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700-302 - Advanced Borderless Network Architecture Field Engineer - Dump Information

Vendor : Cisco
Exam Code : 700-302
Exam Name : Advanced Borderless Network Architecture Field Engineer
Questions and Answers : 86 Q & A
Updated On : February 15, 2019
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700-302 Questions and Answers

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700-302 Advanced Borderless Network Architecture Field Engineer

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700-302 exam Dumps Source : Advanced Borderless Network Architecture Field Engineer

Test Code : 700-302
Test Name : Advanced Borderless Network Architecture Field Engineer
Vendor Name : Cisco
Q&A : 86 Real Questions

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Cisco Advanced Borderless Network Architecture

LaSalle options Recertifies for advanced Specializations From Cisco | killexams.com Real Questions and Pass4sure dumps

ROSEMONT, IL--(Marketwired - Apr 23, 2014) - LaSalle solutions, a leading issuer of lifestyles-cycle administration capabilities for expertise and capital belongings, announced these days that it has recertified in here Cisco superior specializations: advanced records center Specialization, advanced without borderlines network architecture Specialization and advanced Collaboration architecture Specialization. These specializations recognize LaSalle solutions as having fulfilled the working towards necessities and program necessities to promote, design and deploy comprehensive Cisco® data center, without borderlines network structure, and Collaboration structure options.

The recertification comes on the heels of LaSalle options' fresh attention because the Cisco® features accomplice of the year for the united states. This prestigious award acknowledges exemplary companions, like LaSalle solutions, who show optimal-in-class enterprise practices and serve as a model to the industry.

"Our recertification for these critical specializations with Cisco demonstrates LaSalle solutions' commitment to featuring our valued clientele with unmatched performance, potential and repair," stated Steven Robb, president of the options group at LaSalle options. "As we continue to sell, design, carry and aid Cisco facts core, without borders community architecture and Collaboration structure solutions, these specializations will allow our crew of specialists at LaSalle solutions to help our valued clientele meet the business challenges of contemporary ever-altering industry."

advanced facts middle Specialization The specialization provides position-based mostly working towards in three elementary areas: Unified fabric for optimizing facts and storage needs; Unified Computing for streamlining records core materials and uniting network, computational, storage and virtualization structures, and Unified network capabilities for advancing the way utility birth and security functions are supplied within the data core network or computing ambiance.

superior borderless community architecture Specialization The Cisco superior without borders network architecture Specialization is designed to assist companions like LaSalle options meet a wide array of consumer wants. the mixing of the network with enterprise processes and purposes requires a holistic approach in line with the Cisco without boundary lines community structure.

advanced Collaboration structure Specialization Cisco superior Collaboration structure really expert partners, like LaSalle options, work with new and present technologies to align components of this architecture with their clients' strategic dreams, helping transform enterprise strategies, raise organizational efficiency and speed up the time to market.

The Cisco Resale Channel program provides companions like LaSalle options with the training required to build income, design, and technical talent and then validates their expertise via a 3rd-birthday party audit.

The recertifications construct on a couple of Cisco-connected milestones LaSalle options has prior to now accomplished, including:

  • Cisco US functions associate of the yr 2014
  • Cisco grasp Unified Communications Specialization
  • Cisco® Gold licensed associate
  • international companion community accomplice
  • advanced Collaboration architecture Specialization
  • TelePresence Video superior licensed know-how company (ATP) status
  • advanced Unified Computing technology Specialization
  • superior borderless network architecture Specialization
  • customer satisfaction Excellence Gold superstar - 37 consecutive quarters
  • Cisco services partner of the year, primary area - 2010, 2011, 2012, 2013
  • About Cisco Cisco is the worldwide leader in networking that transforms how americans connect, communicate and collaborate. information about Cisco can be discovered at http://www.cisco.com.

    About LaSalle options situated in 1980, LaSalle options is a leading company of lifestyles-cycle administration services for technology and capital belongings. These capabilities encompass acquisition and financing, IT asset management, renovation contracts management, remarketing and disposition. LaSalle solutions' strategies, astonishing client carrier and robust, market-leading cloud-based mostly toolset, LAMP, permit valued clientele to greater economically and conveniently manipulate and plan to satisfy their dreams.

