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Duration 14 hours
Course Outline
Introduction to Generative AI
- Defining Generative AI.
- The history and evolution of the technology.
- Essential concepts and terminology.
- A look at the potential and range of applications.
Machine Learning Fundamentals
- Basics of machine learning.
- Distinctions between Supervised, Unsupervised, and Reinforcement Learning.
- Core algorithms and modeling techniques.
- Data preprocessing strategies and feature engineering.
Deep Learning Essentials
- Understanding neural networks and deep learning.
- Role of activation functions, loss functions, and optimizers.
- Addressing overfitting, underfitting, and applying regularization.
- Getting started with TensorFlow and PyTorch.
Overview of Generative Models
- Classifying different types of generative models.
- Comparing discriminative versus generative approaches.
- Identifying key use cases for generative models.
Variational Autoencoders (VAEs)
- The mechanics of autoencoders.
- Architectural components of VAEs.
- The concept of latent space and its importance.
- Practical exercise: Constructing a simple VAE.
Generative Adversarial Networks (GANs)
- An introduction to GANs.
- GAN architecture: The Generator and Discriminator.
- Training processes and common challenges.
- Practical exercise: Developing a basic GAN.
Advanced Generative Models
- Introduction to Transformer architectures.
- Understanding GPT (Generative Pretrained Transformer) models.
- Utilizing GPT for text generation tasks.
- Practical exercise: Text generation using pre-trained GPT models.
Ethics and Societal Impact
- Ethical considerations specific to Generative AI.
- Addressing bias and ensuring fairness in AI models.
- Future outlook and the importance of responsible AI.
Generative AI in Industry
- Impact on art and creative industries.
- Use cases in business strategy and marketing.
- Applications in scientific research and discovery.
Capstone Project
- Conceptualizing and proposing a generative AI project.
- Gathering and preparing datasets.
- Selecting and training the appropriate model.
- Assessing outcomes and presenting final results.
Conclusion and Future Directions
Requirements
- Familiarity with fundamental Python programming concepts.
- Basic proficiency in mathematics, particularly probability and linear algebra.
Target Audience
- Developers.
Testimonials (3)
Comprehensiveness about the backround; how to do a prompt.
Olaf Kannt - Helios Klinikum Krefeld
Course - Generative AI and Prompt Engineering in Healthcare
the tips and recommended prompts that we can take away from this training
Lee Mei Lin - ST Engineering IHQ Pte Ltd
Course - InVideo AI: Creating Engaging Short-Form Videos
use of proper and effective prompt
