Learn efficiency tricks for neural networks in natural language processing through this lecture from CMU's Neural Networks for NLP course. Explore GPU training tips, parallel training techniques, negative sampling methods, and softmax approximations like hierarchical softmax. Discover practical strategies to reduce operations, optimize CPU-GPU data movement, and manage memory effectively. Dive into various parallelism types, tackle computation challenges with large vocabularies, and examine advanced techniques like importance sampling, noise contrastive estimation, and efficient maximum inner product search. Gain insights into structure-based approximations, including class-based softmax and binary code prediction, to enhance neural network performance in NLP tasks.