AI Engineering ／ Large language model learning
LLM Interviews / Infrastructure and Components
Complete collection of LLM large model interview fundamentals, architecture, and components. Covers three Decoder architectures (prefix/causal/encoder-decoder) + Layer Norm (LN/RMS/Deep Norm) + activation functions (FFN/GeLU/Swish/GLU series) + Attention upgrades (MQA/GQA/Flash/linearized/sparse) + Transformer operations + loss functions (KL/cross-entropy) + similarity functions (cosine/Euclidean/Jaccard) + advanced LLM interview topics (generative models/repetition problem/8 major mitigation strategies) + LLM training experience posts