Algorithms and data structures Books
Manning Publications Inside Deep Learning: Math, Algorithms, Models
Book Synopsis"If you want to learn some of the deeper explanations of deep learning and PyTorch then read this book!" - Tiklu Ganguly Journey through the theory and practice of modern deep learning, and apply innovative techniques to solve everyday data problems. In Inside Deep Learning, you will learn how to: Implement deep learning with PyTorchSelect the right deep learning componentsTrain and evaluate a deep learning modelFine tune deep learning models to maximize performanceUnderstand deep learning terminologyAdapt existing PyTorch code to solve new problems Inside Deep Learning is an accessible guide to implementing deep learning with the PyTorch framework. It demystifies complex deep learning concepts and teaches you to understand the vocabulary of deep learning so you can keep pace in a rapidly evolving field. No detail is skipped—you'll dive into math, theory, and practical applications. Everything is clearly explained in plain English. about the technologyDeep learning isn't just for big tech companies and academics. Anyone who needs to find meaningful insights and patterns in their data can benefit from these practical techniques! The unique ability for your systems to learn by example makes deep learning widely applicable across industries and use-cases, from filtering out spam to driving cars. about the bookInside Deep Learning is a fast-paced beginners' guide to solving common technical problems with deep learning. Written for everyday developers, there are no complex mathematical proofs or unnecessary academic theory. You'll learn how deep learning works through plain language, annotated code and equations as you work through dozens of instantly useful PyTorch examples. As you go, you'll build a French-English translator that works on the same principles as professional machine translation and discover cutting-edge techniques just emerging from the latest research. Best of all, every deep learning solution in this book can run in less than fifteen minutes using free GPU hardware! about the readerFor Python programmers with basic machine learning skills. about the authorEdward Raff is a Chief Scientist at Booz Allen Hamilton, and the author of the JSAT machine learning library. His research includes deep learning, malware detection, reproducibility in ML, fairness/bias, and high performance computing. He is also a visiting professor at the University of Maryland, Baltimore County and teaches deep learning in the Data Science department. Dr Raff has over 40 peer reviewed publications, three best paper awards, and has presented at numerous major conferences.Trade Review“Afantastic book with a colourful and intuitive way of describing how deep learning works.” Richard Vaughan “Amazing at what it does. It's a book for people who not only want to use deep learning, but also understand it!” Adam Slysz “A remarkably clear explanation of practical deep learning showing readers how to quickly and systematically apply deep learning techniques tosolve their everyday data problems.” Jeff Neumann “If you want to learn some of the deeper explanations of deep learning and PyTorch then read this book!” Tiklu Ganguly “A must read if you don't understand how Deep Learning works under the hood.” Abdul Basit Hafeez
£39.99
The Pragmatic Programmers Genetic Algorithms and Machine Learning for
Book SynopsisSelf-driving cars, natural language recognition, and online recommendation engines are all possible thanks to Machine Learning. Now you can create your own genetic algorithms, nature-inspired swarms, Monte Carlo simulations, cellular automata, and clusters. Learn how to test your ML code and dive into even more advanced topics. If you are a beginner-to-intermediate programmer keen to understand machine learning, this book is for you. Discover machine learning algorithms using a handful of self-contained recipes. Build a repertoire of algorithms, discovering terms and approaches that apply generally. Bake intelligence into your algorithms, guiding them to discover good solutions to problems. In this book, you will: Use heuristics and design fitness functions. Build genetic algorithms. Make nature-inspired swarms with ants, bees and particles. Create Monte Carlo simulations. Investigate cellular automata. Find minima and maxima, using hill climbing and simulated annealing. Try selection methods, including tournament and roulette wheels. Learn about heuristics, fitness functions, metrics, and clusters. Test your code and get inspired to try new problems. Work through scenarios to code your way out of a paper bag; an important skill for any competent programmer. See how the algorithms explore and learn by creating visualizations of each problem. Get inspired to design your own machine learning projects and become familiar with the jargon. What You Need: Code in C++ (>= C++11), Python (2.x or 3.x) and JavaScript (using the HTML5 canvas). Also uses matplotlib and some open source libraries, including SFML, Catch and Cosmic-Ray. These plotting and testing libraries are not required but their use will give you a fuller experience. Armed with just a text editor and compiler/interpreter for your language of choice you can still code along from the general algorithm descriptions.
£35.14
The Pragmatic Programmers Genetic Algorithms in Elixir
Book SynopsisFrom finance to artificial intelligence, genetic algorithms are a powerful tool with a wide array of applications. But you don't need an exotic new language or framework to get started; you can learn about genetic algorithms in a language you're already familiar with. Join us for an in-depth look at the algorithms, techniques, and methods that go into writing a genetic algorithm. From introductory problems to real-world applications, you'll learn the underlying principles of problem solving using genetic algorithms. Evolutionary algorithms are a unique and often overlooked subset of machine learning and artificial intelligence. Because of this, most of the available resources are outdated or too academic in nature, and none of them are made with Elixir programmers in mind. Start from the ground up with genetic algorithms in a language you are familiar with. Discover the power of genetic algorithms through simple solutions to challenging problems. Use Elixir features to write genetic algorithms that are concise and idiomatic. Learn the complete life cycle of solving a problem using genetic algorithms. Understand the different techniques and fine-tuning required to solve a wide array of problems. Plan, test, analyze, and visualize your genetic algorithms with real-world applications. Open your eyes to a unique and powerful field - without having to learn a new language or framework. What You Need: You'll need a macOS, Windows, or Linux distribution with an up-to-date Elixir installation.
£30.39
Emerald Publishing Limited Organization and Governance Using Algorithms
Book SynopsisFollowing a recent mathematical, algorithmic, and computational turn in the field of social sciences, and particularly design aspects of contemporary organisations, Organisation and Governance Using Algorithms explores the problem of governance in organisations from a mathematical perspective. Avramopoulos offers a ground-breaking theory and application on organisational systems design, including discussions on organisational systems design requirements, such as productivity, emotion, and reward, the problems of unaccountability, including hierarchical delegation, and the benefits of accountable design. The suggested theoretical approach views organizational actors as computer processors that communicate through a shared infrastructure – both physical and digital – and suggests scientific principles and mechanisms by which to correct inequality and advance democratic governance in organisations.Table of ContentsChapter 1. Introduction Chapter 2. On the cognitive foundation of organization Chapter 3. Organizational systems design requirements Chapter 4. The stigmata of unaccountable presence Chapter 5. Organization based on accountably anonymous delegation Chapter 6. Concluding remarks and future work
£33.75
Springer International Publishing AG Guide to Computer Processor Architecture: A
Book SynopsisThis unique, accessible textbook presents a succession of implementations of the open-source RISC-V processor. Implementations are offered in increasing difficulty (non-pipelined, pipelined, deeply pipelined, multi-threaded, multicore).Each implementation is shown as a High-Level Synthesis (HLS) code in C++. This facilitates synthesis and testing on an FPGA-based development board (Such a board can be freely obtained from the Xilinx University Program targeting university professors).The book can be useful for several reasons. First, it is a novel way to introduce computer architecture: The codes given can serve as labs for a processor architecture course. Second, the book content is based on the RISC-V Instruction Set Architecture, which is an open-source machine language promising to become the main machine language to be taught, replacing DLX and MIPS. Third, all the designs are implemented through the HLS tool, which is able to translate a C program into an intellectual property (IP). Lastly, HLS will become the new standard for IP implementations, replacing Verilog/VHDL; already there are job positions tied to HLS, with the argument of rapid IP development.Hence, in addition to offering undergraduates a firm introduction, the textbook/guide can also serve engineers willing to implement processors on FPGA, as well as researchers willing to develop RISC-V based hardware simulators.Bernard Goossens is Professor in the Faculty of Sciences at the Université de Perpignan, France. He is author of the French-language book from Springer, Architecture et microarchitecture des processeurs, 2002.Table of ContentsPart I. Single core processors.- 1. Getting Ready.- 2. Building a RISC-V Processor.- 3. Building a Pipelined RISC-V Processor.- 4. Building a RISC-V Processor with a Multi-cycle Pipeline.- 5. Building a RISC-V Processor with a Multiple Hart Pipeline.- Part II. Multiple core processors.- 6. Connecting IPs.- 7. A Multi-core RISC-V Processor.- 8. A Multi-core RISC-V Processor with Multi-hart Cores.