    LaSalle solutions is an independently operated company and a subsidiary of MB economic bank, a publicly traded Chicago-primarily based financial institution maintaining business. MB financial is traded on the NASDAQ as "MBFI." For extra advice on LaSalle solutions, please seek advice from www.elasalle.com and www.YouTube.com/LaSalleSolutions.

    LaSalle solutions and LAMP are registered emblems of LaSalle solutions in the united states. Cisco, the Cisco brand and Cisco techniques are registered logos of Cisco programs Inc. in the united states and likely other international locations.


    without boundary lines Networks opportunities For Cisco partners | killexams.com Real Questions and Pass4sure dumps

    Forrester analysis calculated a 3-12 months, risk-adjusted ROI of 121 percent, with a payback duration of under six months, for consumers who applied Cisco borderless Networks.

    Cisco without borders Networks is a next-generation structure that provides the brand new work house experience, enabling personnel, clients, and partners to entry mission-important applications with any gadget, anywhere, every time—securely, reliably, transparently.  It enables companies to easily offer different tiers of entry to users and instruments to satisfy enterprise and regulatory compliance. The Border­less community fits the appropriate consumer on the right device to the appropriate network with the right access. as a result of a quick, comfy, and reputable network is elementary to the without borderlines enter­prise, the Cisco network Optimization service can play a crucial role within the Border­much less network structure.

    partners: we've a functions providing where you could learn the way to promote it to your consumer base (Cisco CCOID is required).

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    LaSalle options Named 2016 Cisco services associate of the yr, Americas relevant | killexams.com Real Questions and Pass4sure dumps

    ROSEMONT, IL--(Marketwired - March 08, 2016) - LaSalle options introduced nowadays that it's as soon as again the recipient of a Cisco accomplice Summit award for functions associate of the yr, Americas vital. Cisco introduced the award at a reception held during its annual partner summit, which took location Feb. 29­ - March 3 in San Diego.

    Awarded to exemplary channel partners, the Cisco companion Summit Geographical place awards are designed to recognize most suitable-in-category company practices and function a model to the trade inside their respective areas. Areas of consideration encompass innovative practices, structure-led strategy successes, strategic company outcome-concentrated courses, seizing new opportunities and earnings methods.

    The Cisco companion Summit awards the accurate-performing partners inside certain technology markets throughout all geographical regions. Award recipients are chosen through a gaggle of Cisco international associate firm and regional executives.

    "we're honored to once more acquire the valuable location functions associate of the yr award," spoke of Steven Robb, president of the options group at LaSalle options. "LaSalle is dedicated to proposing foremost-in-type solutions to our clients, and we are proud to have a partner like Cisco that both recognizes and shares our dedication. We appear forward to our persevered collaboration with Cisco and the outstanding effects it is going to continue to provide."

    LaSalle solutions is a Cisco Gold certified companion with Cisco master Specializations in security and Collaboration. The company holds Cisco advanced Specializations in without boundary lines network structure Specialization, Collaboration architecture Specialization and information center structure. in addition, LaSalle options holds Cisco approved expertise provider (ATP) designations in utility Centric Infrastructure, TelePresence Video grasp, ServiceGrid, information Virtualization Reseller, energy management Suite Integrator and id capabilities Engine.

    LaSalle solutions turned into named U.S. central location commercial enterprise partner of the year in 2015, Cisco services accomplice of the yr in 2014, relevant vicinity Cisco services companion of the 12 months from 2010 to 2013 and has received the Cisco client pride Excellence Award for 40 consecutive quarters.

    About LaSalle options

    centered in 1980, LaSalle options is a leading company of expertise life-cycle asset administration services. LaSalle makes it possible for its valued clientele to increase their expertise operations via improved strategies, management and reporting for superior planning and return on investment. LaSalle solutions' approaches, staggering consumer carrier and robust, market-main cloud-based mostly toolset, LAMP, allow valued clientele to attain more advantageous enterprise effects via transparency and reliable results at their fingertips.

