Learning In Real Time

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Machine Learning and Deep Learning in Real-Time Applications

Author : Mahrishi, Mehul
Publisher : IGI Global
ISBN 13 : 1799830977
Total Pages : 344 pages
Book Rating : 4.7/5 (998 download)

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Book Synopsis Machine Learning and Deep Learning in Real-Time Applications by : Mahrishi, Mehul

Download or read book Machine Learning and Deep Learning in Real-Time Applications written by Mahrishi, Mehul and published by IGI Global. This book was released on 2020-04-24 with total page 344 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial intelligence and its various components are rapidly engulfing almost every professional industry. Specific features of AI that have proven to be vital solutions to numerous real-world issues are machine learning and deep learning. These intelligent agents unlock higher levels of performance and efficiency, creating a wide span of industrial applications. However, there is a lack of research on the specific uses of machine/deep learning in the professional realm. Machine Learning and Deep Learning in Real-Time Applications provides emerging research exploring the theoretical and practical aspects of machine learning and deep learning and their implementations as well as their ability to solve real-world problems within several professional disciplines including healthcare, business, and computer science. Featuring coverage on a broad range of topics such as image processing, medical improvements, and smart grids, this book is ideally designed for researchers, academicians, scientists, industry experts, scholars, IT professionals, engineers, and students seeking current research on the multifaceted uses and implementations of machine learning and deep learning across the globe.

Educational Technology and Resources for Synchronous Learning in Higher Education

Author : Yoon, Jiyoon
Publisher : IGI Global
ISBN 13 : 1522575685
Total Pages : 356 pages
Book Rating : 4.5/5 (225 download)

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Book Synopsis Educational Technology and Resources for Synchronous Learning in Higher Education by : Yoon, Jiyoon

Download or read book Educational Technology and Resources for Synchronous Learning in Higher Education written by Yoon, Jiyoon and published by IGI Global. This book was released on 2019-04-19 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: As more classes move to online instruction, there is a need for research that shows the effectiveness of synchronous learning. Educators must guide students on how to use these new learning tools and become aware of the research trends and opportunities within these developing online and hybrid courses. Educational Technology and Resources for Synchronous Learning in Higher Education provides evidence-based practice on incorporating synchronous teaching tools and practice within online courses to enhance content mastery and community development. Additionally, the book presents a strong theoretical overview of the topic and allows readers to develop a more nuanced understanding of the benefits and constraints of synchronous learning. Covering topics such as game learning, online communication, and professional development, it is designed for online instructors, instructional designers, administrators, students, and researchers and educators in higher education, as well as corporate, military, and government sectors.

Learning in Real Time

Author : Jonathan E. Finkelstein
Publisher : John Wiley & Sons
ISBN 13 : 0470596627
Total Pages : 5 pages
Book Rating : 4.4/5 (75 download)

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Book Synopsis Learning in Real Time by : Jonathan E. Finkelstein

Download or read book Learning in Real Time written by Jonathan E. Finkelstein and published by John Wiley & Sons. This book was released on 2009-10-06 with total page 5 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learning in Real Time is a concise and practical resource for education professionals teaching live and online or those wanting to humanize and improve interaction in their online courses by adding a synchronous learning component. The book offers keen insight into the world of synchronous learning tools, guides instructors in evaluating how and when to use them, and illustrates how educators can develop their own strategies and styles in implementing such tools to improve online learning.

Real-time Iterative Learning Control

Author : Jian-Xin Xu
Publisher : Springer Science & Business Media
ISBN 13 : 1848821751
Total Pages : 204 pages
Book Rating : 4.8/5 (488 download)

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Book Synopsis Real-time Iterative Learning Control by : Jian-Xin Xu

Download or read book Real-time Iterative Learning Control written by Jian-Xin Xu and published by Springer Science & Business Media. This book was released on 2008-12-12 with total page 204 pages. Available in PDF, EPUB and Kindle. Book excerpt: Real-time Iterative Learning Control demonstrates how the latest advances in iterative learning control (ILC) can be applied to a number of plants widely encountered in practice. The book gives a systematic introduction to real-time ILC design and source of illustrative case studies for ILC problem solving; the fundamental concepts, schematics, configurations and generic guidelines for ILC design and implementation are enhanced by a well-selected group of representative, simple and easy-to-learn example applications. Key issues in ILC design and implementation in linear and nonlinear plants pervading mechatronics and batch processes are addressed, in particular: ILC design in the continuous- and discrete-time domains; design in the frequency and time domains; design with problem-specific performance objectives including robustness and optimality; design in a modular approach by integration with other control techniques; and design by means of classical tools based on Bode plots and state space.

