What is involved in TensorFlow

Find out what the related areas are that TensorFlow connects with, associates with, correlates with or affects, and which require thought, deliberation, analysis, review and discussion. This unique checklist stands out in a sense that it is not per-se designed to give answers, but to engage the reader and lay out a TensorFlow thinking-frame.

How far is your company on its Tensorflow Machine Learning journey?

Take this short survey to gauge your organization’s progress toward Tensorflow Machine Learning leadership. Learn your strongest and weakest areas, and what you can do now to create a strategy that delivers results.

To address the criteria in this checklist for your organization, extensive selected resources are provided for sources of further research and information.

Start the Checklist

Below you will find a quick checklist designed to help you think about which TensorFlow related domains to cover and 84 essential critical questions to check off in that domain.

The following domains are covered:

TensorFlow, Alphabet Inc., Apache License, Apache SINGA, Application-specific integrated circuit, Artificial neural network, Central processing unit, Code refactoring, Comparison of deep learning software, Computing platform, Convolutional neural network, Dataflow programming, Deep learning, Directed graph, General-purpose computing on graphics processing units, Google Brain, Google Compute Engine, Low-precision arithmetic, Machine learning, Microsoft Cognitive Toolkit, Neural Designer, Neural networks, Open-source software, Order of magnitude, Performance per watt, Proprietary software, Reference implementation, Software categories, Software developer, Software license, Software release life cycle, Supervised learning, Tensor processing unit, Wolfram Mathematica:

TensorFlow Critical Criteria:

Dissect TensorFlow projects and question.

– When a TensorFlow manager recognizes a problem, what options are available?

– Is TensorFlow Realistic, or are you setting yourself up for failure?

– How can the value of TensorFlow be defined?

Alphabet Inc. Critical Criteria:

Review Alphabet Inc. issues and perfect Alphabet Inc. conflict management.

– What are your results for key measures or indicators of the accomplishment of your TensorFlow strategy and action plans, including building and strengthening core competencies?

– What business benefits will TensorFlow goals deliver if achieved?

– How can you measure TensorFlow in a systematic way?

Apache License Critical Criteria:

Deliberate over Apache License governance and optimize Apache License leadership as a key to advancement.

– Is there a TensorFlow Communication plan covering who needs to get what information when?

– What are the barriers to increased TensorFlow production?

– How is the value delivered by TensorFlow being measured?

Apache SINGA Critical Criteria:

Recall Apache SINGA governance and find the essential reading for Apache SINGA researchers.

– What knowledge, skills and characteristics mark a good TensorFlow project manager?

– How does the organization define, manage, and improve its TensorFlow processes?

– Can Management personnel recognize the monetary benefit of TensorFlow?

Application-specific integrated circuit Critical Criteria:

Illustrate Application-specific integrated circuit failures and maintain Application-specific integrated circuit for success.

– How do you incorporate cycle time, productivity, cost control, and other efficiency and effectiveness factors into these TensorFlow processes?

– For your TensorFlow project, identify and describe the business environment. is there more than one layer to the business environment?

– Do those selected for the TensorFlow team have a good general understanding of what TensorFlow is all about?

Artificial neural network Critical Criteria:

Steer Artificial neural network failures and track iterative Artificial neural network results.

– Will new equipment/products be required to facilitate TensorFlow delivery for example is new software needed?

– Is the TensorFlow organization completing tasks effectively and efficiently?

– Who sets the TensorFlow standards?

Central processing unit Critical Criteria:

Reconstruct Central processing unit risks and report on the economics of relationships managing Central processing unit and constraints.

– Record-keeping requirements flow from the records needed as inputs, outputs, controls and for transformation of a TensorFlow process. ask yourself: are the records needed as inputs to the TensorFlow process available?

– Will TensorFlow deliverables need to be tested and, if so, by whom?

– Do we all define TensorFlow in the same way?

Code refactoring Critical Criteria:

Probe Code refactoring quality and ask what if.

– What are the record-keeping requirements of TensorFlow activities?

Comparison of deep learning software Critical Criteria:

Have a session on Comparison of deep learning software decisions and look for lots of ideas.

