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 85 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:
Adapt TensorFlow leadership and integrate design thinking in TensorFlow innovation.
– Can we add value to the current TensorFlow decision-making process (largely qualitative) by incorporating uncertainty modeling (more quantitative)?
– How do we know that any TensorFlow analysis is complete and comprehensive?
– What are the Essentials of Internal TensorFlow Management?
Alphabet Inc. Critical Criteria:
Deduce Alphabet Inc. projects and assess what counts with Alphabet Inc. that we are not counting.
– Do the TensorFlow decisions we make today help people and the planet tomorrow?
– What are all of our TensorFlow domains and what do they do?
– How do we keep improving TensorFlow?
Apache License Critical Criteria:
Rank Apache License tasks and sort Apache License activities.
– Why is it important to have senior management support for a TensorFlow project?
– How will you know that the TensorFlow project has been successful?
– Do you monitor the effectiveness of your TensorFlow activities?
Apache SINGA Critical Criteria:
Mix Apache SINGA governance and explain and analyze the challenges of Apache SINGA.
– What are your most important goals for the strategic TensorFlow objectives?
– How will we insure seamless interoperability of TensorFlow moving forward?
– How to deal with TensorFlow Changes?
Application-specific integrated circuit Critical Criteria:
Read up on Application-specific integrated circuit strategies and describe which business rules are needed as Application-specific integrated circuit interface.
– Meeting the challenge: are missed TensorFlow opportunities costing us money?
– What are the business goals TensorFlow is aiming to achieve?
– What threat is TensorFlow addressing?
Artificial neural network Critical Criteria:
Accommodate Artificial neural network goals and oversee Artificial neural network management by competencies.
– Do TensorFlow rules make a reasonable demand on a users capabilities?
– Are there TensorFlow Models?
Central processing unit Critical Criteria:
Steer Central processing unit management and balance specific methods for improving Central processing unit results.
– What potential environmental factors impact the TensorFlow effort?
Code refactoring Critical Criteria:
Brainstorm over Code refactoring management and adjust implementation of Code refactoring.
– What other jobs or tasks affect the performance of the steps in the TensorFlow process?
– Do several people in different organizational units assist with the TensorFlow process?
– Can we do TensorFlow without complex (expensive) analysis?
Comparison of deep learning software Critical Criteria:
Reorganize Comparison of deep learning software failures and gather practices for scaling Comparison of deep learning software.
– Which customers cant participate in our TensorFlow domain because they lack skills, wealth, or convenient access to existing solutions?
– Do we all define TensorFlow in the same way?
Computing platform Critical Criteria:
Mine Computing platform tactics and define what do we need to start doing with Computing platform.
– Are there any easy-to-implement alternatives to TensorFlow? Sometimes other solutions are available that do not require the cost implications of a full-blown project?
Convolutional neural network Critical Criteria:
Probe Convolutional neural network quality and intervene in Convolutional neural network processes and leadership.
– Does TensorFlow analysis isolate the fundamental causes of problems?
Dataflow programming Critical Criteria:
Tête-à-tête about Dataflow programming goals and triple focus on important concepts of Dataflow programming relationship management.
– Think about the kind of project structure that would be appropriate for your TensorFlow project. should it be formal and complex, or can it be less formal and relatively simple?
– Will new equipment/products be required to facilitate TensorFlow delivery for example is new software needed?
– Who is responsible for ensuring appropriate resources (time, people and money) are allocated to TensorFlow?
Deep learning Critical Criteria:
Co-operate on Deep learning visions and explore and align the progress in Deep learning.
– At what point will vulnerability assessments be performed once TensorFlow is put into production (e.g., ongoing Risk Management after implementation)?
– Which TensorFlow goals are the most important?
Directed graph Critical Criteria:
Conceptualize Directed graph governance and arbitrate Directed graph techniques that enhance teamwork and productivity.
– Why are TensorFlow skills important?
– How much does TensorFlow help?
General-purpose computing on graphics processing units Critical Criteria:
Unify General-purpose computing on graphics processing units planning and assess and formulate effective operational and General-purpose computing on graphics processing units strategies.
– What are the success criteria that will indicate that TensorFlow objectives have been met and the benefits delivered?
– How do we maintain TensorFlows Integrity?
Google Brain Critical Criteria:
Investigate Google Brain projects and oversee Google Brain management by competencies.