£46.76
Cambridge University Press Introduction to Neuromorphic Computing
a huge range and FREE tracked UK delivery on ALL orders.
£52.24
Manning Publications Algorithms and Data Structures in Action
Book SynopsisAs a software engineer, you’ll encounter countless programming challenges that initially seem confusing, difficult, or even impossible. Don’t despair! Many of these “new” problems already have well-established solutions. Advanced Algorithms and Data Structures teaches you powerful approaches to a wide range of tricky coding challenges that you can adapt and apply to your own applications. Providing a balanced blend of classic, advanced, and new algorithms, this practical guide upgrades your programming toolbox with new perspectives and hands-on techniques. about the technology Data structures and algorithms are the foundations for how programs store and process information. Choosing the optimal algorithms ensures that your programs are fast, efficient, and reliable. about the book Algorithms and Data Structures in Action expands on the basic algorithms you already know to give you a better selection of solutions to different programming problems. In it, you’ll discover techniques for improving priority queues, efficient caching, clustering data, and more. Each example is fully illustrated with graphics, language agnostic pseudo-code, and code samples in various languages. When you’re done, you will be able to implement advanced and little-known algorithms to deliver better performance from your code. what's inside Improving on basic data structures Efficient caching Nearest neighbour search, including k-d trees and S-trees Full ‘pseudo-code’ and samples in multiple languages about the readerFor programmers with basic or intermediate skills. Written in a language-agnostic manner, no specific language knowledge is required. about the author Marcello La Rocca is a research scientist and a full-stack engineer focused on optimization algorithms, genetic algorithms, machine learning and quantum computing. He has contributed to large-scale web applications at companies like Twitter and Microsoft, has undertaken applied research in both academia and industry, and authored the Neatsort adaptive sorting algorithm.
£43.19
Manning Publications Graph Algorithms for Data Science
Book SynopsisGraphs are the natural way to understand connected data. This book explores the most important algorithms and techniques for graphs in data science, with practical examples and concrete advice on implementation and deployment. In Graph Algorithms for Data Science you will learn: Labeled-property graph modeling Constructing a graph from structured data such as CSV or SQL NLP techniques to construct a graph from unstructured data Cypher query language syntax to manipulate data and extract insights Social network analysis algorithms like PageRank and community detection How to translate graph structure to a ML model input with node embedding models Using graph features in node classification and link prediction workflows Graph Algorithms for Data Science is a hands-on guide to working with graph-based data in applications like machine learning, fraud detection, and business data analysis. It's filled with fascinating and fun projects, demonstrating the ins-and-outs of graphs. You'll gain practical skills by analyzing Twitter, building graphs with NLP techniques, and much more. You don't need any graph experience to start benefiting from this insightful guide. These powerful graph algorithms are explained in clear, jargon-free text and illustrations that makes them easy to apply to your own projects. about the technology Graphs reveal the relationships in your data. Tracking these interlinking connections reveals new insights and influences and lets you analyze each data point as part of a larger whole. This interconnected data is perfect for machine learning, as well as analyzing social networks, communities, and even product recommendations. about the book Graph Algorithms for Data Science teaches you how to construct graphs from both structured and unstructured data. You'll learn how the flexible Cypher query language can be used to easily manipulate graph structures, and extract amazing insights. The book explores common and useful graph algorithms like PageRank and community detection/clustering algorithms. Each new algorithm you learn is instantly put into action to complete a hands-on data project, including modeling a social network! Finally, you'll learn how to utilize graphs to upgrade your machine learning, including utilizing node embedding models and graph neural networks.Trade Review'The book covers topics in-depth but is easy to understand. Though delving into theory, it doesn't lose its focus of being a more practical guide. ' Carl Yu 'A good starting point to getting started with network analysis and how to extract the essential information you need easily.' Andrea Paciolla 'A great introduction to how to use graphs and data they can provide.' Marcin SękTable of Contentstable of contents detailed TOC READ IN LIVEBOOK 1GRAPHS AND NETWORK SCIENCE: AN INTRODUCTION READ IN LIVEBOOK 2REPRESENTING NETWORK STRUCTURE - DESIGN YOUR FIRST GRAPH MODEL READ IN LIVEBOOK 3YOUR FIRST STEPS WITH THE CYPHER QUERY LANGUAGE READ IN LIVEBOOK 4CYPHER AGGREGATIONS AND SOCIAL NETWORK ANALYSIS 5 INFERRING NETWORKS AND MONOPARTITE PROJECTIONS 6 CONSTRUCT A GRAPH USING NLP TECHNIQUES 7 NODE EMBEDDINGS AND CLASSIFICATION 8 IMPROVE DOCUMENT CLASSIFICATION WITH GRAPH NEURAL NETWORKS 9 PREDICT NEW CONNECTIONS 10 KNOWLEDGE GRAPH COMPLETION READ IN LIVEBOOK APPENDIX A: ADJACENCY MATRIX
£41.39
Kogan Page Ltd The Enterprise Big Data Framework
Book SynopsisJan-Willem Middelburg is a Dutch entrepreneur and author with a passion for technology and innovation. He is the CEO and co-founder of Cybiant, a global technology that company that helps to create a more sustainable world through analytics, big data and automation. He is also President and Chief Examiner of the Enterprise Big Data Framework, an independent organization dedicated to upskilling individuals with expertise in Big Data. In partnership with APMG-International, the Enterprise Big Data Framework offers vendor-neutral certifications for individuals.Trade Review"The Enterprise Big Data Framework is relevant for everybody within an organisation engaged in driving maximum benefits from data. There is something for everybody; from the board considering governance and ethical behaviour to individuals within the organisation knowing where they fit and the value they can get from better use of their organisation's data. If you are considering a transformation project, this is an excellent guide for your project team." * Richard Pharro, CEO, The APM Group Limited *"If you are looking for a good guide to empower your knowledge on big data and to find a framework to help you on your big data journey, then this book is for you. From learning what big data is to defining a big data strategy, Jan-Willem has built a book to empower the learner on the topic of big data." * Jordan Morrow, Chief Strategy & Transformation Officer, DataPrime and Author of Be Data Literate *"This book is a master piece for those who are familiar and those who discover the world of data. It provides an "a la carte framework" starting with a (big) data strategy and the supporting aspects such as big data functions, architecture and algorithms. It covers in depth data platforms architectures, its management as well as data governance, data catalogue and all the required security considerations associated to the various data classifications. You will find details of data life cycle management, of various machine learning algorithms and an important chapter covering AI ethics when building and deploying sophisticated algorithms using data. The concepts covered in this book apply to on-premises and in the (public) cloud environments, making this book a must read." * Jean-Michel Coeur, APAC Technology Practice Lead, Data Analytics, Google Cloud *Table of Contents Section - ONE: Introduction to Big Data; Chapter - 01: Introduction to Big Data; Chapter - 02: The Big Data framework; Chapter - 03: Big Data strategy; Chapter - 04: Big Data architecture; Chapter - 05: Big Data algorithms; Chapter - 06: Big Data processes; Chapter - 07: Big Data functions; Chapter - 08: Artificial intelligence; Section - TWO: Enterprise Big Data analysis; Chapter - 09: Introduction to Big Data analysis; Chapter - 10: Defining the business objective; Chapter - 11: Data ingestion – importing and reading data sets; Chapter - 12: Data preparation – cleaning and wrangling data; Chapter - 13: Data analysis – model building; Chapter - 14: Data presentation; Section - THREE: Enterprise Big Data engineering; Chapter - 15: Introduction to Big Data engineering; Chapter - 16: Data modelling; Chapter - 17: Constructing the data lake; Chapter - 18: Building an enterprise Big Data warehouse; Chapter - 19: Design and structure of Big Data pipelines; Chapter - 20: Managing data pipelines; Chapter - 21: Cluster technology; Section - FOUR: enterprise Big Data algorithm design; Chapter - 22: Introduction to Big Data algorithm design; Chapter - 23: Algorithm design – fundamental concepts; Chapter - 24: Statistical machine learning algorithms; Chapter - 25: The data science roadmap; Chapter - 26: Programming languages 26 visualization and simple metrics; Chapter - 27: Advanced machine learning algorithms; Chapter - 28: Advanced machine learning classification algorithms; Chapter - 29: Technical communication and documentation; Section - FIVE: Enterprise Big Data architecture; Chapter - 30: Introduction to the Big Data architecture; Chapter - 31: Strength and resilience – the Big Data platform; Chapter - 32: Design principles for Big Data architecture; Chapter - 33: Big Data infrastructure; Chapter - 34: Big Data platforms; Chapter - 35: The Big Data application provider; Chapter - 36: System orchestration in Big Data
