Machine Learning Design Patterns

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Publisher : "O'Reilly Media, Inc."
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ISBN 10 : 9781098115739
Pages : 408 pages
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Rating : 4.1/5 (115 users download)


Download Machine Learning Design Patterns by Valliappa Lakshmanan PDF/Ebook Free clicking on the below button will initiate the downloading process of Machine Learning Design Patterns by Valliappa Lakshmanan. This book is available in ePub and PDF format with a single click unlimited downloads. The design patterns in this book capture best practices and solutions to recurring problems in machine learning. The authors, three Google engineers, catalog proven methods to help data scientists tackle common problems throughout the ML process. These design patterns codify the experience of hundreds of experts into straightforward, approachable advice. In this book, you will find detailed explanations of 30 patterns for data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness. Each pattern includes a description of the problem, a variety of potential solutions, and recommendations for choosing the best technique for your situation. You'll learn how to: Identify and mitigate common challenges when training, evaluating, and deploying ML models Represent data for different ML model types, including embeddings, feature crosses, and more Choose the right model type for specific problems Build a robust training loop that uses checkpoints, distribution strategy, and hyperparameter tuning Deploy scalable ML systems that you can retrain and update to reflect new data Interpret model predictions for stakeholders and ensure models are treating users fairly


Machine Learning Design Patterns

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ISBN 10 : 1098115775
Pages : 158 pages
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Rating : 4.9/5 (98 users download)


Download Machine Learning Design Patterns by Valliappa Lakshmanan PDF/Ebook Free clicking on the below button will initiate the downloading process of Machine Learning Design Patterns by Valliappa Lakshmanan. This book is available in ePub and PDF format with a single click unlimited downloads. The design patterns in this book capture best practices and solutions to recurring problems in machine learning. The authors, three Google engineers, catalog proven methods to help data scientists tackle common problems throughout the ML process. These design patterns codify the experience of hundreds of experts into straightforward, approachable advice. In this book, you will find detailed explanations of 30 patterns for data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness. Each pattern includes a description of the problem, a variety of potential solutions, and recommendations for choosing the best technique for your situation. You'll learn how to: Identify and mitigate common challenges when training, evaluating, and deploying ML models Represent data for different ML model types, including embeddings, feature crosses, and more Choose the right model type for specific problems Build a robust training loop that uses checkpoints, distribution strategy, and hyperparameter tuning Deploy scalable ML systems that you can retrain and update to reflect new data Interpret model predictions for stakeholders and ensure models are treating users fairly.


Machine Learning Design Patterns

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ISBN 10 : 7564195541
Pages : 390 pages
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Rating : 4.6/5 (564 users download)


Download Machine Learning Design Patterns by Valliappa Lakshmanan PDF/Ebook Free clicking on the below button will initiate the downloading process of Machine Learning Design Patterns by Valliappa Lakshmanan. This book is available in ePub and PDF format with a single click unlimited downloads.


Design Patterns Für Machine Learning

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ISBN 10 : OCLC:1287919169
Pages : 432 pages
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Rating : 4./5 ( users download)


Download Design Patterns Für Machine Learning by Valliappa Lakshmanan PDF/Ebook Free clicking on the below button will initiate the downloading process of Design Patterns Für Machine Learning by Valliappa Lakshmanan. This book is available in ePub and PDF format with a single click unlimited downloads. Die Design Patterns in diesem Buch zeigen praxiserprobte Methoden und Lösungen für wiederkehrende Aufgaben beim Machine Learning. Die Autoren, drei Machine-Learning-Experten bei Google, beschreiben bewährte Herangehensweisen, um Data Scientists bei der Lösung gängiger Probleme im gesamten ML-Prozess zu unterstützen. Die Patterns bündeln die Erfahrungen von Hunderten von Experten und bieten einfache, zugängliche Best Practices. In diesem Buch finden Sie detaillierte Erläuterungen zu 30 Patterns für diese Themen: Daten- und Problemdarstellung, Operationalisierung, Wiederholbarkeit, Reproduzierbarkeit, Flexibilität, Erklärbarkeit und Fairness. Jedes Pattern enthält eine Beschreibung des Problems, eine Vielzahl möglicher Lösungen und Empfehlungen für die Auswahl der besten Technik für Ihre Situation.


