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000-M226 exam Dumps Source : IBM Smart Analytics Sales Mastery Test(R) v1

Test Code : 000-M226
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: 34 actual Questions

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Education Expert Rick Wormeli Dazzles in favorite SDE Assessment Seminar | killexams.com actual questions and Pass4sure dumps

By PR Newswire

Article Rating:

September 11, 2015 08:35 AM EDT  

PETERBOROUGH, N.H., Sept. 11, 2015 /PRNewswire/ -- Charting student progress in the classroom can be a major predicament for educators, especially given the traditional report cards and grading systems. Assessment and grading are top of irony for many educators who are looking for truly convincing ways to assess students and report progress. Education expert Rick Wormeli, internationally-acclaimed professional evolution consultant and assessment specialist, can befriend alleviate the solicitude surrounding assessment through a practical study at standards and outcomes-based assessment and grading.

Logo - http://photos.prnewswire.com/prnh/20150910/265542LOGO

Staff evolution for Educators' (SDE) two-day seminar on Standards-based Assessment & Grading: Principles & Practicalities for Todays Classroom, offered this topple and Winter for teachers of grades 5-12, is designed precisely to befriend educators learn how to manage evidence-based assessment and grading, using innovative strategies designed to meet common standards and be tailored to students' unique needs.

Wormeli will deal registrants to an engaging, provocative, and practical study at assessment and grading practices through the lens of diverse student bodies, changing polities, and evolving pedagogy.

"Evidence-based assessment and grading are two frontiers critical to your success," explains Wormeli. "Make positive you intent how you can shape the course of education for you and your students."

Wormeli, one of the first Nationally Board Certified teachers in America, brings energy, validity, and tall standards to his presentations and instructional practice. A teacher for more than 30 years, Wormeli's travail has been reported on ABC's generous Morning America and on Hardball with Chris Matthews, and featured in publications such as the Washington Post, National Geographic, and generous Housekeeping. An exciting and engaging presenter, he is besides the author of the award-winning book Meet Me in the Middle, as well as unbiased Isn't Always Equal: Assessment & Grading in the Differentiated Classroom, and Differentiation: From Planning to Practice.

"This seminar was created in direct response to the needs of teachers across the country," explains Mia Leonard-Solis, SDE Program Planner. "The content is germane and necessary, and Rick is highly entertaining and motivating, not to mention an expert in this area!"

Wormeli will guide teachers through the necessary elements for useful pre-, formative, common, and summative assessments that really travail in today's classrooms, and working with students who learn differently. The seminar will besides address designing rubrics, using grading scales, averaging scores, handling zeros, homework, do-overs, late work, and extra credit.

The thoughtful, research-based solutions provided by Wormeli will befriend educators develop a principled mindset to determine assessment and grading techniques that create transcripts of just integrity that stakeholders in other schools and districts can trust. Wormeli will drill educators how to focus assessments so that they provide actual and convincing evidence of what is (and is not) mastery.

"Rick will befriend teachers tackle how to meaningfully unwrap the standards as they design effectual rubrics," Leonard-Solis explains. "With strategies to tier and scaffold assignments appropriately, Rick will besides guide teachers on how to do just and significant connections between what they are teaching and what their students are learning.

The seminar is designed for novel and veteran teachers alike.

"What to grade, how to grade, when to grade—participants will leave with much more confidence in assessing their students, and they'll luxuriate in every minute of this entertaining educational experience," Leonard-Solis states confidently.

Staff evolution for Educators is America's leading provider of professional evolution for PreK through Grade 12 educators. SDE believes that educators own the most notable job in the world. Through Onsite PD, PD Events, Web-based PD, and PD Resources, the company empowers teachers and administrators across the country with ongoing professional evolution that is not only research-based, innovative, and rigorous, but besides practical, motivating, and fun. complete SDE training is presented by the nation's top authors and educational experts, with the goal of creating extraordinary classrooms that prepare students for the evolving demands of the 21st century.

All SDE events embrace a free resource book of handouts, lesson ideas, reproducibles, and a free 30-day membership at TeachingMadeEasier.com. With the paid registration of a staff member, SDE extends free tuition to a principal from the identical building, and besides offers group discounts for groups of 10+ and 20+.

