Editor's Note: Download this Free eBook: Getting Started with Apache Spark 2.x – from Inception to Production In this blog post, we will give an introduction to machine learning and deep learning, and we will go over the main Spark machine learning algorithms and techniques with some real-world use cases. The goal is to give you a better understanding of what you can do with machine learning. Machine learning is becoming more accessible to developers, and data scientists work with domain experts, architects, developers, and data engineers, so it is important for everyone to have a better understanding of the possibilities. Every piece of information that your business generates has potential to add value. This overview is meant to provoke a review of your own data to identify new opportunities.

High dimensional data can contain a large amount of noise and importantly for visualization, the human brain can only comprehend three dimensions. Thus, there is a need to reduce data into an interpretable format by converting high dimensional data into two or three dimensions, which can subsequently be visualized using a two or three dimensional scatterplot. To meet the need for dimensionality reduction methods, a plethora of algorithms and associated fitting methods have been developed. A researcher wishing to perform dimensionality reduction for visualization will be presented with a choice of hundreds of algorithms. Which algorithm should be used? This paper describes a visualization framework called QVisVis and associated software tools implemented in R to help choose dimensionality reduction methods, tune these methods, and visually evaluate the quality of dimensionality reduction solutions. The major contributions of these paper are to review and synthesize previous work on evaluating and "visualizing" performance metrics, create an overall visualization framework for "visualizing" visualization quality, and implement the framework in an R toolkit.

This article reviews the applications of Bayesian Networks to Intelligent Autonomous Vehicles (IAV) from the decision making point of view, which represents the final step for fully Autonomous Vehicles (currently under discussion). Until now, when it comes making high level decisions for Autonomous Vehicles (AVs), humans have the last word. Based on the works cited in this article and analysis done here, the modules of a general decision making framework and its variables are inferred. Many efforts have been made in the labs showing Bayesian Networks as a promising computer model for decision making. Further research should go into the direction of testing Bayesian Network models in real situations. In addition to the applications, Bayesian Network fundamentals are introduced as elements to consider when developing IAVs with the potential of making high level judgement calls.

Researchers from all over the world contribute to this repository as a prelude to the peer review process for publication in traditional journals. We hope to save you some time by picking out articles that represent the most promise for the typical data scientist. The articles listed below represent a fraction of all articles appearing on the preprint server. They are listed in no particular order with a link to each paper along with a brief overview. Especially relevant articles are marked with a "thumbs up" icon.

The term artificial intelligence is now applied to such a wide range of applications these days, it's difficult to know what AI truly is. We hear that Google is using AI in search, Facebook uses it for facial recognition and Netflix is using AI to'conquer the world'. These examples are all very interesting, but they do leave many wondering what exactly AI is and how can they apply it, now, to their everyday marketing tasks? To help marketers understand AI and how it applies to our craft, Econsultancy recently held a Digital Outlook event in Singapore and invited marketing AI expert Deborah Kay, Founder of Digital Discovery, to give an overview of the state of the art. Helpfully, Ms. Kay provided a summary of the four most exciting areas of AI for marketing as well as many examples of how AI is being used in the real world.

With competitive pressure increasing drastically and the digital economy progressing considerably, enterprises need to figure out new ways to plan, develop, and add value. Therefore, to adapt to digital transformation efficiently, DevOps has become a necessity to eliminate technical and cultural constraints for offering value rapidly. Unfortunately, the conservative nature of IT enterprises has led to slower adoption of DevOps. Additionally, many organizations still need to adopt them for efficient processes. The adoption would enhance the efficiency of operational processes and reduce downtime in the development life cycle of software.

"Scientific research has changed the world. Now it needs to change itself. There has been a growing concern about the validity of scientific findings. A multitude of journals, papers and reports have recognized the ever smaller number of replicable scientific studies. In 2016, one of the giants of scientific publishing, Nature, surveyed about 1,500 researchers across many different disciplines, asking for their stand on the status of reproducibility in their area of research. One of the many takeaways to the worrisome results of this survey is the following: 90% of the respondents agreed that there is a reproducibility crisis, and the overall top answer to boosting reproducibility was "better understanding of statistics". Indeed, many factors contributing to the explosion of irreproducible research stem from the neglect of the fact that statistics is no longer as static as it was in the first half of the 20th century, when statistical hypothesis testing came into prominence as a ...

In discussions I've had with CIOs via my weekly #CIOChat sessions this year, the top 5 priorities are: And while there are differing opinions regarding the ownership of the analytics function, one thing is clear: CIOs need a better understanding regarding the potential for analytics and what is required to get data into a shape for their organization's data scientists. CIOs also need very clear mutual direction established with business leaders – in other words, what questions should be answered with data? Against this backdrop, "AI, Analytics, and New Machine Age" – published by Harvard Business Review earlier this month – is a timely, relevant compendium of HBR articles. The authors' insights should have value for CIOs and business people trying to use analytics in the running their businesses. Davenport contrasts the results obtained from large AI projects versus Robotic Process Automation (RPA).

They say it is ushering in a new age, a Customer Experience 3.0. As a notorious heckler, I thought I would have my team research the market, as it stands, in the closing weeks of 2018. My question was: Who cares about promises; I am exhausted by all this thought leadership; I want specific examples; what are AI-driven solutions capable of achieving for customer experience, right now? So that is exactly what they did. Then we discussed how these solutions change or do not change the game.

Graph Convolutional Networks (GCNs) and their variants have experienced significant attention and have become the de facto methods for learning graph representations. GCNs derive inspiration primarily from recent deep learning approaches, and as a result, may inherit unnecessary complexity and redundant computation. In this paper, we reduce this excess complexity through successively removing nonlinearities and collapsing weight matrices between consecutive layers. We theoretically analyze the resulting linear model and show that it corresponds to a fixed low-pass filter followed by a linear classifier. Notably, our experimental evaluation demonstrates that these simplifications do not negatively impact accuracy in many downstream applications. Moreover, the resulting model scales to larger datasets, is naturally interpretable, and yields up to two orders of magnitude speedup over FastGCN.