The Ultimate Transformer Course for Working Engineers

2026-05-12 · Source: DeepLearningAI · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Intermediate, quick

Summary

The "Transformers in Practice" course, developed in collaboration with Deep Learning AI and AMD, addresses common challenges encountered when working with Large Language Models (LLMs) and other transformer-based models. These issues include slow inference, out-of-memory errors, and model hallucinations. The course aims to provide a comprehensive practical understanding of how transformers and their surrounding LLM systems function, moving beyond theoretical explanations to offer actionable insights. Key topics covered include the token-by-token text generation process of transformers, the mechanics of attention mechanisms, and optimization techniques for running these models efficiently on GPUs. The curriculum incorporates interactive visualizations to enhance understanding of technical components and their integration.

Key takeaway

For AI Engineers and Machine Learning Engineers struggling with practical LLM deployment, this course offers a structured approach to understanding and resolving common issues like slow inference and out-of-memory errors. You should consider enrolling to gain deeper intuition into transformer mechanics, attention, and GPU optimization, which will directly impact your ability to debug and efficiently utilize LLMs in production.

Key insights

The course offers practical understanding of transformers to debug LLM issues like slow inference and OOM errors.

Principles

• Practical understanding is key to debugging LLM issues.
• Optimization is crucial for GPU-based transformer inference.

In practice

• Optimize transformer inference for GPUs.
• Debug LLM hallucinations by understanding attention.

Topics

• Transformers
• Large Language Models
• Inference Optimization
• Attention Mechanism
• GPU Optimization

Best for: Machine Learning Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by DeepLearningAI.