Standards-based Assessment & Grading will be offered in Portland, OR on November 12-13, 2015, Nashville, TN on November 17-18, 2015, Madison, WI on December 1-2, 2015, Springfield, IL on January 21-22, 2016, Joliet, IL on January 26-27, 2016, and Philadelphia, PA on February 23-24, 2016. Educators interested registering for this seminar, or learning about more about SDE's national conferences, upcoming SDE events, or other professional evolution opportunities, can visit www.SDE.com, summon 1-800-462-1478, or email [email protected].

To view the original version on PR Newswire, visit:http://www.prnewswire.com/news-releases/education-expert-rick-wormeli-dazzles-in-popular-sde-assessment-seminar-300141460.html

SOURCE Staff evolution for Educators

Copyright © 2007 PR Newswire. complete rights reserved. Republication or redistribution of PRNewswire content is expressly prohibited without the prior written consent of PRNewswire. PRNewswire shall not be liable for any errors or delays in the content, or for any actions taken in reliance thereon.

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The Feed | killexams.com actual questions and Pass4sure dumps

Sunday Oct 22ndBuilding the Products Your Customers Need — Alluvium

Company focus:- 1. They befriend them understand stability- 2. flawless production

Software is there to help, support, and enable experts in the complete parts of the system.

Under the hood, fundamentally about dimensionality reduction…folks at the edge puss tall dimensionality problems day after day. Started with online learning off streaming data for the edge….but faced cultural and organizational friction.

Risk of adoption stalled conversations and POCs. Turned to, hey, what happens if they evaluate your historical data and immediately provide value/build longterm trust.

Historical event analysis — grounding it in actual problem (stability score + actual sensor data). Using shallow NN (lstm), unsupervised.

Saturday Oct 21st

Listening primarily to Andrew Ng.- Today, the money is in supervised learning, structured data, and ads. He’s spoken before about the revenue Baidu was seeing (even a brace years ago) using abysmal learning for ad targeting, and while it’s not the sexiest consume case, it’s certainly a prudent one.- Ng said he thinks of structure a commerce around AI as a multi-year “chess game,” where the goal is to strategically assemble the best data, build models, build the product, and then learn even more from user data — ultimately creating a virtuous cycle.- just AI company has these components.  — ‘strategic data acquisition’ — ‘centralized data warehouse’ — ‘pervasive automation’ — ‘new job descriptions'

Merlon with Bradford. Similar to skymind with fiscal industry fraud — crimes analysis. - wanted to be here because of how unsexy, actual biz problem it is- catalysts: execs loosing jobs, amount of spending, amount of fines- humans in the loop and ML product to befriend signal if someone opening a bank account should be doing that, notifying staff of users in the news for — potentially disagreeable developments- companies spending $10B+ each year on this problem- risk ranking vs interest ranking (comps to his own startup data crawling, learn to rank implementations)- provide a UI, complete stack product- generate some of their own data- what is defensible. Product that ties within prop data, prop ui. Learning cross competitors. - People/HR side is defensible too

Bradford speaking with Matt Turck. - Largely the identical pitch around vertical AI and why MLaaS/horizontal has — limited potential- Focus on solving higher smooth commerce problem as opposed to low smooth assignment that will become commoditized pretty quickly- Revenue - “There is necessity to befriend people understand how machine learning can unravel their problems and if you don’t reach to them with a solution…but you kind of reach with a really Great piece of technology that could unravel their solution…your ultimately drop the problem back in their lap.”https://www.youtube.com/watch?v=mYu2MnPnCCY

New (to me) commentary on ideal team structures:- Mostly remark polar (very technical team…7 top kids from stanford) or (some guy from trucking business, domain expertise but no clue around tech)- You remark very runt of very smart ML founder and smart commerce founder that realize they necessity to travail together and besides ensnare PM, designer, topic matter expert, etc- Sometimes you can overweight the domain expertise in first 1–2…startup commercial sense and savvy is more notable in the beginning

Friday Oct 20th Command C + Command V for the future tech IPOs. S-1

Thursday Oct 19th

Pod: https://overcast.fm/+HzyVPL4iU — AlluviumNotes:

