Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Introduction to Generative AI
- Defining Generative AI.
- The history and progression of Generative AI.
- Key concepts and industry terminology.
- A survey of applications and the potential of Generative AI.
Fundamentals of Machine Learning
- Introduction to the field of machine learning.
- Types of machine learning: Supervised, Unsupervised, and Reinforcement Learning.
- Core algorithms and models.
- Data preprocessing and feature engineering techniques.
Deep Learning Basics
- Neural networks and the principles of deep learning.
- Activation functions, loss functions, and optimizers.
- Managing overfitting, underfitting, and regularization strategies.
- Introduction to TensorFlow and PyTorch frameworks.
Generative Models Overview
- Categorization of generative models.
- Distinctions between discriminative and generative models.
- Common use cases for generative models.
Variational Autoencoders (VAEs)
- Comprehending how autoencoders work.
- The structural architecture of VAEs.
- The concept of latent space and its importance.
- Practical project: Constructing a simple VAE.
Generative Adversarial Networks (GANs)
- Introduction to GANs.
- The architecture of GANs: Generator and Discriminator components.
- Training GANs and associated challenges.
- Practical project: Developing a basic GAN.
Advanced Generative Models
- Introduction to Transformer architectures.
- Overview of GPT (Generative Pretrained Transformer) models.
- Applications of GPT in text generation.
- Practical project: Text generation using a pre-trained GPT model.
Ethics and Implications
- Ethical considerations within Generative AI.
- Addressing bias and ensuring fairness in AI models.
- Future implications and the push for responsible AI.
Industry Applications of Generative AI
- Generative AI in art and creative fields.
- Applications in business and marketing strategies.
- Generative AI in scientific 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 grasp of fundamental Python programming concepts.
- 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)