£44.99
APress Advanced Data Analytics Using Python
Book Synopsis 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 analyticTable of Contents CHAPTER 1: Overview of Python Language 1.1 Philosophy of Python programming 1.2 Comparison with other languages 1.4 Design patterns in Python 1.4.1 Structural patterns 1.4.2 Behavioral patterns 1.4.3 Creational patterns 1.5 Why Python is so popular? 1.6 Use-case where Python does not fit well 1.7 Interfacing Python with other languages 1.7.1 Running Stanford NLP Java library in Python 1.7.2 Running time series Holt- Winter R module in Python 1.7.3 Expose your Python program as service in 2 minutes 1.8 Essential architectural pattern in data analytics 1. Hot Potato anti pattern 2. Data collector as a service 3. Bridge & proxy patterns. 4. Application layering CHAPTER 2: ETL with Python 2.1 Introduction 2.2 Python &Mysql 2.3 Python & Neo4j 2.4 Python & Elastic Search 2.5 Crawling with Beautiful Soup 2.6 Crawling using selenium 2.7 Regular expressions 2.8 Panda framework 2.9 Cloud Storages 2.9.1 AWS storage 2.10.1 GCP storages 2.9 Topical crawling 2.9.1 Find potential activists for a political party from web CHAPTER 3: Supervised Learning and Unsupervised Learning with Python 3.1. Introduction 3.2 Correlation analysis 3.2.1 Measures of correlation 3.2.2 Threshold for correlation 3.2.3 Dealing uneven cordiality of features 3.3 Principle component analysis 3.3.1 Singular value decomposition algorithm 3. 3.2 Factor analysis 3.3.3 Use case: Measuring impact of change in organization 3.4 Mutual information & dealing with categorical data 3.4.1 Use case: Measuring most significant features in ad price prediction 3.5 Feature engineering in texts and images 3.5.1 Classification 3. 5.2 Decision tree & entropy gain 3. 5.3 Random forest classifier 3. 5.4 Naïve bay’s classifier 3. 5.5 Support vector machine 3. 5.6 Text classification using Python 3. 5.7 Image classification using Python 3. 5.8 Supervised & unsupervised learning 3. 5.9. Semi supervised learning 3. 6.1 Regression 3. 6.2 Least-square estimation 3. 6.3 Logistic regression 3. 6.4 Classification using regression 3.6.5 Feature scaling 3.6.6 Intentionally bias the model to over fit or under fit CHAPTER 4: Clustering with Python 4.1 Introduction 4.2 Distance measures 4.3 Hierarchical clustering 4.3.1 Top to bottom algorithm 4.3.2 Bottom to top algorithm 4.3.3 Dendrogram to cluster 4.3.4 Choosing the threshold 4.4 K-Mean clustering 4.4.1 Algorithm 4.4.2 Choosing K 4.5 Graph theoretic approach 4.6 Measure for good clustering 4.7 Find summary of a paragraph 4.8 Find faces in images CHAPTER 5: Deep Learning & Neural Networks 5.1 History 5.2 Architecture 5.3 Use-case where NN fit well 5.4 Back propagation algorithm 5.5 Quick tour to other NN algorithms 5.6 Regularization techniques 5.7 Recurrent neural network 5.8 Goal oriented dialog system 5. 9.1 Convolution neural network 5. 9.2 Fake image detection Introduction to reinforcement learning 1. Dancing Floor on GCP 2. Dialectic Learning CHAPTER 6: Time Series Analysis 6.1 Introduction 6.2 Smoothing techniques 6.3 Autoregressive model 6.4 Moving average model 6.5 ARMA model 6.6 ARIMA model 6.7. SARIMA model 6.8 Historical practice 6.9 Frequency domain analysis in time series CHAPTER 7: Analytics in Scale 7.1 Introduction 7.2 Hadoop architecture 7.3 Popular design pattern in MapReduce 7.4 Introduction to cloud 7.5. Analytics on cloud 7.6 Introduction to Spark 7.7. Spark architecture - Memory optimization - Problem with memory optimization - Essential parameter in Spark - Naïve Bayes classifier in Spark 7.8 A recommendation system in Spark
£35.99
Princeton University Press Trading at the Speed of Light
Book SynopsisTrade Review"Winner of the Bronze Medal in Business Technology, Axiom Business Book Awards""I loved this book. . . . Trading at the Speed of Light is an amazing, detailed account of why material reality matters for virtual outcomes, and conversely, in the financial markets. Everybody with the slightest interest in modern finance should read it."---Diane Coyle, Enlightened Economist
£19.00
Springer-Verlag Berlin and Heidelberg GmbH & Co. KG Understanding Cryptography: A Textbook for
Book SynopsisCryptography is now ubiquitous – moving beyond the traditional environments, such as government communications and banking systems, we see cryptographic techniques realized in Web browsers, e-mail programs, cell phones, manufacturing systems, embedded software, smart buildings, cars, and even medical implants. Today's designers need a comprehensive understanding of applied cryptography. After an introduction to cryptography and data security, the authors explain the main techniques in modern cryptography, with chapters addressing stream ciphers, the Data Encryption Standard (DES) and 3DES, the Advanced Encryption Standard (AES), block ciphers, the RSA cryptosystem, public-key cryptosystems based on the discrete logarithm problem, elliptic-curve cryptography (ECC), digital signatures, hash functions, Message Authentication Codes (MACs), and methods for key establishment, including certificates and public-key infrastructure (PKI). Throughout the book, the authors focus on communicating the essentials and keeping the mathematics to a minimum, and they move quickly from explaining the foundations to describing practical implementations, including recent topics such as lightweight ciphers for RFIDs and mobile devices, and current key-length recommendations. The authors have considerable experience teaching applied cryptography to engineering and computer science students and to professionals, and they make extensive use of examples, problems, and chapter reviews, while the book’s website offers slides, projects and links to further resources. This is a suitable textbook for graduate and advanced undergraduate courses and also for self-study by engineers.The authors' website (http://www.crypto-textbook.com/) provides extensive notes, slides, video lectures; the authors' YouTube channel (https://www.youtube.com/channel/UC1usFRN4LCMcflV7UjHNuQg) includes video lectures.Trade ReviewFrom the reviews: "The authors have succeeded in creating a highly valuable introduction to the subject of applied cryptography. I hope that it can serve as a guide for practitioners to build more secure systems based on cryptography, and as a stepping stone for future researchers to explore the exciting world of cryptography and its applications." (Bart Preneel, K.U.Leuven) "The material is very well presented so it is clear to understand. The necessary amount of mathematics is used and complete yet simple examples are used by the authors to help the reader understand the topics. ... [The authors] appear to fully understand the concepts and follow a very good pedagogical process that helps the reader not only understand the different topics but motivate you to perform some of the exercises at the end of each chapter and browse some of the reference materials. I fully recommend this book to any software developer/designer working or considering working on a project that requires security." (John Canessa) "The book presents a panoramic of modern Cryptography with a view to practical applications. ... The book is well written, many examples and figures through it illustrate the theory and the book's website offers links and supplementary information. The book also discusses the implementation in software and hardware of the main algorithms described." (Juan Tena Ayuso, Zentralblatt MATH, Vol. 1190, 2010)Table of ContentsIntroduction to Cryptography and Data Security.- Stream Ciphers.- The Data Encryption Standard (DES) and Alternatives.- The Advanced Encryption Standard (AES).- More About Block Ciphers.- to Public-Key Cryptography.- The RSA Cryptosystem.- Public-Key Cryptosystems Based on the Discrete Logarithm Problem.- Elliptic Curve Cryptosystems.- Digital Signatures.- Hash Functions.- Message Authentication Codes (MACs).- Key Establishment.
