A Deep-Q Network (DQN) is a reinforcement learning technique that attempts to model the actions that perform best in each state in real-time.
The recently released 2021 Gartner MQ for Data Science and Machine Learning contains a wealth of information and here are my takes on key market trends from that report for data scientists. This evaluation features SAS Viya with its SAS Data Science offerings.
In this article, we summarize our SAS research paper on the application of reinforcement learning to monitor traffic control signals which was recently accepted to the 34th Conference on Neural Information Processing Systems (NeurIPS 2020), Vancouver, Canada. This annual conference is hosted by the Neural Information Processing Systems Foundation, a non-profit corporation that promotes the exchange of ideas in neural information processing systems across multiple disciplines.
An analyst report offers an unbiased, side-by-side, third-party evaluation of the technology in the market. These analysts know how to put the vendors through the paces and require proof of any claims that are made.
An embedding model is a way to reduce the dimensionality of input data, such as images. Consider this to be a type of data preparation applied to image analysis. When an embedding model is used, input images are converted into low-dimensional vectors that can be more easily used by other computer vision tasks. The key to good embedding is to train the model so that similar images are converted to similar vectors.
Ever since automated machine learning has entered the scene, people are asking, "Will automated machine learning replace data scientists?"
Let's talk about using DLPy to model employee retention through a survival analysis model. Survival analysis is used to model time-to-event. Examples of time-to-event include the time until an employee leaves a company, the time until a disease goes into remission, or the time until a mechanical part fails. The
This blog is a part of a series on the Data Science Pilot Action Set. In this blog, we discuss updates to Visual Data Mining and Machine Learning with the release of Viya 3.5. In the middle of my blog series, SAS released Viya 3.5. Included in Viya 3.5 was the
Computer vision can augment radiologists and make the image interpretation process cheaper, faster and more accurate. The ultimate goal is to achieve a better patient outcome facilitated by the use of computer vision.
This series of videos spotlights a very powerful API that lets you use Python while also having access to the power of SAS Deep Learning.
