by Jake Drew, PhD Introduction This article describes methods for machine learning using bootstrap samples and parallel processing to model very large volumes of data in short periods of time. The R programming language includes many packages for machine learningContinue reading… Machine Learning in Parallel with Support Vector Machines, Generalized Linear Models and Adaptive Boosting
Today, businesses can collect data along every point of the customer journey. This information might include mobile app usage, digital clicks, interactions on social media and more, all contributing to a data fingerprint that is completely unique to its owner.Continue reading… 5 Benefits of Data and Analytics for Positive Business Outcomes
What are Tree Methods? Tree methods are commonly used in data science to understand patterns within data and to build predictive models. The term Tree Methods covers a variety of techniques with different levels of complexity but my aim isContinue reading… Making data science accessible – Machine Learning – Tree Methods
It’s not hyperbole to say that use cases for machine learning and deep learning are only limited by our imaginations. About one year ago, a former embedded systems designer from the Japanese automobile industry named Makoto Koike started helping outContinue reading… How a Japanese cucumber farmer is using deep learning and TensorFlow
Math. Math. Oh and perhaps some more math.
That’s the gist of the advice to students interested in AI from Facebook’s Yann LeCun and Joaquin Quiñonero Candela who run the company’s Artificial Intelligence Lab and Applied Machine Learning group respectively.
Tech companies often advocate STEM (science, technology, engineering and math), but today’s tips are particularly pointed. The pair specifically note that students should
eat their vegetables take Calc I, Calc II, Calc III, Linear Algebra, Probability and Statistics as early as possible.
From this list, probability and statistics are perhaps the most interesting. From what I remember about high-school, those two subjects are regularly dismissed as too-obvious strategies for skirting the informal AP Calculus preference of top colleges and universities (AP Statistics is often thought of as a cop-out by students).
If differential equations represents the electricity that powers machine learning, statistics represents the gears of the machine itself — as the company touches on in a series of AI explainer videos we linked to at the bottom of this post.
To be fair, LeCun and Candela are most likely addressing the college crowd, though its important to consider incentives across all levels of education. Simply, we all could probably use some more statistics in our lives. Beyond math, the two say
more math engineering, computer science, economics and neuroscience are also important subjects in today’s economy. How else would a fledgling machine learning student learn to leverage neuroeconomics and cognitive bias to target ads?
The pair also point to philosophy as a necessary prerequisite to understanding knowledge and learning. Amidst all the talk of News Feed bias, it’s important to remember that there is a human behind every application of machine learning. We don’t yet know how to escape the black box problem, but we do know that it will be humans working to figure it out and it would sure help if those humans understood how learning works before they start manipulating data.
Lastly, Facebook turns its attention to the actual mechanics of getting a job in the field of machine learning. Most of these tips are self-explanatory: find a professor to work with, consider working with PhD students who have more time on their hands and try to secure an industry-focused internship regardless of your future aspirations to understand how AI works in the real world.
When applying to PhD programs the two note that being able to identify a professor you want to work with is far more important than program ranking. Once there, students should work to address a specific problem and try to release a piece of open source code before all is said and done.
For more, click here.
Alexa has evolved beyond the Amazon Echo into one of the hottest new platforms in tech. Learn how developers and businesses can leverage the technology.
The launch of Amazon Echo and its voice service, Alexa, brought virtual assistants out of our smartphones and into our homes and offices. While the Echo is a solid product, Alexa as a voice platform is where the real value is.
After starting off with 100 things the Echo could do, the number of available Alexa Skills now tops 7,000. CES 2017 showed how eager tech companies are to integrate Alexa, as the Amazon virtual assistant was everywhere at CES, despite the fact that neither the Echo or Alexa had booth space on the show floor.
As such, the interest in developing tools for the platform has skyrocketed, with many developers eager to jump into the ecosystem. To help developers and companies better understand how to get started working with Alexa and its related services, we’ve pulled together the most important details and resource
For more click here
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Shutterstock Big data is imperative to business—and the amount of data in circulation and storage needed increases daily. Most forward-thinking businesses recognize the value of this data and leverage it as a decision-making factor for business strategy, but they don’tContinue reading… Data as a Service: The Big Opportunity for Business