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Author: Mark Ryan Publisher: Simon and Schuster ISBN: 163835717X Category : Computers Languages : en Pages : 262
Book Description
Deep Learning with Structured Data teaches you powerful data analysis techniques for tabular data and relational databases. Summary Deep learning offers the potential to identify complex patterns and relationships hidden in data of all sorts. Deep Learning with Structured Data shows you how to apply powerful deep learning analysis techniques to the kind of structured, tabular data you'll find in the relational databases that real-world businesses depend on. Filled with practical, relevant applications, this book teaches you how deep learning can augment your existing machine learning and business intelligence systems. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Here’s a dirty secret: Half of the time in most data science projects is spent cleaning and preparing data. But there’s a better way: Deep learning techniques optimized for tabular data and relational databases deliver insights and analysis without requiring intense feature engineering. Learn the skills to unlock deep learning performance with much less data filtering, validating, and scrubbing. About the book Deep Learning with Structured Data teaches you powerful data analysis techniques for tabular data and relational databases. Get started using a dataset based on the Toronto transit system. As you work through the book, you’ll learn how easy it is to set up tabular data for deep learning, while solving crucial production concerns like deployment and performance monitoring. What's inside When and where to use deep learning The architecture of a Keras deep learning model Training, deploying, and maintaining models Measuring performance About the reader For readers with intermediate Python and machine learning skills. About the author Mark Ryan is a Data Science Manager at Intact Insurance. He holds a Master's degree in Computer Science from the University of Toronto. Table of Contents 1 Why deep learning with structured data? 2 Introduction to the example problem and Pandas dataframes 3 Preparing the data, part 1: Exploring and cleansing the data 4 Preparing the data, part 2: Transforming the data 5 Preparing and building the model 6 Training the model and running experiments 7 More experiments with the trained model 8 Deploying the model 9 Recommended next steps
Author: Mark Ryan Publisher: Simon and Schuster ISBN: 163835717X Category : Computers Languages : en Pages : 262
Book Description
Deep Learning with Structured Data teaches you powerful data analysis techniques for tabular data and relational databases. Summary Deep learning offers the potential to identify complex patterns and relationships hidden in data of all sorts. Deep Learning with Structured Data shows you how to apply powerful deep learning analysis techniques to the kind of structured, tabular data you'll find in the relational databases that real-world businesses depend on. Filled with practical, relevant applications, this book teaches you how deep learning can augment your existing machine learning and business intelligence systems. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Here’s a dirty secret: Half of the time in most data science projects is spent cleaning and preparing data. But there’s a better way: Deep learning techniques optimized for tabular data and relational databases deliver insights and analysis without requiring intense feature engineering. Learn the skills to unlock deep learning performance with much less data filtering, validating, and scrubbing. About the book Deep Learning with Structured Data teaches you powerful data analysis techniques for tabular data and relational databases. Get started using a dataset based on the Toronto transit system. As you work through the book, you’ll learn how easy it is to set up tabular data for deep learning, while solving crucial production concerns like deployment and performance monitoring. What's inside When and where to use deep learning The architecture of a Keras deep learning model Training, deploying, and maintaining models Measuring performance About the reader For readers with intermediate Python and machine learning skills. About the author Mark Ryan is a Data Science Manager at Intact Insurance. He holds a Master's degree in Computer Science from the University of Toronto. Table of Contents 1 Why deep learning with structured data? 2 Introduction to the example problem and Pandas dataframes 3 Preparing the data, part 1: Exploring and cleansing the data 4 Preparing the data, part 2: Transforming the data 5 Preparing and building the model 6 Training the model and running experiments 7 More experiments with the trained model 8 Deploying the model 9 Recommended next steps
Author: Lau, Linda K. Publisher: IGI Global ISBN: 1930708521 Category : Education Languages : en Pages : 258
Book Description
In today's technology-crazed environment, distance learning is touted as a cost-effective option for delivering employee training and higher education programs, such as bachelor's, master's and even doctoral degrees. Distance Learning Technologies: Issues, Trends and Opportunities provides readers with an in-depth understanding of distance learning and the technologies available for this innovative media of learning and instruction. It traces the development of distance learning from its history to suggestions of a solid strategic implementation plan to ensure its successful and effective deployment.
