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Course Outline
Introduction to Generative AI
- Defining Generative AI.
- The history and evolution of Generative AI.
- Key concepts and essential 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.
- Basic algorithms and models.
- Data preprocessing and feature engineering.
Deep Learning Basics
- Neural networks and deep learning.
- Activation functions, loss functions, and optimizers.
- Addressing overfitting, underfitting, and regularization techniques.
- Introduction to TensorFlow and PyTorch.
Generative Models Overview
- Types of generative models.
- Differences between discriminative and generative models.
- Use cases for generative models.
Variational Autoencoders (VAEs)
- Understanding autoencoders.
- The architecture of VAEs.
- Latent space and its significance.
- 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
- Familiarity with fundamental Python programming concepts.
- Understanding of 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)