Real-World Machine Learning

Author : Henrik Brink
Publisher : Simon and Schuster
ISBN 13 : 1638357005
Total Pages : 380 pages
Book Rating : 4.6/5 (383 download)

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Book Synopsis Real-World Machine Learning by : Henrik Brink

Download or read book Real-World Machine Learning written by Henrik Brink and published by Simon and Schuster. This book was released on 2016-09-15 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt: Summary Real-World Machine Learning is a practical guide designed to teach working developers the art of ML project execution. Without overdosing you on academic theory and complex mathematics, it introduces the day-to-day practice of machine learning, preparing you to successfully build and deploy powerful ML systems. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Machine learning systems help you find valuable insights and patterns in data, which you'd never recognize with traditional methods. In the real world, ML techniques give you a way to identify trends, forecast behavior, and make fact-based recommendations. It's a hot and growing field, and up-to-speed ML developers are in demand. About the Book Real-World Machine Learning will teach you the concepts and techniques you need to be a successful machine learning practitioner without overdosing you on abstract theory and complex mathematics. By working through immediately relevant examples in Python, you'll build skills in data acquisition and modeling, classification, and regression. You'll also explore the most important tasks like model validation, optimization, scalability, and real-time streaming. When you're done, you'll be ready to successfully build, deploy, and maintain your own powerful ML systems. What's Inside Predicting future behavior Performance evaluation and optimization Analyzing sentiment and making recommendations About the Reader No prior machine learning experience assumed. Readers should know Python. About the Authors Henrik Brink, Joseph Richards and Mark Fetherolf are experienced data scientists engaged in the daily practice of machine learning. Table of Contents PART 1: THE MACHINE-LEARNING WORKFLOW What is machine learning? Real-world data Modeling and prediction Model evaluation and optimization Basic feature engineering PART 2: PRACTICAL APPLICATION Example: NYC taxi data Advanced feature engineering Advanced NLP example: movie review sentiment Scaling machine-learning workflows Example: digital display advertising

Machine Learning for Data Streams

Author : Albert Bifet
Publisher : MIT Press
ISBN 13 : 0262346052
Total Pages : 255 pages
Book Rating : 4.2/5 (623 download)

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Book Synopsis Machine Learning for Data Streams by : Albert Bifet

Download or read book Machine Learning for Data Streams written by Albert Bifet and published by MIT Press. This book was released on 2018-03-16 with total page 255 pages. Available in PDF, EPUB and Kindle. Book excerpt: A hands-on approach to tasks and techniques in data stream mining and real-time analytics, with examples in MOA, a popular freely available open-source software framework. Today many information sources—including sensor networks, financial markets, social networks, and healthcare monitoring—are so-called data streams, arriving sequentially and at high speed. Analysis must take place in real time, with partial data and without the capacity to store the entire data set. This book presents algorithms and techniques used in data stream mining and real-time analytics. Taking a hands-on approach, the book demonstrates the techniques using MOA (Massive Online Analysis), a popular, freely available open-source software framework, allowing readers to try out the techniques after reading the explanations. The book first offers a brief introduction to the topic, covering big data mining, basic methodologies for mining data streams, and a simple example of MOA. More detailed discussions follow, with chapters on sketching techniques, change, classification, ensemble methods, regression, clustering, and frequent pattern mining. Most of these chapters include exercises, an MOA-based lab session, or both. Finally, the book discusses the MOA software, covering the MOA graphical user interface, the command line, use of its API, and the development of new methods within MOA. The book will be an essential reference for readers who want to use data stream mining as a tool, researchers in innovation or data stream mining, and programmers who want to create new algorithms for MOA.