– What business benefits will TensorFlow goals deliver if achieved?
– What is our formula for success in TensorFlow ?
– What is our TensorFlow Strategy?
Google Compute Engine Critical Criteria:
Communicate about Google Compute Engine adoptions and test out new things.
– How do you incorporate cycle time, productivity, cost control, and other efficiency and effectiveness factors into these TensorFlow processes?
– What are the Key enablers to make this TensorFlow move?
– Are assumptions made in TensorFlow stated explicitly?
Low-precision arithmetic Critical Criteria:
Inquire about Low-precision arithmetic quality and catalog Low-precision arithmetic activities.
– How do senior leaders actions reflect a commitment to the organizations TensorFlow values?
– How will you measure your TensorFlow effectiveness?
– How would one define TensorFlow leadership?
Machine learning Critical Criteria:
Check Machine learning governance and pioneer acquisition of Machine learning systems.
– What are the long-term implications of other disruptive technologies (e.g., machine learning, robotics, data analytics) converging with blockchain development?
– Does TensorFlow appropriately measure and monitor risk?
– How do we Lead with TensorFlow in Mind?
Microsoft Cognitive Toolkit Critical Criteria:
Experiment with Microsoft Cognitive Toolkit quality and plan concise Microsoft Cognitive Toolkit education.
– Is TensorFlow dependent on the successful delivery of a current project?
Neural Designer Critical Criteria:
Chart Neural Designer issues and correct better engagement with Neural Designer results.
– Who will be responsible for making the decisions to include or exclude requested changes once TensorFlow is underway?
– Do we monitor the TensorFlow decisions made and fine tune them as they evolve?
Neural networks Critical Criteria:
Coach on Neural networks governance and prioritize challenges of Neural networks.
– Is there a TensorFlow Communication plan covering who needs to get what information when?
– Is maximizing TensorFlow protection the same as minimizing TensorFlow loss?
Open-source software Critical Criteria:
Tête-à-tête about Open-source software issues and find the essential reading for Open-source software researchers.
– Will TensorFlow have an impact on current business continuity, disaster recovery processes and/or infrastructure?
– How do we go about Securing TensorFlow?
Order of magnitude Critical Criteria:
Confer re Order of magnitude strategies and catalog Order of magnitude activities.
Performance per watt Critical Criteria:
Study Performance per watt risks and correct Performance per watt management by competencies.
– How do we measure improved TensorFlow service perception, and satisfaction?
Proprietary software Critical Criteria:
Extrapolate Proprietary software outcomes and balance specific methods for improving Proprietary software results.
– Who is the main stakeholder, with ultimate responsibility for driving TensorFlow forward?
– Have all basic functions of TensorFlow been defined?
Reference implementation Critical Criteria:
Weigh in on Reference implementation engagements and maintain Reference implementation for success.
– What new services of functionality will be implemented next with TensorFlow ?
– What are the barriers to increased TensorFlow production?
Software categories Critical Criteria:
Be responsible for Software categories management and define Software categories competency-based leadership.
– What are your key performance measures or indicators and in-process measures for the control and improvement of your TensorFlow processes?
– What are the record-keeping requirements of TensorFlow activities?
Software developer Critical Criteria:
Meet over Software developer outcomes and budget the knowledge transfer for any interested in Software developer.
– What management system can we use to leverage the TensorFlow experience, ideas, and concerns of the people closest to the work to be done?
– 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?
Software license Critical Criteria:
Track Software license results and get answers.
– What implementation technologies/resources (e.g., programming languages, development platforms, software licenses) does the provider support?
– When a TensorFlow manager recognizes a problem, what options are available?
– Is our software usage in compliance with software license agreements?
– Think of your TensorFlow project. what are the main functions?
– What are our TensorFlow Processes?
Software release life cycle Critical Criteria:
Nurse Software release life cycle engagements and don’t overlook the obvious.
– How can you negotiate TensorFlow successfully with a stubborn boss, an irate client, or a deceitful coworker?
Supervised learning Critical Criteria:
Debate over Supervised learning governance and look at the big picture.
– What are our needs in relation to TensorFlow skills, labor, equipment, and markets?
– Are we making progress? and are we making progress as TensorFlow leaders?
– Does the TensorFlow task fit the clients priorities?
