Introduction to Generative AI

Description:

This training on Gen AI  provides an introduction to Generative AI, a field of artificial intelligence that focuses on creating models capable of generating new and creative content. Participants will learn about various generative models, including GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders), and gain hands-on experience in building and training generative models.

Duration: 2 days

Course Code: BDT313

Learning Objectives:

By the end of this course, students will be able to:

• Understand the fundamentals of Generative AI and its applications.
• Differentiate between various generative models and their strengths and weaknesses.
• Implement and train Generative Adversarial Networks (GANs).
• Implement and train Variational Autoencoders (VAEs).
• Apply generative models to generate creative content, such as images and text.

• Basic understanding of machine learning concepts.
• Proficiency in a programming language (Python is recommended).

• Data Scientists and Machine Learning Practitioners
• Software Developers
• Computer Science Students
• AI Enthusiasts and Hobbyists
• Professionals in Creative Industries
• Entrepreneurs and Innovators

• Data Scientists and Machine Learning Practitioners
• Software Developers
• Computer Science Students
• AI Enthusiasts and Hobbyists
• Professionals in Creative Industries
• Entrepreneurs and Innovators

Course Outline:

Session 1: Introduction to Generative AI

• What is Generative AI?
• Applications of Generative AI in various fields.
• Overview of generative models (e.g., GANs, VAEs).

Session 2: Generative Adversarial Networks (GANs)

• Understanding GAN architecture (generator and discriminator).
• Training GANs to generate images.
• Variations of GANs (e.g., DCGAN, CycleGAN).
• Hands-on coding exercise: Building and training a GAN.

Session 3: Variational Autoencoders (VAEs)

• Understanding the autoencoder architecture.
• Introduction to VAEs and how they work.
• Training VAEs to generate data.
• Hands-on coding exercise: Building and training a VAE.

Session 4: Creative Applications and Future Trends

• Real-world applications of generative models (e.g., image generation, text generation).
• Ethical considerations in Generative AI.
• Future trends and advancements in Generative AI.
• Q&A and discussion

*Additional Material that can be Included on Set Up Page

Setup / Installation expected

• A computer with Python installed.
• Jupyter Notebook or another code editor.
• Internet access for downloading code examples and resources.

Recommended Reading:

• "Generative Deep Learning" by David Foster
• Relevant research papers and articles in the field of Generative AI.