    LaSalle options is a subsidiary of MB financial bank, N.A., a industrial financial institution headquartered in Chicago, Illinois. MB fiscal, Inc. is the publicly traded conserving enterprise for MB financial bank, N.A. and is traded on the NASDAQ as "MBFI."

    For extra guidance on LaSalle solutions, please consult with www.elasalle.com and www.YouTube.com/LaSalleSolutions.

    LaSalle options and LAMP are registered emblems of LaSalle solutions in the u.s..

    Cisco, the Cisco logo and Cisco TelePresence are trademarks or registered logos of Cisco and/or its associates in the U.S. and different nations. a list of Cisco's trademarks may also be found at www.cisco.com/go/emblems.

    graphic available: http://www.marketwire.com/library/MwGo/2016/three/8/11G086717/photographs/Steve_Summit_Award_2016-aef006ebe867aa0d8907daaf1c873f94.jpg


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    Neural Networks Need a Cookbook. Here Are the Ingredients | killexams.com real questions and Pass4sure dumps

    When we design a skyscraper we expect it will perform to specification: that the tower will support so much weight and be able to withstand an earthquake of a certain strength.

    But with one of the most important technologies of the modern world, we’re effectively building blind. We play with different designs, tinker with different setups, but until we take it out for a test run, we don’t really know what it can do or where it will fail.

    Quanta Magazine author photoAbout

    Original story reprinted with permission from Quanta Magazine, an editorially independent publication of the Simons Foundation whose mission is to enhance public understanding of science by covering research developments and trends in mathematics and the physical and life sciences.

    This technology is the neural network, which underpins today’s most advanced artificial intelligence systems. Increasingly, neural networks are moving into the core areas of society: They determine what we learn of the world through our social media feeds, they help doctors diagnose illnesses, and they even influence whether a person convicted of a crime will spend time in jail.

    Yet “the best approximation to what we know is that we know almost nothing about how neural networks actually work and what a really insightful theory would be,” said Boris Hanin, a mathematician at Texas A&M University and a visiting scientist at Facebook AI Research who studies neural networks.

    He likens the situation to the development of another revolutionary technology: the steam engine. At first, steam engines weren’t good for much more than pumping water. Then they powered trains, which is maybe the level of sophistication neural networks have reached. Then scientists and mathematicians developed a theory of thermodynamics, which let them understand exactly what was going on inside engines of any kind. Eventually, that knowledge took us to the moon.

    “First you had great engineering, and you had some great trains, then you needed some theoretical understanding to go to rocket ships,” Hanin said.

    Within the sprawling community of neural network development, there is a small group of mathematically minded researchers who are trying to build a theory of neural networks—one that would explain how they work and guarantee that if you construct a neural network in a prescribed manner, it will be able to perform certain tasks.

    This work is still in its very early stages, but in the last year researchers have produced several papers which elaborate the relationship between form and function in neural networks. The work takes neural networks all the way down to their foundations. It shows that long before you can certify that neural networks can drive cars, you need to prove that they can multiply.

    The Best Brain Recipe

    Neural networks aim to mimic the human brain—and one way to think about the brain is that it works by accreting smaller abstractions into larger ones. Complexity of thought, in this view, is then measured by the range of smaller abstractions you can draw on, and the number of times you can combine lower-level abstractions into higher-level abstractions—like the way we learn to distinguish dogs from birds.

    “For a human, if you’re learning how to recognize a dog you’d learn to recognize four legs, fluffy,” said Maithra Raghu, a doctoral student in computer science at Cornell University and a member of Google Brain. “Ideally we’d like our neural networks to do the same kinds of things.”

    Maithra Raghu, a member of Google Brain, has been identifying principles that explain how neural networks operate.

    Arun Chaganty

    Abstraction comes naturally to the human brain. Neural networks have to work for it. As with the brain, neural networks are made of building blocks called “neurons” that are connected in various ways. (The neurons in a neural network are inspired by neurons in the brain but do not imitate them directly.) Each neuron might represent an attribute, or a combination of attributes, that the network considers at each level of abstraction.

