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Laravel 5.x Cookbook
by Alfred NutileA recipe-based book to help you efficiently create amazing PHP-based applications with Laravel 5.x About This Book * Leverage the amazing new features of Laravel 5.x to create cutting-edge responsive PHP applications. * Create apps with interoperability features and extend these features to your existing applications as well. * Over 60 recipes that combine tried and tested Laravel tips for getting your app working. Who This Book Is For The ideal target audience for this book is PHP developers who have some basic PHP programming knowledge. No previous experience with Laravel is required for this book. What You Will Learn * Optimize Your Gulp and Elixir Workflow * Use Travis to run tests with every push * Build and test your view-based route in PHPUnit * Explore workflows for migrations and seeding * Implement Angular in your Laravel applications * Set up a user authentication system * Integrate the new Billing library and Stripe in your Laravel application * Use the Artisan command-line tool * Test your App in Production with Behat In Detail Laravel is a prominent member of a new generation of web frameworks. It is one of the most popular PHP frameworks and is also free and an open source. Laravel 5 is a substantial upgrade with a lot of new toys, at the same time retaining the features that made Laravel wildly successful. It comes with plenty of architectural as well as design-based changes. The book is a blend of numerous recipes that will give you all the necessary tips you need to build an application. It starts with basic installation and configuration tasks and will get you up-and-running in no time. You will learn to create and customize your PHP app and tweak and re-design your existing apps for better performance. You will learn to implement practical recipes to utilize Laravel's modular structure, the latest method injection, route caching, and interfacing techniques to create responsive modern-day PHP apps that stand on their own against other apps. Efficient testing and deploying techniques will make you more confident with your Laravel skills as you move ahead with this book. Towards the end of the book, you will understand a number of add-ons and new features essential to finalize your application to make it ready for subscriptions. You will be empowered to get your application out to the world. Style and approach This book will have a practical recipe-based approach with dedicated recipes on your daily Laravel tasks (as well as on more advanced issues) that will help you become a pro with Laravel 5.x
Laravel Application Development Blueprints
by Halil Ibrahim Yılmaz Arda KılıcdagıFollow along as we work together to build 10 different applications using Laravel 4. Since each chapter is devoted to the design of a different application, there is no need to read the book in any particular order. Instead, you can pick and choose the blueprints that are of most interest to you and dive right in.This book is for intermediate to advanced level PHP programmers who want to master Laravel. It's assumed that you will have some experience with PHP already. This book is also for those who are already using a different PHP framework and are looking for better solutions.
Laravel Application Development Cookbook
by Terry MatulaGet to grips with a new technology, understand what it is and what it can do for you, and then get to work with the most important features and tasks.A short and precise guide to get you started with EaselJS , helping you to create some cool applications and games.EaselJS greatly simplifies application development in HTML5 Canvas using a syntax and an architecture very similar to the ActionScript 3.0 language. As a result, Flash / Flex developers will immediately feel at home but it's very easy to learn even if you've never opened Flash in your life. The book targets Web designers, animators, Digital content producers, and Flash and Flex developers.
Laravel Design Patterns and Best Practices
by H. Ibrahim Yilmaz Arda KilicdagiThis book is a practical guide packed with clear examples that will help you get to grips with the best practices in Laravel design patterns to create advanced web applications. This book is intended for web application developers working with Laravel who want to increase the efficiency of their web applications. It assumes that you have some experience with the Laravel PHP framework and are familiar with coding OOP methods.
Laravel Starter
by Shawn MccoolThis book is a practical, task-based, step-by-step tutorial that demonstrates topics ranging from MVC code-separation, to code-modularity, to utilizing ActiveRecord for data abstraction which are explained from the ground-up to provide a strong framework of understanding for creating professional web-applications with Laravel. This book is ideal for programmers familiar with PHP who are interested in learning the Laravel way of solving the common problems faced in their day to day work.