Fuzzy Neural Networks for Real Time Control Applications

Author : Erdal Kayacan
Publisher : Butterworth-Heinemann
ISBN 13 : 0128027037
Total Pages : 266 pages
Book Rating : 4.1/5 (28 download)

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Book Synopsis Fuzzy Neural Networks for Real Time Control Applications by : Erdal Kayacan

Download or read book Fuzzy Neural Networks for Real Time Control Applications written by Erdal Kayacan and published by Butterworth-Heinemann. This book was released on 2015-10-07 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: AN INDISPENSABLE RESOURCE FOR ALL THOSE WHO DESIGN AND IMPLEMENT TYPE-1 AND TYPE-2 FUZZY NEURAL NETWORKS IN REAL TIME SYSTEMS Delve into the type-2 fuzzy logic systems and become engrossed in the parameter update algorithms for type-1 and type-2 fuzzy neural networks and their stability analysis with this book! Not only does this book stand apart from others in its focus but also in its application-based presentation style. Prepared in a way that can be easily understood by those who are experienced and inexperienced in this field. Readers can benefit from the computer source codes for both identification and control purposes which are given at the end of the book. A clear and an in-depth examination has been made of all the necessary mathematical foundations, type-1 and type-2 fuzzy neural network structures and their learning algorithms as well as their stability analysis. You will find that each chapter is devoted to a different learning algorithm for the tuning of type-1 and type-2 fuzzy neural networks; some of which are: • Gradient descent • Levenberg-Marquardt • Extended Kalman filter In addition to the aforementioned conventional learning methods above, number of novel sliding mode control theory-based learning algorithms, which are simpler and have closed forms, and their stability analysis have been proposed. Furthermore, hybrid methods consisting of particle swarm optimization and sliding mode control theory-based algorithms have also been introduced. The potential readers of this book are expected to be the undergraduate and graduate students, engineers, mathematicians and computer scientists. Not only can this book be used as a reference source for a scientist who is interested in fuzzy neural networks and their real-time implementations but also as a course book of fuzzy neural networks or artificial intelligence in master or doctorate university studies. We hope that this book will serve its main purpose successfully. - Parameter update algorithms for type-1 and type-2 fuzzy neural networks and their stability analysis - Contains algorithms that are applicable to real time systems - Introduces fast and simple adaptation rules for type-1 and type-2 fuzzy neural networks - Number of case studies both in identification and control - Provides MATLAB® codes for some algorithms in the book

Concepts and Real-Time Applications of Deep Learning

Author : Smriti Srivastava
Publisher : Springer Nature
ISBN 13 : 3030761673
Total Pages : 212 pages
Book Rating : 4.0/5 (37 download)

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Book Synopsis Concepts and Real-Time Applications of Deep Learning by : Smriti Srivastava

Download or read book Concepts and Real-Time Applications of Deep Learning written by Smriti Srivastava and published by Springer Nature. This book was released on 2021-09-23 with total page 212 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides readers with a comprehensive and recent exposition in deep learning and its multidisciplinary applications, with a concentration on advances of deep learning architectures. The book discusses various artificial intelligence (AI) techniques based on deep learning architecture with applications in natural language processing, semantic knowledge, forecasting and many more. The authors shed light on various applications that can benefit from the use of deep learning in pattern recognition, person re-identification in surveillance videos, action recognition in videos, image and video captioning. The book also highlights how deep learning concepts can be interwoven with more modern concepts to yield applications in multidisciplinary fields. Presents a comprehensive look at deep learning and its multidisciplinary applications, concentrating on advances of deep learning architectures; Includes a survey of deep learning problems and solutions, identifying the main open issues, innovations and latest technologies; Shows industrial deep learning in practice with examples/cases, efforts, challenges, and strategic approaches.

Handbook of Distance Learning for Real-Time and Asynchronous Information Technology Education

Author : Negash, Solomon
Publisher : IGI Global
ISBN 13 : 1599049651
Total Pages : 406 pages
Book Rating : 4.5/5 (99 download)

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Book Synopsis Handbook of Distance Learning for Real-Time and Asynchronous Information Technology Education by : Negash, Solomon

Download or read book Handbook of Distance Learning for Real-Time and Asynchronous Information Technology Education written by Negash, Solomon and published by IGI Global. This book was released on 2008-05-31 with total page 406 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book looks at solutions that provide the best fits of distance learning technologies for the teacher and learner presented by sharing teacher experiences in information technology education"--Provided by publisher.

