Deep Learning Toolkit

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Save time, empower your teams and effectively upgrade your processes with access to this practical Deep Learning Toolkit and guide. Address common challenges with best-practice templates, step-by-step work plans and maturity diagnostics for any Deep Learning related project.

Download the Toolkit and in Three Steps you will be guided from idea to implementation results.

The Toolkit contains the following practical and powerful enablers with new and updated Deep Learning specific requirements:


STEP 1: Get your bearings

Start with...

  • The latest quick edition of the Deep Learning Self Assessment book in PDF containing 49 requirements to perform a quickscan, get an overview and share with stakeholders.

Organized in a data driven improvement cycle RDMAICS (Recognize, Define, Measure, Analyze, Improve, Control and Sustain), check the…

  • Example pre-filled Self-Assessment Excel Dashboard to get familiar with results generation

Then find your goals...


STEP 2: Set concrete goals, tasks, dates and numbers you can track

Featuring 986 new and updated case-based questions, organized into seven core areas of process design, this Self-Assessment will help you identify areas in which Deep Learning improvements can be made.

Examples; 10 of the 986 standard requirements:

  1. Is cyber security protection, detection and response intelligence improved autonomously (e.g., via large-scale machine learning, reinforcement learning) using historical cyber security event data?

  2. Thanks to the evolution of the IoT, machine learning is advancing at a rapid pace. Machines are getting smarter and collecting data themselves, which poses the question; who will manage the data?

  3. How to architect an AI system so as to maximize the odds that, as it radically self-modifies and self-improves, it will not lose track of its originally programmed/taught goal system?

  4. If someone were to have a neural network that could scan information on all aspects of your life, where would that neural network potentially be able to find information about you?

  5. Portfolio Analysis: Given a set of technical papers, patents and press releases covering a field, what are the major topics of consideration and what entities are considering them?

  6. How Does a Serial, Integrated, and Very Limited Stream of Consciousness Emerge from a Nervous System That Is Mostly Unconscious, Distributed, Parallel, and of Enormous Capacity?

  7. A main component of the neural-network approach is the use of embeddings - representing each feature as a vector in a low dimensional space. But where do the vectors come from?

  8. Can biological insight gained from in vitro cultured neural networks on electronic substrates be used for construction of new types of large-scale artificial neural network?

  9. How do techniques from the pre-analytics era differ from this new Data Science and Business Analytics era, or have they just been re-skinned to fit into the new terminology?

  10. What are the most important benefits your organization is looking for when it comes to predictive threat prevention technologies provided through machine and deep learning?


Complete the self assessment, on your own or with a team in a workshop setting. Use the workbook together with the self assessment requirements spreadsheet:

  • The workbook is the latest in-depth complete edition of the Deep Learning book in PDF containing 986 requirements, which criteria correspond to the criteria in...

Your Deep Learning self-assessment dashboard which gives you your dynamically prioritized projects-ready tool and shows your organization exactly what to do next:

  • The Self-Assessment Excel Dashboard; with the Deep Learning Self-Assessment and Scorecard you will develop a clear picture of which Deep Learning areas need attention, which requirements you should focus on and who will be responsible for them:

    • Shows your organization instant insight in areas for improvement: Auto generates reports, radar chart for maturity assessment, insights per process and participant and bespoke, ready to use, RACI Matrix
    • Gives you a professional Dashboard to guide and perform a thorough Deep Learning Self-Assessment
    • Is secure: Ensures offline data protection of your Self-Assessment results
    • Dynamically prioritized projects-ready RACI Matrix shows your organization exactly what to do next:

 

STEP 3: Implement, Track, follow up and revise strategy

The outcomes of STEP 2, the self assessment, are the inputs for STEP 3; Start and manage Deep Learning projects with the 62 implementation resources:

  • 62 step-by-step Deep Learning Project Management Form Templates covering over 1500 Deep Learning project requirements and success criteria:

Examples; 10 of the check box criteria:

  1. Stakeholder Management Plan: In your opinion, do certain Deep Learning project resources hold a higher importance than other resources?

  2. Team Operating Agreement: Do you send out the agenda and meeting materials in advance?

  3. Scope Management Plan: Have all documents been archived in a Deep Learning project repository for each release?

  4. Risk Management Plan: Do the people have the right combinations of skills?

  5. Quality Audit: Is refuse and garbage adequately stored and disposed of with sufficient frequency to prevent contamination?

  6. Procurement Audit: Did the contracting authority verify compliance with the basic requirements of the competition?

  7. Risk Management Plan: Do requirements demand the use of new analysis, design, or testing methods?

  8. Requirements Documentation: Where do system and software requirements come from, what are sources?

  9. Cost Management Plan: Does the business case include how the Deep Learning project aligns with your organizations strategic goals & objectives?