    When joining these neurons together, engineers have many choices to make. They have to decide how many layers of neurons the network should have (or how “deep” it should be). Consider, for example, a neural network with the task of recognizing objects in images. The image enters the system at the first layer. At the next layer, the network might have neurons that simply detect edges in the image. The next layer combines lines to identify curves in the image. Then the next layer combines curves into shapes and textures, and the final layer processes shapes and textures to reach a conclusion about what it’s looking at: woolly mammoth!

    “The idea is that each layer combines several aspects of the previous layer. A circle is curves in many different places, a curve is lines in many different places,” said David Rolnick, a mathematician at the University of Pennsylvania.

    Engineers also have to decide the “width” of each layer, which corresponds to the number of different features the network is considering at each level of abstraction. In the case of image recognition, the width of the layers would be the number of types of lines, curves or shapes it considers at each level.

    Beyond the depth and width of a network, there are also choices about how to connect neurons within layers and between layers, and how much weight to give each connection.

    So if you have a specific task in mind, how do you know which neural network architecture will accomplish it best? There are some broad rules of thumb. For image-related tasks, engineers typically use “convolutional” neural networks, which feature the same pattern of connections between layers repeated over and over. For natural language processing—like speech recognition, or language generation—engineers have found that “recurrent” neural networks seem to work best. In these, neurons can be connected to non-adjacent layers.

    Lucy Reading-Ikkanda/Quanta Magazine

    Beyond those general guidelines, however, engineers largely have to rely on experimental evidence: They run 1,000 different neural networks and simply observe which one gets the job done.

    “These choices are often made by trial and error in practice,” Hanin said. “That’s sort of a tough [way to do it] because there are infinitely many choices and one really doesn’t know what’s the best.”

    A better approach would involve a little less trial and error and a little more upfront understanding of what a given neural network architecture gets you. A few papers published recently have moved the field in that direction.

    “This work tries to develop, as it were, a cookbook for designing the right neural network. If you know what it is that you want to achieve out of the network, then here is the recipe for that network,” Rolnick said.

    To Rope a Red Sheep

    One of the earliest important theoretical guarantees about neural network architecture came three decades ago. In 1989, computer scientists proved that if a neural network has only a single computational layer, but you allow that one layer to have an unlimited number of neurons, with unlimited connections between them, the network will be capable of performing any task you might ask of it.

    It was a sweeping statement that turned out to be fairly intuitive and not so useful. It’s like saying that if you can identify an unlimited number of lines in an image, you can distinguish between all objects using just one layer. That may be true in principle, but good luck implementing it in practice.

    Researchers today describe such wide, flat networks as “expressive,” meaning that they’re capable in theory of capturing a richer set of connections between possible inputs (such as an image) and outputs (such as descriptions of the image). Yet these networks are extremely difficult to train, meaning it’s almost impossible to teach them how to actually produce those outputs. They’re also more computationally intensive than any computer can handle.

    Boris Hanin, a mathematician at Texas A&M University, has studied the tradeoff between depth and width in neural networks.

    Intel AI One Tree Studio

    More recently, researchers have been trying to understand how far they can push neural networks in the other direction — by making them narrower (with fewer neurons per layer) and deeper (with more layers overall). So maybe you only need to pick out 100 different lines, but with connections for turning those 100 lines into 50 curves, which you can combine into 10 different shapes, which give you all the building blocks you need to recognize most objects.

    In a paper completed last year, Rolnick and Max Tegmark of the Massachusetts Institute of Technology proved that by increasing depth and decreasing width, you can perform the same functions with exponentially fewer neurons. They showed that if the situation you’re modeling has 100 input variables, you can get the same reliability using either 2100 neurons in one layer or just 210 neurons spread over two layers. They found that there is power in taking small pieces and combining them at greater levels of abstraction instead of attempting to capture all levels of abstraction at once.

    “The notion of depth in a neural network is linked to the idea that you can express something complicated by doing many simple things in sequence,” Rolnick said. “It’s like an assembly line.”

