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Summary Machine Learning Systems: Designs that scale is an example-rich guide that teaches you how to implement reactive design solutions in your machine learning systems to make them as reliable as a well-built web app. Foreword by Sean Owen, Director of Data Science, Cloudera Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology If you’re building machine learning models to be used on a small scale, you don't need this book. But if you're a developer building a production-grade ML application that needs quick response times, reliability, and good user experience, this is the book for you. It collects principles and practices of machine learning systems that are dramatically easier to run and maintain, and that are reliably better for users. About the Book Machine Learning Systems: Designs that scale teaches you to design and implement production-ready ML systems. You'll learn the principles of reactive design as you build pipelines with Spark, create highly scalable services with Akka, and use powerful machine learning libraries like MLib on massive datasets. The examples use the Scala language, but the same ideas and tools work in Java, as well. What's Inside Working with Spark, MLlib, and Akka Reactive design patterns Monitoring and maintaining a large-scale system Futures, actors, and supervision About the Reader Readers need intermediate skills in Java or Scala. No prior machine learning experience is assumed. About the Author Jeff Smith builds powerful machine learning systems. For the past decade, he has been working on building data science applications, teams, and companies as part of various teams in New York, San Francisco, and Hong Kong. He blogs (https: //medium.com/@jeffksmithjr), tweets (@jeffksmithjr), and speaks (www.jeffsmith.tech/speaking) about various aspects of building real-world machine learning systems. Table of Contents PART 1 - FUNDAMENTALS OF REACTIVE MACHINE LEARNING Learning reactive machine learning Using reactive tools PART 2 - BUILDING A REACTIVE MACHINE LEARNING SYSTEM Collecting data Generating features Learning models Evaluating models Publishing models Responding PART 3 - OPERATING A MACHINE LEARNING SYSTEM Delivering Evolving intelligence
This book describes deep learning systems: the algorithms, compilers, and processor components to efficiently train and deploy deep learning models for commercial applications. The exponential growth in computational power is slowing at a time when the amount of compute consumed by state-of-the-art deep learning (DL) workloads is rapidly growing. Model size, serving latency, and power constraints are a significant challenge in the deployment of DL models for many applications. Therefore, it is imperative to codesign algorithms, compilers, and hardware to accelerate advances in this field with holistic system-level and algorithm solutions that improve performance, power, and efficiency. Advancing DL systems generally involves three types of engineers: (1) data scientists that utilize and develop DL algorithms in partnership with domain experts, such as medical, economic, or climate scientists; (2) hardware designers that develop specialized hardware to accelerate the components in the DL models; and (3) performance and compiler engineers that optimize software to run more efficiently on a given hardware. Hardware engineers should be aware of the characteristics and components of production and academic models likely to be adopted by industry to guide design decisions impacting future hardware. Data scientists should be aware of deployment platform constraints when designing models. Performance engineers should support optimizations across diverse models, libraries, and hardware targets. The purpose of this book is to provide a solid understanding of (1) the design, training, and applications of DL algorithms in industry; (2) the compiler techniques to map deep learning code to hardware targets; and (3) the critical hardware features that accelerate DL systems. This book aims to facilitate co-innovation for the advancement of DL systems. It is written for engineers working in one or more of these areas who seek to understand the entire system stack in order to better collaborate with engineers working in other parts of the system stack. The book details advancements and adoption of DL models in industry, explains the training and deployment process, describes the essential hardware architectural features needed for today's and future models, and details advances in DL compilers to efficiently execute algorithms across various hardware targets. Unique in this book is the holistic exposition of the entire DL system stack, the emphasis on commercial applications, and the practical techniques to design models and accelerate their performance. The author is fortunate to work with hardware, software, data scientist, and research teams across many high-technology companies with hyperscale data centers. These companies employ many of the examples and methods provided throughout the book.
This introduction to the concepts and techniques of formal learning theory is based on a number-theoretical approach to learning and uses the tools of recursive function theory to understand how learners come to an accurate view of reality.
