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Duration 14 hours
Course Outline
Introduction
- Defining generative AI.
- Distinguishing generative AI from other AI categories.
- Review of key techniques and models within generative AI.
- Exploring applications and real-world use cases of generative AI.
- Identifying challenges and limitations associated with generative AI.
Image Creation with Generative AI
- Synthesizing images from textual descriptions.
- Employing GANs to produce realistic and varied imagery.
- Leveraging VAEs to generate images using latent variables.
- Applying style transfer techniques to impose artistic styles on images.
Text Generation with Generative AI
- Producing text from prompt inputs.
- Utilizing transformer-based models to ensure contextual and coherent text creation.
- Employing text summarization to condense lengthy documents into concise summaries.
- Using text paraphrasing to rephrase meanings in diverse ways.
Audio Generation with Generative AI
- Converting text into speech.
- Transcribing speech into text.
- Composing music based on textual or audio inputs.
- Generating speech with specific vocal characteristics.
Other Content Creation with Generative AI
- Generating code from natural language descriptions.
- Creating product sketches from textual prompts.
- Producing video content from text or image inputs.
- Constructing 3D models from text or images.
Evaluation of Generative AI
- Measuring content quality and diversity in generative AI outputs.
- Applying metrics such as inception score, Fréchet inception distance, and BLEU score.
- Incorporating human assessment via crowdsourcing and surveys.
- Using adversarial evaluation methods like Turing tests and discriminators.
Ethical and Social Implications of Generative AI
- Promoting fairness and accountability in AI systems.
- Preventing misuse and abuse of generative capabilities.
- Protecting the rights and privacy of both content creators and consumers.
- Encouraging creative collaboration between humans and AI.
Summary and Future Directions
Requirements
- A foundational grasp of basic AI concepts and terminology
- Proficiency in Python programming and data analysis techniques
- Experience 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