– Among the TensorFlow product and service cost to be estimated, which is considered hardest to estimate?

– Do several people in different organizational units assist with the TensorFlow process?

– How to Secure TensorFlow?

Computing platform Critical Criteria:

Probe Computing platform risks and look at it backwards.

– Does TensorFlow appropriately measure and monitor risk?

– How can skill-level changes improve TensorFlow?

– How do we Lead with TensorFlow in Mind?

Convolutional neural network Critical Criteria:

Set goals for Convolutional neural network adoptions and proactively manage Convolutional neural network risks.

– What will drive TensorFlow change?

Dataflow programming Critical Criteria:

Apply Dataflow programming risks and frame using storytelling to create more compelling Dataflow programming projects.

– At what point will vulnerability assessments be performed once TensorFlow is put into production (e.g., ongoing Risk Management after implementation)?

– What are specific TensorFlow Rules to follow?

– What about TensorFlow Analysis of results?

Deep learning Critical Criteria:

Guard Deep learning visions and finalize specific methods for Deep learning acceptance.

– How important is TensorFlow to the user organizations mission?

– Is TensorFlow Required?

Directed graph Critical Criteria:

Incorporate Directed graph engagements and observe effective Directed graph.

– Who is the main stakeholder, with ultimate responsibility for driving TensorFlow forward?

General-purpose computing on graphics processing units Critical Criteria:

Adapt General-purpose computing on graphics processing units leadership and interpret which customers can’t participate in General-purpose computing on graphics processing units because they lack skills.

– What role does communication play in the success or failure of a TensorFlow project?

– What tools and technologies are needed for a custom TensorFlow project?

– What potential environmental factors impact the TensorFlow effort?

Google Brain Critical Criteria:

Devise Google Brain visions and diversify disclosure of information – dealing with confidential Google Brain information.

– What tools do you use once you have decided on a TensorFlow strategy and more importantly how do you choose?

– Have all basic functions of TensorFlow been defined?

Google Compute Engine Critical Criteria:

Discourse Google Compute Engine goals and modify and define the unique characteristics of interactive Google Compute Engine projects.

– Where do ideas that reach policy makers and planners as proposals for TensorFlow strengthening and reform actually originate?

Low-precision arithmetic Critical Criteria:

Refer to Low-precision arithmetic failures and mentor Low-precision arithmetic customer orientation.

– What are the Key enablers to make this TensorFlow move?

– Are assumptions made in TensorFlow stated explicitly?

– How to deal with TensorFlow Changes?

Machine learning Critical Criteria:

Transcribe Machine learning goals and assess and formulate effective operational and Machine learning strategies.

– A compounding model resolution with available relevant data can often provide insight towards a solution methodology; which TensorFlow models, tools and techniques are necessary?

– What are the long-term implications of other disruptive technologies (e.g., machine learning, robotics, data analytics) converging with blockchain development?

– How much does TensorFlow help?

Microsoft Cognitive Toolkit Critical Criteria:

Chat re Microsoft Cognitive Toolkit projects and reinforce and communicate particularly sensitive Microsoft Cognitive Toolkit decisions.

– What are the top 3 things at the forefront of our TensorFlow agendas for the next 3 years?

Neural Designer Critical Criteria:

Weigh in on Neural Designer tactics and simulate teachings and consultations on quality process improvement of Neural Designer.

– What are the business goals TensorFlow is aiming to achieve?

– What are current TensorFlow Paradigms?

Neural networks Critical Criteria:

Focus on Neural networks visions and oversee Neural networks requirements.

– What are your key performance measures or indicators and in-process measures for the control and improvement of your TensorFlow processes?

– How will we insure seamless interoperability of TensorFlow moving forward?

– What sources do you use to gather information for a TensorFlow study?

Open-source software Critical Criteria:

Map Open-source software decisions and customize techniques for implementing Open-source software controls.

– What are the key elements of your TensorFlow performance improvement system, including your evaluation, organizational learning, and innovation processes?

– Marketing budgets are tighter, consumers are more skeptical, and social media has changed forever the way we talk about TensorFlow. How do we gain traction?

Order of magnitude Critical Criteria:

Consult on Order of magnitude outcomes and don’t overlook the obvious.