Laravel: Up & Running
by Matt StaufferWhat sets Laravel apart from other PHP web frameworks? Speed and simplicity, for starters. This rapid application development framework and its ecosystem of tools let you quickly build new sites and applications with clean, readable code. Fully updated to include Laravel 10, the third edition of this practical guide provides the definitive introduction to one of today's most popular web frameworks.Matt Stauffer, a leading teacher and developer in the Laravel community, delivers a high-level overview and concrete examples to help experienced PHP web developers get started with this framework right away. This updated edition covers the entirely new auth and frontend tooling and other first-party tools introduced since the second edition.Dive into features, including:Blade, Laravel's powerful custom templating toolTools for gathering, validating, normalizing, and filtering user-provided dataThe Eloquent ORM for working with application databasesThe Illuminate request object and its role in the application lifecyclePHPUnit, Mockery, and Dusk for testing your PHP codeTools for writing JSON and RESTful APIsInterfaces for filesystem access, sessions, cookies, caches, and searchTools for implementing queues, jobs, events, and WebSocket event publishingSpecialty packages including Scout, Passport, Cashier, and more
Large Deviations For Performance Analysis: Queues, Communication and Computing (Routledge Revivals)
by Alan Weiss Adam ShwartzOriginally published in 1995, Large Deviations for Performance Analysis consists of two synergistic parts. The first half develops the theory of large deviations from the beginning, through recent results on the theory for processes with boundaries, keeping to a very narrow path: continuous-time, discrete-state processes. By developing only what is needed for the applications, the theory is kept to a manageable level, both in terms of length and in terms of difficulty. Within its scope, the treatment is detailed, comprehensive and self-contained. As the book shows, there are sufficiently many interesting applications of jump Markov processes to warrant a special treatment. The second half is a collection of applications developed at Bell Laboratories. The applications cover large areas of the theory of communication networks: circuit switched transmission, packet transmission, multiple access channels, and the M/M/1 queue. Aspects of parallel computation are covered as well including, basics of job allocation, rollback-based parallel simulation, assorted priority queueing models that might be used in performance models of various computer architectures, and asymptotic coupling of processors. These applications are thoroughly analysed using the tools developed in the first half of the book.
Large-Eddy Simulation Based on the Lattice Boltzmann Method for Built Environment Problems
by Mengtao Han Ryozo OokaThis book details the lattice Boltzmann method (LBM) applied to the built environment problems. It provides the fundamental theoretical knowledge and specific implementation methods of LBM from the engineering perspective of the built environment. It covers comprehensive issues of built environment with three detailed cases, solving practical problems. It can be used as a reference book for teachers, students, and engineering technicians to study LBM and conduct architecture and urban wind environments simulations, in the fields of architecture, building technology science, urban planning, HVAC, built environment engineering, and civil engineering.
Large Group Decision Making: Creating Decision Support Approaches at Scale (SpringerBriefs in Computer Science)
by Iván Palomares CarrascosaThis SpringerBrief provides a pioneering, central point of reference for the interested reader in Large Group Decision Making trends such as consensus support, fusion and weighting of relevant decision information, subgroup clustering, behavior management, and implementation of decision support systems, among others. Based on the challenges and difficulties found in classical approaches to handle large decision groups, the principles, families of techniques, and newly related disciplines to Large-Group Decision Making (such as Data Science, Artificial Intelligence, Social Network Analysis, Opinion Dynamics, Behavioral and Cognitive Sciences), are discussed. Real-world applications and future directions of research on this novel topic are likewise highlighted.
Large Language Model-Based Solutions: How to Deliver Value with Cost-Effective Generative AI Applications (Tech Today)
by Shreyas SubramanianLearn to build cost-effective apps using Large Language Models In Large Language Model-Based Solutions: How to Deliver Value with Cost-Effective Generative AI Applications, Principal Data Scientist at Amazon Web Services, Shreyas Subramanian, delivers a practical guide for developers and data scientists who wish to build and deploy cost-effective large language model (LLM)-based solutions. In the book, you'll find coverage of a wide range of key topics, including how to select a model, pre- and post-processing of data, prompt engineering, and instruction fine tuning. The author sheds light on techniques for optimizing inference, like model quantization and pruning, as well as different and affordable architectures for typical generative AI (GenAI) applications, including search systems, agent assists, and autonomous agents. You'll also find: Effective strategies to address the challenge of the high computational cost associated with LLMs Assistance with the complexities of building and deploying affordable generative AI apps, including tuning and inference techniques Selection criteria for choosing a model, with particular consideration given to compact, nimble, and domain-specific models Perfect for developers and data scientists interested in deploying foundational models, or business leaders planning to scale out their use of GenAI, Large Language Model-Based Solutions will also benefit project leaders and managers, technical support staff, and administrators with an interest or stake in the subject.
