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Course Outline
Foundations of Generative AI
- Defining Generative AI.
- The historical development and evolution of the field.
- Essential terminology and core concepts.
- A broad look at current applications and future potential.
Machine Learning Essentials
- An introduction to machine learning fundamentals.
- Categories of machine learning: Supervised, Unsupervised, and Reinforcement Learning.
- Foundational algorithms and modeling techniques.
- Strategies for data preprocessing and feature engineering.
Deep Learning Principles
- Neural networks and the architecture of deep learning systems.
- Activation functions, loss metrics, and optimization strategies.
- Managing overfitting, underfitting, and regularization methods.
- Getting started with TensorFlow and PyTorch.
Overview of Generative Modeling
- Different types of generative models.
- Distinguishing between discriminative and generative approaches.
- Practical use cases for generative modeling.
Variational Autoencoders (VAEs)
- The mechanics of standard autoencoders.
- Architectural components of VAEs.
- The role and significance of latent space.
- Practical exercise: Developing a basic VAE.
Generative Adversarial Networks (GANs)
- Introduction to the GAN framework.
- GAN architecture: The roles of the Generator and Discriminator.
- Training processes and common challenges.
- Practical exercise: Building a fundamental GAN.
Advanced Generative Architectures
- Introduction to Transformer-based models.
- Overview of GPT (Generative Pretrained Transformer) systems.
- Utilizing GPTs for text generation tasks.
- Practical exercise: Generating text using pre-trained GPT models.
Ethics and Societal Impact
- Ethical frameworks for Generative AI.
- Addressing bias and ensuring fairness in AI models.
- Future trends and the practice of responsible AI.
Industry Use Cases
- The role of Generative AI in artistic and creative endeavors.
- Applications in corporate strategy and marketing.
- Contributions to 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
- A solid grasp of fundamental programming concepts in Python.
- Familiarity with essential mathematical theories, particularly probability theory and linear algebra.
Target Audience
- Software Developers.
14 Hours
Testimonials (2)
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
