- Using text paraphrasing to express the same meaning in different ways.
Creating Audio with Generative AI
- Generating speech from text.
- Transcribing text from speech.
- Composing music from text or audio inputs.
- Generating speech with specific voice characteristics.
Creating Other Content with Generative AI
- Generating code from natural language.
- Creating product sketches from text descriptions.
- Generating video content from text or images.
- Producing 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.
- Preventing 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 basic AI concepts and terminology.
- Proficiency 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)
