Sebastian Raschka

Python has been at the center of my work in machine learning and AI for more than a decade. It is where I start from scratch, experiment with ideas, and build systems that help me understand how large language models really work.

Chinese and American open-source LLMs are competing head-to-head — from DeepSeek and Qwen to Llama and Mistral. The model landscape is broader than ever, yet in Germany the debate still circles around waiting for the next breakthrough.

In this talk, you’ll learn about the latest trends in large language model (LLM) architectures. We'll look at how the architectural building blocks have evolved this year, with a focus on current transformer-based models (including Llama, GPT-OSS, Gemma, Qwen, and DeepSeek). The talk will also...

The evolution of large language models (LLMs) from the original Generative Pre-trained Transformer (GPT) series to the recent advancements seen in models like Llama 3 has been accompanied by several architectural and methodological innovations. This talk aims to catch attendees up on the latest AI...

This tutorial is aimed at coders interested in understanding the building blocks of large language models (LLMs), how LLMs work, and how to code them from the ground up in PyTorch. We will kick off this tutorial with an introduction to LLMs, recent milestones, and their use cases.

We will kick off this tutorial with an introduction to deep learning and highlight its primary strengths and use cases compared to traditional machine learning. In recent years, PyTorch has emerged as the most widely used deep learning library for research.