Large-Scale Agile Frameworks: Agile Frameworks, agile Infrastruktur und pragmatische Lösungen zur digitalen Transformation
by Sascha BlockWarum sollten Sie sich mit Large-Scale Agile Frameworks auseinandersetzen? Agilität in Unternehmen und Organisationen gewinnt zunehmend an Bedeutung. Dieses Buch unterstützt Sie mit praxisnahen Lösungen und Werkzeugen zur übergreifenden Priorisierung von Anforderungen zur Software-Entwicklung. So ermöglichen Sie bestmögliche digitale Lösungen und etablieren einen optimalen Informationsfluss in Ihrer Organisation.Mit agilen Organisationsstrukturen - auch als Large-Scale Agile Frameworks bekannt - werden Unternehmen und Organisationen anpassungsfähiger. Damit können sie schneller auf Veränderungen reagieren und werden wettbewerbsfähiger. Unterstützen Sie somit die bestmögliche Priorisierung der in agilen Teams organisierten Unternehmenseinheiten. Wissenschaftsorientiert werden Large-Scale Agile Frameworks im Kontext des Large-Scale Agile Development und dazugehöriger Agile Frameworks und agiler Methodik und Tools beleuchtet. Mit dem Domänenmodell (Domain Driven Design), dem Scaled Agile Framework (SAFe) und dem Spotify Engineering Model werden drei bekannte Frameworks vorgestellt. Dabei wird das Zusammenspiel aktueller Technologie-Themen wie des Cloud-Trends oder die organisatorischen Anforderungen im Hinblick auf Microservices und IT-Security reflektiert.
Large-Scale Agile Frameworks: Agile Frameworks, Agile Infrastructure and Pragmatic Solutions for Digital Transformation
by Sascha BlockThe book Large-Scale Agile Frameworks provides practical solutions for cross-team and cross-functional prioritization of requirements and documentation for enterprises. It reflects the interplay of current technology trends such as cloud computing and organizational requirements for microservices. Organizations are increasingly required to align their IT strategy with customer needs for customer-centric and service-oriented products and services. The book analyzes the unique requirements of a differentiated software service offering and shows how agile principles are effective in addressing these issues. The book also highlights the importance of large-scale agile development and provides guidance to organizations on how to transform their structure towards agile prioritization. The book covers various appropriate models, methodologies, and agile tools and provides recommendations for cross-functional prioritization of requirements. It also considers the need for IT security and shows how it can be integrated into the overall agile development process.
Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention: International Workshops, LABELS 2019, HAL-MICCAI 2019, and CuRIOUS 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13 and 17, 2019, Proceedings (Lecture Notes in Computer Science #11851)
by Luping Zhou Nicholas Heller Yiyu Shi Yiming Xiao Raphael Sznitman Veronika Cheplygina Diana Mateus Emanuele Trucco X. Sharon Hu Danny Chen Matthieu Chabanas Hassan Rivaz Ingerid ReinertsenThis book constitutes the refereed joint proceedings of the 4th International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2019, the First International Workshop on Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention, HAL-MICCAI 2019, and the Second International Workshop on Correction of Brainshift with Intra-Operative Ultrasound, CuRIOUS 2019, held in conjunction with the 22nd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2019, in Shenzhen, China, in October 2019. The 8 papers presented at LABELS 2019, the 5 papers presented at HAL-MICCAI 2019, and the 3 papers presented at CuRIOUS 2019 were carefully reviewed and selected from numerous submissions. The LABELS papers present a variety of approaches for dealing with a limited number of labels, from semi-supervised learning to crowdsourcing. The HAL-MICCAI papers cover a wide set of hardware applications in medical problems, including medical image segmentation, electron tomography, pneumonia detection, etc. The CuRIOUS papers provide a snapshot of the current progress in the field through extended discussions and provide researchers an opportunity to characterize their image registration methods on newly released standardized datasets of iUS-guided brain tumor resection.
