Generative AI Advanced

• An understanding of fundamental machine learning concepts and algorithms
• Experience with Python programming and basic usage of TensorFlow or PyTorch
• Familiarity with the principles of neural networks and deep learning

Audience

• Data scientists
• Machine learning engineers
• AI practitioners

Generative AI is an exciting and rapidly evolving field that focuses on creating new, synthetic instances of data that can pass for real data.

This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level data scientists, machine learning engineers, and AI practitioners who wish to explore complex generative models and their applications in various industries.

By the end of this training, participants will be able to:

• Implement and train advanced generative models, including GANs, VAEs, and diffusion models.
• Understand and apply the Transformer architecture in generative tasks.
• Explore the ethical implications and challenges of Generative AI.
• Apply advanced generative techniques to solve real-world problems in various industries.
• Stay abreast of current research trends and future directions in Generative AI.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Review of Generative AI Basics

• Quick recap of Generative AI concepts
• Advanced applications and case studies

Deep Dive into Generative Adversarial Networks (GANs)

• In-depth study of GAN architectures
• Techniques to improve GAN training
• Conditional GANs and their applications
• Hands-on project: Designing a complex GAN

Advanced Variational Autoencoders (VAEs)

• Exploring the limits of VAEs
• Disentangled representations in VAEs
• Beta-VAEs and their significance
• Hands-on project: Building an advanced VAE

Transformers and Generative Models

• Understanding the Transformer architecture
• Generative Pretrained Transformers (GPT) and BERT for generative tasks
• Fine-tuning strategies for generative models
• Hands-on project: Fine-tuning a GPT model for a specific domain

Diffusion Models

• Introduction to diffusion models
• Training diffusion models
• Applications in image and audio generation
• Hands-on project: Implementing a diffusion model

Reinforcement Learning in Generative AI

• Reinforcement learning basics
• Integrating reinforcement learning with generative models
• Applications in game design and procedural content generation
• Hands-on project: Creating content with reinforcement learning

Advanced Topics in Ethics and Bias

• Deepfakes and synthetic media
• Detecting and mitigating bias in generative models
• Legal and ethical considerations

Industry-Specific Applications

• Generative AI in healthcare
• Creative industries and entertainment
• Generative AI in scientific research

Research Trends in Generative AI

• Latest advancements and breakthroughs
• Open problems and research opportunities
• Preparing for a research career in Generative AI

Capstone Project

• Identifying a problem suitable for Generative AI
• Advanced dataset preparation and augmentation
• Model selection, training, and fine-tuning
• Evaluation, iteration, and presentation of the project

Summary and Next Steps