Generative AI with PyTorch: From GANs to LLMs, Multimodal AI

About this course

Generative AI is transforming how machines create images, text and multimodal content. This course gives you both the conceptual foundations and the practical coding experience needed to understand how these systems work, moving step by step from foundational neural networks and GANs to multimodal AI, visual LLM exploration and modern LLM fine-tuning techniques.

Build and Understand Generative AI with Python and PyTorch

Understand the foundations of machine learning, deep learning and generative modelling

Compare GANs, autoregressive models (LLMs), variational autoencoders (VAEs), diffusion models and flow-based models

Build basic and advanced generative adversarial networks from scratch with Python and PyTorch

Understand adversarial training, generators, discriminators, loss functions and latent representations

Explore latent spaces, representation learning, interpolation and creative generation

Understand CLIP and the foundations of multimodal AIFine-tune open-source LLMs using LoRA and QLoRAVisually explore large language models (LLMs), including attention mechanisms, token relationships and internal model behaviour

Follow the code line by line while connecting every implementation choice to the underlying theory

The course begins with a broad introduction to Generative AI, covering its major architecture families, machine learning foundations, applications, ethical challenges and future possibilities.

We then move into hands-on coding. Two sections are dedicated to building and understanding generative adversarial networks: first a foundational GAN architecture and then a more advanced system. These projects allow you to understand adversarial training, generators and discriminators, loss functions, latent spaces and the practical challenges involved in training generative neural networks.

From there, the course expand

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