Building Recommendation Engines and Deep Learning Models Using Python, R and SAS
Building Recommendation Engines and Deep Learning Models Using Python, R and SAS


Deep learning is the newest area of machine learning and has become ubiquitous in predictive modeling. The complex, brainlike structure of deep learning models is used to find intricate patterns in large volumes of data. These models have heavily improved the performance of general supervised models, time series, speech recognition, object detection and classification, and sentiment analysis.

Factorization machines are a relatively new and powerful tool for modeling high-dimensional and sparse data. Most commonly they are used as recommender systems by modeling the relationship between users and items. For example, factorization machines can be used to recommend your next Netflix binge based on how you and other streamers rate content.

In this session, participants will use recurrent neural networks to analyze sequential data and improve the forecast performance of time series data, and use convolutional neural networks for image classification. Participants will also use a genetic algorithm to efficiently tune the hyperparameters of both deep learning models. Finally, students will use factorization machines to model the relationship between movies and viewers to make recommendations.
Demonstrations are provided in both R and Python, and will be administered from a Jupyter notebook. Students will use the open source SWAT package (SAS Wrapper for Analytics Transfer) to access SAS CAS (Cloud Analytic Services) in order to take advantage of the in-memory distributed environment. CAS provides a fast and scalable environment to build complex models and analyze big data by using algorithms designed for parallel processing.


Ari holds bachelor’s degrees in both physics and mathematics from UNC-Chapel Hill. His research focused on collecting and analyzing low energy physics data to better understand the neutrino. Ari taught introductory and advanced physics and scientific programming courses at UC-Berkeley while working on a master’s in physics with a focus on nonlinear dynamics. While at SAS, Ari has worked to develop courses that teach how to use Python code to control SAS analytical procedures.

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