# Week 10 - Transformers for NLP

### This week you will...

* take a look at some of the unique considerations involved when handling sequential time series data -- where values change over time, like the temperature on a particular day, or the number of visitors to your web site. We'll discuss various methodologies for predicting future values in these time series.
* begin to teach neural networks to recognize and predict on time series.
* explore using Recurrent Neural networks (RNN) and Long Short Term Memory (LSTM) networks and see how useful they are to classify and predict on sequential data.
* add CNNs on top of Dnns and RNNs and put it together using a real world data series -- one which measures sunspot activity over hundreds of years,

### Learning Resources

* Week 1 and 2 of the course [Sequences, Time Series and Prediction](https://www.coursera.org/learn/tensorflow-sequences-time-series-and-prediction)

### Until next week you should...

* complete your project work
* prepare your project presentation according to the instructions given [here](https://github.com/opencampus-sh/ml-project-template/blob/main/4_Presentation/INSTRUCTIONS.md)