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Course Outline
Introduction to Generative AI
- Defining Generative AI
- The history and evolution of Generative AI
- Key concepts and terminology
- Overview of applications and the potential of Generative AI
Fundamentals of Machine Learning
- An 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
- Activation functions, loss functions, and optimizers
- Managing overfitting, underfitting, and employing regularization techniques
- Getting started with TensorFlow and PyTorch
Generative Models Overview
- Types of generative models
- Distinctions between discriminative and generative models
- Use cases for generative models
Variational Autoencoders (VAEs)
- Understanding autoencoders
- The architectural structure of VAEs
- The latent space and its importance
- Practical project: Constructing a simple VAE
Generative Adversarial Networks (GANs)
- An introduction to GANs
- GAN architecture: Generator and Discriminator
- Training GANs and associated challenges
- Practical project: Developing a basic GAN
Advanced Generative Models
- An introduction to Transformer models
- Overview of GPT (Generative Pretrained Transformer) models
- Applications of GPT in text generation
- Practical project: Generating text using a pre-trained GPT model
Ethics and Implications
- Ethical considerations in Generative AI
- Bias and fairness within 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
- 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)