Application of FPGA to Real‐Time Machine Learning

Author : Piotr Antonik
Publisher : Springer
ISBN 13 : 3319910531
Total Pages : 187 pages
Book Rating : 4.3/5 (199 download)

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Book Synopsis Application of FPGA to Real‐Time Machine Learning by : Piotr Antonik

Download or read book Application of FPGA to Real‐Time Machine Learning written by Piotr Antonik and published by Springer. This book was released on 2018-05-18 with total page 187 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book lies at the interface of machine learning – a subfield of computer science that develops algorithms for challenging tasks such as shape or image recognition, where traditional algorithms fail – and photonics – the physical science of light, which underlies many of the optical communications technologies used in our information society. It provides a thorough introduction to reservoir computing and field-programmable gate arrays (FPGAs). Recently, photonic implementations of reservoir computing (a machine learning algorithm based on artificial neural networks) have made a breakthrough in optical computing possible. In this book, the author pushes the performance of these systems significantly beyond what was achieved before. By interfacing a photonic reservoir computer with a high-speed electronic device (an FPGA), the author successfully interacts with the reservoir computer in real time, allowing him to considerably expand its capabilities and range of possible applications. Furthermore, the author draws on his expertise in machine learning and FPGA programming to make progress on a very different problem, namely the real-time image analysis of optical coherence tomography for atherosclerotic arteries.

Real-Time Phoenix

Author : Stephen Bussey
Publisher : Pragmatic Bookshelf
ISBN 13 : 1680507753
Total Pages : 405 pages
Book Rating : 4.6/5 (85 download)

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Book Synopsis Real-Time Phoenix by : Stephen Bussey

Download or read book Real-Time Phoenix written by Stephen Bussey and published by Pragmatic Bookshelf. This book was released on 2020-03-25 with total page 405 pages. Available in PDF, EPUB and Kindle. Book excerpt: Give users the real-time experience they expect, by using Elixir and Phoenix Channels to build applications that instantly react to changes and reflect the application's true state. Learn how Elixir and Phoenix make it easy and enjoyable to create real-time applications that scale to a large number of users. Apply system design and development best practices to create applications that are easy to maintain. Gain confidence by learning how to break your applications before your users do. Deploy applications with minimized resource use and maximized performance. Real-time applications come with real challenges - persistent connections, multi-server deployment, and strict performance requirements are just a few. Don't try to solve these challenges by yourself - use a framework that handles them for you. Elixir and Phoenix Channels provide a solid foundation on which to build stable and scalable real-time applications. Build applications that thrive for years to come with the best-practices found in this book. Understand the magic of real-time communication by inspecting the WebSocket protocol in action. Avoid performance pitfalls early in the development lifecycle with a catalog of common problems and their solutions. Leverage GenStage to build a data pipeline that improves scalability. Break your application before your users do and confidently deploy them. Build a real-world project using solid application design and testing practices that help make future changes a breeze. Create distributed apps that can scale to many users with tools like Phoenix Tracker. Deploy and monitor your application with confidence and reduce outages. Deliver an exceptional real-time experience to your users, with easy maintenance, reduced operational costs, and maximized performance, using Elixir and Phoenix Channels. What You Need: You'll need Elixir 1.9+ and Erlang/OTP 22+ installed on a Mac OS X, Linux, or Windows machine.

Deep Learning for Coders with fastai and PyTorch

Author : Jeremy Howard
Publisher : O'Reilly Media
ISBN 13 : 1492045497
Total Pages : 624 pages
Book Rating : 4.4/5 (92 download)

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Book Synopsis Deep Learning for Coders with fastai and PyTorch by : Jeremy Howard

Download or read book Deep Learning for Coders with fastai and PyTorch written by Jeremy Howard and published by O'Reilly Media. This book was released on 2020-06-29 with total page 624 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala

Teaching and Learning in Real Time

Author : Carla Meskill
Publisher : Athelstan
ISBN 13 : 0940753170
Total Pages : 216 pages
Book Rating : 4.9/5 (47 download)

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Book Synopsis Teaching and Learning in Real Time by : Carla Meskill

Download or read book Teaching and Learning in Real Time written by Carla Meskill and published by Athelstan. This book was released on 2002 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: This title explores technology use for second language learners, focussing on sociocognitive development, media awareness, second language acquisition strategies and interpersonal interactions. Topics include: instructional media and teachnology and language learning; The Media as a Second Language; principled uses of media and technologies; the aural -- talking about, around and through audio technologies; video -- the What, the Why, the How; computers in language learning -- from Constructed to Constructing; computer communication tools; multimedia spaces, performances, and characters; electronic literacy as a Second Language.

