Deep learning frameworks: PyTorch vs. TensorFlow

Not every regression or classification problem needs to be solved with . For that matter, not every regression or classification problem needs to be solved with . After all, many...

How to do real-time analytics across historical and live data

Today’s analytical requirements are putting unprecedented pressures on existing data infrastructures. Performing real-time analytics across operational and stored data is typically critical to success but always challenging to implement.Consider an...

Julia vs. Python: Which is best for data science?

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TensorFlow 2 review: Easier, end-to-end machine learning

The importance of and is no longer in doubt. After decades of promise, hype, and disappointment, both have led to practical applications. We haven’t gotten to the point...

Time series analysis: A simple example with KNIME and Spark

Demand predictionI think we all agree that knowing what lies ahead in the future makes life much easier. This is true for life events as well as for prices of...

Supervised learning explained

is a branch of artificial intelligence that includes for automatically creating models from data. At a high level, there are four kinds of machine learning: supervised learning, unsupervised learning,...

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,...

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...

Deep learning explained

What is deep learning?Deep learning is a form of that models patterns in data as complex, multi-layered networks. Because deep learning is the most general way to model a...

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...

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