Supervised learning explained

Machine learning is a branch of artificial intelligence that includes algorithms for automatically creating models from data. At a high level, there are four kinds of machine learning: supervised learning, unsupervised learning, reinforcement learning, and active machine learning. Since reinforcement learning and active machine learning are relatively new, they are sometimes omitted from lists of… Continue reading Supervised learning explained

What is TensorFlow? The machine learning library explained

Machine learning is a complex discipline. But implementing machine learning models is far less daunting and difficult than it used to be, thanks to machine learning frameworks—such as Google’s TensorFlow—that ease the process of acquiring data, training models, serving predictions, and refining future results. Created by the Google Brain team, TensorFlow is an open source… Continue reading What is TensorFlow? The machine learning library explained

Hadoop runs out of gas

Big data remains a big deal, but that fact is somewhat obscured by the recent stumbling of its former poster children: Cloudera, Hortonworks, and MapR. Once the darlings of data, able to raise gargantuan piles of cash—Intel pumped $766 million into Cloudera in just one investment round!—the heavyweights have been forced to skinny down, whether… Continue reading Hadoop runs out of gas

Natural language processing explained

From a friend on Facebook: Me: Alexa please remind me my morning yoga sculpt class is at 5:30am. Alexa: I have added Tequila to your shopping list. We talk to our devices, and sometimes they recognize what we are saying correctly. We use free services to translate foreign language phrases encountered online into English, and… Continue reading Natural language processing explained

Deep learning explained

What is deep learning? Deep learning is a form of machine learning that models patterns in data as complex, multi-layered networks. Because deep learning is the most general way to model a problem, it has the potential to solve difficult problems—such as computer vision and natural language processing—that outstrip both conventional programming and other machine… Continue reading Deep learning explained

4 reasons big data projects fail—and 4 ways to succeed

Big data projects are, well, big in size and scope, often very ambitious, and all too often, complete failures. In 2016, Gartner estimated that 60 percent of big data projects failed. A year later, Gartner analyst Nick Heudecker‏ said his company was “too conservative” with its 60 percent estimate and put the failure rate at… Continue reading 4 reasons big data projects fail—and 4 ways to succeed