Machine Learning Bookcamp

Author : Alexey Grigorev
Publisher : Simon and Schuster
ISBN 13 : 1638351058
Total Pages : 470 pages
Book Rating : 4.6/5 (383 download)

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Book Synopsis Machine Learning Bookcamp by : Alexey Grigorev

Download or read book Machine Learning Bookcamp written by Alexey Grigorev and published by Simon and Schuster. This book was released on 2021-11-23 with total page 470 pages. Available in PDF, EPUB and Kindle. Book excerpt: Time to flex your machine learning muscles! Take on the carefully designed challenges of the Machine Learning Bookcamp and master essential ML techniques through practical application. Summary In Machine Learning Bookcamp you will: Collect and clean data for training models Use popular Python tools, including NumPy, Scikit-Learn, and TensorFlow Apply ML to complex datasets with images Deploy ML models to a production-ready environment The only way to learn is to practice! In Machine Learning Bookcamp, you’ll create and deploy Python-based machine learning models for a variety of increasingly challenging projects. Taking you from the basics of machine learning to complex applications such as image analysis, each new project builds on what you’ve learned in previous chapters. You’ll build a portfolio of business-relevant machine learning projects that hiring managers will be excited to see. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Master key machine learning concepts as you build actual projects! Machine learning is what you need for analyzing customer behavior, predicting price trends, evaluating risk, and much more. To master ML, you need great examples, clear explanations, and lots of practice. This book delivers all three! About the book Machine Learning Bookcamp presents realistic, practical machine learning scenarios, along with crystal-clear coverage of key concepts. In it, you’ll complete engaging projects, such as creating a car price predictor using linear regression and deploying a churn prediction service. You’ll go beyond the algorithms and explore important techniques like deploying ML applications on serverless systems and serving models with Kubernetes and Kubeflow. Dig in, get your hands dirty, and have fun building your ML skills! What's inside Collect and clean data for training models Use popular Python tools, including NumPy, Scikit-Learn, and TensorFlow Deploy ML models to a production-ready environment About the reader Python programming skills assumed. No previous machine learning knowledge is required. About the author Alexey Grigorev is a principal data scientist at OLX Group. He runs DataTalks.Club, a community of people who love data. Table of Contents 1 Introduction to machine learning 2 Machine learning for regression 3 Machine learning for classification 4 Evaluation metrics for classification 5 Deploying machine learning models 6 Decision trees and ensemble learning 7 Neural networks and deep learning 8 Serverless deep learning 9 Serving models with Kubernetes and Kubeflow

Lifelong Machine Learning, Second Edition

Author : Zhiyuan Sun
Publisher : Springer Nature
ISBN 13 : 3031015819
Total Pages : 187 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Lifelong Machine Learning, Second Edition by : Zhiyuan Sun

Download or read book Lifelong Machine Learning, Second Edition written by Zhiyuan Sun and published by Springer Nature. This book was released on 2022-06-01 with total page 187 pages. Available in PDF, EPUB and Kindle. Book excerpt: Lifelong Machine Learning, Second Edition is an introduction to an advanced machine learning paradigm that continuously learns by accumulating past knowledge that it then uses in future learning and problem solving. In contrast, the current dominant machine learning paradigm learns in isolation: given a training dataset, it runs a machine learning algorithm on the dataset to produce a model that is then used in its intended application. It makes no attempt to retain the learned knowledge and use it in subsequent learning. Unlike this isolated system, humans learn effectively with only a few examples precisely because our learning is very knowledge-driven: the knowledge learned in the past helps us learn new things with little data or effort. Lifelong learning aims to emulate this capability, because without it, an AI system cannot be considered truly intelligent. Research in lifelong learning has developed significantly in the relatively short time since the first edition of this book was published. The purpose of this second edition is to expand the definition of lifelong learning, update the content of several chapters, and add a new chapter about continual learning in deep neural networks—which has been actively researched over the past two or three years. A few chapters have also been reorganized to make each of them more coherent for the reader. Moreover, the authors want to propose a unified framework for the research area. Currently, there are several research topics in machine learning that are closely related to lifelong learning—most notably, multi-task learning, transfer learning, and meta-learning—because they also employ the idea of knowledge sharing and transfer. This book brings all these topics under one roof and discusses their similarities and differences. Its goal is to introduce this emerging machine learning paradigm and present a comprehensive survey and review of the important research results and latest ideas in the area. This book is thus suitable for students, researchers, and practitioners who are interested in machine learning, data mining, natural language processing, or pattern recognition. Lecturers can readily use the book for courses in any of these related fields.

