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C2020-003 - IBM Algo Fundamantals - BrainDump Information

Vendor Name : IBM
Exam Code : C2020-003
Exam Name : IBM Algo Fundamantals
Questions and Answers : 60 Q & A
Updated On : January 16, 2019
PDF Download Mirror : C2020-003 Braindumps
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C2020-003 exam Dumps Source : IBM Algo Fundamantals

Test Code : C2020-003
Test Name : IBM Algo Fundamantals
Vendor Name : IBM
Q&A : 60 Real Questions

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IBM IBM Algo Fundamantals

AI Nanodegree software Syllabus: term 2 (Deep getting to know), extensive | killexams.com Real Questions and Pass4sure dumps

So let’s get started! right here is our first three-month curriculum extensive, together with all the initiatives and labs that you just’ll be constructing.

Core application

  • Deep Neural Networks
  • Convolutional Neural Networks
  • Recurrent Neural Networks
  • Concentrations

  • Voice consumer Interfaces — with Amazon Alexa
  • natural Language Processing — with IBM Watson
  • computing device vision — with Affectiva
  • Core software Introduction to Deep Neural Networks

    Neural Networks are the fundamental constructing block of Deep researching. during this class, you’ll be taught their structure, and the way to build them and train them. We’ll delivery the class by fixing linear classification problems using perceptrons and logistic regression. From there, we’ll extend to solving totally non-linear classification complications the use of deep neural networks. We’ll study their structure, a way to coach them using thoughts akin to gradient descent and backpropagation, and the way to optimize the practising procedure the use of distinct error capabilities and regularization suggestions.

    Lab: constructing Neural Networks in Keras

    You’ll conclude this lesson with a coding lab the place you’ll have the chance to practice all these ideas in Keras, one of the most regular Deep discovering programs in Python.

    Convolutional Neural Networks

    Convolutional Neural Networks are frequent for photograph classification, amongst many different entertaining functions. in this area you’ll learn the way they fluctuate from average neural networks. After exploring some new sorts of layers, we’ll discover a number of different community architectures. You’ll find how to augment your statistics to enrich performance, before researching recommendations to imagine what your networks study. You’ll study to tap into the energy of switch gaining knowledge of, with the aid of benefitting from pre-trained networks reminiscent of VGGNet or ResNet.

    task: build a Dog consciousness App

    You’ll apply what you’ve learned to build an conclusion-to-conclusion algorithm to manner any person-offered image. Given an image of a dog, your algorithm will establish an estimate of the canines’s breed. If offered a picture of a human, the code will establish the equivalent to dog breed. What type of dog will your algorithm suppose you seem like? :)

    Recurrent Neural Networks

    Recurrent Neural Networks are used for some very wonderful applications, similar to time sequence predictions and sequence technology. Their architecture is different from feedforward neural networks, as the information cycles through the network, enabling it to be aware previous states.

    project — half 1: Predicting Apple inventory

    You’ll observe what you’ve learned to construct an algorithm with the intention to function time sequence predictions, and may forecast the stock rate of Apple 7 days in advance.

    task — part 2: generating Sherlock Holmes textual content

    You’ll additionally implement a recurrent neural network to create an English language generator, so one can build semi-coherent English sentences from scratch. You’ll be capable of verify it by way of feeding it with the textual content of Sir Arthur Conan Doyle’s basic ebook, The Adventures of Sherlock Holmes, with the intention to attain textual content that resembles a Sherlock Holmes novel! basic, my expensive IBM Watson! :)

    Concentrations attention 1: Voice person Interfaces with Amazon Alexa

    during this awareness, you’ll find out how computer systems can manner speech, turn it into text, and vice versa. within the first half, you’ll get an overview of Voice user Interfaces (VUI), focus on Conversational AI, and learn how Alexa operates. Then, you’ll dive deeper into the interesting field of Speech recognition, getting to know signal analysis and Phonetics, single be aware classification using Dynamic Time Warping, and sentence recognition the use of Hidden Markov fashions. eventually, you’ll gain knowledge of in regards to the leading edge in computerized Speech cognizance, leveraging deep neural networks.

    Amazon Alexa Lab — building an Alexa ability

    in this lab, you’ll get your palms on the the Alexa potential equipment, where you’ll build and installation your first Alexa ability, for you to stick with it a small conversation with the user and supply a fact from a given 12 months.

    Capstone task — Speech attention with Neural Networks

    in this challenge, you will construct a deep neural community that features as part of an end-to-end automated speech attention (ASR) pipeline! Your accomplished pipeline will settle for uncooked audio as enter and return a expected transcription of the spoken language. the entire pipeline is summarized in the figure under.