Large-Scale Data Analytics
by Aris Gkoulalas-Divanis Abderrahim LabbiThis edited book collects state-of-the-art research related to large-scale data analytics that has been accomplished over the last few years. This is among the first books devoted to this important area based on contributions from diverse scientific areas such as databases, data mining, supercomputing, hardware architecture, data visualization, statistics, and privacy. There is increasing need for new approaches and technologies that can analyze and synthesize very large amounts of data, in the order of petabytes, that are generated by massively distributed data sources. This requires new distributed architectures for data analysis. Additionally, the heterogeneity of such sources imposes significant challenges for the efficient analysis of the data under numerous constraints, including consistent data integration, data homogenization and scaling, privacy and security preservation. The authors also broaden reader understanding of emerging real-world applications in domains such as customer behavior modeling, graph mining, telecommunications, cyber-security, and social network analysis, all of which impose extra requirements for large-scale data analysis. Large-Scale Data Analytics is organized in 8 chapters, each providing a survey of an important direction of large-scale data analytics or individual results of the emerging research in the field. The book presents key recent research that will help shape the future of large-scale data analytics, leading the way to the design of new approaches and technologies that can analyze and synthesize very large amounts of heterogeneous data. Students, researchers, professionals and practitioners will find this book an authoritative and comprehensive resource.
Large-Scale Disk Failure Prediction: PAKDD 2020 Competition and Workshop, AI Ops 2020, February 7 – May 15, 2020, Revised Selected Papers (Communications in Computer and Information Science #1261)
by Cheng He Mengling Feng Patrick P. C. Lee Pinghui Wang Shujie Han Yi LiuThis book constitutes the thoroughly refereed post-competition proceedings of the AI Ops Competition on Large-Scale Disk Failure Prediction, conducted between February 7th and May 15, 2020 on the Alibaba Cloud Tianchi Platform. A dedicated workshop, featuring the best performing teams of the competition, was held at the 24th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2020, in Singapore, in April 2019. Due to the COVID-19 pandemic, the workshop was hosted online. This book includes 13 selected contributions: an introduction to dataset, selected approaches of the competing teams and the competition summary, describing the competition task, practical challenges, evaluation metrics, etc.
Large-scale Distributed Systems and Energy Efficiency
by Jean-Marc PiersonAddresses innovations in technology relating to the energy efficiency of a wide variety of contemporary computer systems and networks With concerns about global energy consumption at an all-time high, improving computer networks energy efficiency is becoming an increasingly important topic. Large-Scale Distributed Systems and Energy Efficiency: A Holistic View addresses innovations in technology relating to the energy efficiency of a wide variety of contemporary computer systems and networks. After an introductory overview of the energy demands of current Information and Communications Technology (ICT), individual chapters offer in-depth analyses of such topics as cloud computing, green networking (both wired and wireless), mobile computing, power modeling, the rise of green data centers and high-performance computing, resource allocation, and energy efficiency in peer-to-peer (P2P) computing networks. Discusses measurement and modeling of the energy consumption method Includes methods for energy consumption reduction in diverse computing environments Features a variety of case studies and examples of energy reduction and assessment Timely and important, Large-Scale Distributed Systems and Energy Efficiency is an invaluable resource for ways of increasing the energy efficiency of computing systems and networks while simultaneously reducing the carbon footprint.
Large-scale Graph Analysis: System, Algorithm and Optimization (Big Data Management)
by Yingxia Shao Bin Cui Lei ChenThis book introduces readers to a workload-aware methodology for large-scale graph algorithm optimization in graph-computing systems, and proposes several optimization techniques that can enable these systems to handle advanced graph algorithms efficiently. More concretely, it proposes a workload-aware cost model to guide the development of high-performance algorithms. On the basis of the cost model, the book subsequently presents a system-level optimization resulting in a partition-aware graph-computing engine, PAGE. In addition, it presents three efficient and scalable advanced graph algorithms – the subgraph enumeration, cohesive subgraph detection, and graph extraction algorithms. This book offers a valuable reference guide for junior researchers, covering the latest advances in large-scale graph analysis; and for senior researchers, sharing state-of-the-art solutions based on advanced graph algorithms. In addition, all readers will find a workload-aware methodology for designing efficient large-scale graph algorithms.
