In a Q&A with SAS' Udo Sglavo, Xilong Chen of SAS parses the work of 2021 Nobel Prize winners for Economics.
SAS' Hamza Ghadyali introduces you to JupICL, a SAS field-tested, easy-to-use, customizable image labelling tool that runs entirely inside a Jupyter notebook.
[Editor's note: this post was co-authored by Marinela Profi and Wilbram Hazejager] Data science teams are multidisciplinary, each with different skills and technologies of choice. Some of them use SAS, others may have analytical assets already built in Python or R. Let's just say each team is unique. As part
SAS' Rajesh Selukar introduces you to a new scoring feature.
SAS' Bahar Biller, an operations researcher, details how to develop a supply chain digital twin.
Generative adversarial networks (GANs) are one of the newer machine learning algorithms that data scientists are tapping into. When I first heard it, I wondered how can networks be adversarial? I envisioned networks with swords drawn going at it. Close… but I can assure you that no networks were harmed in the making of this article.
SAS' Udo Sglavo and Jan Chvosta discuss the power of a regression framework and choosing the correct regression model.
SAS' Udo Sglavo interviews colleague Jan Chvosta, director of Scientific Computing at SAS, on regression analysis and how it works.
IDC measures advanced & predictive analytics in its annual Worldwide Business Intelligence and Analytics Software Market Shares* report – and has consistently ranked SAS as the #1 market leader for over two decades!
Word embeddings are the learned representations of words within a set of documents. Each word or term is represented as a real-valued vector within a vector space. Terms or words that reside closer to each other within that vector space are expected to share similar meanings. Thus, embeddings try to capture the meaning of each word or term through its relationships with the other words in the corpus.