Real-Time Student Assessment

Author : Peggy L. Maki
Publisher : Taylor & Francis
ISBN 13 : 1000977919
Total Pages : 198 pages
Book Rating : 4.0/5 (9 download)

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Book Synopsis Real-Time Student Assessment by : Peggy L. Maki

Download or read book Real-Time Student Assessment written by Peggy L. Maki and published by Taylor & Francis. This book was released on 2023-07-03 with total page 198 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book challenges institutions and their programs to prioritize the use of chronological assessment results to benefit enrolled students in comparison with the more common practice of prolonged assessment cycles that generally benefit future students. Peggy Maki advocates for real-time assessment processes to identify patterns of underperformance and obstacles that require timely interventions for enrolled students to succeed. In tandem with the sets of educational practices and policies that many institutions have now undertaken to close achievement and graduation rates across our diverse student demographics, such as developing clear degree pathways, she calls on all higher education providers – if they are to remain relevant and meet their social purpose in our complex world – to urgently recalibrate their assessment processes to focus on currently enrolled students’ progress towards achieving a high-quality degree, regardless of when they matriculate or re-enter higher education. She demonstrates that we already have sufficient examples and evidence to implement real-time assessment of students as they progress through their studies. She draws on the practices of specialized accredited programs, such as those in the professions that assess in real time; on the experiences of institutions that have adopted competency-based education; and on the affordances of technologies that now provide faculty and students with up-to-the-minute diagnostics. She identifies the six principles necessary to implement a real-time assessment process, illustrated by case studies of how campuses have operationalized them to advance students’ equitable progress towards achieving a high-quality degree; and demonstrates the benefits of real-time assessment compared to more future-oriented processes, among which is engaging students in reflecting on their own progress along their degree pathways.She advocates for the use of well documented national outcomes-based frameworks such as Liberal Education and America’s Promise (LEAP), its aligned Valid Assessment of Learning in Undergraduate Education scoring rubrics ( VALUE), the Degree Qualifications Profile, and discipline-based outcomes assessments to ensure high-quality degrees that meet well-defined standards and criteria. She also identifies how data systems and technological developments help to monitor closely and respond in time to students’ patterns of underperformance.The book is an urgent call for higher education to achieve the values of equity, transparency and quality it espouses; and ensure that all students graduate in a timely fashion with the competencies they need to be active and productive citizens.

Data Science on the Google Cloud Platform

Author : Valliappa Lakshmanan
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491974532
Total Pages : 403 pages
Book Rating : 4.4/5 (919 download)

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Book Synopsis Data Science on the Google Cloud Platform by : Valliappa Lakshmanan

Download or read book Data Science on the Google Cloud Platform written by Valliappa Lakshmanan and published by "O'Reilly Media, Inc.". This book was released on 2017-12-12 with total page 403 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how easy it is to apply sophisticated statistical and machine learning methods to real-world problems when you build on top of the Google Cloud Platform (GCP). This hands-on guide shows developers entering the data science field how to implement an end-to-end data pipeline, using statistical and machine learning methods and tools on GCP. Through the course of the book, you’ll work through a sample business decision by employing a variety of data science approaches. Follow along by implementing these statistical and machine learning solutions in your own project on GCP, and discover how this platform provides a transformative and more collaborative way of doing data science. You’ll learn how to: Automate and schedule data ingest, using an App Engine application Create and populate a dashboard in Google Data Studio Build a real-time analysis pipeline to carry out streaming analytics Conduct interactive data exploration with Google BigQuery Create a Bayesian model on a Cloud Dataproc cluster Build a logistic regression machine-learning model with Spark Compute time-aggregate features with a Cloud Dataflow pipeline Create a high-performing prediction model with TensorFlow Use your deployed model as a microservice you can access from both batch and real-time pipelines