    Rolnick and Tegmark proved the utility of depth by asking neural networks to perform a simple task: multiplying polynomial functions. (These are just equations that feature variables raised to natural-number exponents, for example y = x3 + 1.) They trained the networks by showing them examples of equations and their products. Then they asked the networks to compute the products of equations they hadn’t seen before. Deeper neural networks learned the task with far fewer neurons than shallower ones.

    And while multiplication isn’t a task that’s going to set the world on fire, Rolnick says the paper made an important point: “If a shallow network can’t even do multiplication then we shouldn’t trust it with anything else.”

    David Rolnick, a mathematician at the University of Pennsylvania, proved that increasing a network’s depth allowed a network to accomplish tasks with exponentially fewer neurons.

    Stephanie Ku

    Other researchers have been probing the minimum amount of width needed. At the end of September, Jesse Johnson, formerly a mathematician at Oklahoma State University and now a researcher with the pharmaceutical company Sanofi, proved that at a certain point, no amount of depth can compensate for a lack of width.

    To get a sense of his result, imagine sheep in a field, except these are punk-rock sheep: Their wool has been dyed one of several colors. The task for your neural network is to draw a border around all sheep of the same color. In spirit, this task is similar to image classification: The network has a collection of images (which it represents as points in higher-dimensional space), and it needs to group together similar ones.

    Johnson proved that a neural network will fail at this task when the width of the layers is less than or equal to the number of inputs. So for our sheep, each can be described with two inputs: an x and a y coordinate to specify its position in the field. The neural network then labels each sheep with a color and draws a border around sheep of the same color. In this case, you will need three or more neurons per layer to solve the problem.

    More specifically, Johnson showed that if the width-to-variable ratio is off, the neural network won’t be able to draw closed loops—the kind of loops the network would need to draw if, say, all the red sheep were clustered together in the middle of the pasture. “If none of the layers are thicker than the number of input dimensions, there are certain shapes the function will never be able to create, no matter how many layers you add,” Johnson said.

    Papers like Johnson’s are beginning to build the rudiments of a theory of neural networks. At the moment, researchers can make only very basic claims about the relationship between architecture and function—and those claims are in small proportion to the number of tasks neural networks are taking on.

    So while the theory of neural networks isn’t going to change the way systems are built anytime soon, the blueprints are being drafted for a new theory of how computers learn—one that’s poised to take humanity on a ride with even greater repercussions than a trip to the moon.

    Original story reprinted with permission from Quanta Magazine, an editorially independent publication of the Simons Foundation whose mission is to enhance public understanding of science by covering research developments and trends in mathematics and the physical and life sciences.

    More Great WIRED Stories

    Strategies in Light helps lighting industry find its place in the Internet of Things | killexams.com real questions and Pass4sure dumps

    The LED and solid-state lighting (SSL) community continues to seek its place in the grand Internet of Things (IoT) scheme. With each related news story and feature article we publish, we at LEDs Magazine look for the central thread: How is the lighting infrastructure connected in a system architecture and imbued with intelligent capabilities, driven by software, hardware, and communications methods working in tandem?

    One of those themes that keeps popping up is Bluetooth connectivity, in particular Bluetooth mesh networking because of its extended range and many-to-many device communication functionality. While the potential of Bluetooth has been discussed for some time now, we’ve also observed recently that commercial products — and projects — equipped with Bluetooth mesh capability have been slow to roll out. This is not unusual, in our experience; there is a reason why we call them “emerging applications” or “emerging technologies.” But implementation is underway.

    If you are looking to learn more about how Bluetooth mesh networking factors into connected lighting and enables lighting to merge with the IoT, you ought to make it a point to visit the IoT + Connected Lighting Zone on the exhibit floor at Strategies in Light (Mandalay Bay Hotel and Convention Center, Las Vegas, NV; Feb. 27–Mar. 1, 2019).

    [Native Advertisement]

    On Thursday, Feb. 28, from 1:00–3:30 PM in the IoT Pavilion Theater, partners and connectivity and controls specialists from Silvair, Fulham, and McWong will be presenting their collective vision on how the Bluetooth Mesh standard is poised to enable robust, secure connected lighting installations.