In this seminal volume, leading authorities strategize about how to create early childhood systems that transcend politics and economics to serve the needs of all young children. The authors offer different interpretations of the nature of early childhood systems, discuss the elements necessary to support their development, and examine how effectiveness can be assessed. With a combination of cutting-edge scholarship and practical examples of systems-building efforts taking place in the field, this book provides the foundation educators and policymakers need to take important steps toward developing more conceptually integrated approaches to early childhood care, education, and comprehensive services. Book Features: Provides the only up-to-date, comprehensive examination of early childhood systems.Considers new efforts to expand services, improve quality, maximize resources, and reduce inequities in early childhood.Offers a forum for the field to come together to frame a set of cogent recommendations for the future. Contributors: Kimberly Boller, Andrew Brodsky, Charles Bruner, Dean Clifford, Julia Coffman, Jeanine Coleman, Harriet Dichter, Sangree Froelicher, Eugene García, Stacie Goffin, Jodi Hardin, Karen Hill Scott, Janice Gruendel, Marilou Hyson, Amy Kershaw, Lisa G. Klein, Denise Mauzy, Geoffrey Nagle, Karen Ponder, Ann Reale, Sue Russell, Diana Schaack, Helene M. Stebbins, Jennifer M. Stedron, Kate Tarrant, Kathy R. Thornburg, Kathryn Tout, Fasaha Traylor, Jessica Vick Whittaker Sharon Lynn Kagan is the Virginia and Leonard Marx Professor of Early Childhood and Family Policy and Co-Director of the National Center for Children and Families at Teachers College, Columbia University. Kristie Kauerz is the program director for PreK-3rd Education at Harvard Graduate School of Education (HGSE). “A veritable encyclopedia of ideas on early childhood system building.” —Barbara T. Bowman,Irving B. Harris Professor of Child Development, Erikson Institute “The key to successful change is continued development of the frames of reference. Both editors have respected the past, listened to the implementers, and provided a context for moving forward. Like efforts to build systems of child development, which we must now link to growth in specific children we know by name, the book ends with robust examples of the work in progress. Sharon Lynn Kagan and Kristie Kauerz don't just talk about the work, they participate in the creation of change.” —Sherri Killins, Ed.D, Commissioner, Department of Early Education and Care, Massachusetts
"The book provides A guidelines approach on how to implement the proposed theory and tools in e-learning programs"--Provided by publisher.
Describes how to evaluate interactive learning systems, both in their initial development and later in regard to effectiveness and efficiency. These include web-based systems, computer-aided learning, etc.
This is a tutorial-driven and practical, but well-grounded book showcasing good Machine Learning practices. There will be an emphasis on using existing technologies instead of showing how to write your own implementations of algorithms. This book is a scenario-based, example-driven tutorial. By the end of the book you will have learnt critical aspects of Machine Learning Python projects and experienced the power of ML-based systems by actually working on them.This book primarily targets Python developers who want to learn about and build Machine Learning into their projects, or who want to pro.
Learning to perform complex action strategies is an important problem in the fields of artificial intelligence, robotics and machine learning. Presenting interesting, new experimental results, Learning in Embedded Systems explores algorithms that learn efficiently from trial and error experience with an external world. The text is a detailed exploration of the problem of learning action strategies in the context of designing embedded systems that adapt their behaviour to a complex, changing environment. Such systems include mobile robots, factory process controllers and long-term software databases.
Artificial Intelligence is concerned with producing devices that help or replace human beings in their daily activities. Neural-symbolic learning systems play a central role in this task by combining, and trying to benefit from, the advantages of both the neural and symbolic paradigms of artificial intelligence. This book provides a comprehensive introduction to the field of neural-symbolic learning systems, and an invaluable overview of the latest research issues in this area. It is divided into three sections, covering the main topics of neural-symbolic integration - theoretical advances in knowledge representation and learning, knowledge extraction from trained neural networks, and inconsistency handling in neural-symbolic systems. Each section provides a balance of theory and practice, giving the results of applications using real-world problems in areas such as DNA sequence analysis, power systems fault diagnosis, and software requirements specifications. Neural-Symbolic Learning Systems will be invaluable reading for researchers and graduate students in Engineering, Computing Science, Artificial Intelligence, Machine Learning and Neurocomputing. It will also be of interest to Intelligent Systems practitioners and anyone interested in applications of hybrid artificial intelligence systems.
Provides practical tools and protocols for focusing districts on their role in providing meaningful instruction so that more students achieve at higher levels.