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Course Outline
Introduction to Generative AI
- Defining Generative AI
- The history and evolution of Generative AI
- Core concepts and key terminology
- An overview of applications and the potential of Generative AI
Fundamentals of Machine Learning
- Introduction to machine learning
- Types of machine learning: Supervised, Unsupervised, and Reinforcement Learning
- Foundational 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 regularization techniques
- Introduction to TensorFlow and PyTorch
Generative Models Overview
- Different types of generative models
- Distinctions between discriminative and generative models
- Practical use cases for generative models
Variational Autoencoders (VAEs)
- Understanding autoencoders
- The architecture of VAEs
- The significance of latent space
- Hands-on project: Building a simple VAE
Generative Adversarial Networks (GANs)
- Introduction to GANs
- The architecture of GANs: Generator and Discriminator
- Training GANs and associated challenges
- Hands-on project: Creating a basic GAN
Advanced Generative Models
- Introduction to Transformer models
- Overview of GPT (Generative Pretrained Transformer) models
- Applications of GPT in text generation
- Hands-on project: 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
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 solid understanding of basic Python programming concepts
- Familiarity with fundamental mathematical concepts, particularly probability and linear algebra
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)
