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IBM C2090-930 : IBM SPSS Modeler Professional v3 Exam

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Test Number : C2090-930
Test Name : IBM SPSS Modeler Professional v3
Vendor Name : IBM
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Exam Name : IBM Certified Specialist - SPSS Modeler Professional v3
Exam Code : C2090-930
Number of questions: 60
Number of questions to pass: 40
Time allowed: 90 mins
Status: Live

SPSS Modeler Professional Functionality 10%
Identify the purpose of each palette
Describe the use of SuperNodes
Describe the advantages of SPSS Modeler scripting

Business Understanding and Planning 10%
Describe the CRISP-DM process
Describe how to map business objectives to data mining goals

Data Understanding 15%
Describe appropriate nodes for summary statistics, distributions, and visualizations (for example, graph nodes, output nodes)
Describe data quality issues (for example, outliers and missing data)

Data Preparation 20%
Describe methods for data transformation (for example, Derive node, Auto Data Prep node, Data Audit node and Filler node)
Describe how to integrate data (for example, Merge node and Append node)
Describe sampling, partitioning, and balancing data (for example, demo node, Balance node and Partition node)
Describe methods for refining data (for example, Select node, Filter node and Aggregate node)

Modeling 20%
Describe classification models (including GLM and regression)
Describe segmentation models
Describe association models
Describe auto modeling nodes
Demonstrate how to combine models using the Ensemble node

Evaluation and Analysis 15%
Demonstrate how to interpret SPSS Modeler results (for example, using Evaluation node, Analysis node, and data visualizations)
Describe how to use model nugget interfaces

Deployment 10%
Describe how to use Export nodes (tools for exporting data)
Identify how to score new data using models
Identify SPSS Modeler reporting methods

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IBM certification

IBM Boosts Deep getting to know Accuracy on Memristive Chips | C2090-930 Dumps and PDF Braindumps

IBM researchers have taken one other step towards making in-reminiscence computing according to part change (PCM) memory instruments a fact. Papers in Nature and Frontiers in Neuroscience this month existing IBM work using a mixed-signal structure with PCM gadgets to run deep neural networks at excessive accuracy. IBM validated a novel method to coping with herbal adaptations existing in PCM (memristive) devices to enrich accuracy via working towards fashions to accommodate noise.

The large aim of these “neuromorphic” processing tactics is to ‘mimic’ the low-power processing concepts used by means of organic techniques and dispose of some of the statistics stream required by using usual compute architectures. IBM researcher Manuel LeGallo, an creator on each papers, described the work in a weblog final week.

Manuel LeGallo, IBM

“whereas there was significant progress in the development of hardware-accelerator architectures for inference, lots of the present set-united states of americabodily cut up the reminiscence and processing devices. This capability that DNN fashions are typically saved in off-chip reminiscence, and that computational projects require a constant shuffling of data between the memory and computing contraptions – a system that slows down computation and boundaries the maximum viable power effectivity,” wrote LeGallo.

“Our analysis, featured in Nature Communications, exploits in-memory computing strategies using resistance-based (memristive) storage devices as a promising non-von Neumann strategy for establishing hardware that may efficiently support DNN inference fashions. in particular, they propose an structure in line with section-change reminiscence (PCM) that, like the human mind, has no separate cubicles to shop and compute records, and for this reason consumes tremendously much less energy.”

a major problem in using PCM contraptions is achieving and conserving computational accuracy. PCM technology is analog in nature, and computational precision is limited as a result of device variability as well as read and write conductance noise. IBM become in search of a means to coach the neural networks so that transferring the digitally expert weights to the analog resistive memory devices would now not effect in gigantic lack of accuracy.

Wrote LeGallo, “Our method changed into to discover injecting noise to the synaptic weights right through the training of DNNs in utility as a popular components to Strengthen the community resilience against analog in-memory computing hardware non-idealities. Their assumption became that injecting noise comparable to the machine noise all the way through the practising of DNNs would increase the robustness of the fashions.”

