Whilst most businesses don’t earn revenue by processing data, they do spend a large amount of their hard earned revenue in manually processing data, validating it and ultimately performing manual tasks that don’t scale. But at what point does this manual involvement become a burden of cost upon your business?

This article is part of a media partnership with PyData Berlin, a group helping support open-source data science libraries and tools. To learn more about this topic, please consider attending our fourth annual PyData Berlin conference on June 30-July 2, 2017. Matti Lyra and other experts will be giving talks

R is ubiquitous in the machine learning community. Its ecosystem of more than 8,000 packages makes it the Swiss Army knife of modeling applications. Similarly, Apache Spark has rapidly become the big data platform of choice for data scientists. Its ability to perform calculations relatively quickly (due to features like in-memory

For people in the know, machine learning is old hat. Even so, it’s set to become the data buzzword of the year — for a rather mundane reason. When things get complex, people expect technology to ‘automagically’ solve the problem. Whether it’s automated financial product consultation or shopping in the supermarket of

The Estimators API in tf.contrib.learn (See tutorial here) is a very convenient way to get started using TensorFlow. The really cool thing from my perspective about the Estimators API is that using it is a very easy way to create distributed TensorFlow models. Many of the TensorFlow samples that you

As buzzwords become ubiquitous they become easier to tune out. We’ve finely honed this defense mechanism, for good purpose. It’s better to focus on what’s in front of us than the flavor of the week. CRISPR might change our lives, but knowing how it works doesn’t help you. VR could

According to the prediction of IDC Futurescapes, two-thirds of Global 2000 Enterprises CEOs will center their corporate strategy on digital transformation. A major part of the strategy should include machine learning (ML) solutions. The implementation of these solutions could change how these enterprises view customer value and internal operating model

In May of 2012, just 4 weeks before the official date, the opening of Berlin’s new international airport is announced to be delayed for another couple of weeks. Weeks became months and months became years. The latest prediction for its actual opening is late 2018. There is a huge mismatch

It is no doubt that the sub-field of machine learning / artificial intelligence has increasingly gained more popularity in the past couple of years. As Big Data is the hottest trend in the tech industry at the moment, machine learning is incredibly powerful to make predictions or calculated suggestions based

Deep neural networks and Deep Learning are powerful and popular algorithms. And a lot of their success lays in the careful design of the neural network architecture. I wanted to revisit the history of neural network design in the last few years and in the context of Deep Learning. For