    First, you’ll preprocess the raw audio to convert it to certainly one of two feature representations that are conventional for ASR. Then, you are going to be taught in regards to the simple types of neural networks that are often used for acoustic modeling, and be capable of build your own acoustic model! This model will be used as enter into the decoder, on the way to return the anticipated transcription.

    awareness 2: natural Language Processing with IBM Watson

    herbal Language Processing (NLP) is a desirable container through which we teach computer systems to be aware and analyze textual content. in this awareness, you’ll learn to decompose an issue that involves inspecting natural language text into projects, perform fundamental NLP operations, such as building an N-gram language mannequin from a given corpus, and labelling words in a sentence with half-of-Speech (POS) tags and as named entities. you will accomplish end-to-end NLP tasks such as doc classification, machine translation, and so forth., the usage of a combination of customized processing and cloud-based APIs.

    IBM Watson Lab — query-Answering Agent with Watson

    in this lab, you'll construct an easy query-answering agent it really is in a position to learn from any textual content facts you give, and answer queries posed in herbal language. you are going to use IBM Watson’s cloud-based mostly capabilities to technique the enter text facts and find valuable responses.

    Capstone challenge — desktop Translation

    during this task, you are going to construct a deep neural community that functions as part of an conclusion-to-end computing device translation pipeline. Your accomplished pipeline will accept English text as enter and return the French translation.

    First, you’ll preprocess the facts to transform text to a chain of integers. Then, you’ll create fashions which accept a series of integers as enter, and return the probability distribution over feasible translations. After getting to know concerning the primary forms of neural networks that are often used for laptop translations, you might be able to interact for your personal investigations, to design your personal machine translation model that you can run on English textual content!

    concentration three: laptop vision with Affectiva

    impressed by way of human vision, computer vision goals to supply machines the capacity to peer and interpret the area by means of extracting counsel from photographs. during this concentration, you’ll gain knowledge of the fundamentals of computer imaginative and prescient and the function it performs in synthetic intelligence programs. desktop vision is used in many purposes, from detecting dermis cancer to emotion recognition (and even self-using car navigation!). throughout this time period, you’ll increase useful advantage and get arms-on coding experience with a lot of these actual-world purposes. You’ll study to spoil down any problem that comprises visible perception into laptop imaginative and prescient tasks, corresponding to: bettering images, making use of color and geometric transformations to alternate the appearance of a picture, detecting object boundaries, computing gradients and filtering photos, and extracting facets like object edges and exciting visible patterns. We’ll go over these foundational computing device vision recommendations in aspect. Then, you’ll utilize what you’ve realized to create an entire AI system that makes use of computer vision to perform smart object detection and exercise awareness.

    Affectiva Emotion API Lab — Mimic Me!

    in this lab, you’ll be trained to track faces in a video and identify facial expressions (joy, unhappiness, surprise, etc.) the usage of AffedexMe from Affectiva. As a fun visualization, it will be up to you to tag each recognized face with its acceptable emoji! Then, you’ll turn this into a video game the place a player should mimic a random emoji displayed with the aid of the computing device.

    Capstone task — Facial Keypoint Detection

    during this task, you’ll combine your expertise of computer imaginative and prescient strategies and deep discovering to build an end-to-conclusion facial keypoint focus gadget! Facial keypoints encompass elements across the eyes, nose, and mouth on a face and are used in lots of functions, from facial tracking to emotion recognition.

    This challenge is broken into a few leading components. First, you’ll use your skills of laptop vision and OpenCV to discover faces. You’ll also use picture filtering strategies to de-noise pictures and seriously change the appearance of a face in a picture. second, you’ll define and teach a Convolutional Neural network (CNN) to observe facial keypoints. ultimately, you’ll put these two parts together so so you might reliably establish facial keypoints on any graphic!

    You’ll even be given not obligatory workout routines that e-book you in extending this mission to video (or a laptop digicam feed); this offers you the capacity to put in force enjoyable face filters and keypoint detection in actual time!

    college students during this software will grasp an in-depth curriculum that covers each the foundations and the reducing-edge developments of Deep gaining knowledge of. This sort of palms-on publicity to one of the most transformational applied sciences of our time makes this software a unique getting to know probability for anyone excited through the chances of AI. We invite you to be a part of us as we put together the next generation of AI experts who will vigour the wonderful improvements forward!

    think free to attain out to us if you have any questions, and see you in the program!

    practice now to be a part of the AI Nanodegree program!