Author: Stephen Boyd Publisher: Now Publishers Inc ISBN: 160198460X Category : Computers Languages : en Pages : 138
Book Description
Surveys the theory and history of the alternating direction method of multipliers, and discusses its applications to a wide variety of statistical and machine learning problems of recent interest, including the lasso, sparse logistic regression, basis pursuit, covariance selection, support vector machines, and many others.
Author: Andrew S Tanenbaum Publisher: Maarten Van Steen ISBN: 9789081540636 Category : Languages : en Pages : 0
Book Description
This is the fourth edition of "Distributed Systems." We have stayed close to the setup of the third edition, including examples of (part of) existing distributed systems close to where general principles are discussed. For example, we have included material on blockchain systems, and discuss their various components throughout the book. We have, again, used special boxed sections for material that can be skipped at first reading. The text has been thoroughly reviewed, revised, and updated. In particular, all the Python code has been updated to Python3, while at the same time the channel package has been almost completely revised and simplified. Additional material, including coding examples, figures, and slides, are available at www.distributed-systems.net.
Author: Hien Luu Publisher: Apress ISBN: 1484235797 Category : Computers Languages : en Pages : 398
Book Description
Develop applications for the big data landscape with Spark and Hadoop. This book also explains the role of Spark in developing scalable machine learning and analytics applications with Cloud technologies. Beginning Apache Spark 2 gives you an introduction to Apache Spark and shows you how to work with it. Along the way, you’ll discover resilient distributed datasets (RDDs); use Spark SQL for structured data; and learn stream processing and build real-time applications with Spark Structured Streaming. Furthermore, you’ll learn the fundamentals of Spark ML for machine learning and much more. After you read this book, you will have the fundamentals to become proficient in using Apache Spark and know when and how to apply it to your big data applications. What You Will Learn Understand Spark unified data processing platform How to run Spark in Spark Shell or Databricks Use and manipulate RDDs Deal with structured data using Spark SQL through its operations and advanced functions Build real-time applications using Spark Structured Streaming Develop intelligent applications with the Spark Machine Learning library Who This Book Is For Programmers and developers active in big data, Hadoop, and Java but who are new to the Apache Spark platform.
Author: Caroline Howard Publisher: IGI Global ISBN: 9781591402213 Category : Education Languages : en Pages : 372
Book Description
Distance Learning and University Effectiveness: Changing Educational Paradigms for Online Learning addresses the challenges and opportunities associated with information and communication technologies (ICTs) as related to education. From discussing new and innovative educational paradigms and learning models resulting from ICTs to addressing future student needs and international issues, this book provides comprehensive coverage of the paradigm, teaching, technology and other changes that may be required of universities to remain in the new competitive marketplace of online learning.
Author: Ian Goodfellow Publisher: MIT Press ISBN: 0262337371 Category : Computers Languages : en Pages : 801
Book Description
An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.
Author: Tomei, Lawrence A. Publisher: IGI Global ISBN: 1599049368 Category : Business & Economics Languages : en Pages : 4032
Book Description
"This comprehensive, six-volume collection addresses all aspects of online and distance learning, including information communication technologies applied to education, virtual classrooms, pedagogical systems, Web-based learning, library information systems, virtual universities, and more. It enables libraries to provide a foundational reference to meet the information needs of researchers, educators, practitioners, administrators, and other stakeholders in online and distance learning"--Provided by publisher.
Author: Louise Spear-Swerling Publisher: Guilford Publications ISBN: 1462548792 Category : EDUCATION Languages : en Pages : 266
Book Description
"In this book, structured literacy is conceptualized as an umbrella term encompassing a variety of intervention methods, instructional approaches, and commercial programs. In addition to focusing on SL approaches to intervention, this book is organized around common poor reader profiles that have been identified in research. The chapters in this volume are written by experts who are well known as researchers but who are also highly skilled at writing for practitioners. Chapters were written with a strong foundation of research that is summarized, but with a concentration on translating research into practice, including case studies, sample intervention activities, and lesson plans. Each chapter includes application activities at the end to check for and extend readers' understanding"--
Author: Mark Ryan
Author: Mark Ryan
Author: Lau, Linda K.
Author: Stephen Boyd
Author: Andrew S Tanenbaum
Author: Hien Luu
Author: Gene V Glass
Author: Caroline Howard
Author: Ian Goodfellow
Author: Tomei, Lawrence A.
Author: Louise Spear-Swerling