MapReduce Design Patterns

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Publisher : "O'Reilly Media, Inc."
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ISBN 10 : 9781449341992
Pages : 250 pages
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Rating : 4.4/5 (341 users download)


Download MapReduce Design Patterns by Donald Miner PDF/Ebook Free clicking on the below button will initiate the downloading process of MapReduce Design Patterns by Donald Miner. This book is available in ePub and PDF format with a single click unlimited downloads. Until now, design patterns for the MapReduce framework have been scattered among various research papers, blogs, and books. This handy guide brings together a unique collection of valuable MapReduce patterns that will save you time and effort regardless of the domain, language, or development framework you’re using. Each pattern is explained in context, with pitfalls and caveats clearly identified to help you avoid common design mistakes when modeling your big data architecture. This book also provides a complete overview of MapReduce that explains its origins and implementations, and why design patterns are so important. All code examples are written for Hadoop. Summarization patterns: get a top-level view by summarizing and grouping data Filtering patterns: view data subsets such as records generated from one user Data organization patterns: reorganize data to work with other systems, or to make MapReduce analysis easier Join patterns: analyze different datasets together to discover interesting relationships Metapatterns: piece together several patterns to solve multi-stage problems, or to perform several analytics in the same job Input and output patterns: customize the way you use Hadoop to load or store data "A clear exposition of MapReduce programs for common data processing patterns—this book is indespensible for anyone using Hadoop." --Tom White, author of Hadoop: The Definitive Guide


Machine Learning Engineering in Action

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Publisher : Simon and Schuster
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ISBN 10 : 9781638356585
Pages : 576 pages
File Format : PDF, EPUB, TEXT, KINDLE or MOBI
Rating : 4.5/5 (356 users download)


Download Machine Learning Engineering in Action by Ben Wilson PDF/Ebook Free clicking on the below button will initiate the downloading process of Machine Learning Engineering in Action by Ben Wilson. This book is available in ePub and PDF format with a single click unlimited downloads. Field-tested tips, tricks, and design patterns for building machine learning projects that are deployable, maintainable, and secure from concept to production. In Machine Learning Engineering in Action, you will learn: Evaluating data science problems to find the most effective solution Scoping a machine learning project for usage expectations and budget Process techniques that minimize wasted effort and speed up production Assessing a project using standardized prototyping work and statistical validation Choosing the right technologies and tools for your project Making your codebase more understandable, maintainable, and testable Automating your troubleshooting and logging practices Ferrying a machine learning project from your data science team to your end users is no easy task. Machine Learning Engineering in Action will help you make it simple. Inside, you'll find fantastic advice from veteran industry expert Ben Wilson, Principal Resident Solutions Architect at Databricks. Ben introduces his personal toolbox of techniques for building deployable and maintainable production machine learning systems. You'll learn the importance of Agile methodologies for fast prototyping and conferring with stakeholders, while developing a new appreciation for the importance of planning. Adopting well-established software development standards will help you deliver better code management, and make it easier to test, scale, and even reuse your machine learning code. Every method is explained in a friendly, peer-to-peer style and illustrated with production-ready source code. About the technology Deliver maximum performance from your models and data. This collection of reproducible techniques will help you build stable data pipelines, efficient application workflows, and maintainable models every time. Based on decades of good software engineering practice, machine learning engineering ensures your ML systems are resilient, adaptable, and perform in production. About the book Machine Learning Engineering in Action teaches you core principles and practices for designing, building, and delivering successful machine learning projects. You'll discover software engineering techniques like conducting experiments on your prototypes and implementing modular design that result in resilient architectures and consistent cross-team communication. Based on the author's extensive experience, every method in this book has been used to solve real-world projects. What's inside Scoping a machine learning project for usage expectations and budget Choosing the right technologies for your design Making your codebase more understandable, maintainable, and testable Automating your troubleshooting and logging practices About the reader For data scientists who know machine learning and the basics of object-oriented programming. About the author Ben Wilson is Principal Resident Solutions Architect at Databricks, where he developed the Databricks Labs AutoML project, and is an MLflow committer.


Proceedings

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ISBN 10 : UOM:39015058776736
Pages : pages
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Rating : 4./5 ( users download)


Download Proceedings by PDF/Ebook Free clicking on the below button will initiate the downloading process of Proceedings by . This book is available in ePub and PDF format with a single click unlimited downloads.


