Archive for the ‘Deep Learning’ Category

Architectural Tenets of Deep Learning

Keith Manthey

Keith Manthey

CTO - Unstructured Storage Division
Keith has spent 25+ years building distributing computing and high performance computing systems for the Financial Services industry and in support of the US Government. He built his first machine learning system in 2009 and has been fascinated by data driven technology since then. Keith holds 6 issued patents and a few still pending around distributed analytics and high performance computing. Keith holds degrees from Virginia Tech and the University of Georgia
Keith Manthey

Latest posts by Keith Manthey (see all)

Lately, I have spent large swaths of my time focused around Deep Learning and Neural Networks (either with customers or in our lab).   One of the most common questions that I get is around underperforming model training with regard to “wall clock time”.  This has more to do with focusing on only one aspect of their architecture, say GPUs. As such, I will spend a little time writing about the 3 fundamental tenets for a successful Deep Learning architecture.  These fundamental tenants are compute, file access, and bandwidth. Hopefully this will resonate and help provide some thoughts for those customers on their journey.


Deep Learning (DL) is certainly all the rage. We are defining DL as a type of Machine Learning (ML) built on a deep hierarchy of layers, with each layer solving different pieces of a complex problem. These layers are interconnected into a “neural network”.

The use cases that I am presented with continue to grow exponentially with very compelling financial return on investments. Whether it is Convolutional Neural Networks (CNNs) for Computer Vision or Recurrent Neural Networks (RNNs) for Natural Language Processing (NLP) or Deep Belief Networks (DBN) for Restricted Boltzmann Machines (RBMs), Deep Learning has many architectural structures and acronyms. There is some great Neural Network information out there.  Pic 1 is a good representation of the structural layers for Deep Learning on Neural Networks:


Pic 1


Orchestration tools like BlueData, Kubernetes, Mesosphere, or Spark Cluster Manager are the top of the layer cake of (more…)

Democratizing Artificial Intelligence, Deep Learning and Machine Learning with Dell EMC Ready Solutions

Bill Schmarzo

Bill Schmarzo

CTO, Dell EMC Services (aka “Dean of Big Data”)
Bill Schmarzo, author of “Big Data: Understanding How Data Powers Big Business” and “Big Data MBA: Driving Business Strategies with Data Science”, is responsible for setting strategy and defining the Big Data service offerings for Dell EMC’s Big Data Practice. As a CTO within Dell EMC’s 2,000+ person consulting organization, he works with organizations to identify where and how to start their big data journeys. He’s written white papers, is an avid blogger and is a frequent speaker on the use of Big Data and data science to power an organization’s key business initiatives. He is a University of San Francisco School of Management (SOM) Executive Fellow where he teaches the “Big Data MBA” course. Bill also just completed a research paper on “Determining The Economic Value of Data”. Onalytica recently ranked Bill as #4 Big Data Influencer worldwide. Bill has over three decades of experience in data warehousing, BI and analytics. Bill authored the Vision Workshop methodology that links an organization’s strategic business initiatives with their supporting data and analytic requirements. Bill serves on the City of San Jose’s Technology Innovation Board, and on the faculties of The Data Warehouse Institute and Strata. Previously, Bill was vice president of Analytics at Yahoo where he was responsible for the development of Yahoo’s Advertiser and Website analytics products, including the delivery of “actionable insights” through a holistic user experience. Before that, Bill oversaw the Analytic Applications business unit at Business Objects, including the development, marketing and sales of their industry-defining analytic applications. Bill holds a Masters Business Administration from University of Iowa and a Bachelor of Science degree in Mathematics, Computer Science and Business Administration from Coe College.

Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) are at the heart of digital transformation by enabling organizations to exploit their growing wealth of big data to optimize key business and operational use cases.

• AI is the theory and development of computer systems able to perform tasks normally requiring human intelligence (e.g. visual perception, speech recognition, translation between languages, etc.).
• ML is a sub-field of AI that provides systems the ability to learn and improve by itself from experience without being explicitly programmed.
• DL is a type of ML built on a deep hierarchy of layers, with each layer solving different pieces of a complex problem. These layers are interconnected into a “neural network.” A DL framework is SW that accelerates the development and deployment of these models.

See “Artificial Intelligence is not Fake Intelligence” for more details on AI | ML | DL.

And the business ramifications are staggering (see Figure 1)!

Figure 1: Source : McKinsey

And Senior Executives seem to have gotten the word.  BusinessWeek (October 23, 2017) reported a dramatic increase in mentions of  (more…)

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