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Book Review: Python Data Science Handbook

I recently had a need for a Python language resource to supplement a series of courses on Deep Learning I was evaluating that depended on this widely used language. As a long-time data science practitioner, my language of choice has been R, so I relished the opportunity to dig into Python to see first hand how the other side of the data science world did machine learning. The book I settled on was “Python Data Science Handbook: Essential Tools for Working with Data” by Jake VanderPlas.

State of Artificial Intelligence for the Enterprise

A survey conducted in July 2017 by Vanson Bourne, the Teradata “State of Artificial Intelligence for Enterprises” report captures the here and now for AI — how executive decision-makers are investing in AI today, the expected return on investment (ROI), what barriers lie ahead and how businesses craft a solid game plan to realize AI’s full potential.

Probing the Wisdom of Apple, Inc., Crowds Using Alternative Data Sources

In this contributed article, Anasse Bari, clinical assistant professor of computer science at New York University, and software engineer Lihao Liu, provide a detailed look at the competitive analysis they performed for four major smartphone contenders: iPhone X and 8, Samsung Galaxy Note 8, Nokia 8 and Google Pixel 2 using alternative data sources.

Tableau Celebrates 10th Anniversary of Global Customer Conference with 14,000 Data Enthusiasts

Tableau Software (NYSE: DATA), a leading analytics platform, is coming together this week with customers, partners, and data rockstars from around the globe for its 10th annual conference in Las Vegas. More than 14,000 passionate attendees will be on hand as Tableau showcases upcoming innovations to its analytics platform.

How Can We Trust Machine Learning?

How Can We Trust Machine Learning? In this talk, Carlos Guestrin, CEO of Dato, Inc. and Amazon Professor of Machine Learning at the University of Washington, describes recent research and new tools with which companies can start to have the means to gain trust and confidence in the models and predictions behind their core business applications.

Strata Data Conference New York City 2017 – Roundup

In this field report, I wanted to share the insideBIGDATA experience last week at the Strata Data Conference 2017 in New York City, September 25-28, sponsored by O’Reilly and Cloudera. These shows are always recognized extravaganzas and never disappoint with respect to the technology being showcased. Below is a series of vignettes that highlight breaking news items that we feel are important for our readers. I hope you’ll feel like you were on the exhibition floor!

New Theory Unveils the Black Box of Deep Learning

In the video presentation below (courtesy of Yandex) – “Deep Learning: Theory, Algorithms, and Applications” – Naftali Tishby, a computer scientist and neuroscientist from the Hebrew University of Jerusalem, provides evidence in support of a new theory explaining how deep learning works. Tishby argues that deep neural networks learn according to a procedure called the “information bottleneck.”

Making Computers Smarter with Google’s AI chief John Giannandrea

In the video below from the recent TechCrunch Disrupt SF 2017 event, Google’s John Giannandrea sits down with Frederic Lardinois to discuss the AI hype/worry cycle and the importance, limitations, and acceleration of machine learning. Giannandrea addresses the huge amount of hype surrounding AI right now, specifically the fear-mongering by some of Silicon Valley’s elite.

Field Report: Deep Learning Specialization on Coursera

This “Field Report” is a bit difference from all the other reports I’ve done for insideBIGDATA.com because it is more of a “virtual” report that chronicles my experiences going through the content of an exciting new learning resource designed to get budding AI technologists jump started into the field of Deep Learning. Renowned MOOC platform Coursera just launched a new Deep Learning Specialization series consisting of 5 courses.

RMSprop Optimization Algorithm for Gradient Descent with Neural Networks

The video lecture below on the RMSprop optimization method is from the course Neural Networks for Machine Learning, as taught by Geoffrey Hinton (University of Toronto) on Coursera in 2012. For all you AI practitioners out there, this technique should supplement your toolbox in a very useful way.