£29.69
Penguin Books Ltd The Master Algorithm
Book Synopsis''Pedro Domingos demystifies machine learning and shows how wondrous and exciting the future will be'' Walter Isaacson, author of Steve JobsSociety is changing, one learning algorithm at a time, from search engines to online dating, personalized medicine to predicting the stock market. But learning algorithms are not just about Big Data - these algorithms take raw data and make it useful by creating more algorithms. This is something new under the sun: a technology that builds itself. In The Master Algorithm, Pedro Domingos reveals how machine learning is remaking business, politics, science and war. And he takes us on an awe-inspiring quest to find ''The Master Algorithm'' - a universal learner capable of deriving all knowledge from data.Trade ReviewPedro Domingos demystifies machine learning and shows how wondrous and exciting the future will be -- Walter Isaacson, author of Steve Jobs and The InnovatorsMachine learning is a fascinating world never before glimpsed by outsiders. Pedro Domingos initiates you to the mysterious languages spoken by its five tribes, and invites you to join in his plan to unite them, creating the most powerful technology our civilization has ever seen -- Sebastian Seung, Professor, Princeton, and author of 'Connectome'Machine learning, known in commercial use as predictive analytics, is changing the world. This riveting, far-reaching, and inspiring book introduces the deep scientific concepts to even non-technical readers, and yet also satisfies experts with a fresh, profound perspective that reveals the most promising research directions. It's a rare gem indeed -- Eric Siegel, founder of Predictive Analytics World and author of 'Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die'With terms like 'Machine Learning' and 'Big Data' regularly making headlines, there is no shortage of hype-filled business books on the subject. There are also textbooks that are too technical to be accessible. For those in the middle-from executives to college students-this is the ideal book, showing how and why things really work without the heavy math. Unlike other books that proclaim a bright future, this one actually gives you what you need to understand the changes that are coming -- Peter Norvig, Director of Research, Google and coauthor of 'Artificial Intelligence: A Modern Approach'[The Master Algorithm] does a good job of examining the field's five main techniques...The subject is meaty and the author ... has a knack for introducing concepts at the right moment * Economist *Machine learning is the single most transformative technology that will shape our lives over the next fifteen years. This book is a must-read-a bold and beautifully written new framework for looking into the future -- Geoffrey Moore, author of 'Crossing the Chasm'This is an incredibly important and useful book. Machine learning is already critical to your life and work, and will only become more so. Finally, Pedro Domingos has written about it in a clear and understandable fashion -- Thomas H. Davenport, Distinguished Professor, Babson College and author of 'Competing on Analytics and Big Data @ Work'Starting with the audacious claim that all knowledge can be derived from data by a single 'master algorithm,' Domingos takes the reader on a fast-paced journey through the brave new world of machine learning. Writing breezily but with deep authority, Domingos is the perfect tour guide from whom you will learn everything you need to know about this exciting field, and a surprising amount about science and philosophy as well -- Duncan Watts, Principal Researcher, Microsoft Research, and author of 'Six Degrees and Everything Is Obvious *Once You Know the Answer'A delightful book by one of the leading experts in the field. If you wonder how AI will change your life, read this book -- Sebastian Thrun, Research Professor, Stanford, Google Fellow and Inventor of the Self-Driving CarAn exhilarating venture into groundbreaking computer science * Booklist (starred review) *Domingos writes with verve and passion, and the book has a strong narrative * New Scientist *Pedro Domingos is a man with a quest, and a hypothesis, which is likely - one day - to change the world . . . Domingos is a genial and amusing guide . . . This is a highly inclusive book, aimed at a wide range of readers from the merely curious to those who might be interested in pursuing a career in the field . . . Descriptions and discussions are presented with a commendable lack of jargon and the examples are clear and accessible -- John Gilbey * Times Higher Education *Wonderfully erudite, humorous, and easy to read. * KDNuggets *The Master Algorithm does a good job of examining the field's five main techniques...The subject is meaty and the author...has a knack for introducing concepts at the right moment * Economist *
£10.44
Taylor & Francis Ltd Handbook of Computer Programming with Python
Book SynopsisThis handbook provides a hands-on experience based on the underlying topics, and assists students and faculty members in developing their algorithmic thought process and programs for given computational problems. It can also be used by professionals who possess the necessary theoretical and computational thinking background but are presently making their transition to Python.Key Features: Discusses concepts such as basic programming principles, OOP principles, database programming, GUI programming, application development, data analytics and visualization, statistical analysis, virtual reality, data structures and algorithms, machine learning, and deep learning Provides the code and the output for all the concepts discussed Includes a case study at the end of each chapter This handbook will benefit students of computer science, information systems, and information technology, or anyone who is Table of ContentsChapter 1 IntroductionDimitrios Xanthidis, Christos Manolas, Ourania K. Xanthidou, and Han-I WangChapter 2 Introduction to Programming with PythonAmeur Bensefia, Muath Alrammal, and Ourania K. XanthidouChapter 3 Object-Oriented Programming in PythonGhazala Bilquise, Thaeer Kobbaey, and Ourania K. XanthidouChapter 4 Graphical User Interface Programming with PythonOurania K. Xanthidou, Dimitrios Xanthidis, and Sujni PaulChapter 5 Application Development with PythonDimitrios Xanthidis, Christos Manolas, and Hanêne Ben-AbdallahChapter 6 Data Structures and Algorithms with PythonThaeer Kobbaey, Dimitrios Xanthidis, and Ghazala BilquiseChapter 7 Database Programming with PythonDimitrios Xanthidis, Christos Manolas, and Tareq AlhousaryChapter 8 Data Analytics and Data Visualization with PythonDimitrios Xanthidis, Han-I Wang, and Christos ManolasChapter 9 Statistical Analysis with PythonHan-I Wang, Christos Manolas, and Dimitrios XanthidisChapter 10 Machine Learning with PythonMuath Alrammal, Dimitrios Xanthidis, and Munir NaveedChapter 11 Introduction to Neural Networks and Deep LearningDimitrios Xanthidis, Muhammad Fahim, and Han-I WangChapter 12 Virtual Reality Application Development with PythonChristos Manolas, Ourania K. Xanthidou, and Dimitrios XanthidisAppendix: Case Studies Solutions