    Quantum computing, facet analytics, and meta learning: key developments in facts science and large information in 2019 | killexams.com Real Questions and Pass4sure dumps

    When historians look at modern notions of data in the early twenty first century, 2018 might smartly be a landmark yr. in many approaches this turned into the 12 months when massive and important concerns – from the very own to the political – all started to floor. The techlash, a term which has described the 12 months, arguably emerged from conversations and debates concerning the makes use of and abuses of records.

    however whereas cynicism casts a shadow on the brightly lit facts science landcape, there’s nonetheless lots of optimism out there. And more importantly, records isn’t going to drop off the agenda any time quickly.

    youngsters, the altering dialog in 2018 does mean that the manner statistics scientists, analysts, and engineers use data and construct solutions for it'll change. A renewed emphasis on ethics and safety is now appearing, so as to possible shape 2019 tendencies.

    however what will these traits be? Let’s take a glance at one of the vital most essential areas to maintain a watch on within the new 12 months.

    Meta learning and automatic machine gaining knowledge of

    one of the most key subject matters of information science and artificial intelligence in 2019 may be doing greater with less.

    There are a couple of methods in which this can manifest itself. the primary is meta learning. here is a concept that aims to improve the way that laptop researching programs basically work by way of working machine studying on laptop gaining knowledge of techniques. essentially this allows for a computing device getting to know algorithm to learn the way to study. with the aid of doing this, that you may greater come to a decision which algorithm is most appropriate for a given problem.

    Hands On Meta Learning with Python

    learn how to place meta discovering into practice. study with fingers On Meta gaining knowledge of with Python.

    automated desktop learning is closely aligned with meta studying. one way of knowing it is to see it as putting the theory of automating the software of meta getting to know. So, if meta studying can help improved check which desktop discovering algorithms may still be utilized and the way they should still be designed, automatic computer researching makes that technique a little smoother. It builds the decision making into the desktop discovering solution. basically, it’s all about “algorithm option, hyper-parameter tuning, iterative modelling, and mannequin assessment,” as Matthew Mayo explains on KDNuggets.

    automatic laptop researching equipment

    What’s chiefly unique about automatic computer learning is that there are already a number of equipment that make it fairly convenient to do. AutoML is a group of tools developed by using Google that can be used on the Google Cloud Platform, while auto-sklearn, developed across the scikit-gain knowledge of library, provides an identical out of the box answer for automated machine studying.

    besides the fact that children each AutoML and auto-sklearn are very new, there are newer equipment attainable that could dominate the panorama: AutoKeras and AdaNet. AutoKeras is developed on Keras (the Python neural community library), whereas AdaNet is constructed on TensorFlow. each could be extra most economical open source alternate options to AutoML.

    Whichever automatic computer learning library good points the most recognition will stay to be considered, but one issue is certain: it makes deep learning obtainable to many companies who prior to now wouldn’t have had the substances or inclination to rent a group of PhD computing device scientists.

    nevertheless it’s critical to remember that computerized computer getting to know certainly doesn’t mean automated statistics science. whereas tools like AutoML will support many companies construct deep learning models for simple initiatives, for agencies that want a more developed data approach, the function of the data scientist will stay a must have. which you could’t in any case, automate away approach and decision making.

    gain knowledge of automatic desktop researching with these titles:

    Hands on automated machine learning

    palms-On automated computing device LearningTensorFlow 1.x Deep learning Cookbook

    Quantum computing

    Quantum computing, whilst an idea, feels well-nigh fantastical. It’s no longer simply slicing-part, it’s mind-bending. but in true-world terms it additionally continues the theme of doing more with much less.

    Explaining quantum computing may also be complicated, however the fundamentals are this: in its place of a binary gadget (the foundation of computing as we at present realize it), which will also be either 0 or 1, in a quantum gadget you have got qubits, which can also be 0, 1 or each simultaneously. (in case you wish to study greater, examine this article).

    What Quantum computing potential for developers

    So, what does this mean in follow? just about, because the qubits in a quantum gadget can also be diverse issues at the identical time, you are then capable of run tons extra complicated computations. believe in regards to the difference in scale: working a deep discovering system on a binary device has clear limits. yes, which you could scale up in processing vigor, however you’re even so restrained by means of the foundational truth of zeros and ones. In a quantum equipment where that limit now not exists, the dimensions of the computing vigour at your disposal increases astronomically.

    when you remember the primary proposition, it becomes a whole lot simpler to see why the likes of IBM and Google are clamouring to strengthen and installation quantum know-how. one of the most mentioned use instances is the use of Quantum computers to find even better best numbers (a flow which consists of dangers given prime numbers are the groundwork for plenty up to date encryption). however there different functions, equivalent to in chemistry, where advanced subatomic interactions are too certain to be modelled by way of a standard desktop.

    It’s essential to observe that Quantum computing continues to be very much in its infancy. while Google and IBM are main the style, they are in reality most effective getting to know the enviornment. It actually hasn’t been deployed or utilized in any colossal or sustained manner.

    however this isn’t to assert that it's going to be not noted. It’s going to have a big impact on the longer term, and more importantly it’s plain unique. although you don’t think you’ll be attending to grips with quantum programs at work for some time (a decade at most suitable), knowing the principles and the way it really works in practice will not most effective offer you an excellent basis for fundamental adjustments in the future, it will additionally aid you enhanced be aware probably the most current challenges in scientific computing. And, of route, it will also make you an honest conversationalist at dinner parties.