  10. Project or Phase Close-Out: Did the delivered product meet the specified requirements and goals of the Deep Learning project?

 
Step-by-step and complete Deep Learning Project Management Forms and Templates including check box criteria and templates.

1.0 Initiating Process Group:

  • 1.1 Deep Learning project Charter
  • 1.2 Stakeholder Register
  • 1.3 Stakeholder Analysis Matrix


2.0 Planning Process Group:

  • 2.1 Deep Learning project Management Plan
  • 2.2 Scope Management Plan
  • 2.3 Requirements Management Plan
  • 2.4 Requirements Documentation
  • 2.5 Requirements Traceability Matrix
  • 2.6 Deep Learning project Scope Statement
  • 2.7 Assumption and Constraint Log
  • 2.8 Work Breakdown Structure
  • 2.9 WBS Dictionary
  • 2.10 Schedule Management Plan
  • 2.11 Activity List
  • 2.12 Activity Attributes
  • 2.13 Milestone List
  • 2.14 Network Diagram
  • 2.15 Activity Resource Requirements
  • 2.16 Resource Breakdown Structure
  • 2.17 Activity Duration Estimates
  • 2.18 Duration Estimating Worksheet
  • 2.19 Deep Learning project Schedule
  • 2.20 Cost Management Plan
  • 2.21 Activity Cost Estimates
  • 2.22 Cost Estimating Worksheet
  • 2.23 Cost Baseline
  • 2.24 Quality Management Plan
  • 2.25 Quality Metrics
  • 2.26 Process Improvement Plan
  • 2.27 Responsibility Assignment Matrix
  • 2.28 Roles and Responsibilities
  • 2.29 Human Resource Management Plan
  • 2.30 Communications Management Plan
  • 2.31 Risk Management Plan
  • 2.32 Risk Register
  • 2.33 Probability and Impact Assessment
  • 2.34 Probability and Impact Matrix
  • 2.35 Risk Data Sheet
  • 2.36 Procurement Management Plan
  • 2.37 Source Selection Criteria
  • 2.38 Stakeholder Management Plan
  • 2.39 Change Management Plan


3.0 Executing Process Group:

  • 3.1 Team Member Status Report
  • 3.2 Change Request
  • 3.3 Change Log
  • 3.4 Decision Log
  • 3.5 Quality Audit
  • 3.6 Team Directory
  • 3.7 Team Operating Agreement
  • 3.8 Team Performance Assessment
  • 3.9 Team Member Performance Assessment
  • 3.10 Issue Log


4.0 Monitoring and Controlling Process Group:

  • 4.1 Deep Learning project Performance Report
  • 4.2 Variance Analysis
  • 4.3 Earned Value Status
  • 4.4 Risk Audit
  • 4.5 Contractor Status Report
  • 4.6 Formal Acceptance


5.0 Closing Process Group:

  • 5.1 Procurement Audit
  • 5.2 Contract Close-Out
  • 5.3 Deep Learning project or Phase Close-Out
  • 5.4 Lessons Learned

 

Results

With this Three Step process you will have all the tools you need for any Deep Learning project with this in-depth Deep Learning Toolkit.

In using the Toolkit you will be better able to:

  • Diagnose Deep Learning projects, initiatives, organizations, businesses and processes using accepted diagnostic standards and practices
  • Implement evidence-based best practice strategies aligned with overall goals
  • Integrate recent advances in Deep Learning and put process design strategies into practice according to best practice guidelines

Defining, designing, creating, and implementing a process to solve a business challenge or meet a business objective is the most valuable role; In EVERY company, organization and department.

Unless you are talking a one-time, single-use project within a business, there should be a process. Whether that process is managed and implemented by humans, AI, or a combination of the two, it needs to be designed by someone with a complex enough perspective to ask the right questions. Someone capable of asking the right questions and step back and say, 'What are we really trying to accomplish here? And is there a different way to look at it?'

This Toolkit empowers people to do just that - whether their title is entrepreneur, manager, consultant, (Vice-)President, CxO etc... - they are the people who rule the future. They are the person who asks the right questions to make Deep Learning investments work better.

This Deep Learning All-Inclusive Toolkit enables You to be that person.

 

Includes lifetime updates

Every self assessment comes with Lifetime Updates and Lifetime Free Updated Books. Lifetime Updates is an industry-first feature which allows you to receive verified self assessment updates, ensuring you always have the most accurate information at your fingertips.