Large-Scale Graph Processing Using Apache Giraph
by Sherif Sakr Faisal Moeen Orakzai Ibrahim Abdelaziz Zuhair KhayyatThis book takes its reader on a journey through Apache Giraph, a popular distributed graph processing platform designed to bring the power of big data processing to graph data. Designed as a step-by-step self-study guide for everyone interested in large-scale graph processing, it describes the fundamental abstractions of the system, its programming models and various techniques for using the system to process graph data at scale, including the implementation of several popular and advanced graph analytics algorithms. The book is organized as follows: Chapter 1 starts by providing a general background of the big data phenomenon and a general introduction to the Apache Giraph system, its abstraction, programming model and design architecture. Next, chapter 2 focuses on Giraph as a platform and how to use it. Based on a sample job, even more advanced topics like monitoring the Giraph application lifecycle and different methods for monitoring Giraph jobs are explained. Chapter 3 then provides an introduction to Giraph programming, introduces the basic Giraph graph model and explains how to write Giraph programs. In turn, Chapter 4 discusses in detail the implementation of some popular graph algorithms including PageRank, connected components, shortest paths and triangle closing. Chapter 5 focuses on advanced Giraph programming, discussing common Giraph algorithmic optimizations, tunable Giraph configurations that determine the system's utilization of the underlying resources, and how to write a custom graph input and output format. Lastly, chapter 6 highlights two systems that have been introduced to tackle the challenge of large scale graph processing, GraphX and GraphLab, and explains the main commonalities and differences between these systems and Apache Giraph. This book serves as an essential reference guide for students, researchers and practitioners in the domain of large scale graph processing. It offers step-by-step guidance, with several code examples and the complete source code available in the related github repository. Students will find a comprehensive introduction to and hands-on practice with tackling large scale graph processing problems using the Apache Giraph system, while researchers will discover thorough coverage of the emerging and ongoing advancements in big graph processing systems.
Large-Scale Group Decision-Making: State-to-the-Art Clustering and Consensus Paths
by Su-Min Yu Zhi-Jiao DuThis book explores clustering operations in the context of social networks and consensus-reaching paths that take into account non-cooperative behaviors. This book focuses on the two key issues in large-scale group decision-making: clustering and consensus building. Clustering aims to reduce the dimension of a large group. Consensus reaching requires that the divergent individual opinions of the decision makers converge to the group opinion. This book emphasizes the similarity of opinions and social relationships as important measurement attributes of clustering, which makes it different from traditional clustering methods with single attribute to divide the original large group without requiring a combination of the above two attributes. The proposed consensus models focus on the treatment of non-cooperative behaviors in the consensus-reaching process and explores the influence of trust loss on the consensus-reaching process.The logic behind is as follows: firstly, a clustering algorithm is adopted to reduce the dimension of decision-makers, and then, based on the clusters’ opinions obtained, a consensus-reaching process is carried out to obtain a decision result acceptable to the majority of decision-makers. Graduates and researchers in the fields of management science, computer science, information management, engineering technology, etc., who are interested in large-scale group decision-making and consensus building are potential audience of this book. It helps readers to have a deeper and more comprehensive understanding of clustering analysis and consensus building in large-scale group decision-making.
Large Scale Hierarchical Classification: State of the Art (SpringerBriefs in Computer Science)
by Azad Naik Huzefa RangwalaThis SpringerBrief covers the technical material related to large scale hierarchical classification (LSHC). HC is an important machine learning problem that has been researched and explored extensively in the past few years. In this book, the authors provide a comprehensive overview of various state-of-the-art existing methods and algorithms that were developed to solve the HC problem in large scale domains. Several challenges faced by LSHC is discussed in detail such as: 1. High imbalance between classes at different levels of the hierarchy 2. Incorporating relationships during model learning leads to optimization issues 3. Feature selection 4. Scalability due to large number of examples, features and classes 5. Hierarchical inconsistencies 6. Error propagation due to multiple decisions involved in making predictions for top-down methods The brief also demonstrates how multiple hierarchies can be leveraged for improving the HC performance using different Multi-Task Learning (MTL) frameworks. The purpose of this book is two-fold: 1. Help novice researchers/beginners to get up to speed by providing a comprehensive overview of several existing techniques. 2. Provide several research directions that have not yet been explored extensively to advance the research boundaries in HC. New approaches discussed in this book include detailed information corresponding to the hierarchical inconsistencies, multi-task learning and feature selection for HC. Its results are highly competitive with the state-of-the-art approaches in the literature.
