The Ultimate Transformer Course for Working Engineers

Why It Matters

By turning transformer theory into actionable debugging tools, the course helps engineers cut inference costs and avoid production failures, accelerating AI product rollouts.

Key Takeaways

• LLM engineers face slow inference, OOM errors, and hallucinations.
• Course links transformer theory to real‑world debugging strategies.
• Interactive visualizations illustrate token‑by‑token generation and attention mechanisms.
• Optimization techniques focus on GPU execution with AMD hardware.
• Practical insights aim to improve deployment efficiency for working engineers.

Summary

The video announces "Transformers in Practice," a hands‑on course built with Deep Learning AI and AMD aimed at engineers wrestling with large language model (LLM) deployment issues such as latency, out‑of‑memory crashes, and hallucinations. It positions the curriculum as a bridge between abstract transformer concepts and the concrete debugging tactics engineers need on the job.

The program walks learners through how transformers generate text one token at a time, demystifies the role of attention, and shows how these operations are optimized for GPU execution, particularly on AMD hardware. Interactive visualizations let participants manipulate attention maps and token streams, turning theory into observable behavior.

Sharon Joe emphasizes that “understanding the pieces isn’t the same as understanding how they all fit together,” and promises that students will “see and play with key technical pieces” throughout the course. Real‑world examples illustrate how small architectural tweaks can cut inference time and prevent memory overruns.

For engineers, the course promises faster, more reliable LLM deployments, reducing costly trial‑and‑error cycles and enabling businesses to bring AI‑driven products to market with greater confidence.

Original Description

Learn more: https://bit.ly/4tts8MQ

Large language models can feel opaque, especially when you’re dealing with slow inference, hallucinations, memory bottlenecks, or output you can’t fully explain.

Today, we’re launching Transformers in Practice, a course taught by Sharon Zhou, VP of Engineering & AI at AMD.

The course focuses on understanding what’s actually happening inside transformer-based models so you can reason about their behavior, debug issues more effectively, and make better deployment decisions.

You’ll learn:

- How transformers generate text one token at a time, and how sampling affects output

- What attention, positional encoding, and transformer layers are actually doing

- Why hallucinations happen and how techniques like RAG and constrained generation help

- How optimizations like quantization, KV caching, flash attention, and speculative decoding improve inference efficiency on GPUs

Throughout the course, interactive visualizations help build intuition for concepts that are often difficult to grasp through theory alone.

This course will give you a practical understanding of transformers from both the model and systems perspectives.

Enroll now: https://bit.ly/4tts8MQ