14—Time Series & Segmentation
Deep learning has generally been associated with unstructured data such as images, language, and audio. However it turns out that the structured data found in the columns of a database table or spreadsheet, where the columns can each represent different types of information in different ways (e.g. sales in dollars, area as zip code, product id, etc), can also be used very effectively by a neural network. This is equally true if the data can be represented as a time series (i.e. the rows represent different times or time periods).
In particular, what we learnt in part 1 about embeddings can be used not just for collaborative filtering and word encodings, but also for arbitrary categorical variables representing products, places, channels, and so forth. This has been highlighted by the results of two Kaggle competitions that were won by teams using this approach. We will study both of these datasets and competition winning strategies in this lesson.
Finally, we’ll look at how the Densenet architecture we studied in the last lesson can be used for image segmentation - that is, exactly specifying the location of every object in an image. This is another type of generative model, as we learnt in lesson 9, so many of the basic ideas from there will be equally applicable here.
We hope you enjoyed your deep learning journey with us! Now that you’ve finished, be sure to drop by the forums to tell us how you’re using deep learning in your life or work, or what projects you’re considering working on now.