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Course Outline
Introduction
- Defining generative AI
- Comparing generative AI with other AI paradigms
- Overview of key techniques and models in the generative AI landscape
- Real-world applications and use cases of generative AI
- Current challenges and limitations in generative AI
Image Creation with Generative AI
- Synthesising images from textual descriptions
- Leveraging GANs to produce realistic and varied imagery
- Utilising VAEs for image creation involving latent variables
- Applying style transfer to impart artistic styles to images
Text Generation with Generative AI
- Generating text from textual prompts
- Employing transformer-based models to ensure contextual and coherent text
- Using text summarisation to distill long texts into concise summaries
- Applying text paraphrasing to express concepts in varied ways
Audio Generation with Generative AI
- Converting text into speech
- Transcribing speech into text
- Creating music from text or audio inputs
- Generating speech with specific voice characteristics
Other Content Creation with Generative AI
- Producing code from natural language
- Generating product sketches from text descriptions
- Creating video content from text or images
- Generating 3D models from text or image inputs
Evaluating Generative AI
- Measuring content quality and diversity in generative AI outputs
- Applying metrics such as inception score, Fréchet inception distance, and BLEU score
- Conducting human evaluation via crowdsourcing and surveys
- Implementing adversarial evaluation techniques like Turing tests and discriminators
Ethical and Social Dimensions of Generative AI
- Ensuring fairness and accountability
- Preventing misuse and abuse
- Safeguarding the rights and privacy of content creators and consumers
- Encouraging collaborative creativity between humans and AI
Summary and Future Directions
Requirements
- A grasp of fundamental AI concepts and terminology
- Practical 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 tips and recommended prompts that we can take away from this training
Lee Mei Lin - ST Engineering IHQ Pte Ltd
Course - InVideo AI: Creating Engaging Short-Form Videos
use of proper and effective prompt
