In this article, I present a few modern techniques that have been used in various business contexts, comparing performance with traditional methods. The advanced techniqu...
Data science today is a lot like the Wild West: there’s endless opportunity and excitement, but also a lot of chaos and confusion. If you’re new to data science and a...
Guest blog by Kevin Gray.. Kevin is president of Cannon Gray, a marketing science and analytics consultancy. Regression is arguably the workhorse of statistics. Despit...
Summary: In this multi-part series we walk through the full landscape of Recommenders. In this article we cover business considerations as well as issues for Recommen...
AI systems need to continually learn from new data to perform well in real-world scenarios. However, it is non-trivial to decide what new data needs to be labeled for tra...
This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, Hadoop, decision trees, ensembles, ...
This article was written by Ian Goodfellow, Yoshua Bengio, and Aaron Courville. It consists of summaries, dozens of formulas, and numerous small sections that will he...
The combination of Deep Learning with Apache Spark has the potential for tremendous impact in many sectors of the industry. This webinar, based on the experience gained i...
Today, data scientists are generally divided among two languages — some prefer R, some prefer Python. I will not try to explain in this article which one is better....
This is part of a new series of articles: once or twice a month, we post previous articles that were very popular when first published. These articles are at least 6 mont...