£85.49
John Wiley & Sons Inc Algorithmic Problem Solving
Book Synopsis* Novel approach to the mathematics of problem solving, in particular how to do logical calculations. * Many of the problems are well-known from (mathematical) puzzle books. * The solution method in the book is new and more relevant to the true nature of problem solving in the modern IT-dominated world.Table of ContentsPreface xi PART I Algorithmic Problem Solving 1 CHAPTER 1 – Introduction 3 1.1 Algorithms 3 1.2 Algorithmic Problem Solving 4 1.3 Overview 5 1.4 Bibliographic Remarks 6 CHAPTER 2 – Invariants 7 2.1 Chocolate Bars 10 2.1.1 The Solution 10 2.1.2 The Mathematical Solution 11 2.2 Empty Boxes 16 2.2.1 Review 19 2.3 The Tumbler Problem 22 2.3.1 Non-deterministic Choice 23 2.4 Tetrominoes 24 2.5 Summary 30 2.6 Bibliographic Remarks 34 CHAPTER 3 – Crossing a River 35 3.1 Problems 36 3.2 Brute Force 37 3.2.1 Goat, Cabbage and Wolf 37 3.2.2 State-Space Explosion 39 3.2.3 Abstraction 41 3.3 Nervous Couples 42 3.3.1 What Is the Problem? 42 3.3.2 Problem Structure 43 3.3.3 Denoting States and Transitions 44 3.3.4 Problem Decomposition 45 3.3.5 A Review 48 3.4 Rule of Sequential Composition 50 3.5 The Bridge Problem 54 3.6 Conditional Statements 63 3.7 Summary 65 3.8 Bibliographic Remarks 65 CHAPTER 4 – Games 67 4.1 Matchstick Games 67 4.2 Winning Strategies 69 4.2.1 Assumptions 69 4.2.2 Labelling Positions 70 4.2.3 Formulating Requirements 72 4.3 Subtraction-Set Games 74 4.4 Sums of Games 78 4.4.1 A Simple Sum Game 79 4.4.2 Maintain Symmetry! 81 4.4.3 More Simple Sums 82 4.4.4 Evaluating Positions 83 4.4.5 Using the Mex Function 87 4.5 Summary 91 4.6 Bibliographic Remarks 92 CHAPTER 5 – Knights and Knaves 95 5.1 Logic Puzzles 95 5.2 Calculational Logic 96 5.2.1 Propositions 96 5.2.2 Knights and Knaves 97 5.2.3 Boolean Equality 98 5.2.4 Hidden Treasures 100 5.2.5 Equals for Equals 101 5.3 Equivalence and Continued Equalities 102 5.3.1 Examples of the Associativity of Equivalence 104 5.3.2 On Natural Language 105 5.4 Negation 106 5.4.1 Contraposition 109 5.4.2 Handshake Problems 112 5.4.3 Inequivalence 113 5.5 Summary 117 5.6 Bibliographic Remarks 117 CHAPTER 6 – Induction 119 6.1 Example Problems 120 6.2 Cutting the Plane 123 6.3 Triominoes 126 6.4 Looking for Patterns 128 6.5 The Need for Proof 129 6.6 From Verification to Construction 130 6.7 Summary 134 6.8 Bibliographic Remarks 134 CHAPTER 7 – Fake-Coin Detection 137 7.1 Problem Formulation 137 7.2 Problem Solution 139 7.2.1 The Basis 139 7.2.2 Induction Step 139 7.2.3 The Marked-Coin Problem 140 7.2.4 The Complete Solution 141 7.3 Summary 146 7.4 Bibliographic Remarks 146 CHAPTER 8 – The Tower of Hanoi 147 8.1 Specification and Solution 147 8.1.1 The End of the World! 147 8.1.2 Iterative Solution 148 8.1.3 Why? 149 8.2 Inductive Solution 149 8.3 The Iterative Solution 153 8.4 Summary 156 8.5 Bibliographic Remarks 156 CHAPTER 9 – Principles of Algorithm Design 157 9.1 Iteration, Invariants and Making Progress 158 9.2 A Simple Sorting Problem 160 9.3 Binary Search 163 9.4 Sam Loyd’s Chicken-Chasing Problem 166 9.4.1 Cornering the Prey 170 9.4.2 Catching the Prey 174 9.4.3 Optimality 176 9.5 Projects 177 9.6 Summary 178 9.7 Bibliographic Remarks 180 CHAPTER 10 – The Bridge Problem 183 10.1 Lower and Upper Bounds 183 10.2 Outline Strategy 185 10.3 Regular Sequences 187 10.4 Sequencing Forward Trips 189 10.5 Choosing Settlers and Nomads 193 10.6 The Algorithm 196 10.7 Summary 199 10.8 Bibliographic Remarks 200 CHAPTER 11 – Knight’s Circuit 201 11.1 Straight-Move Circuits 202 11.2 Supersquares 206 11.3 Partitioning the Board 209 11.4 Summary 216 11.5 Bibliographic Remarks 218 PART II Mathematical Techniques 219 CHAPTER 12 – The Language of Mathematics 221 12.1 Variables, Expressions and Laws 222 12.2 Sets 224 12.2.1 The Membership Relation 224 12.2.2 The Empty Set 224 12.2.3 Types/Universes 224 12.2.4 Union and Intersection 225 12.2.5 Set Comprehension 225 12.2.6 Bags 227 12.3 Functions 227 12.3.1 Function Application 228 12.3.2 Binary Operators 230 12.3.3 Operator Precedence 230 12.4 Types and Type Checking 232 12.4.1 Cartesian Product and Disjoint Sum 233 12.4.2 Function Types 235 12.5 Algebraic Properties 236 12.5.1 Symmetry 237 12.5.2 Zero and Unit 238 12.5.3 Idempotence 239 12.5.4 Associativity 240 12.5.5 Distributivity/Factorisation 241 12.5.6 Algebras 243 12.6 Boolean Operators 244 12.7 Binary Relations 246 12.7.1 Reflexivity 247 12.7.2 Symmetry 248 12.7.3 Converse 249 12.7.4 Transitivity 249 12.7.5 Anti-symmetry 251 12.7.6 Orderings 252 12.7.7 Equality 255 12.7.8 Equivalence Relations 256 12.8 Calculations 257 12.8.1 Steps in a Calculation 259 12.8.2 Relations between Steps 260 12.8.3 ‘‘If’’ and ‘‘Only If’’ 262 12.9 Exercises 264 CHAPTER 13 – Boolean Algebra 267 13.1 Boolean Equality 267 13.2 Negation 269 13.3 Disjunction 270 13.4 Conjunction 271 13.5 Implication 274 13.5.1 Definitions and Basic Properties 275 13.5.2 Replacement Rules 276 13.6 Set Calculus 279 13.7 Exercises 281 CHAPTER 14 – Quantifiers 285 14.1 DotDotDot and Sigmas 285 14.2 Introducing Quantifier Notation 286 14.2.1 Summation 287 14.2.2 Free and Bound Variables 289 14.2.3 Properties of Summation 291 14.2.4 Warning 297 14.3 Universal and Existential Quantification 297 14.3.1 Universal Quantification 298 14.3.2 Existential Quantification 300 14.4 Quantifier Rules 301 14.4.1 The Notation 302 14.4.2 Free and Bound Variables 303 14.4.3 Dummies 303 14.4.4 Range Part 303 14.4.5 Trading 304 14.4.6 Term Part 304 14.4.7 Distributivity Properties 304 14.5 Exercises 306 CHAPTER 15 – Elements of Number Theory 309 15.1 Inequalities 309 15.2 Minimum and Maximum 312 15.3 The Divides Relation 315 15.4 Modular Arithmetic 316 15.4.1 Integer Division 316 15.4.2 Remainders and Modulo Arithmetic 320 15.5 Exercises 322 CHAPTER 16 – Relations, Graphs and Path Algebras 325 16.1 Paths in a Directed Graph 325 16.2 Graphs and Relations 328 16.2.1 Relation Composition 330 16.2.2 Union of Relations 332 16.2.3 Transitive Closure 334 16.2.4 Reflexive Transitive Closure 338 16.3 Functional and Total Relations 339 16.4 Path-Finding Problems 341 16.4.1 Counting Paths 341 16.4.2 Frequencies 343 16.4.3 Shortest Distances 344 16.4.4 All Paths 345 16.4.5 Semirings and Operations on Graphs 347 16.5 Matrices 351 16.6 Closure Operators 353 16.7 Acyclic Graphs 354 16.7.1 Topological Ordering 355 16.8 Combinatorics 357 16.8.1 Basic Laws 358 16.8.2 Counting Choices 359 16.8.3 Counting Paths 361 16.9 Exercises 366 Solutions to Exercises 369 References 405 Index 407
£41.75
University of California Press The Feel of Algorithms
Book SynopsisTable of ContentsContents Preface Acknowledgments Introduction 1 Structures of Feeling in Algorithmic Culture 2 Coevolving with Algorithms 3 The Digital Geography of Fear 4 Friction in Algorithmic Relations 5 Care for Algorithmic Futures Ways Forward References Index
£22.50
Cambridge University Press How to Think about Algorithms
a huge range and FREE tracked UK delivery on ALL orders.
