Course Outline
Introduction to Generative AI <\/p>
- What is Generative AI? <\/li>
- History and evolution of Generative AI <\/li>
- Key concepts and terminology <\/li>
-
Overview of applications and potential of Generative AI
<\/li>
<\/ul>
Fundamentals of Machine Learning <\/p>
- Introduction to machine learning <\/li>
- Types of machine learning: Supervised, Unsupervised, and Reinforcement Learning <\/li>
- Basic algorithms and models <\/li>
-
Data preprocessing and feature engineering
<\/li>
<\/ul>
Deep Learning Basics <\/p>
- Neural networks and deep learning <\/li>
- Activation functions, loss functions, and optimizers <\/li>
- Overfitting, underfitting, and regularization techniques <\/li>
-
Introduction to TensorFlow and PyTorch
<\/li>
<\/ul>
Generative Models Overview <\/p>
- Types of generative models <\/li>
- Differences between discriminative and generative models <\/li>
-
Use cases for generative models
<\/li>
<\/ul>
Variational Autoencoders (VAEs) <\/p>
- Understanding autoencoders <\/li>
- The architecture of VAEs <\/li>
- Latent space and its significance <\/li>
-
Hands-on project: Building a simple VAE
<\/li>
<\/ul>
Generative Adversarial Networks (GANs) <\/p>
- Introduction to GANs <\/li>
- The architecture of GANs: Generator and Discriminator <\/li>
- Training GANs and challenges <\/li>
-
Hands-on project: Creating a basic GAN
<\/li>
<\/ul>
Advanced Generative Models <\/p>
- Introduction to Transformer models <\/li>
- Overview of GPT (Generative Pretrained Transformer) models <\/li>
- Applications of GPT in text generation <\/li>
-
Hands-on project: Text generation with a pre-trained GPT model
<\/li>
<\/ul>
Ethics and Implications <\/p>
- Ethical considerations in Generative AI <\/li>
- Bias and fairness in AI models <\/li>
-
Future implications and responsible AI
<\/li>
<\/ul>
Industry Applications of Generative AI <\/p>
- Generative AI in art and creativity <\/li>
- Applications in business and marketing <\/li>
-
Generative AI in science and research
<\/li>
<\/ul>
Capstone Project <\/p>
- Ideation and proposal of a generative AI project <\/li>
- Dataset collection and preprocessing <\/li>
- Model selection and training <\/li>
-
Evaluation and presentation of results
<\/li>
<\/ul>
Summary and Next Steps <\/p>
Requirements
- A solid understanding of basic programming concepts in Python <\/li>
-
Familiarity with fundamental mathematical concepts, particularly probability and linear algebra
<\/li>
<\/ul>
Target Audience<\/strong> <\/p>
- Developers <\/li> <\/ul>
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)
