Generative AI Training
Join our Corporate Generative AI Practical Training to explore the principles, techniques, and applications of generative AI. Led by industry experts, this intensive program combines theory and hands-on exercises, equipping participants with skills to develop AI models for generating text, images, and more. Stay ahead in AI innovation!
Description
Corporate Generative AI Practical Training program, designed to immerse participants in the cutting-edge field of generative artificial intelligence. This intensive training is tailored for individuals looking to delve deep into the principles, techniques, and applications of generative AI. Throughout this program, participants will gain hands-on experience in developing AI models capable of generating creative and realistic outputs, ranging from images and text to music and more. Led by industry experts, this training combines theoretical insights with practical exercises to empower participants with the skills needed to harness the full potential of generative AI technologies. Whether you are a data scientist, AI enthusiast, or industry professional looking to stay ahead in the rapidly evolving landscape of artificial intelligence, this training will equip you with the tools and knowledge to innovate and excel in the field of generative AI.
Benefits of Attending This Training
- Gain practical skills in building and deploying generative AI models.
- Hands-on experience with industry-standard frameworks like TensorFlow and PyTorch.
- Understand the ethical implications and challenges of generative AI.
- Certification of completion to demonstrate proficiency in Generative AI.
This outline covers foundational to advanced topics in Generative AI, providing participants with comprehensive knowledge and practical skills necessary to work on real-world generative AI projects. Adjustments can be made based on specific audience needs and expertise levels.
Course Content
Introduction to Generative AI
- Introduction to Generative AI
- What is Generative AI?
- Applications and use cases in various industries
- Overview of Generative Models
- Types of generative models (e.g., GANs, VAEs, autoregressive models)
- Understanding the architecture and components
- Hands-on Workshop: Setting Up Environment
- Installing necessary libraries (e.g., TensorFlow, PyTorch)
- Setting up GPU acceleration (if applicable)
Fundamentals of GANs (Generative Adversarial Networks)
- Understanding GANs
- How GANs work: Generator and Discriminator
- Training GANs: Loss functions and optimization
- GAN Variants
- Conditional GANs
- CycleGANs
- StyleGANs
- Hands-on Workshop: Building Your First GAN
- Implementing a basic GAN model using TensorFlow or PyTorch
Variational Autoencoders (VAEs)
- Introduction to Variational Autoencoders
- Principles and objectives of VAEs
- Probabilistic framework and inference
- Applications of VAEs
- Image generation and reconstruction
- Anomaly detection and data augmentation
- Hands-on Workshop: Implementing VAEs
- Constructing a VAE model for image generation tasks
Deep Reinforcement Learning for Generative Tasks
- Introduction to Deep Reinforcement Learning (DRL)
- Basics of reinforcement learning
- Policy gradients and Q-learning
- Applications of DRL in Generative AI
- Autonomous agents and game playing
- Task optimization and control
- Hands-on Workshop: Reinforcement Learning with Generative Models
- Implementing DRL techniques for generative tasks
Advanced Topics and Practical Applications
- Advanced Generative Models
- Flow-based models
- Attention mechanisms in generative models
- Ethical Considerations and Challenges in Generative AI
- Bias and fairness
- Privacy and security concerns
- Hands-on Project: Real-world Generative AI Application
- Capstone project integrating concepts learned throughout the training
Reviews
There are no reviews yet.