    Who’s riding Quantum computing ahead?

    if you wish to get begun, Microsoft has put collectively the Quantum development kit, which comprises the first quantum-certain programming language Q#. IBM, meanwhile, has developed its own Quantum event, which enables engineers and researchers to run quantum computations within the IBM cloud.

    As you investigate these tools you’ll probably get the experience that no person’s reasonably sure what to do with these technologies. And that’s exceptional – if anything it makes it the superb time to get worried and help extra research and pondering on the theme.

    Quantum computing with IBM QX

    Get a head birth within the Quantum Computing revolution. Pre-order mastering Quantum Computing with IBM QX.

    area analytics and digital twins

    whereas Quantum lingers on the horizon, the conception of the edge has quietly planted itself at the very middle of the IoT revolution. IoT might still be the time period that business leaders and, indeed, wider society are speakme about, for technologists and engineers, none of its benefits would be viable without the edge.

    side computing or edge analytics is very nearly about processing records on the edge of a network as opposed to inside a centralized data warehouse. once again, as that you can begin to look, the theory of the edge lets you do extra with much less. greater pace, less bandwidth (as contraptions now not need to communicate with facts facilities), and, in thought, more records.in the context of IoT, where just about each object in existence is usually a supply of information, moving processing and analytics to the area can only be a very good element.

    Will the side substitute the cloud?

    There’s loads of dialog about no matter if side will substitute cloud. It won’t. however it doubtless will replace the cloud because the region where we run synthetic intelligence. as an instance, as an alternative of operating powerful analytics models in a centralized space, which you can run them at diverse aspects throughout the community. this can dramatically improve pace and performance, primarily for these applications that run on artificial intelligence.

    A greater dispensed world

    believe of it this way: just as utility has develop into greater distributed within the ultimate few years, because of the emergence of the area, records itself is going to be greater allotted. We’ll have billions of pockets of pastime, whether from buyers or industrial machines, a locus of information-era.

    learn how to position the ideas of aspect analytics into practice:

    azure iot development cookbook

    Azure IoT development Cookbook

    Digital twins

    An emerging a part of the facet computing and analytics trend is the idea of digital twins. this is, admittedly, nevertheless some thing in its infancy, however in 2019 it’s seemingly that you simply’ll be hearing much more about digital twins.

    A digital twin is a digital replica of a tool that engineers and application architects can monitor, model and verify. for instance, if you have a digital twin of a computer, you could run exams on it to improved take into account its aspects of failure. You may also investigate ways you might make the computing device more efficient. extra importantly, a digital twin will also be used to aid engineers control the connection between centralized cloud and programs on the facet – the digital twin is very nearly a layer of abstraction that lets you more suitable take into account what’s occurring at the aspect without needing to go into the aspect of the device.

    For these of us working in statistics science, digital twins deliver more desirable clarity and visibility on how disconnected aspects of a community interact. If we’re going to make 2019 the 12 months we use statistics more intelligently – probably much more humanely – then this is exactly the type of aspect we need.

    Interpretability, explainability, and ethics

    Doing greater with much less could be one of the ongoing subject matters in data science and massive facts in 2019, however we are able to’t ignore the proven fact that ethics and security will continue to be firmly on the agenda. even though it’s convenient to dismiss these concerns concerns as break away the technical elements of statistics mining, processing, and analytics, nevertheless it is, truly, deeply integrated into it.

    one of the most key aspects of ethics are two linked ideas: explainability and interpretability.

    the two phrases are often used interchangeably, but there are some subtle variations. Explainability is the extent to which the internal-working of an algorithm will also be explained in human terms, whereas interpretability is the extent to which possible take note the way wherein it's working (eg. predict the outcomes in a given situation). So, an algorithm may also be interpretable, but you may not reasonably be in a position to explain why some thing is occurring. (feel about this within the context of scientific research: once in a while, scientists understand that a factor is really happening, but they can’t deliver a transparent reason for why it is.)

    enhancing transparency and accountability

    both means, interpretability and explainability are critical as a result of they can assist to increase transparency in computer discovering and deep gaining knowledge of algorithms. In an international where deep researching algorithms are being applied to complications in areas from drugs to justice – the place the difficulty of accountability is peculiarly fraught – this transparency isn’t an option, it’s essential.

    In apply, this potential engineers should tweak the algorithm development procedure to make it less difficult for those backyard the procedure to have in mind why definite issues are occurring and why they aren’t. To a certain extent, this sooner or later requires the information science world to take the scientific method greater critically than it has completed. as opposed to just aiming for accuracy (which is itself often open to contestation), the goal is to normally manage that gap between what we’re trying to achieve with an algorithm and how it goes about definitely doing that.

