Explore a comprehensive lecture on conditioned generation in neural networks for natural language processing. Delve into encoder-decoder models, conditional generation techniques, and search algorithms. Learn about ensembling methods, evaluation strategies, and various types of data used for conditioning. Access accompanying slides and code examples for hands-on learning. Gain insights into language models, the generation problem, and evaluation paradigms, including human evaluation and perplexity. Part of CMU's Neural Networks for NLP course, this lecture provides essential knowledge for understanding and implementing advanced NLP techniques.