.NET Design Patterns

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Publisher : Packt Publishing Ltd
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ISBN 10 : 9781786461865
Pages : 314 pages
File Format : PDF, EPUB, TEXT, KINDLE or MOBI
Rating : 4.6/5 (461 users download)


Download .NET Design Patterns by Praseed Pai PDF/Ebook Free clicking on the below button will initiate the downloading process of .NET Design Patterns by Praseed Pai. This book is available in ePub and PDF format with a single click unlimited downloads. Explore the world of .NET design patterns and bring the benefits that the right patterns can offer to your toolkit today About This Book Dive into the powerful fundamentals of .NET framework for software development The code is explained piece by piece and the application of the pattern is also showcased. This fast-paced guide shows you how to implement the patterns into your existing applications Who This Book Is For This book is for those with familiarity with .NET development who would like to take their skills to the next level and be in the driver's seat when it comes to modern development techniques. Basic object-oriented C# programming experience and an elementary familiarity with the .NET framework library is required. What You Will Learn Put patterns and pattern catalogs into the right perspective Apply patterns for software development under C#/.NET Use GoF and other patterns in real-life development scenarios Be able to enrich your design vocabulary and well articulate your design thoughts Leverage object/functional programming by mixing OOP and FP Understand the reactive programming model using Rx and RxJs Writing compositional code using C# LINQ constructs Be able to implement concurrent/parallel programming techniques using idioms under .NET Avoiding pitfalls when creating compositional, readable, and maintainable code using imperative, functional, and reactive code. In Detail Knowing about design patterns enables developers to improve their code base, promoting code reuse and making their design more robust. This book focuses on the practical aspects of programming in .NET. You will learn about some of the relevant design patterns (and their application) that are most widely used. We start with classic object-oriented programming (OOP) techniques, evaluate parallel programming and concurrency models, enhance implementations by mixing OOP and functional programming, and finally to the reactive programming model where functional programming and OOP are used in synergy to write better code. Throughout this book, we'll show you how to deal with architecture/design techniques, GoF patterns, relevant patterns from other catalogs, functional programming, and reactive programming techniques. After reading this book, you will be able to convincingly leverage these design patterns (factory pattern, builder pattern, prototype pattern, adapter pattern, facade pattern, decorator pattern, observer pattern and so on) for your programs. You will also be able to write fluid functional code in .NET that would leverage concurrency and parallelism! Style and approach This tutorial-based book takes a step-by-step approach. It covers the major patterns and explains them in a detailed manned along with code examples.


Data Science Design Patterns

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Publisher : Addison-Wesley Professional
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ISBN 10 : 0134000056
Pages : 512 pages
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Rating : 4.3/5 (134 users download)


Download Data Science Design Patterns by Todd Morley PDF/Ebook Free clicking on the below button will initiate the downloading process of Data Science Design Patterns by Todd Morley. This book is available in ePub and PDF format with a single click unlimited downloads. Data Science Design Patterns brings together several dozen proven patterns for building successful decision-support and decision-automation systems in the enterprise. Like Martin Fowler's classic Patterns of Enterprise Application Architecture, it helps you rapidly hone in on proven solutions to common problems, leveraging the hard-won expertise of those who have come before you. Todd Morley helps you draw upon and integrate diverse domains including statistics, machine learning, information retrieval, compression, optimization, and other areas of software development and business consulting. His patterns address many common challenges, including categorization, prediction, optimization, testing, and human factors. They link directly to key goals for data science and analytics: increasing revenue, decreasing costs, reducing risk, choosing strategies, and making key decisions. Each pattern offers a high-level design for an application module or layer that either directly solves an enterprise-scale data science problem, or offers a higher-level approach to solving it. Throughout, Morley presents wide-ranging examples, links to real-world case studies, and extensive bibliographic references for deepening your understanding. This guide's patterns will substantially shorten the learning curve faced by software developers, architects, and IT professionals who have limited mathematical background, and are tasked with solving large-scale data science business problems. They will be equally valuable to experienced data scientists interested in applying best practices to become even more effective.


