> For the complete documentation index, see [llms.txt](https://opencampus.gitbook.io/opencampus-machine-learning-program/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://opencampus.gitbook.io/opencampus-machine-learning-program/courses/machine-learning-with-tensorflow/week-8-sequences-time-series-and-prediction-part-i.md). # 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) --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://opencampus.gitbook.io/opencampus-machine-learning-program/courses/machine-learning-with-tensorflow/week-8-sequences-time-series-and-prediction-part-i.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.