    Getting Started with Machine Learning in Python

    which you could be trained the fundamentals of building explainable desktop getting to know models in the Getting began with laptop researching in Python video. 

    Transparency and innovation ought to go hand in hand in 2019

    So, there are two fundamental things for information science in 2019: improving efficiency, and enhancing transparency. however the two ideas could seem like the conflict with each and every other, it’s truly a little of a false dichotomy. If we realised that 365 days ago, we might have prevented lots of the concerns which have come to easy this yr.

    Transparency has to be a core consideration for any person constructing systems for inspecting and processing information. without it, the work your doing might be flawed or pointless. You’re simplest going to deserve to add additional iterations to rectify your errors or alter the affect of your biases.

    With this in intellect, now could be the time to gain knowledge of the classes of 2018’s techlash. We should decide to stopping the miserable conveyor belt of scandal and failure. now could be the time to locate new how you can construct superior synthetic intelligence methods.


    Android business gadget administration cheat sheet | killexams.com Real Questions and Pass4sure dumps

    Google has been continuously including commercial enterprise management capabilities to the Android operating gadget, however it's no longer...

    all the time clear how the tools and technologies fluctuate from one an extra or the way it can with no trouble administer Android contraptions and apps with them.

    This cheat sheet goals to make clear one of the variations between Android points particular to the commercial enterprise to support directors more advantageous be mindful the alternate options for Android commercial enterprise machine management.

    Android device management APIs and features

    machine Administration API: Google delivered this API in Android 2.2 to deliver builders with gadget administration features on the equipment level.

    youngsters many groups continue to use this API, it isn't any longer mighty enough to meet ultra-modern safety and administration necessities. as an example, the API can not securely reset gadget passwords on encrypted contraptions or set up administrator-defined passcodes to lock a person out of a tool.

    in its place, developers should transition to more moderen Android applied sciences when interfacing with the Android working equipment (OS). Google plans to birth depreciating equipment Administration guidelines within the subsequent Android free up.

    modern administration APIs: Android provides a couple of APIs for working with the Android and Google Play ecosystems. one of the crucial important is the Android administration API, which carriers can incorporate into their enterprise mobility administration (EMM) structures to deliver clients with tools to provision, at ease and support with Android commercial enterprise machine administration.

    For potent Android enterprise machine management, IT groups should still look for EMM items that take full knowledge of those APIs, akin to Google cell administration, VMware AirWatch, MobileIron, IBM MaaS360 or different products.

    Google cell administration: directors can use Google's EMM platform to comfy and display screen Android contraptions, and to support with Android business gadget management, in a similar way to 3rd-birthday party EMM items that use the Android management APIs.

    Google cell management is covered with all G Suite editions and supports Apple iOS and home windows cellphone contraptions moreover Android.

    Android commercial enterprise is an umbrella term that covers the big selection of security and administration elements obtainable in the latest Android OS releases.

    Android commercial enterprise: Google renamed Android for Work Android enterprise and accelerated the product to include greater mighty commercial enterprise elements. Android commercial enterprise is an umbrella time period that covers the wide selection of protection and management facets purchasable within the latest Android OS releases.

    Android commercial enterprise recommended: Google created the recommended software to ensure that Android instruments and their linked features adhere to dependent necessities and most efficient practices for Android commercial enterprise equipment administration. The application defines an elevated set of requisites for validating EMM items, mobile features and Android contraptions. Google gives a listing of validated products and functions on its Android commercial enterprise site.

    enterprise provisioning: Google supports a couple of alternatives to provision Android devices, akin to the use of QR codes or near-box communique provisioning apps.

    With the release of Android 8.0 Oreo, administrators can also configure company-owned gadgets for zero-contact enrollment, which allows a tool to immediately enroll itself in EMM when the gadget is first grew to become on.

    equipment deployment scenarios

    Work-managed: Work-managed deployments apply to company-owned gadgets that employees use solely for conducting business. directors handle the entire gadget – including the information and applications -- and may preclude the machine's utilization to accredited work-connected operations. conclusion clients should still no longer use work-managed gadgets for personal company.

    Work profile: directors can use work profiles to aid BYOD situations. a piece profile is a self-contained, wholly encrypted workspace installed on the user's machine. The work profile limits administrative control to the workspace in preference to to the whole gadget. It additionally incorporates corporate apps, records and policy settings within the profile separate from very own counsel and operations.

    corporate-owned, single use (COSU): The COSU method aims corporate-owned instruments used for single use instances, corresponding to kiosks, equipment delivery capabilities or stock management programs. under this model, administrators can lock down an Android device to a confined variety of apps and services, while fighting clients from enabling specific aspects or taking different moves.