Deep Learning Patterns and Practices

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Publisher : Simon and Schuster
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ISBN 10 : 9781638356677
Pages : 472 pages
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Rating : 4.5/5 (356 users download)


Download Deep Learning Patterns and Practices by Andrew Ferlitsch PDF/Ebook Free clicking on the below button will initiate the downloading process of Deep Learning Patterns and Practices by Andrew Ferlitsch. This book is available in ePub and PDF format with a single click unlimited downloads. Discover best practices, reproducible architectures, and design patterns to help guide deep learning models from the lab into production. In Deep Learning Patterns and Practices you will learn: Internal functioning of modern convolutional neural networks Procedural reuse design pattern for CNN architectures Models for mobile and IoT devices Assembling large-scale model deployments Optimizing hyperparameter tuning Migrating a model to a production environment The big challenge of deep learning lies in taking cutting-edge technologies from R&D labs through to production. Deep Learning Patterns and Practices is here to help. This unique guide lays out the latest deep learning insights from author Andrew Ferlitsch’s work with Google Cloud AI. In it, you'll find deep learning models presented in a unique new way: as extendable design patterns you can easily plug-and-play into your software projects. Each valuable technique is presented in a way that's easy to understand and filled with accessible diagrams and code samples. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Discover best practices, design patterns, and reproducible architectures that will guide your deep learning projects from the lab into production. This awesome book collects and illuminates the most relevant insights from a decade of real world deep learning experience. You’ll build your skills and confidence with each interesting example. About the book Deep Learning Patterns and Practices is a deep dive into building successful deep learning applications. You’ll save hours of trial-and-error by applying proven patterns and practices to your own projects. Tested code samples, real-world examples, and a brilliant narrative style make even complex concepts simple and engaging. Along the way, you’ll get tips for deploying, testing, and maintaining your projects. What's inside Modern convolutional neural networks Design pattern for CNN architectures Models for mobile and IoT devices Large-scale model deployments Examples for computer vision About the reader For machine learning engineers familiar with Python and deep learning. About the author Andrew Ferlitsch is an expert on computer vision, deep learning, and operationalizing ML in production at Google Cloud AI Developer Relations. Table of Contents PART 1 DEEP LEARNING FUNDAMENTALS 1 Designing modern machine learning 2 Deep neural networks 3 Convolutional and residual neural networks 4 Training fundamentals PART 2 BASIC DESIGN PATTERN 5 Procedural design pattern 6 Wide convolutional neural networks 7 Alternative connectivity patterns 8 Mobile convolutional neural networks 9 Autoencoders PART 3 WORKING WITH PIPELINES 10 Hyperparameter tuning 11 Transfer learning 12 Data distributions 13 Data pipeline 14 Training and deployment pipeline


Advanced Python Programming

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Publisher : Packt Publishing Ltd
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ISBN 10 : 9781801817776
Pages : 606 pages
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Rating : 4.1/5 (817 users download)


Download Advanced Python Programming by Quan Nguyen PDF/Ebook Free clicking on the below button will initiate the downloading process of Advanced Python Programming by Quan Nguyen. This book is available in ePub and PDF format with a single click unlimited downloads. Write fast, robust, and highly reusable applications using Python's internal optimization, state-of-the-art performance-benchmarking tools, and cutting-edge libraries Key Features Benchmark, profile, and accelerate Python programs using optimization tools Scale applications to multiple processors with concurrent programming Make applications robust and reusable using effective design patterns Book Description Python's powerful capabilities for implementing robust and efficient programs make it one of the most sought-after programming languages. In this book, you'll explore the tools that allow you to improve performance and take your Python programs to the next level. This book starts by examining the built-in as well as external libraries that streamline tasks in the development cycle, such as benchmarking, profiling, and optimizing. You'll then get to grips with using specialized tools such as dedicated libraries and compilers to increase your performance at number-crunching tasks, including training machine learning models. The book covers concurrency, a major solution to making programs more efficient and scalable, and various concurrent programming techniques such as multithreading, multiprocessing, and asynchronous programming. You'll also understand the common problems that cause undesirable behavior in concurrent programs. Finally, you'll work with a wide range of design patterns, including creational, structural, and behavioral patterns that enable you to tackle complex design and architecture challenges, making your programs more robust and maintainable. By the end of the book, you'll be exposed to a wide range of advanced functionalities in Python and be equipped with the practical knowledge needed to apply them to your use cases. What you will learn Write efficient numerical code with NumPy, pandas, and Xarray Use Cython and Numba to achieve native performance Find bottlenecks in your Python code using profilers Optimize your machine learning models with JAX Implement multithreaded, multiprocessing, and asynchronous programs Solve common problems in concurrent programming, such as deadlocks Tackle architecture challenges with design patterns Who this book is for This book is for intermediate to experienced Python programmers who are looking to scale up their applications in a systematic and robust manner. Programmers from a range of backgrounds will find this book useful, including software engineers, scientific programmers, and software architects.


