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Course Outline
Introduction to Generative AI
- Defining Generative AI
- Historical context and evolution of Generative AI
- Essential concepts and terminology
- Survey of applications and potential within Generative AI
Machine Learning Fundamentals
- Overview of machine learning
- Categories of machine learning: Supervised, Unsupervised, and Reinforcement Learning
- Fundamental algorithms and models
- Data preprocessing and feature engineering techniques
Basics of Deep Learning
- Neural networks and the field of deep learning
- Activation functions, loss functions, and optimization strategies
- Addressing overfitting, underfitting, and regularization methods
- Introduction to TensorFlow and PyTorch frameworks
Overview of Generative Models
- Classifications of generative models
- Distinctions between discriminative and generative models
- Practical use cases for generative models
Variational Autoencoders (VAEs)
- Comprehension of autoencoders
- The architectural structure of VAEs
- The latent space and its importance
- Practical exercise: Constructing a simple VAE
Generative Adversarial Networks (GANs)
- Introduction to GANs
- GAN architecture: Generator and Discriminator components
- Training GANs and associated challenges
- Practical exercise: Building 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 Implications
- Ethical considerations in Generative AI
- Bias and fairness issues in AI models
- Future implications and the importance of responsible AI
Industry Applications of Generative AI
- Generative AI in art and creative fields
- Applications in business and marketing
- Generative AI in science and research
Capstone Project
- Conceptualizing and proposing a generative AI project
- Collecting and preprocessing datasets
- Selecting and training models
- Evaluating and presenting outcomes
Summary and Next Steps
Requirements
- A foundational understanding of Python programming concepts
- Familiarity with fundamental mathematical principles, particularly probability and linear algebra
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)