    Managed instruments with work profiles: In Android 8.0, Google added the potential to use work profiles together with work-managed contraptions to separate company records and apps from those for private use. below this model, administrators manage the complete machine, which allows them to give protection to company elements whereas proposing clients with a less restrictive workspace for personal use.

    Android utility management

    Managed Google Play: The managed edition of Google Play combines basic app store performance with management capabilities to give IT with a company app store option.

    directors can installation and approve apps, buy app licenses, manipulate permissions and carry out different administration projects. conclusion clients can browse apps, view app details, deploy apps on their devices and take other actions, similar to how they may use the general public Google Play store.

    Google Play EMM API: When the Google Play API is incorporated into an EMM product, directors can specify which apps users can down load, can manage app installations, can manipulate bulk licensing and might function a variety of different projects. The API works along with Managed Google Play to support the entire app management lifecycle.

    EMM utility administration: The Android administration API contains a few aspects specific to app administration. An EMM platform that incorporates the API permits administrators to provision work profiles, observe app-degree administration guidelines, at ease apps and statistics, instantly install apps, evade apps from being uninstalled, distribute public and private apps, and perform different administrative projects.

    EMM equipment policy controller: a tool coverage controller is an software installed on an Android machine that allows directors to control entry to corporate apps and records. The controller works along with EMM to provision work profiles on own contraptions and implement a company's security guidelines.




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    Quantum computing, edge analytics, and meta learning: key trends in data science and big data in 2019 | killexams.com real questions and Pass4sure dumps

    When historians study contemporary notions of data in the early 21st century, 2018 might well be a landmark year. In many ways this was the year when Big and Important Issues – from the personal to the political – began to surface. The techlash, a term which has defined the year, arguably emerged from conversations and debates about the uses and abuses of data.

    But while cynicism casts a shadow on the brightly lit data science landcape, there’s still a lot of optimism out there. And more importantly, data isn’t going to drop off the agenda any time soon.

    However, the changing conversation in 2018 does mean that the way data scientists, analysts, and engineers use data and build solutions for it will change. A renewed emphasis on ethics and security is now appearing, which will likely shape 2019 trends.

    But what will these trends be? Let’s take a look at some of the most important areas to keep an eye on in the new year.

    Meta learning and automated machine learning

    One of the key themes of data science and artificial intelligence in 2019 will be doing more with less.

    There are a number of ways in which this will manifest itself. The first is meta learning. This is a concept that aims to improve the way that machine learning systems actually work by running machine learning on machine learning systems. Essentially this allows a machine learning algorithm to learn how to learn. By doing this, you can better decide which algorithm is most appropriate for a given problem.

    Hands On Meta Learning with Python

    Find out how to put meta learning into practice. Learn with Hands On Meta Learning with Python.

    Automated machine learning is closely aligned with meta learning. One way of understanding it is to see it as putting the concept of automating the application of meta learning. So, if meta learning can help better determine which machine learning algorithms should be applied and how they should be designed, automated machine learning makes that process a little smoother. It builds the decision making into the machine learning solution. Fundamentally, it’s all about “algorithm selection, hyper-parameter tuning, iterative modelling, and model assessment,” as Matthew Mayo explains on KDNuggets.

    Automated machine learning tools

    What’s particularly exciting about automated machine learning is that there are already a number of tools that make it relatively easy to do. AutoML is a set of tools developed by Google that can be used on the Google Cloud Platform, while auto-sklearn, built around the scikit-learn library, provides a similar out of the box solution for automated machine learning.

    Although both AutoML and auto-sklearn are very new, there are newer tools available that could dominate the landscape: AutoKeras and AdaNet. AutoKeras is built on Keras (the Python neural network library), while AdaNet is built on TensorFlow. Both could be more affordable open source alternatives to AutoML.

    Whichever automated machine learning library gains the most popularity will remain to be seen, but one thing is certain: it makes deep learning accessible to many organizations who previously wouldn’t have had the resources or inclination to hire a team of PhD computer scientists.

    But it’s important to remember that automated machine learning certainly doesn’t mean automated data science. While tools like AutoML will help many organizations build deep learning models for basic tasks, for organizations that need a more developed data strategy, the role of the data scientist will remain vital. You can’t after all, automate away strategy and decision making.

    Learn automated machine learning with these titles:

    Hands on automated machine learning

    Hands-On Automated Machine LearningTensorFlow 1.x Deep Learning Cookbook

    Quantum computing

    Quantum computing, even as a concept, feels almost fantastical. It’s not just cutting-edge, it’s mind-bending. But in real-world terms it also continues the theme of doing more with less.

    Explaining quantum computing can be tricky, but the fundamentals are this: instead of a binary system (the foundation of computing as we currently know it), which can be either 0 or 1, in a quantum system you have qubits, which can be 0, 1 or both simultaneously. (If you want to learn more, read this article).

