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Course Outline
Introduction to Generative AI
- Defining Generative AI
- Historical context and development of Generative AI
- Essential concepts and terminology
- Survey of applications and potential of Generative AI
Machine Learning Fundamentals
- Overview of machine learning
- Categories of machine learning: Supervised, Unsupervised, and Reinforcement Learning
- Core algorithms and models
- Data preprocessing and feature engineering
Essentials of Deep Learning
- Neural networks and deep learning principles
- Activation functions, loss functions, and optimizers
- Addressing overfitting, underfitting, and regularization methods
- Introduction to TensorFlow and PyTorch
Overview of Generative Models
- Classifications of generative models
- Distinctions between discriminative and generative models
- Scenarios for applying generative models
Variational Autoencoders (VAEs)
- Comprehending autoencoders
- The structural design of VAEs
- The concept and importance of latent space
- Practical exercise: Constructing a simple VAE
Generative Adversarial Networks (GANs)
- Introductory concepts of GANs
- The structure of GANs: Generator and Discriminator
- Training GANs and associated challenges
- Practical exercise: Developing a basic GAN
Advanced Generative Models
- Introduction to Transformer models
- Overview of GPT (Generative Pretrained Transformer) models
- Applications of GPT in text generation
- Practical exercise: Generating text with a pre-trained GPT model
Ethics and Societal Impact
- Ethical factors in Generative AI
- Bias and fairness within AI models
- Future impacts and responsible AI development
Real-World Applications of Generative AI
- Generative AI in art and creative fields
- Applications in business and marketing
- Generative AI in scientific research
Capstone Project
- Conceptualizing and proposing a generative AI project
- Gathering and preprocessing datasets
- Selecting and training models
- Evaluating and presenting outcomes
Summary and Future Directions
Requirements
- Familiarity with fundamental programming concepts in Python
- Competence in basic mathematical principles, particularly linear algebra and probability
Target Audience
- Software Developers
14 Hours
Testimonials (2)
The interactive style, the exercises
Tamas Tutuntzisz
Course - Introduction to Prompt Engineering
A great repository of resources for future use, instructor's style (full of good sense of humor, great level of detail)
