Recurrent Neural Networks: Neural Language Models

This lecture covers fixed-context neural language models, recurrent neural networks (RNNs), and their applications in natural language processing. The instructor discusses the advantages and disadvantages of RNNs, the challenges of long-range dependencies, and the issue of vanishing gradients. The lecture also explores sequence labeling, backpropagation through time, and solutions to the vanishing gradient problem. Additionally, it delves into the practical aspects of computing gradients and the use of automatic differentiation in deep learning software packages. The lecture concludes with a preview of upcoming topics on more powerful RNN architectures and encoder-decoder models.