Generative AI Accelerated Course

Goals

What will you learn in this course:

Our journey begins with an introduction to the fundamentals of generative AI. We'll explore the different types of generative models, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and recurrent neural networks (RNNs) and Transformers. Understanding the principles behind each type of generative model will lay the foundation for further exploration and experimentation.

As we delve deeper into the world of generative AI, we'll discover its wide range of applications across various domains. From generating realistic images and synthesizing music to creating lifelike characters and virtual environments, generative AI is transforming industries and enabling new forms of artistic expression and storytelling. We'll explore real-world examples and case studies to understand how generative AI is being used to solve complex problems and drive innovation.

However, with great power comes great responsibility. Generative AI also presents unique challenges and ethical considerations that must be addressed. From mode collapse and training instability to concerns about the misuse of AI-generated content for malicious purposes, navigating these challenges requires careful consideration and ethical decision-making. We'll discuss strategies for mitigating these challenges and promoting responsible AI development practices.

To embark on this journey, it's essential to equip ourselves with the right tools and knowledge. We'll explore popular deep learning frameworks like TensorFlow and PyTorch, as well as specialized libraries like GANLib and pre-trained models like OpenAI's GPT series. Through hands-on experimentation and exploration, we'll gain practical experience and develop the skills needed to build and train our own generative AI models.

Prerequisites

What are the prerequisites for this course?

None