    Silvair executive Szymon Slupik has written articles for us explaining the foundations of the standard, mesh networking principles, and how Bluetooth can forge the path for lighting in the IoT, as well as how the system and network architecture changes the commissioning process for connected lighting. In the IoT & Connected Lighting Zone presentation “Bluetooth mesh lighting network: A practical guide for lighting manufacturers,” Slupik is prepared to demonstrate how Bluetooth technology can simplify smart lighting concepts and controls. “We're here to show lighting manufacturers how easy they can go wireless today, and how reliable wireless controls have become. Bluetooth mesh provides the shortest way to sensor-driven systems and advanced control strategies. It’s not about wired versus wireless anymore. It’s about democratizing lighting control and making lighting truly efficient,” said Slupik.

    Fulham’s Jeremy Ludyjan added, “The Internet of Things will have a dramatic impact on building automation, but for a successful IoT infrastructure you need a reliable data communications ecosystem. A number of vendors, including Fulham, are already offering LED components with onboard Bluetooth mesh designed in to create a wireless data infrastructure. We expect to see more wireless capability being used in lighting retrofits in preparation for IoT building management.” Expect to hear Ludyjan explain how components can add Bluetooth connectivity to existing luminaires during the show-floor talks.

    2018 Sapphire Awards finalist McWong partnered with Silvair last year to embed the company's firmware into sensors for smart lighting control platforms. McWong vice president Blane Goettle said, "McWong is coming to SIL this year with a growing line of IoT solutions; our goal is to partner with our customers to form the backbone of a new Bluetooth mesh-enabled IoT infrastructure. Visit the IoT pavilion to hear how we are helping make installation, commissioning, and activation more intuitive and accessible."

    Szymon Slupik, CTO and co-founder at Silvair — Slupik also chairs the Mesh Working Group at the Bluetooth Special Interest Group (SIG), and is the author of the Mesh Model specification that lays the ground for interoperability of multivendor smart lighting systems. A serial entrepreneur with a strong engineering background, Slupik has been positioning Silvair as a provider of a smart lighting control platform as a service, based on interoperable and open standards. He drove the development of Bluetooth mesh networking, elevating smart lighting as the primary application for this wireless technology.

    Jeremy Ludyjan, LC, senior director of global field marketing, Fulham Co., Inc. — Ludyjan is responsible for marketing and business development of Fulham’s full line of lighting controls and IoT products. Before Fulham, Ludyjan was a consultant and the general manager of Velocity SSL. Previously, he held various roles at Bulbrite, Illumitex, Samsung LED, Everlight, TTI, and Mouser Electronics. Ludyjan studied international business at Texas Wesleyan University for his Bachelor’s degree in business administration and has an MBA in sustainable business. He is an active member of the Illuminating Engineering Society (IES) and has his Lighting Certificate (LC) from the National Council on Qualifications for the Lighting Professions (NCQLP).

    Blane Goettle, vice president, McWong International, Inc. — At McWong, Goettle manages the business development and marketing programs as well as strategic product development and partner relationships. Prior to serving in this capacity, he worked for the company as a senior project manager. Before joining McWong, Goettle provided marketing and business communications services to a range of clients in Shanghai. Goettle holds an MBA from the Moore School of Business, University of South Carolina, and a B.S. from Stanford University.

    Strategies in Light 2019 is just around the corner. Register now to gain access to these exhibit-floor presentations, booth demonstrations, focused educational workshops, conference sessions, and more.

    *Updated Feb. 8, 2019 12:15 PM for additional comment.


    Foundations Built for a General Theory of Neural Networks | killexams.com real questions and Pass4sure dumps

    When we design a skyscraper we expect it will perform to specification: that the tower will support so much weight and be able to withstand an earthquake of a certain strength.

    But with one of the most important technologies of the modern world, we’re effectively building blind. We play with different designs, tinker with different setups, but until we take it out for a test run, we don’t really know what it can do or where it will fail.

    This technology is the neural network, which underpins today’s most advanced artificial intelligence systems. Increasingly, neural networks are moving into the core areas of society: They determine what we learn of the world through our social media feeds, they help doctors diagnose illnesses, and they even influence whether a person convicted of a crime will spend time in jail.