Manufacturing, broadly speaking, falls into two buckets. - Discrete — monorail (car + plane assembly)- Process/Additive — focus for Alluvium- Producing a gist or material (refining oil, chemical processing), this has highly interdependent systems.- assume of a visual aid…thousands of pipes and boilers wrangled together

Who the user is falls into two groups:

- someone working in oil refinery…typically relying on traditional threshold based results (alarm going off)- team of individuals looking back at historical (corporate or incident resolution)

Product goal is to consolidate that and provide collective intelligence — system smooth analysis in actual time…humans in the loop looking at feedback. + — labels

Not necessarily considered anomaly detection (thresholds own already been hit), rather — series of smaller changes and heads up

Technical under the hood:- time progression data of different frequency- asset emitting state or sensor sharing temperature- used unsupervised or semi machine learning (user)- reality is most folks aren’t in a position to own that data..need to start from scratch and start injecting human expertise into the loop- traditional ML (forecasting) and shallow NN (fluffy retort here, miracle if most folks worry about being perceived as not having abysmal tech)- built abysmal empathy and humility for operators (most team lacked industrial DNA and domain knowledge)

General fears of a solution of this type

Corporate execs: dismay that these tools that don’t actually know the problem and dismay of how accomplish they retain domain erudition amongst workers that are aging / retire- looking ways at software can build institutional knowledge

Front line side: machines are coming to supersede job- stress and messaging that system has to travail alongside the human

Wednesday Oct 18th

and Part 2. Very well received by /r/machinelearing

- Neuron — thing that holds a number (between 0–1)…function - Learning: computer finding perquisite weights and biases - Relu replaced sigmoid

- Cost office tall when not training well, low when performant- local minimum: doable- global minimum: very hard

  • find downhill direction and steepest descent- a network learning is minimizing a cost office using gradient descent
  • Tuesday Oct 17thConversation with software engineer spending last year seriously exploring the field.Draw to DL:- Potential repercussion over the longterm- Can play a role in bringing software eng practices to ml workflows- Find ML problems to be more entertaining than APIs/backend plumbing- commerce reality that folks are using ML perquisite now

    Shallow and novel field~1 yr of in-depth study can bring you to the edge of the field.

    Commentary on the state of tooling being completely lacking…state of javascript 10 years ago - model training, deployment, tracking, reproducibility

    Monday Oct 16th

    PodcastNotes:- They own a lot of resources, this is a astronomical field that we’re well poised to play in.- A lot of corporate talk oddly concluded with a candid and genuine comment that they don’t know of how some (non-silicon) commerce models will develop

    Found this helpful in trying to understand automatic speech recognition vs natural language processing.

    ASR — attempt to determine what, exactly, was said- besides known as speech to textNLP —tries to determine the intent of what was said

    Saturday October 14th

    Post from Erik Bernhardsson who I’ve reach to really respect from afar for discussions about ML, data, and org building.

    This piece does a nice job of setting the scene. MOAR DATA =/ perquisite or useful

    #anotherone

    Post.A tweet from a month prior

    again…real candid and grounded thoughts from someone who has actually built these systems. valuable to ensnare this perspective.

    Anecdotally organize his reasoning as to why he took the CTO position to be one of the better I’m sharing a evolution in my professional life kind of updates.

    Friday October 13th

    Pod.Notes — ProductAI

    Applied computer vision - Special focus in protest detection- Matt is former longtime MSFT researcher. SEA → Beijing office- 2012 mainstream debut / breakthrough for DL on-stage…NYT front page press- ‘I silent assume it’s the early days of DL’ — Supervised is a bit more mature, semi/weakly semi supervision is needed

    Applications- Textiles trait control (much higher interval testing vs random human sampling) material classification, fiber classification, and color analysis — acquired data via human labels (microscope hooked up to inspection)- protest detection within x-rays for safety within transportation- Fashion — style/cust/other attributes- Ecommerce — dealing with billions of images/products on marketplaces and auction sites — semi supervised learning is needed to scale with this volume…models trained via this. - Semi-supervised learning is their key technical innovation - “semi-supervised learning = subset of your data which is labeled with reliable trait and then using that to inform the rest of the data set”- “weakly, in their definition….when you essentially own labels for complete of your data but it’s unreliable (unbalanced, noisy) so that’s where actually find the biggest value…because the internet is complete of it.”- Scalability…moving away from dependence on human labeling

    - China landscape is a bit different in perception. Far more open that folks think. Everyone is sharing notes. Rising tide overall. Information reaches the east but research (published in mandarin) in china doesn’t current back through to US. Andrew Ng highlighted this on identical pod.

