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Course Outline
Introduction to Generative AI
- Defining Generative AI
- Historical context and evolution of Generative AI
- Essential concepts and terminology
- Overview of applications and potential of Generative AI
Machine Learning Fundamentals
- Introduction to machine learning
- Categories of machine learning: Supervised, Unsupervised, and Reinforcement Learning
- Core algorithms and models
- Data preprocessing and feature engineering
Deep Learning Basics
- Neural networks and deep learning frameworks
- Activation functions, loss functions, and optimizers
- Addressing overfitting, underfitting, and regularization techniques
- Introduction to TensorFlow and PyTorch
Overview of Generative Models
- Classifications of generative models
- Distinctions between discriminative and generative models
- Use cases for generative models
Variational Autoencoders (VAEs)
- Comprehending 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
- The architecture of GANs: Generator and Discriminator components
- Training methodologies 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: Text generation with a pre-trained GPT model
Ethics and Implications
- Ethical considerations in Generative AI
- Bias and fairness in AI models
- Future implications and responsible AI practices
Industry Applications of Generative AI
- Generative AI in art and creativity
- Applications in business and marketing
- Generative AI in science and research
Capstone Project
- Ideation and proposal of a generative AI project
- Dataset collection and preprocessing
- Model selection and training
- Evaluation and presentation of results
Summary and Next Steps
Requirements
- A working knowledge of fundamental programming concepts in Python.
- Familiarity with basic 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)
