This course is an introduction to sequence models and their applications, including an overview of sequence model architectures and how to handle inputs of variable length. Predict future values of a time-series Classify free form text Address time-series and text problems with recurrent neural networks Choose between RNNs/LSTMs and simpler models Train and reuse word embeddings in text problems You will get hands-on practice building and optimizing your own text classification and sequence models on a variety of public datasets in the labs we ll work on together. Prerequisites: Basic SQL, familiarity with Python and TensorFlow