Transformers in Practice

If you’ve worked with LLMs, you’ve probably run into slow inference, out-of-memory errors, or hallucinations you couldn’t explain. There’s no shortage of resources on how transformers work, but most of them either ask you to build one from scratch or get lost in theory that doesn’t connect to the problems you’re actually facing.

Transformers in Practice is different. Taught by Sharon Zhou, VP of Engineering & AI at AMD, this course gives you a complete practical view of how transformers work, from how they generate text to what’s happening inside the model to how it all gets optimized to run on real hardware. Interactive visualizations throughout let you see key concepts in action and build intuition that actually sticks.

Here’s what you’ll learn:

• Model Behavior: You’ll learn how LLMs generate text through an autoregressive loop, selecting one token at a time from a probability distribution. You’ll see how sampling parameters like temperature shape the output, why hallucinations happen, and how techniques like RAG, constrained generation, and chain-of-thought reasoning all work within this same loop.
• Model Architecture and Attention: You’ll look inside the transformer to understand what attention is really doing, how positional encoding tracks token order, and how multiple layers and attention heads work together to turn an input sequence into a next-token prediction.
• Scaling and Deploying: You’ll learn why GPUs are well-suited for transformer inference and where the real bottlenecks are. You’ll build practical intuition for quantization, KV caching, flash attention, and speculative decoding, including the tradeoffs each one introduces for cost, speed, and output quality.

You’ll earn a certificate upon completing the course, recognizing your skills in transformer-based language models.

This course is designed for software engineers, ML engineers, and developers who work with LLMs and want to understand what’s actually happening under the hood.

You don’t need to have built a model from scratch, but you should be comfortable using LLMs through an API or chat interface and have a basic understanding of neural network concepts like weights, layers, and training.