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Course Outline
Introduction
- Defining generative AI.
- Comparing generative AI with other AI paradigms.
- An overview of key techniques and models in the generative AI landscape.
- Exploring real-world applications and use cases.
- Identifying current challenges and limitations.
Image Creation with Generative AI
- Synthesizing images from textual descriptions.
- Leveraging GANs for realistic and varied image generation.
- Employing VAEs to manipulate latent variables for image creation.
- Implementing style transfer to apply artistic aesthetics to images.
Text Generation with Generative AI
- Converting prompts into coherent text outputs.
- Utilizing transformer-based models to ensure context and coherence.
- Applying text summarization to condense lengthy documents.
- Using text paraphrasing to rephrase ideas with different expressions.
Audio Generation with Generative AI
- Synthesizing speech from text input.
- Converting speech into text.
- Composing music from text or audio references.
- Generating speech with specific voice characteristics.
Creating Other Content Types
- Generating code from natural language instructions.
- Developing product sketches based on textual descriptions.
- Producing videos from text or image inputs.
- Building 3D models from text or images.
Evaluating Generative AI
- Measuring the quality and diversity of generated content.
- Applying metrics such as inception score, Fréchet inception distance, and BLEU score.
- Incorporating human feedback via crowdsourcing and surveys.
- Employing adversarial methods, including Turing tests and discriminators, for assessment.
Ethical and Social Implications
- Maintaining fairness and accountability in AI systems.
- Mitigating risks of misuse and abuse.
- Protecting the rights and privacy of creators and consumers.
- Encouraging collaborative creativity between humans and AI.
Summary and Future Directions
Requirements
- A solid grasp of fundamental AI concepts and terminology.
- Practical experience with Python programming and data analysis.
- Familiarity with deep learning frameworks like TensorFlow or PyTorch.
Intended 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
