Working in an interactive learning environment, led by our engaging AI expert you’ll:

• Build a solid understanding of generative AI techniques and their applications in software.
• Gain hands-on experience with popular models, including GANs, VAEs, and Transformers.
• Use AI libraries and frameworks like TensorFlow, Keras, and Hugging Face Transformers for implementing generative models.
• Design, train, optimize, and evaluate custom generative AI models for specific development tasks.
• Fine-tune pre-trained models for targeted applications and deploy them on cloud-based or on-premises servers.
• Address ethical, legal, and safety considerations of generative AI, including bias mitigation and responsible content generation.

The ideal audience for this intermediate and beyond level course consists of experienced software developers, programmers, and engineers who are eager to learn and adopt cutting-edge generative AI techniques in their projects. The course is tailored for experienced professionals with a background in programming and a basic understanding of artificial intelligence and machine learning concepts.

Attendee roles might include:

• Software Developers/Programmers: Those wanting to integrate AI into tasks like code generation, documentation, and testing.
• Data Scientists: Professionals expanding their skillset by incorporating generative models in analysis and prediction.
• Machine Learning Engineers: Individuals focused on developing and deploying generative AI for various applications.
• AI Researchers: Those exploring advancements in generative AI and its applications in software.
• UI/UX Designers: Professionals interested in creating dynamic, adaptive interfaces using AI.
• Technical Product Managers: Managers looking to enhance AI-driven products.
• Technical Team Leads: Leaders seeking innovative ways to incorporate generative AI into team projects.

Please note that this topics, agenda and labs are subject to change, and may adjust during live delivery based on audience skill level, interests and participation.

1. Introduction to Generative AI

• Understand generative AI concepts and applications.
• Trace the evolution of generative AI technologies.
• Identify types of generative models and their uses.
• Learn key concepts: machine learning, neural networks, transformers.
• Review popular generative models like GPT and Codex.

2. Introduction to Prompt Engineering

• Explore prompts' role in guiding AI outputs.
• Craft effective prompts for various tasks.
• See how prompt specificity shapes results.
• Experiment with prompt variations for desired outcomes.

3. Deep Dive into AI Models

• Understand architectures of popular AI models.
• Learn to fine-tune models for tasks like code generation.
• Examine transformers’ role in generative AI.
• Assess model performance and limitations.

4. Ethics and Responsible AI

• Explore ethical considerations in generative AI.
• Detect and address biases in AI content.
• Apply best practices for privacy and fairness.
• Understand regulatory impacts of generative AI.

5. • Variational Autoencoders (VAEs)

• Learn VAE principles and mechanisms.
• Use VAEs for generating code and test scenarios.
• Apply VAEs in UI enhancement and content personalization.
• Generate variations in code and UI elements.

6. Deep Learning and GANs

• Learn GAN concepts and applications.
• Generate synthetic data for testing.
• Use GANs for realistic UI elements and media.
• Explore advanced GANs for complex code generation.

7. Natural Language Generation (NLG)

• Understand NLG's role in software development.
• Generate readable documentation from code.
• Create code from high-level descriptions.
• Build interactive, personalized user experiences.

8. Automated Code Generation

• Generate boilerplate code across languages.
• Use AI for refactoring and optimizing code.
• Learn best practices for code integration.
• Develop integration strategies for generated code.

9. Automating Documentation Creation

• Generate API documentation and comments with AI.
• Maintain accurate, up-to-date documentation.
• Create user guides and technical manuals.
• Integrate AI-generated docs into workflows.

10. Test Generation and Automation

• Generate unit, integration, and end-to-end tests.
• Ensure test coverage and reliability with AI.
• Automate testing using generative AI.
• Integrate AI-generated tests into CI/CD pipelines.

11. AI in UI/UX Design

• Use AI for dynamic, adaptive interfaces.
• Enhance UX and accessibility with AI.
• Personalize UI/UX based on user preferences.
• Review case studies of AI-driven UI/UX improvement.

12. Multimodal Generative AI

• Generate combined text, images, and data.
• Leverage multimodal AI for comprehensive content.
• Enhance UI/UX design with multimodal AI.
• Integrate multimodal content into applications.

13. Style Transfer and Neural Art

• Learn style transfer principles and applications.
• Apply neural art in UI/UX design.
• Create customizable UI themes with style transfer.
• Explore creative content generation techniques.

14. Integrating AI into Existing Projects

• Incorporate generative AI into ongoing projects.
• Balance AI content with manual development.
• Maintain integrity while integrating AI.
• Manage AI-driven changes in long-term projects.

15. Customizing AI Models for Specific Use Cases

• Adapt generative models for specific needs.
• Train and deploy customized models.
• Fine-tune models for specialized tasks.

16. Generative AI in the Real World

• Explore generative AI applications across industries.
• Apply techniques to real-world challenges.
• Review case studies of successful implementations.
• Develop strategies for practical applications.

17. Pulling it All Together: Building and Deploying Generative AI Models

• Synthesize concepts to build and deploy AI models.