£94.99
CRC Press AI for the Sustainable Development Goals
Book SynopsisArtificial Intelligence has a real impact on our lives and on our environment, and the Sustainable Development Goals enable us to evaluate these impacts in a systematic manner. AI for the Sustainable Development Goals shows how AI potentially affects all SDGs. Positively, but also negatively.Trade Review"Artificial Intelligence is continuously presented as the technology that will help solve some of the most complex problems of contemporary society, including the fight against climate change and global warming. However, reducing modern problems to mere engineering solutions is not straightforward and may have adverse consequences too. In this book, Sætra shows in an elegant and simple way how AI can indeed be a potential solution to some of the most pressing issues humanity is facing today. Yet, it also alerts about the dangers AI may entail without appropriate oversight and highlights the role and responsibilities of those wielding influence over such great powers. An excellent read and a stepping stone towards having AI serving the Sustainable Development Goals." -- Dr. Eduard Fosch-Villaronga, Assistant Professor, eLaw Center for Law and Digital Technologies, Leiden University.Table of ContentsAuthor. 1 Introduction. 2 AI and the SDGs in Context. 3 The Challenge of Evaluating AI Impact. 4 Sustainable Economic Development. 5 Sustainable Social Development. 6 Sustainable Environmental Development. 7 Assessing the Overall Impact of AI. 8 Conclusion. References. Index.
£22.99
Taylor & Francis Ltd Internet of Things
Book SynopsisToday, Internet of Things (IoT) is ubiquitous as it is applied in practice in everything from Industrial Control Systems (ICS) to e-Health, e-commerce, Cyber Physical Systems (CPS), smart cities, smart parking, healthcare, supply chain management and many more. Numerous industries, academics, alliances and standardization organizations make an effort on IoT standardization, innovation and development. But there is still a need for a comprehensive framework with integrated standards under one IoT vision. Furthermore, the existing IoT systems are vulnerable to huge range of malicious attacks owing to the massive numbers of deployed IoT systems, inadequate data security standards and the resource-constrained nature. Existing security solutions are insufficient and therefore it is necessary to enable the IoT devices to dynamically counter the threats and save the system.Apart from illustrating the diversified IoT applications, this book also addresses the issue of data safekeepinTable of Contents1. IoT Conceptual Model and Application. 2. Standardization of IoT Ecosystems: Open Challenges, Current Solutions, and Future Directions. 3. A Node Reduction Technique for Trojan Detection and Diagnosis in IoT Hardware Devices. 4. Deep-Learning-Empowered Edge Computing-Based IoT Frameworks. 5. A Geo-Referenced Data Collection Microservice Based on IoT Protocols for Smart HazMat Transportation. 6. Impact of Dimentionality Reduction on Performance of IoT Intrusion Detection System. 7. IoT-Based Resources Management and Monitoring for a Smart City. 8. Internet of Things Applications in Marketing. 9. Internet of Things (IoT) for Sustainable Smart Cities. 10. An Integration of IOT and Machine Learning in Smart City Planning. 11. The Internet of Medical Things for Monitoring Health. 12. Secured Multimedia and IoT in Healthcare Computing Paradigms. 13. Designing Contactless Automated Systems Using IoT, Sensors and Artificial Intelligence to Mitigate COVID-19. 14. Analysis Of the Framework for the Development, Security and Efficacy Of IoT-Based Mobile Health-Care Solutions for Antenatal Care.
£99.00
Taylor & Francis Ltd Stochastic Optimization for Largescale Machine
Book SynopsisAdvancements in the technology and availability of data sources have led to the `Big Data'' era. Working with large data offers the potential to uncover more fine-grained patterns and take timely and accurate decisions, but it also creates a lot of challenges such as slow training and scalability of machine learning models. One of the major challenges in machine learning is to develop efficient and scalable learning algorithms, i.e., optimization techniques to solve large scale learning problems.Stochastic Optimization for Large-scale Machine Learning identifies different areas of improvement and recent research directions to tackle the challenge. Developed optimisation techniques are also explored to improve machine learning algorithms based on data access and on first and second order optimisation methods.Key Features: Bridges machine learning and Optimisation. Bridges theory and practice in machine learning. Identifies key reTable of ContentsList of FiguresList of TablesPreface Section I BACKGROUND Introduction1.1 LARGE-SCALE MACHINE LEARNING 1.2 OPTIMIZATION PROBLEMS 1.3 LINEAR CLASSIFICATION1.3.1 Support Vector Machine (SVM) 1.3.2 Logistic Regression 1.3.3 First and Second Order Methods1.3.3.1 First Order Methods 1.3.3.2 Second Order Methods 1.4 STOCHASTIC APPROXIMATION APPROACH 1.5 COORDINATE DESCENT APPROACH 1.6 DATASETS 1.7 ORGANIZATION OF BOOK Optimisation Problem, Solvers, Challenges and Research Directions2.1 INTRODUCTION 2.1.1 Contributions 2.2 LITERATURE 2.3 PROBLEM FORMULATIONS 2.3.1 Hard Margin SVM (1992) 2.3.2 Soft Margin SVM (1995) 2.3.3 One-versus-Rest (1998) 2.3.4 One-versus-One (1999) 2.3.5 Least Squares SVM (1999) 2.3.6 v-SVM (2000) 2.3.7 Smooth SVM (2001) 2.3.8 Proximal SVM (2001) 2.3.9 Crammer Singer SVM (2002) 2.3.10 Ev-SVM (2003) 2.3.11 Twin SVM (2007) 2.3.12 Capped lp-norm SVM (2017) 2.4 PROBLEM SOLVERS 2.4.1 Exact Line Search Method 2.4.2 Backtracking Line Search 2.4.3 Constant Step Size 2.4.4 Lipschitz & Strong Convexity Constants 2.4.5 Trust Region Method 2.4.6 Gradient Descent Method 2.4.7 Newton Method 2.4.8 Gauss-Newton Method 2.4.9 Levenberg-Marquardt Method 2.4.10 Quasi-Newton Method 2.4.11 Subgradient Method 2.4.12 Conjugate Gradient Method 2.4.13 Truncated Newton Method 2.4.14 Proximal Gradient Method 2.4.15 Recent Algorithms 2.5 COMPARATIVE STUDY 2.5.1 Results from Literature 2.5.2 Results from Experimental Study 2.5.2.1 Experimental Setup and Implementation Details 2.5.2.2 Results and Discussions 2.6 CURRENT CHALLENGES AND RESEARCH DIRECTIONS 2.6.1 Big Data Challenge 2.6.2 Areas of Improvement 2.6.2.1 Problem Formulations 2.6.2.2 Problem Solvers 2.6.2.3 Problem Solving Strategies/Approaches 2.6.2.4 Platforms/Frameworks 2.6.3 Research Directions 2.6.3.1 Stochastic Approximation Algorithms 2.6.3.2 Coordinate Descent Algorithms 2.6.3.3 Proximal Algorithms 2.6.3.4 Parallel/Distributed Algorithms 2.6.3.5 Hybrid Algorithms 2.7 CONCLUSION Section II FIRST ORDER METHODSMini-batch and Block-coordinate Approach 3.1 INTRODUCTION 3.1.1 Motivation 3.1.2 Batch Block Optimization Framework (BBOF) 3.1.3 Brief Literature Review 3.1.4 Contributions 3.2 STOCHASTIC AVERAGE ADJUSTED GRADIENT (SAAG) METHODS3.3 ANALYSIS 3.4 NUMERICAL EXPERIMENTS 3.4.1 Experimental setup 3.4.2 Convergence against epochs 3.4.3 Convergence against Time 3.5 CONCLUSION AND FUTURE SCOPE Variance Reduction Methods 4.1 INTRODUCTION 4.1.1 Optimization Problem 4.1.2 Solution Techniques for Optimization Problem 4.1.3 Contributions 4.2 NOTATIONS AND RELATED WORK 4.2.1 Notations 4.2.2 Related Work 4.3 SAAG-I, II AND PROXIMAL