    What Quantum computing means for developers

    So, what does this mean in practice? Essentially, because the qubits in a quantum system can be multiple things at the same time, you are then able to run much more complex computations. Think about the difference in scale: running a deep learning system on a binary system has clear limits. Yes, you can scale up in processing power, but you’re nevertheless constrained by the foundational fact of zeros and ones. In a quantum system where that restriction no longer exists, the scale of the computing power at your disposal increases astronomically.

    Once you understand the fundamental proposition, it becomes much easier to see why the likes of IBM and Google are clamouring to develop and deploy quantum technology. One of the most talked about use cases is using Quantum computers to find even larger prime numbers (a move which contains risks given prime numbers are the basis for much modern encryption). But there other applications, such as in chemistry, where complex subatomic interactions are too detailed to be modelled by a traditional computer.

    It’s important to note that Quantum computing is still very much in its infancy. While Google and IBM are leading the way, they are really only researching the area. It certainly hasn’t been deployed or applied in any significant or sustained way.

    But this isn’t to say that it should be ignored. It’s going to have a huge impact on the future, and more importantly it’s plain interesting. Even if you don’t think you’ll be getting to grips with quantum systems at work for some time (a decade at best), understanding the principles and how it works in practice will not only give you a solid foundation for major changes in the future, it will also help you better understand some of the existing challenges in scientific computing. And, of course, it will also make you a decent conversationalist at dinner parties.

    Who’s driving Quantum computing forward?

    If you want to get started, Microsoft has put together the Quantum Development Kit, which includes the first quantum-specific programming language Q#. IBM, meanwhile, has developed its own Quantum experience, which allows engineers and researchers to run quantum computations in the IBM cloud.

    As you investigate these tools you’ll probably get the sense that no one’s quite sure what to do with these technologies. And that’s fine – if anything it makes it the perfect time to get involved and help further research and thinking on the topic.

    Quantum computing with IBM QX

    Get a head start in the Quantum Computing revolution. Pre-order Mastering Quantum Computing with IBM QX.

    Edge analytics and digital twins

    While Quantum lingers on the horizon, the concept of the edge has quietly planted itself at the very center of the IoT revolution. IoT might still be the term that business leaders and, indeed, wider society are talking about, for technologists and engineers, none of its advantages would be possible without the edge.

    Edge computing or edge analytics is essentially about processing data at the edge of a network rather than within a centralized data warehouse. Again, as you can begin to see, the concept of the edge allows you to do more with less. More speed, less bandwidth (as devices no longer need to communicate with data centers), and, in theory, more data.In the context of IoT, where just about every object in existence could be a source of data, moving processing and analytics to the edge can only be a good thing.

    Will the edge replace the cloud?

    There’s a lot of conversation about whether edge will replace cloud. It won’t. But it probably will replace the cloud as the place where we run artificial intelligence. For example, instead of running powerful analytics models in a centralized space, you can run them at different points across the network. This will dramatically improve speed and performance, particularly for those applications that run on artificial intelligence.

    A more distributed world

    Think of it this way: just as software has become more distributed in the last few years, thanks to the emergence of the edge, data itself is going to be more distributed. We’ll have billions of pockets of activity, whether from consumers or industrial machines, a locus of data-generation.

    Find out how to put the principles of edge analytics into practice:

    azure iot development cookbook

    Azure IoT Development Cookbook

    Digital twins

    An emerging part of the edge computing and analytics trend is the concept of digital twins. This is, admittedly, still something in its infancy, but in 2019 it’s likely that you’ll be hearing a lot more about digital twins.

    A digital twin is a digital replica of a device that engineers and software architects can monitor, model and test. For example, if you have a digital twin of a machine, you could run tests on it to better understand its points of failure. You could also investigate ways you could make the machine more efficient. More importantly, a digital twin can be used to help engineers manage the relationship between centralized cloud and systems at the edge – the digital twin is essentially a layer of abstraction that allows you to better understand what’s happening at the edge without needing to go into the detail of the system.

    For those of us working in data science, digital twins provide better clarity and visibility on how disconnected aspects of a network interact. If we’re going to make 2019 the year we use data more intelligently – maybe even more humanely – then this is precisely the sort of thing we need.

    Interpretability, explainability, and ethics

    Doing more with less might be one of the ongoing themes in data science and big data in 2019, but we can’t ignore the fact that ethics and security will remain firmly on the agenda. Although it’s easy to dismiss these issues issues as separate from the technical aspects of data mining, processing, and analytics, but it is, in fact, deeply integrated into it.

    One of the key facets of ethics are two related concepts: explainability and interpretability.

