This post is authored by Xibin Gao, Data Scientist, Debraj GuhaThakurta, Senior Data Scientist, Gopi Kumar, Principal Program Manager, and Hang Zhang, Senior Data Science Manager, at Microsoft. Data scientists typically spend a significant amount of time writing code seeking answers to the above questions. Although datasets differ between projects, much of the code can be generalized into data science utilities that can be reused across projects, thus helping with productivity. Additionally, such utilities can help data scientists work on specific tasks in a project in a guided mode, ensuring consistency and completeness of the underlying tasks. These two utilities, which run in CRAN-R, can be accessed from this GitHub site.

After you make predictions, you need to know if they are any good. There are standard measures that we can use to summarize how good a set of predictions actually are. In this tutorial, you will discover how to implement four standard prediction evaluation metrics from scratch in Python. How To Implement Machine Learning Algorithm Performance Metrics From Scratch With Python Photo by Hernán Piñera, some rights reserved. You must estimate the quality of a set of predictions when training a machine learning model.

We tend to think of R and other such ML tools only in the context of the workplace, to do "weighty" things aimed at saving millions. A little judicious use of R may help us hugely in our personal lives too. The ideas of regression, classification trees etc. can be powerful tools in valuation, as I found out. Recently, I was in a five-car accident on the infamous 101 in the San Francisco bay area. Luckily, none of us required an ambulance and all of us walked away.

To recap what we have learned from the WikiLeaks emails so far: How to make creamy risotto. That CNN's Donna Brazile might have slipped the Hillary Clinton campaign a question before a town hall debate. Oh, and how long it takes Clinton's team to figure out how to reply to a single Marco Rubio tweet (eight and a half hours, approximately). The emails apparently showed that at the end of July, the Clinton campaign put their heads down when Rubio tweeted "After Clinton's failed'reset' with Putin, now she wants to do a'reset' with Castro. She is making another mistake" around 7:30 a.m.

NVIDIA, which is Tesla Motors' supplier for the Visual Computing Modules (VCM), is also working on autonomous driving technology. NVIDIA's approach is different from conventional methods and relies someewhat on artificial intelligence. As Tesla abandons MobileEye hardware, NVIDIA is hinted as a possible new supplier for new generation Autopilot. "In contrast to the usual approach to operating self-driving cars, we did not program any explicit object detection, mapping, path planning or control components into this car. Instead, the car learns on its own to create all necessary internal representations necessary to steer, simply by observing human drivers. Similarly, the car can drive on the road that is overgrown with grass and bushes without the need to create a vegetation detection system. All it takes is about twenty example runs driven by humans at different times of the day. Learning to drive in these complex environments demonstrates new capabilities of deep neural networks. The car also learns to generalize its driving behavior. This video includes a clip that shows a car that was trained only on California roads successfully driving itself in New Jersey."

In the West, we rely on the grid, which provides most people with ample power. In off-grid communities such as those in large parts of rural Africa, energy is a highly valued resource and consumers are much more aware of energy efficiency. The limited energy that is available is disproportionately expensive and innovative solutions, such as small-scale renewable power, that cannot yet compete in the West, are gaining ground where, compared to the cost of the fossil fuels they replace, they are seen as a bargain. Developments such as small solar home systems are bringing power for the first time to millions of off-grid consumers in sub-Saharan Africa, thanks to technological advances in LED lights, batteries and mobile payment, creating what many are referring to as the "clean energy revolution" in rural electrification. While LED lighting and phone charging were the first volume application in this rapidly evolving market, consumers of the clean energy revolution in Africa want, and increasingly demand, more. Customers now expect to be able to access the latest technology, media and communications through the growth of the largely familiar pay-as-you-go technology, even in areas where there is no little or no grid access.

Bright Computing, the leading provider of hardware-agnostic cluster and cloud management software, today announced that it will be exhibiting at Huawei Connect, Paris, October 20-21, 2016. Huawei Connect, a conference for Huawei's European ICT ecosystem, focuses on business innovation and open partnerships. This year's theme is "Shape the Cloud", and policy makers, industry leaders, academics and technology elites will gather together to share, discuss and debate the future technologies and new business models that are driving the world's digital transformation. At the event, Bright Computing will showcase its latest solution, Bright for Deep Learning, which makes it easy to build an enterprise-grade deep learning environment, quickly and efficiently, enabling organizations to focus on gaining actionable insights from rich, complex data. Bright will explain how it helps to find, configure, and deploy all of the dependent pieces needed to run deep learning libraries and frameworks, in order to gain advantage from the deep learning evolution.

President Obama thinks artificial intelligence could solve many of the world's biggest problems -- like disease, climate change, even economic inequality. To that end, his administration is recommending more investment in the technology across all levels of government, including funding STEM education to have a prepared workforce, advanced research projects, local grants and new federal infrastructure. The White House released a 48-page report today featuring 24 recommendations for how the government can be involved in an increasingly AI-powered future, as well as ways to regulate the budding technology. For one, the White House predicts artificial intelligence and robotics will upend some jobs, noting that low- and medium-skilled workers are most vulnerable to threats of automation. The administration doesn't offer a solution, but says it's an issue that deserves careful attention and pledges to investigate appropriate policy responses.

After decades of dystopian science fiction novels and movies where sentient machines end up turning on humanity, we can't help but worry as real world AI continues to improve at such a rapid rate. That's why Amazon, Facebook, Google's DeepMind division, IBM, and Microsoft have founded a new organization called the Partnership on Artificial Intelligence to Benefit People and Society. Williams is encouraged that tech giants like Facebook and Google are even asking questions about ethics and bias in AI. Ideally, the group will help establish new standards for thinking about artificial intelligence, big data, and algorithms that can weed out harmful assumptions and biases.

This is Patricia Murphy, Vice President of Investor Relations for IBM. I'd like to welcome you to our third quarter earnings presentation. The prepared remarks will be available within a couple of hours and a replay of the webcast will be posted by this time tomorrow. I'll remind you that certain comments made in this presentation may be characterized as forward-looking under the Private Securities Litigation Reform Act of 1995. Those statements involve a number of factors that could cause actual results to differ materially. Additional information concerning these factors is contained in the Company's filings with the SEC. Copies are available from the SEC, from the IBM website, or from us in Investor Relations. Our presentation also includes certain non-GAAP financial measures in an effort to provide additional information to investors. All non-GAAP measures have been reconciled to their related GAAP measures in accordance with SEC rules. You'll find reconciliation charts at the end of the presentation and in the form 8-K submitted to the SEC today. So with that, I'll turn the call over to Martin Schroeter. In the third quarter, we generated 19.2 billion in revenues, 3.7 billion in pre-tax income and 3.29 of operating earnings per share. As we think back to the discussion 90 days ago, it was around Brexit and its impact on Europe, global spending and sectors like banking and the attractiveness of investment in the emerging markets, all of these topics have the capacity to drive some volatility and results, but what you see in our third quarter results is stability in our revenue with continued strong growth and strategic imperatives and a top and bottom line consistent with what we expected. Our revenue was essentially flat relative to last year. Looking at the revenue dynamics, I want to point out a few things. Our clients are focussed on becoming digital businesses and have strong growth in cloud, security, mobile, and across our analytics portfolio reflects this. In total, we continue to deliver double-digit revenue growth in our strategic imperatives led by our cloud business. Cloud delivered as-a-service is part of a solid recurring revenue base across software and services, and our annuity revenue continued to grow. Of course, the acquisitions we made in the last 12 months contributed to growth about the same amount as last quarter and for the first time in quite a while currency was a modest tailwind to revenue growth.