@AYi_AInotes: Fellow developers working on LLM production deployment, check out Andrew Ng's new course. The free version gives you access to all videos and base code. This course is not another rerun of the 'Attention is All You Need' math derivation, nor another set of mystical prompt-tuning tricks, nor yet another toy...

Fellow developers working on LLM production deployment, check out Andrew Ng's new course. The free version gives you access to all videos and base code. This course is not another rerun of the 'Attention is All You Need' math derivation, nor another set of mystical prompt-tuning tricks, nor another toy project for building a Transformer from scratch. It directly cracks open the LLM black box. You'll get to play with the autoregressive loop yourself, watch the model generate one token at a time, see how a probability sampling step goes awry, and observe how hallucinations gradually emerge from nothing. It even lets you drag a slider to adjust temperature and see the changes in output diversity in real time, showing you what different sampling strategies actually change. And you can click into each layer and each attention head to see which head handles grammar, which handles facts, and which handles logical reasoning. The most impressive part is the inference optimization section — the very pitfalls that every production engineer deals with daily: slow inference, out-of-memory errors, exploding costs. Previously, everyone told you to get bigger GPUs or add more machines. This course tells you that over 70% of latency is not due to parameter count but memory bandwidth and attention computation. Quantization, KV Cache, Flash Attention, speculative decoding — each of these techniques can speed up your model by 2 to 5 times with almost negligible accuracy loss. And this course is a deep collaboration with AMD, personally taught by AMD's VP of Engineering. Finally, a course that doesn't just talk about CUDA; finally, someone is teaching hardware-aware optimization. While people who can call APIs are everywhere, those who can see into the model internals, diagnose issues, and optimize costs will be the most scarce talent in the next three years. I think the greatest value of this course is that it finally turns Transformer from an academic concept into an engineering tool that you can touch, debug, and optimize.