    Yet “the best approximation to what we know is that we know almost nothing about how neural networks actually work and what a really insightful theory would be,” said Boris Hanin, a mathematician at Texas A&M University and a visiting scientist at Facebook AI Research who studies neural networks.

    He likens the situation to the development of another revolutionary technology: the steam engine. At first, steam engines weren’t good for much more than pumping water. Then they powered trains, which is maybe the level of sophistication neural networks have reached. Then scientists and mathematicians developed a theory of thermodynamics, which let them understand exactly what was going on inside engines of any kind. Eventually, that knowledge took us to the moon.

    “First you had great engineering, and you had some great trains, then you needed some theoretical understanding to go to rocket ships,” Hanin said.

    Within the sprawling community of neural network development, there is a small group of mathematically minded researchers who are trying to build a theory of neural networks — one that would explain how they work and guarantee that if you construct a neural network in a prescribed manner, it will be able to perform certain tasks.

    This work is still in its very early stages, but in the last year researchers have produced several papers which elaborate the relationship between form and function in neural networks. The work takes neural networks all the way down to their foundations. It shows that long before you can certify that neural networks can drive cars, you need to prove that they can multiply.

    The Best Brain Recipe

    Neural networks aim to mimic the human brain — and one way to think about the brain is that it works by accreting smaller abstractions into larger ones. Complexity of thought, in this view, is then measured by the range of smaller abstractions you can draw on, and the number of times you can combine lower-level abstractions into higher-level abstractions — like the way we learn to distinguish dogs from birds.

    “For a human, if you’re learning how to recognize a dog you’d learn to recognize four legs, fluffy,” said Maithra Raghu, a doctoral student in computer science at Cornell University and a member of Google Brain. “Ideally we’d like our neural networks to do the same kinds of things.”

    Abstraction comes naturally to the human brain. Neural networks have to work for it. As with the brain, neural networks are made of building blocks called “neurons” that are connected in various ways. (The neurons in a neural network are inspired by neurons in the brain but do not imitate them directly.) Each neuron might represent an attribute, or a combination of attributes, that the network considers at each level of abstraction.

    When joining these neurons together, engineers have many choices to make. They have to decide how many layers of neurons the network should have (or how “deep” it should be). Consider, for example, a neural network with the task of recognizing objects in images. The image enters the system at the first layer. At the next layer, the network might have neurons that simply detect edges in the image. The next layer combines lines to identify curves in the image. Then the next layer combines curves into shapes and textures, and the final layer processes shapes and textures to reach a conclusion about what it’s looking at: woolly mammoth!

    “The idea is that each layer combines several aspects of the previous layer. A circle is curves in many different places, a curve is lines in many different places,” said David Rolnick, a mathematician at the University of Pennsylvania.

    Engineers also have to decide the “width” of each layer, which corresponds to the number of different features the network is considering at each level of abstraction. In the case of image recognition, the width of the layers would be the number of types of lines, curves or shapes it considers at each level.

    Beyond the depth and width of a network, there are also choices about how to connect neurons within layers and between layers, and how much weight to give each connection.

    So if you have a specific task in mind, how do you know which neural network architecture will accomplish it best? There are some broad rules of thumb. For image-related tasks, engineers typically use “convolutional” neural networks, which feature the same pattern of connections between layers repeated over and over. For natural language processing — like speech recognition, or language generation — engineers have found that “recurrent” neural networks seem to work best. In these, neurons can be connected to non-adjacent layers.

    Beyond those general guidelines, however, engineers largely have to rely on experimental evidence: They run 1,000 different neural networks and simply observe which one gets the job done.

    “These choices are often made by trial and error in practice,” Hanin said. “That’s sort of a tough [way to do it] because there are infinitely many choices and one really doesn’t know what’s the best.”

    A better approach would involve a little less trial and error and a little more upfront understanding of what a given neural network architecture gets you. A few papers published recently have moved the field in that direction.

    “This work tries to develop, as it were, a cookbook for designing the right neural network. If you know what it is that you want to achieve out of the network, then here is the recipe for that network,” Rolnick said.