– What other organizational variables, such as reward systems or communication systems, affect the performance of this TensorFlow process?

– How do we make it meaningful in connecting TensorFlow with what users do day-to-day?

– Who will provide the final approval of TensorFlow deliverables?

Performance per watt Critical Criteria:

Guard Performance per watt tactics and drive action.

– Are there any disadvantages to implementing TensorFlow? There might be some that are less obvious?

Proprietary software Critical Criteria:

Cut a stake in Proprietary software management and report on the economics of relationships managing Proprietary software and constraints.

– What new services of functionality will be implemented next with TensorFlow ?

– Why is TensorFlow important for you now?

Reference implementation Critical Criteria:

Categorize Reference implementation engagements and optimize Reference implementation leadership as a key to advancement.

– Meeting the challenge: are missed TensorFlow opportunities costing us money?

Software categories Critical Criteria:

Mix Software categories planning and frame using storytelling to create more compelling Software categories projects.

– Is maximizing TensorFlow protection the same as minimizing TensorFlow loss?

– Does the TensorFlow task fit the clients priorities?

– What are the usability implications of TensorFlow actions?

Software developer Critical Criteria:

Give examples of Software developer quality and be persistent.

– Do we aggressively reward and promote the people who have the biggest impact on creating excellent TensorFlow services/products?

– Pick an experienced Unix software developer, show him all the algorithms and ask him which one he likes the best?

– Why should we adopt a TensorFlow framework?

Software license Critical Criteria:

Distinguish Software license decisions and define what our big hairy audacious Software license goal is.

– Think about the people you identified for your TensorFlow project and the project responsibilities you would assign to them. what kind of training do you think they would need to perform these responsibilities effectively?

– What are our best practices for minimizing TensorFlow project risk, while demonstrating incremental value and quick wins throughout the TensorFlow project lifecycle?

– What implementation technologies/resources (e.g., programming languages, development platforms, software licenses) does the provider support?

– Is our software usage in compliance with software license agreements?

– What is our TensorFlow Strategy?

Software release life cycle Critical Criteria:

Illustrate Software release life cycle decisions and integrate design thinking in Software release life cycle innovation.

– What is the total cost related to deploying TensorFlow, including any consulting or professional services?

Supervised learning Critical Criteria:

Check Supervised learning risks and assess and formulate effective operational and Supervised learning strategies.

– How do mission and objectives affect the TensorFlow processes of our organization?

– How likely is the current TensorFlow plan to come in on schedule or on budget?

Tensor processing unit Critical Criteria:

Survey Tensor processing unit adoptions and acquire concise Tensor processing unit education.

– What are our needs in relation to TensorFlow skills, labor, equipment, and markets?

– Do the TensorFlow decisions we make today help people and the planet tomorrow?

– Do you monitor the effectiveness of your TensorFlow activities?

Wolfram Mathematica Critical Criteria:

Substantiate Wolfram Mathematica engagements and suggest using storytelling to create more compelling Wolfram Mathematica projects.


This quick readiness checklist is a selected resource to help you move forward. Learn more about how to achieve comprehensive insights with the Tensorflow Machine Learning Self Assessment:


Author: Gerard Blokdijk

CEO at The Art of Service | theartofservice.com

[email protected]


Gerard is the CEO at The Art of Service. He has been providing information technology insights, talks, tools and products to organizations in a wide range of industries for over 25 years. Gerard is a widely recognized and respected information expert. Gerard founded The Art of Service consulting business in 2000. Gerard has authored numerous published books to date.