Data Science on the Google Cloud Platform

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Publisher : "O'Reilly Media, Inc."
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ISBN 10 : 9781098118914
Pages : 462 pages
File Format : PDF, EPUB, TEXT, KINDLE or MOBI
Rating : 4.1/5 (118 users download)


Download Data Science on the Google Cloud Platform by Valliappa Lakshmanan PDF/Ebook Free clicking on the below button will initiate the downloading process of Data Science on the Google Cloud Platform by Valliappa Lakshmanan. This book is available in ePub and PDF format with a single click unlimited downloads. Learn how easy it is to apply sophisticated statistical and machine learning methods to real-world problems when you build using Google Cloud Platform (GCP). This hands-on guide shows data engineers and data scientists how to implement an end-to-end data pipeline with cloud native tools on GCP. Throughout this updated second edition, you'll work through a sample business decision by employing a variety of data science approaches. Follow along by building a data pipeline in your own project on GCP, and discover how to solve data science problems in a transformative and more collaborative way. You'll learn how to: Employ best practices in building highly scalable data and ML pipelines on Google Cloud Automate and schedule data ingest using Cloud Run Create and populate a dashboard in Data Studio Build a real-time analytics pipeline using Pub/Sub, Dataflow, and BigQuery Conduct interactive data exploration with BigQuery Create a Bayesian model with Spark on Cloud Dataproc Forecast time series and do anomaly detection with BigQuery ML Aggregate within time windows with Dataflow Train explainable machine learning models with Vertex AI Operationalize ML with Vertex AI Pipelines


Advanced Data Analytics Using Python

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Publisher : Apress
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ISBN 10 : 1484280040
Pages : pages
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Rating : 4.8/5 (484 users download)


Download Advanced Data Analytics Using Python by Sayan Mukhopadhyay PDF/Ebook Free clicking on the below button will initiate the downloading process of Advanced Data Analytics Using Python by Sayan Mukhopadhyay. This book is available in ePub and PDF format with a single click unlimited downloads. Understand advanced data analytics concepts such as time series and principal component analysis with ETL, supervised learning, and PySpark using Python. This book covers architectural patterns in data analytics, text and image classification, optimization techniques, natural language processing, and computer vision in the cloud environment. Generic design patterns in Python programming is clearly explained, emphasizing architectural practices such as hot potato anti-patterns. You'll review recent advances in databases such as Neo4j, Elasticsearch, and MongoDB. You'll then study feature engineering in images and texts with implementing business logic and see how to build machine learning and deep learning models using transfer learning. Advanced Analytics with Python, 2nd edition features a chapter on clustering with a neural network, regularization techniques, and algorithmic design patterns in data analytics with reinforcement learning. Finally, the recommender system in PySpark explains how to optimize models for a specific application. What You'll Learn Build intelligent systems for enterprise Review time series analysis, classifications, regression, and clustering Explore supervised learning, unsupervised learning, reinforcement learning, and transfer learning Use cloud platforms like GCP and AWS in data analytics Understand Covers design patterns in Python Who This Book Is For Data scientists and software developers interested in the field of data analytics.


Data Algorithms with Spark

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Publisher : "O'Reilly Media, Inc."
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ISBN 10 : 9781492082354
Pages : 438 pages
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Rating : 4.8/5 (82 users download)


Download Data Algorithms with Spark by Mahmoud Parsian PDF/Ebook Free clicking on the below button will initiate the downloading process of Data Algorithms with Spark by Mahmoud Parsian. This book is available in ePub and PDF format with a single click unlimited downloads. Apache Spark's speed, ease of use, sophisticated analytics, and multilanguage support makes practical knowledge of this cluster-computing framework a required skill for data engineers and data scientists. With this hands-on guide, anyone looking for an introduction to Spark will learn practical algorithms and examples using PySpark. In each chapter, author Mahmoud Parsian shows you how to solve a data problem with a set of Spark transformations and algorithms. You'll learn how to tackle problems involving ETL, design patterns, machine learning algorithms, data partitioning, and genomics analysis. Each detailed recipe includes PySpark algorithms using the PySpark driver and shell script. With this book, you will: Learn how to select Spark transformations for optimized solutions Explore powerful transformations and reductions including reduceByKey(), combineByKey(), and mapPartitions() Understand data partitioning for optimized queries Build and apply a model using PySpark design patterns Apply motif-finding algorithms to graph data Analyze graph data by using the GraphFrames API Apply PySpark algorithms to clinical and genomics data Learn how to use and apply feature engineering in ML algorithms Understand and use practical and pragmatic data design patterns