    To Rope a Red Sheep

    One of the earliest important theoretical guarantees about neural network architecture came three decades ago. In 1989, computer scientists proved that if a neural network has only a single computational layer, but you allow that one layer to have an unlimited number of neurons, with unlimited connections between them, the network will be capable of performing any task you might ask of it.

    It was a sweeping statement that turned out to be fairly intuitive and not so useful. It’s like saying that if you can identify an unlimited number of lines in an image, you can distinguish between all objects using just one layer. That may be true in principle, but good luck implementing it in practice.

    Researchers today describe such wide, flat networks as “expressive,” meaning that they’re capable in theory of capturing a richer set of connections between possible inputs (such as an image) and outputs (such as descriptions of the image). Yet these networks are extremely difficult to train, meaning it’s almost impossible to teach them how to actually produce those outputs. They’re also more computationally intensive than any computer can handle.

    More recently, researchers have been trying to understand how far they can push neural networks in the other direction — by making them narrower (with fewer neurons per layer) and deeper (with more layers overall). So maybe you only need to pick out 100 different lines, but with connections for turning those 100 lines into 50 curves, which you can combine into 10 different shapes, which give you all the building blocks you need to recognize most objects.

    In a paper completed last year, Rolnick and Max Tegmark of the Massachusetts Institute of Technology proved that by increasing depth and decreasing width, you can perform the same functions with exponentially fewer neurons. They showed that if the situation you’re modeling has 100 input variables, you can get the same reliability using either 2100 neurons in one layer or just 210 neurons spread over two layers. They found that there is power in taking small pieces and combining them at greater levels of abstraction instead of attempting to capture all levels of abstraction at once.

    “The notion of depth in a neural network is linked to the idea that you can express something complicated by doing many simple things in sequence,” Rolnick said. “It’s like an assembly line.”

    Rolnick and Tegmark proved the utility of depth by asking neural networks to perform a simple task: multiplying polynomial functions. (These are just equations that feature variables raised to natural-number exponents, for example y = x3 + 1.) They trained the networks by showing them examples of equations and their products. Then they asked the networks to compute the products of equations they hadn’t seen before. Deeper neural networks learned the task with far fewer neurons than shallower ones.

    And while multiplication isn’t a task that’s going to set the world on fire, Rolnick says the paper made an important point: “If a shallow network can’t even do multiplication then we shouldn’t trust it with anything else.”

    Other researchers have been probing the minimum amount of width needed. At the end of September, Jesse Johnson, formerly a mathematician at Oklahoma State University and now a researcher with the pharmaceutical company Sanofi, proved that at a certain point, no amount of depth can compensate for a lack of width.

    To get a sense of his result, imagine sheep in a field, except these are punk-rock sheep: Their wool has been dyed one of several colors. The task for your neural network is to draw a border around all sheep of the same color. In spirit, this task is similar to image classification: The network has a collection of images (which it represents as points in higher-dimensional space), and it needs to group together similar ones.

    Johnson proved that a neural network will fail at this task when the width of the layers is less than or equal to the number of inputs. So for our sheep, each can be described with two inputs: an x and a y coordinate to specify its position in the field. The neural network then labels each sheep with a color and draws a border around sheep of the same color. In this case, you will need three or more neurons per layer to solve the problem.

    More specifically, Johnson showed that if the width-to-variable ratio is off, the neural network won’t be able to draw closed loops — the kind of loops the network would need to draw if, say, all the red sheep were clustered together in the middle of the pasture. “If none of the layers are thicker than the number of input dimensions, there are certain shapes the function will never be able to create, no matter how many layers you add,” Johnson said.

    Papers like Johnson’s are beginning to build the rudiments of a theory of neural networks. At the moment, researchers can make only very basic claims about the relationship between architecture and function — and those claims are in small proportion to the number of tasks neural networks are taking on.

    So while the theory of neural networks isn’t going to change the way systems are built anytime soon, the blueprints are being drafted for a new theory of how computers learn — one that’s poised to take humanity on a ride with even greater repercussions than a trip to the moon.



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    Cisco 700-302 Exam (Advanced Borderless Network Architecture Field Engineer) Detailed Information



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