It turned out they had been correct. “working towards ResNet-classification networks this fashion resulted in no appreciable accuracy loss when transferring weights to PCM gadgets. They done an accuracy of 93.7% on the CIFAR-10 dataset and a properly-1 accuracy on the ImageNet benchmark of 71.6% after mapping the proficient weights to analog PCM synapses. And after programing the proficient weights of ResNet-32 on 723,444 PCM contraptions of a prototype chip, the accuracy computed from the measured hardware,” weights stayed above 92.6% over a period of 1 day. To the better of their capabilities, here is the maximum accuracy experimentally reported to-date on the CIFAR-10 dataset by way of any analog resistive reminiscence hardware.”

IBM has lengthy performed productive research into the usage of PCM know-how for in-memory computing. As defined within the Nature paper, (accurate deep neural network inference the use of computational part-trade reminiscence), PCM is a memristive technology, which records records in a nanometric quantity of section-trade cloth sandwiched between two electrodes. The phase-alternate cloth is within the low-resistive crystalline section in an as-fabricated equipment. by applying a present pulse of satisfactory amplitude (typically noted as the RESET pulse) an amorphous vicinity across the slim bottom electrode is created via a melt-quench process. The gadget can be in a low conductance state if the excessive-resistive amorphous vicinity blocks the latest route between both electrodes. The size of the amorphous region may also be modulated in a nearly completely analog method via the utility of relevant electrical pulses.

here is the formal description of test device used taken from the nature paper:

“The experimental platform is built round a prototype PCM chip that comprises 3 million PCM contraptions. The PCM array is equipped as a matrix of be aware strains (WL) and bit lines (BL). moreover the PCM gadgets, the prototype chip integrates the circuitry for machine addressing, and for write and read operations. The PCM chip is interfaced to a hardware platform comprising a field programmable gate array (FPGA) board and an analog-front-conclusion (AFE) board. The AFE board contains the digital-to-analog converters, and gives the vigour components as well as the voltage and existing reference sources to the PCM chip. The FPGA board implements the facts acquisition and the digital logic to interface with the PCM gadget beneath examine and with the entire electronics of the AFE board.

“The PCM gadgets are integrated into the chip in 90-nm CMOS expertise the usage of the key-hole method described in the ref. fifty two. The part-alternate material is doped Ge2Sb2C5. The bottom electrode has a radius of ~20 nm and a height of ~50 nm. The part-alternate fabric is ~a hundred nm thick and extends to the proper electrode, whose radius is ~one hundred nm. All experiments performed during this work are finished on an array containing 1 million contraptions accessed by means of transistors, which is geared up as a matrix of 512 WL and 2048 BL.”

practicing the fashions to accommodate noise is an important achievement. LeGallo stated, “In an period transitioning more and more in opposition t AI-based technologies, including internet-of-issues battery-powered devices and self reliant automobiles, such technologies would highly improvement from speedy, low-powered, and reliably correct DNN inference engines. The recommendations developed in their stories display extremely good abilities against realizing correct AI hardware-accelerator architectures to aid DNN practicing and inferencing in an power-effective method.”

“Fellow researchers affiliated with King’s college London, The Swiss Federal Institute of technology in Zurich (ETH Zürich), and École Polytechnique Fédérale de Lausanne (EPFL) also contributed to this work. Their analysis is part of the IBM AI Hardware middle, which changed into launch twelve months ago. The center specializes in enabling subsequent-technology chips and methods that aid the huge processing vigor and unheard of speed that AI requires to recognize its full knowledge,” wrote LeGallo.

plenty work continues to be to be carried out but the exact work is significant and may enhance PCM-primarily based getting to know ideas.

link to IBM blog: https://www.ibm.com/blogs/analysis/2020/05/iso-accuracy-deep-getting to know-inference-with-in-memory-computing/

hyperlink to Nature paper: https://www.nature.com/articles/s41467-020-16108-9

hyperlink to Frontiers in Neuroscience paper: https://www.frontiersin.org/articles/10.3389/fnins.2020.00406/full

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