Technology-enhanced Learning

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Publisher : Brill / Sense
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ISBN 10 : OSU:32435081358954
Pages : 318 pages
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Rating : 4./5 ( users download)


Download Technology-enhanced Learning by Peter Goodyear PDF/Ebook Free clicking on the below button will initiate the downloading process of Technology-enhanced Learning by Peter Goodyear. This book is available in ePub and PDF format with a single click unlimited downloads. Designing for technology enhanced learning (TEL) is often a demanding process. It involves creating challenging learning tasks, making sure that students have access to the right tools and resources, and ensuring there are appropriate opportunities for them to learn with and from each other. Good design is creative, and it also depends on deep experience, sound evidence about learning and an understanding of the capabilities of technology. This book introduces the use of design patterns and pattern languages as ways of capturing and sharing TEL design knowledge. The editors have assembled a team of authors who have pioneered research and development in this rapidly expanding field. The book surveys the state-of-the art and identifies productive lines for future research. It will be invaluable to researchers, teachers, students and professional TEL designers.


Design Patterns and Best Practices in Java

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Publisher : Packt Publishing Ltd
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ISBN 10 : 9781786469014
Pages : 280 pages
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Rating : 4.6/5 (469 users download)


Download Design Patterns and Best Practices in Java by Kamalmeet Singh PDF/Ebook Free clicking on the below button will initiate the downloading process of Design Patterns and Best Practices in Java by Kamalmeet Singh. This book is available in ePub and PDF format with a single click unlimited downloads. Create various design patterns to master the art of solving problems using Java Key Features This book demonstrates the shift from OOP to functional programming and covers reactive and functional patterns in a clear and step-by-step manner All the design patterns come with a practical use case as part of the explanation, which will improve your productivity Tackle all kinds of performance-related issues and streamline your development Book Description Having a knowledge of design patterns enables you, as a developer, to improve your code base, promote code reuse, and make the architecture more robust. As languages evolve, new features take time to fully understand before they are adopted en masse. The mission of this book is to ease the adoption of the latest trends and provide good practices for programmers. We focus on showing you the practical aspects of smarter coding in Java. We'll start off by going over object-oriented (OOP) and functional programming (FP) paradigms, moving on to describe the most frequently used design patterns in their classical format and explain how Java’s functional programming features are changing them. You will learn to enhance implementations by mixing OOP and FP, and finally get to know about the reactive programming model, where FP and OOP are used in conjunction with a view to writing better code. Gradually, the book will show you the latest trends in architecture, moving from MVC to microservices and serverless architecture. We will finish off by highlighting the new Java features and best practices. By the end of the book, you will be able to efficiently address common problems faced while developing applications and be comfortable working on scalable and maintainable projects of any size. What you will learn Understand the OOP and FP paradigms Explore the traditional Java design patterns Get to know the new functional features of Java See how design patterns are changed and affected by the new features Discover what reactive programming is and why is it the natural augmentation of FP Work with reactive design patterns and find the best ways to solve common problems using them See the latest trends in architecture and the shift from MVC to serverless applications Use best practices when working with the new features Who this book is for This book is for those who are familiar with Java development and want to be in the driver’s seat when it comes to modern development techniques. Basic OOP Java programming experience and elementary familiarity with Java is expected.


Machine Learning Systems

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Publisher : Pearson Professional
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ISBN 10 : 1617293334
Pages : 224 pages
File Format : PDF, EPUB, TEXT, KINDLE or MOBI
Rating : 4.1/5 (617 users download)


Download Machine Learning Systems by Jeff Smith PDF/Ebook Free clicking on the below button will initiate the downloading process of Machine Learning Systems by Jeff Smith. This book is available in ePub and PDF format with a single click unlimited downloads. 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


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