Large-Scale Machine Learning in the Earth Sciences (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)
by Ashok N. Srivastava Ramakrishna Nemani Karsten SteinhaeuserFrom the Foreword: "While large-scale machine learning and data mining have greatly impacted a range of commercial applications, their use in the field of Earth sciences is still in the early stages. This book, edited by Ashok Srivastava, Ramakrishna Nemani, and Karsten Steinhaeuser, serves as an outstanding resource for anyone interested in the opportunities and challenges for the machine learning community in analyzing these data sets to answer questions of urgent societal interest…I hope that this book will inspire more computer scientists to focus on environmental applications, and Earth scientists to seek collaborations with researchers in machine learning and data mining to advance the frontiers in Earth sciences." --Vipin Kumar, University of Minnesota Large-Scale Machine Learning in the Earth Sciences provides researchers and practitioners with a broad overview of some of the key challenges in the intersection of Earth science, computer science, statistics, and related fields. It explores a wide range of topics and provides a compilation of recent research in the application of machine learning in the field of Earth Science. Making predictions based on observational data is a theme of the book, and the book includes chapters on the use of network science to understand and discover teleconnections in extreme climate and weather events, as well as using structured estimation in high dimensions. The use of ensemble machine learning models to combine predictions of global climate models using information from spatial and temporal patterns is also explored. The second part of the book features a discussion on statistical downscaling in climate with state-of-the-art scalable machine learning, as well as an overview of methods to understand and predict the proliferation of biological species due to changes in environmental conditions. The problem of using large-scale machine learning to study the formation of tornadoes is also explored in depth. The last part of the book covers the use of deep learning algorithms to classify images that have very high resolution, as well as the unmixing of spectral signals in remote sensing images of land cover. The authors also apply long-tail distributions to geoscience resources, in the final chapter of the book.
Large Scale Machine Learning with Python
by Luca Massaron Alberto Boschetti Bastiaan SjardinLearn to build powerful machine learning models quickly and deploy large-scale predictive applications About This Book * Design, engineer and deploy scalable machine learning solutions with the power of Python * Take command of Hadoop and Spark with Python for effective machine learning on a map reduce framework * Build state-of-the-art models and develop personalized recommendations to perform machine learning at scale Who This Book Is For This book is for anyone who intends to work with large and complex data sets. Familiarity with basic Python and machine learning concepts is recommended. Working knowledge in statistics and computational mathematics would also be helpful. What You Will Learn * Apply the most scalable machine learning algorithms * Work with modern state-of-the-art large-scale machine learning techniques * Increase predictive accuracy with deep learning and scalable data-handling techniques * Improve your work by combining the MapReduce framework with Spark * Build powerful ensembles at scale * Use data streams to train linear and non-linear predictive models from extremely large datasets using a single machine In Detail Large Python machine learning projects involve new problems associated with specialized machine learning architectures and designs that many data scientists have yet to tackle. But finding algorithms and designing and building platforms that deal with large sets of data is a growing need. Data scientists have to manage and maintain increasingly complex data projects, and with the rise of big data comes an increasing demand for computational and algorithmic efficiency. Large Scale Machine Learning with Python uncovers a new wave of machine learning algorithms that meet scalability demands together with a high predictive accuracy. Dive into scalable machine learning and the three forms of scalability. Speed up algorithms that can be used on a desktop computer with tips on parallelization and memory allocation. Get to grips with new algorithms that are specifically designed for large projects and can handle bigger files, and learn about machine learning in big data environments. We will also cover the most effective machine learning techniques on a map reduce framework in Hadoop and Spark in Python. Style and approach This efficient and practical title is stuffed full of the techniques, tips and tools you need to ensure your large scale Python machine learning runs swiftly and seamlessly. Large-scale machine learning tackles a different issue to what is currently on the market. Those working with Hadoop clusters and in data intensive environments can now learn effective ways of building powerful machine learning models from prototype to production. This book is written in a style that programmers from other languages (R, Julia, Java, Matlab) can follow.