Tensor processing unit Critical Criteria:
Distinguish Tensor processing unit engagements and do something to it.
– 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?
– How can we improve TensorFlow?
Wolfram Mathematica Critical Criteria:
Wrangle Wolfram Mathematica visions and observe effective Wolfram Mathematica.
– 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?
– How can we incorporate support to ensure safe and effective use of TensorFlow into the services that we provide?
– Does TensorFlow create potential expectations in other areas that need to be recognized and considered?
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 | http://theartofservice.com
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.
To address the criteria in this checklist, these selected resources are provided for sources of further research and information:
TensorFlow External links:
[1603.04467] TensorFlow: Large-Scale Machine Learning …
[PDF]TensorFlow: Large-Scale Machine Learning on …
TensorFlow – Official Site
Alphabet Inc. External links:
Alphabet Inc. (GOOG) After Hours Trading – NASDAQ.com
Alphabet Inc. – GOOGL – Stock Price Today – Zacks
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.
Artificial neural network External links:
[PDF]Artificial Neural Network Travel Time Prediction …
[PDF]J3.4 USE OF AN ARTIFICIAL NEURAL NETWORK TO …
Training an Artificial Neural Network – Intro | solver
Central processing unit External links:
Central Processing Unit (CPU) – Lifewire
What is Central Processing Unit (CPU)? Webopedia
Central processing unit
http://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.
Code refactoring External links:
Code Refactoring C# – Stack Overflow
Comparison of deep learning software External links:
“Comparison of deep learning software” on Revolvy.com
https://broom02.revolvy.com/topic/Comparison of deep learning software
Comparison of deep learning software/Resources – …
Comparison of deep learning software/Resources – …
Computing platform External links:
Private Social and Computing Platform | Appiyo
MCP50 | Mobile Computing Platform | USA Fleet Solutions
Microsoft Azure Cloud Computing Platform & Services
Deep learning External links:
Deep Learning for Computer Vision with TensorFlow
Lambda Labs – Deep Learning Machines
Deep Learning White Paper
http://Ad · www.sas.com/deep-learning
Directed graph External links:
D3.js Titles on Collapsible Force-Directed graph
Force-Directed Graph – bl.ocks.org
directed graph – Everything2.com
Google Brain External links:
Google Brain team prepares for machine-learning-driven …
Google Brain Team – Google.ai
Google Compute Engine External links:
Deploying Applications Using Google Compute Engine
Microsoft Cognitive Toolkit External links:
Blog – Microsoft Cognitive Toolkit
The Microsoft Cognitive Toolkit | Microsoft Docs
Intro to Microsoft Cognitive Toolkit – YouTube
Neural Designer External links:
Neural Designer – Download
Neural Designer | Advanced analytics software
Examples | Neural Designer
Neural networks External links:
Neural Networks – ScienceDirect.com
Open-source software External links:
What is open-source software – Answers.com
Order of magnitude External links:
[PDF]Order of magnitude – Rensselaer at Hartford – ewp.rpi.edu
Rounding to Order of Magnitude in Matlab – Mike Soltys, …
“Carmilla” Order of Magnitude (TV Episode 2016) – IMDb
Performance per watt External links:
Which CPU gives the most performance per watt? – Quora
Proprietary software External links:
Proprietary Software for Free | USC Spatial Sciences Institute
Proprietary Software and the Institutional Researcher. – …
Proprietary Software Definition – LINFO
Reference implementation External links:
reference implementation – Wiktionary
reference implementation – Everything2.com
[PDF]Connected Vehicle Reference Implementation …
Software categories External links:
Browse Software Categories | Crozdesk
NCH Software Categories for Windows, Mac, Android & iOS
Software developer External links:
Title Software Developer Jobs, Employment | Indeed.com
[PDF]Job Description for Software Developer. Title: …
Software Developer Salary – PayScale
Software license External links:
Microsoft Software License Terms
QuickBooks Terms of Service & Software License …
Software release life cycle External links:
7011FI-1.xRelease.pdf | Software Release Life Cycle | Bit
Software Release Life Cycle | Scribd
Skill Pages – Software release life cycle | Dice.com
Supervised learning External links:
Supervised Learning with scikit-learn – DataCamp
Wolfram Mathematica External links:
Wolfram Mathematica – Official Site
Wolfram Mathematica | Division of Information Technology