External links:

To address the criteria in this checklist, these selected resources are provided for sources of further research and information:

TensorFlow External links:

TensorFlow – Official Site

[1603.04467] TensorFlow: Large-Scale Machine Learning …

Title: TensorFlow: A system for large-scale machine learning

Alphabet Inc. External links:

Alphabet Inc. (GOOG) After Hours Trading – NASDAQ.com

Alphabet Inc. – GOOGL – Stock Price Today – Zacks

Apache License External links:

Apache License 2.0 | Software Package Data Exchange …

Apache License 2.0 (Apache-2.0) Explained in Plain …

Apache SINGA External links:

AWS Marketplace: Apache SINGA

Application-specific integrated circuit External links:

An ASIC (application-specific integrated circuit) is a microchip designed for a special application, such as a particular kind of transmission protocol or a hand-held computer. You might contrast it with general integrated circuits, such as the microprocessor and the random access memory chips in your PC.
Reference: whatis.techtarget.com/definition/ASIC-application-specific-integ…

Artificial neural network External links:

Artificial neural network – ScienceDaily

Training an Artificial Neural Network – Intro | solver

Best Artificial Neural Network Software in 2017 | G2 Crowd

Central processing unit External links:

Central Processing Unit (CPU) – Montgomery County, MD

Central processing unit
A central processing unit (CPU) is the electronic circuitry within a computer that carries out the instructions of a computer program by performing the basic arithmetic, logical, control and input/output (I/O) operations specified by the instructions. The term has been used in the computer industry at least since the early 1960s.

What is Central Processing Unit (CPU)? Webopedia

Code refactoring External links:

Code Refactoring – PowerTheShell

Comparison of deep learning software External links:

Comparison of deep learning software/Resources – …

Comparison of deep learning software/Resources – …

Comparison of deep learning software – WOW.com

Computing platform External links:

Cloud Foundry Training – Cloud Computing Platform | …

MCP50 | Mobile Computing Platform | USA Fleet Solutions

Microsoft Azure Cloud Computing Platform & Services

Convolutional neural network External links:

Convolutional Neural Network example — neon …

Motif-based Convolutional Neural Network on Graphs

Dataflow programming External links:

Dataflow Programming Model – Google Cloud Platform

Deep learning External links:

Deep Learning for Computer Vision with TensorFlow

deepjazz: deep learning for jazz

Lambda Labs – Deep Learning Machines

Directed graph External links:

D3.js directed graph editor – CodePen

Directed graph definition – Math Insight

Directed Graph Editor · GitHub

Google Brain External links:

Google Brain Team – Google.ai

Google Brain Team – Research at Google

Google Compute Engine External links:

Google Compute Engine Snapshots

Deploying Applications Using Google Compute Engine

Machine learning External links:

Machine Learning, Cognitive Search & Text Analytics | Attivio

DataRobot – Automated Machine Learning for Predictive …

Microsoft Azure Machine Learning Studio

Microsoft Cognitive Toolkit External links:

Blog – Microsoft Cognitive Toolkit

The Microsoft Cognitive Toolkit | Microsoft Docs

Microsoft Cognitive Toolkit

Neural Designer External links:

Neural Designer | Advanced analytics software

Examples | Neural Designer

Neural networks External links:

Neural Networks – ScienceDirect.com

Artificial Neural Networks – ScienceDirect


Open-source software External links:

What is open-source software – Answers.com

Order of magnitude External links:

“Carmilla” Order of Magnitude (TV Episode 2016) – IMDb

Order of magnitude (Musical CD, 1990) [WorldCat.org]

[PDF]Order of Magnitude of a Function – Mathorama

Proprietary software External links:

Proprietary Software Definition – LINFO

Proprietary Software for Free | USC Spatial Sciences Institute

Proprietary Software and the Institutional Researcher. – …

Reference implementation External links:

[PDF]Connected Vehicle Reference Implementation …

reference implementation – Wiktionary

Software categories External links:

How to Create Custom Software Categories

B2B Software Categories – Financesonline.com

How to Manage Software Categories – technet.microsoft.com

Software developer External links:

[PDF]Job Description for Software Developer. Title: …

Title Software Developer Jobs, Employment | Indeed.com

Software Developer Salary – PayScale

Software license External links:

QuickBooks Terms of Service & Software License …

Software License Management & more | X-Formation

Autodesk Software License Review Portal :: Welcome

Software release life cycle External links:

Skill Pages – Software release life cycle | Dice.com

Supervised learning External links:

What is supervised learning? – Quora

Supervised Learning with scikit-learn – DataCamp

Wolfram Mathematica External links:

Wolfram Mathematica: Contact Us

Wolfram Mathematica | Division of Information Technology

Wolfram Mathematica – Official Site

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