Large Scale Machine Learning with Spark
by Md. Mahedi Kaysar Md. Rezaul KarimDiscover everything you need to build robust machine learning applications with Spark 2.0 About This Book * Get the most up-to-date book on the market that focuses on design, engineering, and scalable solutions in machine learning with Spark 2.0.0 * Use Spark's machine learning library in a big data environment * You will learn how to develop high-value applications at scale with ease and a develop a personalized design Who This Book Is For This book is for data science engineers and scientists who work with large and complex data sets. You should be familiar with the basics of machine learning concepts, statistics, and computational mathematics. Knowledge of Scala and Java is advisable. What You Will Learn * Get solid theoretical understandings of ML algorithms * Configure Spark on cluster and cloud infrastructure to develop applications using Scala, Java, Python, and R * Scale up ML applications on large cluster or cloud infrastructures * Use Spark ML and MLlib to develop ML pipelines with recommendation system, classification, regression, clustering, sentiment analysis, and dimensionality reduction * Handle large texts for developing ML applications with strong focus on feature engineering * Use Spark Streaming to develop ML applications for real-time streaming * Tune ML models with cross-validation, hyperparameters tuning and train split * Enhance ML models to make them adaptable for new data in dynamic and incremental environments In Detail Data processing, implementing related algorithms, tuning, scaling up and finally deploying are some crucial steps in the process of optimising any application. Spark is capable of handling large-scale batch and streaming data to figure out when to cache data in memory and processing them up to 100 times faster than Hadoop-based MapReduce. This means predictive analytics can be applied to streaming and batch to develop complete machine learning (ML) applications a lot quicker, making Spark an ideal candidate for large data-intensive applications. This book focuses on design engineering and scalable solutions using ML with Spark. First, you will learn how to install Spark with all new features from the latest Spark 2.0 release. Moving on, you'll explore important concepts such as advanced feature engineering with RDD and Datasets. After studying developing and deploying applications, you will see how to use external libraries with Spark. In summary, you will be able to develop complete and personalised ML applications from data collections,model building, tuning, and scaling up to deploying on a cluster or the cloud. Style and approach This book takes a practical approach where all the topics explained are demonstrated with the help of real-world use cases.
Large Scale Network-Centric Distributed Systems
by Albert Y. Zomaya Hamid Sarbazi-AzadA highly accessible reference offering a broad range of topics and insights on large scale network-centric distributed systemsEvolving from the fields of high-performance computing and networking, large scale network-centric distributed systems continues to grow as one of the most important topics in computing and communication and many interdisciplinary areas. Dealing with both wired and wireless networks, this book focuses on the design and performance issues of such systems.Large Scale Network-Centric Distributed Systems provides in-depth coverage ranging from ground-level hardware issues (such as buffer organization, router delay, and flow control) to the high-level issues immediately concerning application or system users (including parallel programming, middleware, and OS support for such computing systems). Arranged in five parts, it explains and analyzes complex topics to an unprecedented degree:Part 1: Multicore and Many-Core (Mc) Systems-on-ChipPart 2: Pervasive/Ubiquitous Computing and Peer-to-Peer SystemsPart 3: Wireless/Mobile NetworksPart 4: Grid and Cloud ComputingPart 5: Other Topics Related to Network-Centric Computing and Its ApplicationsLarge Scale Network-Centric Distributed Systems is an incredibly useful resource for practitioners, postgraduate students, postdocs, and researchers.
Large Scale Networks: Modeling and Simulation
by Radu Dobrescu Florin IonescuThis book offers a rigorous analysis of the achievements in the field of traffic control in large networks, oriented on two main aspects: the self-similarity in traffic behaviour and the scale-free characteristic of a complex network. Additionally, the authors propose a new insight in understanding the inner nature of things, and the cause-and-effect based on the identification of relationships and behaviours within a model, which is based on the study of the influence of the topological characteristics of a network upon the traffic behaviour. The effects of this influence are then discussed in order to find new solutions for traffic monitoring and diagnosis and also for traffic anomalies prediction. Although these concepts are illustrated using highly accurate, highly aggregated packet traces collected on backbone Internet links, the results of the analysis can be applied for any complex network whose traffic processes exhibit asymptotic self-similarity, perceived as an adaptability of traffic in networks. However, the problem with self-similar models is that they are computationally complex. Their fitting procedure is very time-consuming, while their parameters cannot be estimated based on the on-line measurements. In this aim, the main objective of this book is to discuss the problem of traffic prediction in the presence of self-similarity and particularly to offer a possibility to forecast future traffic variations and to predict network performance as precisely as possible, based on the measured traffic history.