    Thursday October 12thBox movements in ML space the past year.

    2013…

    Box has now done technical integrations / biz partnerships with - IBM (text, audio)- MSFT (video+audio)- Goog (mostly image)

    tl;dr~ Machine learning as a service technology is effectual and robust enough~ Customers can remark value in better searching, discovering, organizing enriched media. Far more efficient than humans (or no interaction at all)~ Box can leverage best in class APIs…simply build out the plumbing on top of content management platform. ~ Said APIs are commoditized and become plug+play modular. Box here is an aggregator (HT Ben T) with near user relationship (billing, legal, UX, sales force).

    Good mp;A with Aaron this summer.

    PostThe more content you own and the more you consume the system, the harder it gets to use. It’s counter-intuitive that this should preserve progressing this way. The course to unravel that problem, that scale, is through machine learning

    Wednesday October 11thSkymind — deep learning for the enterprise…an org and CTO I’ve been very intrigued by.

    Notes from variety of sources.- Podcast on Archillect.- Tech talk, talk 2- HN and /r/ML

    Business:- Largely serving China (healthcare) and Japan (banking, tech)-US is more stable but conservative line of business- 20 distributed people. - Long sales in suits…working with systems integrators to scale selling to these large organizations. - Tech agnostic…go to where data and dollars are. JVM systems.- Open-core

    Applications and solutions:- Fraud detection (credit cards, online banking)- Detecting illegal mining activity via satellite- Network intrusion (packet analysis)- Predicting machine failure- Stock market trend prediction (up/down)- Simbox fraud- Finding similar parts for large vehicles

    DL can be generous at time progression data, generous at finding patterns in behaviorProblems largely revolving around analytics, churn prediction, recommendation engines

    Technology:- Created DL4J - Main thing is production deployment software and a service smooth agreement for models they build.- Not necessarily always DL, whatever the problem calls for- own built a lot of the software stack

    Tuesday October 10thhttps://www.youtube.com/watch?v=IMoQPvXMkJw

    Building robust machine learning systems- Ravelin — Protect merchants from credit card fraud- 1–3% of revenue…cost for the merchant- serious precision/recall tradeoff. FP as a consumer (just checking, they assume there may be fraud…False Neg — you don’t spot the fraud)- I assume most engineers would be horrified at the state of machine learning tooling (training, testing, deploying)- ML systems — High complexity — Current decisions repercussion future performance — Loss office doesn’t limn just goal

    - Training models — How accomplish they do positive the world they present to the model is the view of the world they actually want to learn?

    - Labels & truth — Delayed labels….takes many weeks/months to acocunt for so if you just train on last 2–3mos…you actually don’t own that captured view of labels.

    - Testing — pull of set of examples you should *always* ensnare right — use as unit test — generate synthetic data for worst case scenarios (loading up credit cards in 10min, spending 100k)

    Deploying- Model provenance — who trained the model…when…what git hash….on what data…which hyperparameters…..train/test performance — Shipping model…share as much code as viable between offline and online — Release gradually. A/B test.  — Score in live with both models. Inspect biggest disagreements.

    Automation:- Unfortunately near up training on *data_1_final_edited_really_final(1)(2).csv*

    Monday October 9th

    #AI methods ranked by econ value Supervised Learning Transfer Learning Unsupervised Learning Reinforcement Learning

    Older notes to be backfilled



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    VCE [6 Certification Exam(s) ]
    Veeam [2 Certification Exam(s) ]
    Veritas [33 Certification Exam(s) ]
    Vmware [58 Certification Exam(s) ]
    Wonderlic [2 Certification Exam(s) ]
    Worldatwork [2 Certification Exam(s) ]
    XML-Master [3 Certification Exam(s) ]
    Zend [6 Certification Exam(s) ]





    References :


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