Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans: learn by example. We know our brain consists of a complex, highly parallel, and a highly connected network of neurons. There are billions of such interconnected neurons, which take numerous inputs and give numerous outputs to further neurons. In Deep Learning, we try to teach a machine the same way by which we learn ie., by a network of such neurons. Let’s understand Neural Network with a small story. Say you have a 5 year old kid and you 5-year-oldt the school daily. Every day he sees this on the road he asks you, “Dad what is this ?”

This article is intended for practitioners who might not necessarily be statisticians or statistically-savvy. The mathematical level is kept as simple as possible, yet I present an original, simple approach to test for randomness, with an interesting application to illustrate the methodology. This material is not something usually discussed in textbooks or classrooms (even for statistical students), offering a fresh perspective, and out-of-the-box tools that are useful in many contexts, as an addition or alternative to traditional tests that are widely used. This article is written as a tutorial, but it also features an interesting research result in the last section.

The EU’s new data privacy rules, the General Data Protection Regulation (GDPR), will have a negative impact on the development and use of artificial intelligence (AI) in Europe, putting EU firms at a competitive disadvantage compared with their competitors in North America and Asia. The GDPR’s AI-limiting provisions do little to protect consumers, and may, in some cases, even harm them. The EU should reform the GDPR so that these rules do not tie down its digital economy in the coming years.

Deep learning has emerged as a powerful machine learning technique that learns multiple layers of representations or features of the data and produces state-of-the-art prediction results. Along with the success of deep learning in many application domains, deep learning is also used in sentiment analysis in recent years. This paper gives an overview of deep learning and then provides a comprehensive survey of its current applications in sentiment analysis.

There are amazing introductions, courses and blog posts on Deep Learning. I will name some of them in the resources sections, but this is a different kind of introduction.

Since its inception over 40 years ago, when S (R’s predecessor) was just a sketch on John Chambers’ wall at Bell Labs, R has always been a language for providing interfaces.