Generative AI for Developers

At Course Completion

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

· Develop a strong foundational understanding of generative AI techniques and their applications in software development.

· Gain hands-on experience working with popular generative AI models, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformer models.

· Master the use of leading AI libraries and frameworks, such as TensorFlow, Keras, and Hugging Face Transformers, for implementing generative AI models.

· Acquire the skills to design, train, optimize, and evaluate custom generative AI models tailored to specific software development tasks.

· Learn to fine-tune pre-trained generative AI models for targeted applications and deploy them effectively in various environments, including cloud-based services and on-premises servers.

· Understand and address the ethical, legal, and safety considerations of using generative AI, including mitigating biases and ensuring responsible AI-generated content.

Audience Profile

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: Professionals who want to integrate generative AI into their projects for tasks like code generation, documentation, and testing.

· Data Scientists: Those looking to expand their skillset by incorporating generative AI models into their data analysis and prediction tasks.

· Machine Learning Engineers: Individuals who want to specialize in developing and deploying generative AI models for various applications.

· AI Researchers: Academics and researchers interested in exploring the latest advancements in generative AI and their potential applications in software development.

· User Interface (UI) and User Experience (UX) Designers: Professionals who want to leverage generative AI for creating dynamic and adaptive interfaces.

· Technical Product Managers: Managers who oversee the development of AI-driven products and want to understand how generative AI can enhance their offerings.

· Technical Team Leads: Supervisors responsible for guiding development teams and looking for innovative ways to incorporate generative AI into their projects.

Prerequisites

This course is highly technical in nature. In order to gain the most from attending you should possess the following incoming skills:

· Python programming experience (Python syntax and constructs, experience with NumPy and Pandas)

· Basic understanding of artificial intelligence and machine learning concepts (supervised and unsupervised learning, neural networks, optimization techniques)

· Some experience with data manipulation and preprocessing , including working with various data formats, such as text, images, and structured data, preprocessing and cleaning data for use in machine learning models.

Take Before: You should have incoming skills aligned with the topics in the course(s) below, or should attend as a pre-requisite:

· TTML5503 AI & Machine Learning JumpStart | Introduction to AI, AI Programming & Machine Learning (3 days)

· TTPS4873 Fast Track to Python in Data Science (3 days)

Next Steps / Follow-on Courses: We offer a wide variety of follow-on courses and learning paths for Generative AI, AI programming, machine learning, analytics, intelligent automation and other related topics. Please see our AI & Machine Learning Suite of courses and Learning Paths for options based on your specific role and goals.

Outline

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.