What you'll learn:
- How to code generative A.I architectures from scratch using Python and Pytorch
- How generative architectures work, in great depth, from GANs to multimodal A.I and large language models (LLMs), understanding every little detail
- In addition to the coding, every section begins with an in-depth review of the key concepts related to these architectures
- Examples: We will code a generative network that produces human faces, and also combine two advanced networks to transform text prompts into amazing images.
- Examples: We will learn to edit the clothes of a person in a picture by combining a segmentation architecture with the Stable Diffusion generative model
- Visual Exploration of Large Language Models (LLMs) : Dive inside models like ChatGPT and understand their attention mechanisms
- Practical fine-tuning of open-source Large Language Models using QLoRA including data preparation, training, evaluation and validation
- Special Bonus Section: Journey to the latent space of a neural network, learn in depth how the networks that power Generative AI learn their mappings
- Special Bonus Section: Experience a guided visualization to exercise the generative model in your head while you learn many things about neural networks
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 AI
Fine-tune open-source LLMs using LoRA and QLoRA
Visually 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 expands beyond GANs into other areas of the Generative AI ecosystem. You will explore CLIP and multimodal AI, work with architectures that connect images, text and semantic representations, and study practical applications such as image generation, segmentation and the modification of visual elements such as a person’s clothing.
The modern LLM sections take you inside transformer-based language models through visual explorations of attention, token relationships and internal model behaviour. You will then move from visualization and conceptual understanding to practical application by preparing data and fine-tuning open-source language models with LoRA and QLoRA.
The course also includes an optional deeper journey into latent spaces and representation learning, together with distinctive visual and guided experiences—including an origami-based section designed to make AI processes more intuitive and memorable.
At the beginning of each section, I explain the key concepts in depth. After that, we work through the implementations together in Python and PyTorch. You will not simply run finished notebooks: the goal is to understand what every component does, why it is needed and how the complete system fits together.
By the end of the course, you will have a broad understanding of the Generative AI ecosystem and practical experience building GANs, working with CLIP and multimodal AI, visually exploring LLMs and fine-tuning language models using LoRA and QLoRA.
What a time to be alive! We are able to code and understand architectures that bring us home, home to our own human nature, capable of creating and imagining. Together, we will make it happen. Let's do it!