EXTENSIONS 4.4 SAAG-III AND IV ALGORITHMS 4.5 ANALYSIS 4.6 EXPERIMENTAL RESULTS 4.6.1 Experimental Setup 4.6.2 Results with Smooth Problem 4.6.3 Results with non-smooth Problem 4.6.4 Mini-batch Block-coordinate versus mini-batch setting 4.6.5 Results with SVM 4.7 CONCLUSION Learning and Data Access 5.1 INTRODUCTION 5.1.1 Optimization Problem 5.1.2 Literature Review 5.1.3 Contributions 5.2 SYSTEMATIC SAMPLING 5.2.1 Definitions 5.2.2 Learning using Systematic Sampling 5.3 ANALYSIS 5.4 EXPERIMENTS 5.4.1 Experimental Setup 5.4.2 Implementation Details 5.4.3 Results 5.5 CONCLUSION Section III SECOND ORDER METHODS Mini-batch Block-coordinate Newton Method 6.1 INTRODUCTION 6.1.1 Contributions 6.2 MBN 6.3 EXPERIMENTS 6.3.1 Experimental Setup 6.3.2 Comparative Study 6.4 CONCLUSION Stochastic Trust Region Inexact Newton Method 7.1 INTRODUCTION 7.1.1 Optimization Problem 7.1.2 Solution Techniques 7.1.3 Contributions 7.2 LITERATURE REVIEW 7.3 TRUST REGION INEXACT NEWTON METHOD 7.3.1 Inexact Newton Method 7.3.2 Trust Region Inexact Newton Method 7.4 STRON 7.4.1 Complexity 7.4.2 Analysis 7.5 EXPERIMENTAL RESULTS 7.5.1 Experimental Setup 7.5.2 Comparative Study 7.5.3 Results with SVM 7.6 EXTENSIONS 7.6.1 PCG Subproblem Solver 17.6.2 Stochastic Variance Reduced Trust Region Inexact Newton Method 7.7 CONCLUSION Section IV CONCLUSIONConclusion and Future Scope 8.1 FUTURE SCOPE 142 Bibliography Index
£135.00
CRC Press Advances in Distance Learning in Times of
Book SynopsisThe book Advances in Distance Learning in Times of Pandemic is devoted to the issues and challenges faced by universities in the field of distance learning in COVID-19 times. It covers both the theoretical and practical aspects connected to distance education. It elaborates on issues regarding distance learning, its challenges, assessment by students and their expectations, the use of tools to improve distance learning, and the functioning of e-learning in the industry 4.0 and society 5.0 eras. The book also devotes a lot of space to the issues of Web 3.0 in university e-learning, quality assurance, and knowledge management. The aim and scope of this book is to draw a holistic picture of ongoing online teaching-activities before and during the lockdown period and present the meaning and future of e-learning from studentsâ points of view, taking into consideration their attitudes and expectations as well as industry 4.0 and society 5.0 aspects. The book presents the approach to distance learning and how it has changed, especially during a pandemic that revolutionized education. It highlights â the function of online education and how that has changed before and during the pandemic. â how e-learning is beneficial in promoting digital citizenship. â distance learning characteristic in the era of industry 4.0 and society 5.0. â how the era of industry 4.0 treats distance learning as a desirable form of education. The book covers both scientific and educational aspects and can be useful for university-level undergraduate, postgraduate and research-grade courses and can be referred to by anyone interested in exploring the diverse aspects of distance learning.
£52.40
Taylor & Francis Ltd Machine Learning for the Physical Sciences
Book SynopsisMachine learning is an exciting topic with a myriad of applications. However, most textbooks are targeted towards computer science students. This, however, creates a complication for scientists across the physical sciences that also want to understand the main concepts of machine learning and look ahead to applica- tions and advancements in their fields.This textbook bridges this gap, providing an introduction to the mathematical foundations for the main algorithms used in machine learning for those from the physical sciences, without a formal background in computer science. It demon- strates how machine learning can be used to solve problems in physics and engineering, targeting senior undergraduate and graduate students in physics and electrical engineering, alongside advanced researchers.All codes are available on the author''s website: CLab (nau.edu)They areTable of ContentsChapter 1: Multivariate Calculus. Chapter 2: Probability Theory. Chapter 3: Dimensionality Reduction. Chapter 4: Cluster Analysis. Chapter 5: Vector Quantization Techniques. Chapter 6: Regression Models. Chapter 7: Classification. Chapter 8: Feedforward Networks. Chapter 9: Advanced Network Architectures. Chapter 10: Value Methods. Chapter 11: Gradient Methods. Chapter 12: Population-Based Metaheuristic Methods. Chapter 13: Local Search methods. Appendix A: Sufficient Statistic. Appendix B: Graphs. Appendix C: Sequential Minimization Optimization. Appendix D: Algorithmic Differentiation. Appendix E: Batch Normalizing Transform. Appendix F: Divergence of Two Gaussian Distributions. Appendix G: Continuous-time Bellman's Equation. Appendix H: Conjugate Gradient. Appendix I: Importance Sampling. References. Index.
£63.64
CRC Press New Perspectives in Behavioral Cybersecurity
Book SynopsisNew Perspectives in Behavioral Cybersecurity offers direction for readers in areas related to human behavior and cybersecurity, by exploring some of the new ideas and approaches in this subject, specifically with new techniques in this field coming from scholars with very diverse backgrounds in dealing with these issues. It seeks to show an understanding of motivation, personality, and other behavioral approaches to understand cyberattacks and create cyberdefenses.This book:â Elaborates cybersecurity concerns in the work environment and cybersecurity threats to individuals. â Presents personality characteristics of cybersecurity attackers, cybersecurity behavior, and behavioral interventions. â Highlights the applications of behavioral economics to cybersecurity. â Captures the management and security of financial data through integrated software solutions. â Examines the importance of studying fake news proliferation by detecting cTable of ContentsSection I. Cybersecurity Concerns in the Work Environment. 1. Management and Security of Financial Data Through Integrated Software Solutions. 2. An Efficient Scheme For Detecting And Mitigating Insider Threats. 3. (Figures query) Phishing Through URLs: An Instance Based Learning Model Approach to Detecting Phishing. Section II. Cybersecurity Threats to the Individual. 4. Video Games in Digital Forensics. 5. Dances with the Illuminati: Hands-On Social Engineering in Classroom Setting. 6. Studying Fake News Proliferation by Detecting Coordinated Inauthentic Behavior. 7. Refining the Sweeney Approach on Data Privacy. Section III. Cybersecurity Concerns in the Home and Work Environment. 8. Cybersecurity Hygiene: Blending Home and Work Computing. 9. Will a Cybersecurity Mindset shift build and sustain a Cybersecurity Pipeline?. Section IV. Ethical Behavior. 10. Cybersecurity Behavior and Behavioral Interventions. Section V. Differences in Languages in Cyberattacks. 11. Using Language Differences to Detect Cyberattacks: Ukrainian and Russian. Section VI. Applications of Behavioral Economics to Cybersecurity. 12. Using Economic Prospect Theory To Quantify Side Channel Attacks. 13. A Game-Theoretic Approach to Detecting Romance Scams. Section VII. New Approaches for Future Research. 14. (Unfinished?) Human-Centered Artificial intelligence: Threats and Opportunities for Cybersecurity.
