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Duration 14 hours
Course Outline
Introduction to Generative AI
- Defining the scope of Generative AI.
- The historical progression and evolution of the field.
- Essential concepts and industry terminology.
- A broad view of current applications and future potential.
Machine Learning Foundations
- An introductory overview of machine learning principles.
- Distinguishing between Supervised, Unsupervised, and Reinforcement Learning paradigms.
- Core algorithms and standard models.
- Techniques for data preprocessing and feature engineering.
Essentials of Deep Learning
- The role of neural networks within deep learning frameworks.
- Exploring activation functions, loss functions, and optimization strategies.
- Addressing overfitting, underfitting, and applying regularization methods.
- Getting started with TensorFlow and PyTorch libraries.
Overview of Generative Models
- Classifying the various types of generative models.
- Contrasting generative models with discriminative ones.
- Identifying key use cases for generative approaches.
Variational Autoencoders (VAEs)
- How autoencoders function.
- Detailed examination of VAE architecture.
- The concept of latent space and its importance.
- Practical exercise: Developing a basic VAE implementation.
Generative Adversarial Networks (GANs)
- Introduction to the GAN concept.
- Analyzing the Generator and Discriminator components.
- Navigating the training process and common challenges.
- Practical exercise: Constructing a foundational GAN.
Advanced Generative Architectures
- Understanding Transformer model structures.
- Reviewing Generative Pretrained Transformer (GPT) models.
- Utilizing GPT models for text synthesis tasks.
- Practical exercise: Generating text using pre-trained GPT models.
Ethics and Societal Impact
- Key ethical considerations in the deployment of Generative AI.
- Managing bias and ensuring fairness in AI systems.
- Future trends and the principles of responsible AI usage.
Real-World Industry Applications
- The role of Generative AI in artistic and creative industries.
- Strategic applications in business operations and marketing.
- Contributions to scientific discovery and research.
Final Capstone Project
- Conceptualizing and proposing a complete Generative AI project.
- Sourcing and preparing necessary datasets.
- Selecting the appropriate model and executing training processes.
- Assessing performance and presenting final outcomes.
Conclusions and Future Directions
Requirements
- A solid grasp of fundamental Python programming principles.
- Familiarity with essential mathematical concepts, particularly probability theory and linear algebra.
Target Audience
- Software 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