    The two terms are often used interchangeably, but there are some subtle differences. Explainability is the extent to which the inner-working of an algorithm can be explained in human terms, while interpretability is the extent to which one can understand the way in which it is working (eg. predict the outcome in a given situation). So, an algorithm can be interpretable, but you might not quite be able to explain why something is happening. (Think about this in the context of scientific research: sometimes, scientists know that a thing is definitely happening, but they can’t provide a clear explanation for why it is.)

    Improving transparency and accountability

    Either way, interpretability and explainability are important because they can help to improve transparency in machine learning and deep learning algorithms. In a world where deep learning algorithms are being applied to problems in areas from medicine to justice – where the problem of accountability is particularly fraught – this transparency isn’t an option, it’s essential.

    In practice, this means engineers must tweak the algorithm development process to make it easier for those outside the process to understand why certain things are happening and why they aren’t. To a certain extent, this ultimately requires the data science world to take the scientific method more seriously than it has done. Rather than just aiming for accuracy (which is itself often open to contestation), the aim is to constantly manage that gap between what we’re trying to achieve with an algorithm and how it goes about actually doing that.

    Getting Started with Machine Learning in Python

    You can learn the basics of building explainable machine learning models in the Getting Started with Machine Learning in Python video. 

    Transparency and innovation must go hand in hand in 2019

    So, there are two fundamental things for data science in 2019: improving efficiency, and improving transparency. Although the two concepts might look like the conflict with each other, it’s actually a bit of a false dichotomy. If we realised that 12 months ago, we might have avoided many of the issues that have come to light this year.

    Transparency has to be a core consideration for anyone developing systems for analyzing and processing data. Without it, the work your doing might be flawed or unnecessary. You’re only going to need to add further iterations to rectify your mistakes or modify the impact of your biases.

    With this in mind, now is the time to learn the lessons of 2018’s techlash. We need to commit to stopping the miserable conveyor belt of scandal and failure. Now is the time to find new ways to build better artificial intelligence systems.


    IBM releases Watson Machine Learning for a general audience | killexams.com real questions and Pass4sure dumps

    Machine learning is everywhere these days. Whether it’s recommending your next movie on Netflix or beating Ken Jennings at Jeopardy, ML is here to stay. But how do you get in on this wave?

    Interested in training your own little neural network? IBM has just made their new Watson Machine Learning (WML) service generally available this week. I do have to point out that you will need to create an account with Bluemix to start playing around with the service, but there’s a 30 day free trial and it’s pretty fun.

    So, what is Watson Machine Learning?

    Basically, this is a general machine learning system that is run on IBM’s Bluemix. It’s capable of two different functions of machine learning: training and scoring. Training means that you can use WML to refine an algorithm so that it can ‘learn’ from a datatset. The results of these processes are called models. And scoring is the operation of predicting an outcome using a trained model.

    WML is designed to help two different, but complimentary groups: data scientists and developers. The notebook tools is specifically designed for researchers that are looking to learn more about machine learning algorithms. Developers, of course, want to build smart apps that can use predictions made by the machine learning algorithms.

    SEE MORE: Machine learning A-team: TensorFlow, Apache Spark MLlib, MOA and more

    IBM is pretty up front about the fact that there are a lot of open source projects out there that can enable the training of machine learning models. (In fact, we’ve talked about some here and here!) “Data scientists already have a significant set of quality tools they can use in order to perform training operations “, said Armand Ruiz, Lead Product Manager IBM Data Science Experience & Watson ML. “The real challenge facing data scientists is how to operationalize those models.”

    According to Ruiz, WML is intended to address questions of deployment, operationalization, and even deriving business value from machine learning models

    What’s new?

    The new WML has a few cool trciks up its circuity sleeve. Here are a few of the cool things:

  • Models as First-Class Entities
  • A new user interface called “Model Builder” meant to simplify the creation of Machine Learning models with a more intuitive visual builder experience.
  • Notebooks, much like the ones used to train algorithms in Scala, R and Python. “Now it is possible to cover the end-to-end flow without leaving the notebook. Train the model, save the model to a project and deploy that model simply by calling our intuitive APIs!”
  • Associate Watson Machine Learning service with Projects: making collaboration between data scientists a snap.
  • Collaboration between the App Developer and Data Scientist: App developers will now have access in the Bluemix Dashboard to all of the models created in DSX, as well as the ability to create to easily integrate the ML APIs in to their applications.
  • So, if you’re interested in trying it out, head on over and give IBM’s Watson Machine Learning a whirl.

    Okay, ladies, now let’s get in formation


    Leadership In Cognitive Cybersecurity Makes IBM A Worthy Investment | killexams.com real questions and Pass4sure dumps

    No result found, try new keyword!Watson, IBM's most famous creation for Artificial Intelligence and machine learning, is now at the forefront of cognitive cybersecurity. The company's unique of approach to creating a self-learning en...


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