Explore advanced search algorithms for neural networks in natural language processing through this comprehensive lecture. Delve into search errors and model errors, examining their impact on output optimization. Learn about beam search and its variants, understanding how to determine optimal beam size. Discover the concept of minimum Bayes risk and its application in reranking. Investigate heuristics modification techniques to enhance search performance. Explore sampling-based search methods and their role in improving diversity. Analyze common model errors like length bias and learn strategies to mitigate them, including length normalization and output length prediction. Gain insights into the pros and cons of sampling-based search approaches and alternative methods for achieving diversity in results.