Generative AI: Creating Novel Content with AI Models Training Course
Generative AI (GenAI) refers to a subset of AI algorithms capable of producing new and realistic content based on existing data, including images, text, audio, and more. These models analyze the patterns and structure within their training data to generate new data with similar attributes.
This instructor-led live training (online or in-person) is designed for beginners to intermediate-level data scientists, AI developers, and enthusiasts who want to leverage generative AI to produce innovative and varied content across different applications.
Upon completion of this course, participants will be able to:
- Establish a development environment that incorporates generative AI models and tools.
- Create images, text, audio, and other forms of content from textual prompts using generative AI techniques.
- Apply generative AI in various fields such as art, design, entertainment, and education.
- Assess the quality and diversity of content produced by generative AI systems.
- Grasp the ethical and societal implications associated with generative AI technologies.
Course Format
- Interactive lectures and discussions.
- A substantial amount of exercises and practical application.
- Hands-on implementation in a live-lab setting.
Customization Options for the Course
- To arrange a customized training session, please contact us to discuss your requirements.
Course Outline
Introduction
- What is generative AI?
- Generative AI vs other types of AI
- Overview of main 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
- Using GANs to create realistic and diverse images
- Using VAEs to create images with latent variables
- Using style transfer to apply artistic styles to images
Creating Text with Generative AI
- Generating text from text prompts
- Using transformer-based models to create text with context and coherence
- Using text summarization to create concise summaries of long texts
- Using text paraphrasing to create different ways of expressing the same meaning
Creating Audio with Generative AI
- Generating speech from text
- Generating text from speech
- Generating music from text or audio
- Generating speech with a specific voice
Creating Other Content with Generative AI
- Generating code from natural language
- Generating product sketches from text
- Generating video from text or images
- Generating 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
- Avoiding misuse and abuse
- Respecting the rights and privacy of content creators and consumers
- Fostering creativity and collaboration of human and AI
Summary and Next Steps
Requirements
- An understanding of basic AI concepts and terminology
- Experience with Python programming and data analysis
- Familiarity with deep learning frameworks such as TensorFlow or PyTorch
Audience
- Data scientists
- AI developers
- AI enthusiasts
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