Thanks Daymond. Awesome questions. In a subsequent tip I talk about data fusing where the goal is to infuse all kinds of features together in your training data set like images, text and typical structured data. Nothing too elaborate -- trying to keep these tips simple and easy. I also plan to write a whole separate tip series on deep learning after these 10 ML tips run. For brevity, SAS has released image processing now as part of SAS Visual Data Mining and Machine Learning. You can read and store, resize, normalize, filter, apply contours, mutate and so on your images. In November 2017 we will have support for the DICOM format for biomedical images. We have deep network learning now and will extend it in November 2017 with CNNs. You can train your network on a GPU.

To go along with CNNs we are also going to support RNNs which as you know are very good a sequence analyses like speech to text and also time series. Another effort we have underway is delivering natural language interaction to enable the computer to map voice or text commands to the right machine learning library. :For example, What are the most important factors why a customer might churn -- here we run something like a simple decision tree and bring back variable importance measures with some simple narrative annotations. Anyway great question partially answered -- SAS is providing AI toolkits to integrate all kinds of data and deep learning is key. For those of you want to read a bit more bout deep learning at SAS see https://www.sas.com/en_us/insights/analytics/deep-learning.html

Lastly you are such a well known practical data miner. We had the fortune of building out SAS product together. It would be awesome if you could share one or two of your tips if you found time. thanks and would love to see you again.