Archive for the ‘Data Analytics’ Category

If you looked at TensorFlow as a deep learning framework last year and decided that it was too hard or too immature to use, it might be time to give it another look.

Since I reviewed TensorFlow r0.10 in October 2016, Google’s open source framework for deep learning has become more mature, implemented more algorithms and deployment options, and become easier to program. TensorFlow is now up to version r1.4.1 (stable version and web documentation), r1.5 (release candidate), and pre-release … Read the rest

Cloud services are moving from the initial “we’re doing it because everyone else is” state to a more cautious, planned migration, one where IT departments have done a careful assessment of their needs and determined what to move to the cloud and what will stay on-premises.

Getting there takes some hard lessons. A study by IDG Research found that as much as 40 percent of workloads moved off the cloud and back to an on-premises setting. That’s because companies had … Read the rest

Modern ethos is that all data is valuable, should be stored forever, and that machine learning will one day magically find the value of it. You’ve probably seen that EMC picture about how there will be 44 zettabytes of data by 2020? Remember how everyone had Fitbits and Jawbone Ups for about a minute? Now Jawbone is out of business. Have you considered this “all data is valuable” fad might be the corporate equivalent? Maybe we shouldn’t take a … Read the rest

TensorFlow, Google’s contribution to the world of machine learning and data science, is a general framework for quickly developing neural networks. Despite being relatively new, TensorFlow has already found wide adoption as a common platform for such work, thanks to its powerful abstractions and ease of use.

TensorFlow 1.4 API additions

TensorFlow Keras API

The biggest changes in TensorFlow 1.4 involve two key additions to the core TensorFlow API. The tf.keras API allows users to employ the Keras API, a … Read the rest

From its humble beginnings in the AMPLab at U.C. Berkeley in 2009, Apache Spark has become one of the key big data distributed processing frameworks in the world. Spark can be deployed in a variety of ways, provides native bindings for the Java, Scala, Python, and R programming languages, and supports SQL, streaming data, machine learning, and graph processing. You’ll find it used by banks, telecommunications companies, games companies, governments, and all of the major tech giants such as Apple, … Read the rest

Every day human beings eat, sleep, work, play, and produce data—lots and lots of data. According to IBM, the human race generates 2.5 quintillion (25 billion billion) bytes of data every day. That’s the equivalent of a stack of DVDs reaching to the moon and back, and encompasses everything from the texts we send and photos we upload to industrial sensor metrics and machine-to-machine communications.

That’s a big reason why “big data” has become such a common catch phrase. … Read the rest

You’ve probably encountered the term “machine learning” more than a few times lately. Often used interchangeably with artificial intelligence, machine learning is in fact a subset of AI, both of which can trace their roots to MIT in the late 1950s.

Machine learning is something you probably encounter every day, whether you know it or not. The Siri and Alexa voice assistants, Facebook’s and Microsoft’s facial recognition, Amazon and Netflix recommendations, the technology that keeps self-driving cars from crashing into … Read the rest

Big data and analytics initiatives can be game-changing, giving you insights to help blow past the competition, generate new revenue sources, and better serve customers.

Big data and analytics initiatives can also be colossal failures, resulting in lots of wasted money and time—not to mention the loss of talented technology professionals who become fed up at frustrating management blunders.

How can you avoid big data failures? Some of the best practices are the obvious ones from a basic business management … Read the rest

There’s now a JavaScript library for executing neural networks inside a webpage, using the hardware-accelerated graphics API available in modern web browsers.

Developed by a team of MIT graduate students, TensorFire can run TensorFlow-style machine learning models on any GPU, without requiring the GPU-specific middleware typically needed by machine learning libraries such as Keras-js.

TensorFire is another step towards making machine learning available to the broadest possible audience, using hardware and software people are already likely to possess, and … Read the rest

No one doubts that software engineering shapes every last facet of our 21st century existence. Given his vested interest in companies whose fortunes were built on software engineering, it was no surprise when Marc Andreessen declared that “software is eating the world.”

But what does that actually mean, and, just as important, does it still apply, if it ever did? These questions came to me recently when I reread Andreessen’s op-ed piece and noticed that he equated “software” with … Read the rest