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Duration 14 hours
Course Outline
Introduction
- Defining generative AI.
- Distinguishing generative AI from other AI paradigms.
- A review of key techniques and architectures in the generative AI landscape.
- Real-world applications and use cases.
- Identifying current challenges and inherent limitations.
Image Generation with Generative AI
- Synthesizing images from textual descriptions.
- Leveraging GANs to produce high-fidelity and diverse visual content.
- Utilizing VAEs for image creation involving latent variables.
- Applying style transfer techniques to impose artistic aesthetics onto images.
Text Generation with Generative AI
- Generating text outputs based on specific prompts.
- Employing transformer-based models to ensure contextual relevance and coherence.
- Creating concise summaries of lengthy documents using text summarization.
- Producing alternative phrasings of identical meaning through text paraphrasing.
Audio Generation with Generative AI
- Synthesizing speech from written text.
- Converting spoken audio into text.
- Composing music driven by textual or audio inputs.
- Generating speech with specific vocal characteristics.
Creating Diverse Content with Generative AI
- Generating code snippets from natural language instructions.
- Producing product sketches from textual descriptions.
- Creating video content from text or image inputs.
- Generating 3D models from textual or visual references.
Evaluating Generative AI Performance
- Assessing the quality and variety of generated content.
- Applying quantitative metrics such as inception score, Fréchet inception distance, and BLEU score.
- Incorporating human feedback via crowdsourcing and survey methods.
- Utilizing adversarial evaluation techniques, including Turing tests and discriminator models.
Ethical and Social Considerations of Generative AI
- Maintaining fairness and ensuring accountability in AI systems.
- Preventing malicious misuse or abuse of generative tools.
- Protecting the rights and privacy of both content creators and consumers.
- Promoting synergistic creativity and collaboration between humans and AI.
Conclusion and Future Directions
Requirements
- Fundamental knowledge of core AI concepts and terminology.
- Proficiency in Python programming and data analysis techniques.
- Working familiarity with deep learning frameworks such as TensorFlow or PyTorch.
Target Audience
- Data scientists
- AI developers
- AI enthusiasts
Testimonials (3)
Comprehensiveness about the backround; how to do a prompt.
Olaf Kannt - Helios Klinikum Krefeld
Course - Generative AI and Prompt Engineering in Healthcare
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