£76.99
Cambridge University Press Finite Elements
Book SynopsisWritten in easy to understand language, this self-explanatory guide introduces the fundamentals of finite element methods and its application to differential equations. Beginning with a brief introduction to Sobolev spaces and elliptic scalar problems, the text progresses through an explanation of finite element spaces and estimates for the interpolation error. The concepts of finite element methods for parabolic scalar parabolic problems, object-oriented finite element algorithms, efficient implementation techniques, and high dimensional parabolic problems are presented in different chapters. Recent advances in finite element methods, including non-conforming finite elements for boundary value problems of higher order and approaches for solving differential equations in high dimensional domains are explained for the benefit of the reader. Numerous solved examples and mathematical theorems are interspersed throughout the text for enhanced learning.Trade Review'The book is written in a very traditional and straightforward style of theory and proof. The organization of the material makes it accessible for the reader to gain a foundational understanding of the topics … This book provides a readable, concise introduction to finite elements. Summing Up: Recommended.' S. L. Sullivan, CHOICETable of ContentsPreface; 1. Sobolev spaces; 1.1. Banach and Hilbert spaces; 1.2. Weak derivatives; 1.3. Sobolev spaces; 2. Elliptic scalar problems; 2.1. A general elliptic problem of second order; 2.2. Weak solution; 2.3. Standard Galerkin method; 2.4. Abstract error estimate; 3. Finite element spaces; 3.1. Simplices and barycentric coordinates; 3.2. Simplicial finite elements and local spaces; 3.3. Construction of finite elements spaces; 3.4. The concept of mapped finite elements: affine mappings; 3.5. Finite elements on rectangular and brick meshes; 3.6. Mapped finite elements: general bijective mappings; 3.7. Mapped Qk finite elements; 3.8. Isoparametric finite elements; 3.9. Further examples of finite elements in C0 and C1; 4. Interpolation and discretization error; 4.1. Transformation formulas; 4.2. Affine equivalent finite elements; 4.3. Canonical interpolation; 4.4. Local and global interpolation error; 4.5. Improved L2 error estimates by duality; 4.6. Interpolation of less smooth functions; 5. Biharmonic equation; 5.1. Deflection of a thin clamped plate; 5.2. Weak formulation of the biharmonic equation; 5.3. Conforming finite element methods; 5.4. Nonconforming finite element methods; 6. Parabolic problems; 6.1. Conservation of energy; 6.2. A general parabolic problem of initial boundary value problems; 6.3. Weak formulation of initial boundary value problems; 6.4. Semidiscretization by finite elements; 6.5. Time discretization; 6.6. Finite elements for high-dimensional parabolic problems; 7. Systems in solid mechanics; 7.1. Linear elasticity; 7.2. Mindlin–Reissner plate; 8. Systems in fluid mechanics; 8.1. Conservation of mass and momentum; 8.2. Weak formulation of the Stokes problem; 8.3. Conforming discretizations of the Stokes problem; 8.4. Nonconforming discretizations of the Stokes problem; 8.5. The nonconforming Crouzeix–Raviart element; 8.6. Further inf–sup stable finite element pairs; 8.7. Equal order stabilized finite elements; 8.8. Navier–Stokes problem with mixed boundary conditions; 8.9. Time discretization and linearization of the Navier–Stokes problem; 9. Implementation of the finite element method; 9.1. Mesh handling and data structure; 9.2. Numerical integration; 9.3. Sparse matrix storage; 9.4. Assembling of system matrices and load vectors; 9.5. Inclusion of boundary conditions; 9.6. Solution of the algebraic systems; 9.7. Object-oriented C++ programming; Bibliography; Index.
£53.19
Cambridge University Press Algorithmic Information Dynamics
Book SynopsisAimed at graduate students and researchers, this book offers a model-driven approach to the study and manipulation of dynamical systems. Based on an online course hosted by the Complexity Explorer, it uses analytical tools from information theory and complexity science to tackle key challenges in network and systems biology.Table of ContentsIntroduction; Part I. Preliminaries: 1. A computational approach to causality; 2. Networks: from structure to dynamics; 3. Information and computability theories; Part II. Theory and Methods: 4. Algorithmic information theory; 5. The coding theorem method (CTM); 6. The block decomposition method (BDM); 7. Graph and tensor complexity; 8. Algorithmic information dynamics (AID); Part III. Applications: 9. From theory to practice; 10. Algorithmic dynamics in artificial environments; 11. Applications to integer and behavioural sequences; 12. Applications to evolutionary biology; Postface; Appendix: Mutual and conditional BDM; Glossary.
£56.99
Springer-Verlag Berlin and Heidelberg GmbH & Co. KG Entity Framework 6 Recipes
Book SynopsisEntity Framework 6 Recipes provides an exhaustive collection of ready-to-use code solutions for Entity Framework, Microsoft's model-centric, data-access platform for the .NET Framework and ASP.NET development.Table of Contents Getting Started with Entity Framework Entity Data Modeling Fundamentals Querying an Entity Data Model Using Entity Framework in ASP.NET Loading Entities and Navigation Properties Beyond the Basics with Modeling and Inheritance Working with Object Services Plain Old CLR Objects Using the Entity Framework in N-Tier Applications Stored Procedures Functions Customizing Entity Framework Objects Improving Performance Concurrency
£52.24
APress Data versus Democracy
Book Synopsis Human attention is in the highest demand it has ever been. The drastic increase in available information has compelled individuals to find a way to sift through the media that is literally at their fingertips. Content recommendation systems have emerged as the technological solution to this social and informational problem, but they''ve also created a bigger crisis in confirming our biases by showing us only, and exactly, what it predicts we want to see. Data versus Democracy investigates and explores how, in the era of social media, human cognition, algorithmic recommendation systems, and human psychology are all working together to reinforce (and exaggerate) human bias. The dangerous confluence of these factors is driving media narratives, influencing opinions, and possibly changing election results. In this book, algorithmic recommendations, clickbait, familiarity bias, propaganda, Trade Review“A very well written book that has an engaging style of writing, doesn’t become dry or bogged down in the details, but still showcases the depth of knowledge that Shaffer has on the subject. … It’s accessible and it provides a satisfying read to those looking for deep analysis of this emerging problem faced by the world.” (The Robotics Law Journal, Vol. 5 (2), September - October, 2019) Table of ContentsPart I: The Propaganda Problem.- Chapter 1: Pay Attention: How Information Abundance Affects the Way We Consume Media .- Chapter 2: Cog in the System: How the Limits of Our Brains Leave Us Vulnerable to Cognitive Hacking.- Chapter 3: Swimming Upstream: How Content Recommendation Engines Impact Information and Manipulate Our Attention.- Part II: Case Studies.- Chapter 4: Domestic Disturbance: Ferguson, GamerGate, and the Rise of the American Alt-Right.- Chapter 5: Democracy Hacked, Part 1: Russian Interference and the New Cold War .- Chapter 6: Democracy Hacked, Part 2: Rumors, Bots, and Genocide in the Global South .- Chapter 7: Conclusion: Where Do We Go from Here?.-
£22.49
O'Reilly Media Introduction to Machine Learning with R
Book SynopsisMachine learning is an intimidating subject until you know the fundamentals. If you understand basic coding concepts, this introductory guide will help you gain a solid foundation in machine learning principles.
£33.74
Springer-Verlag New York Inc. An Introduction to Mathematical Cryptography
Book SynopsisPreface.- Introduction.- 1 An Introduction to Cryptography.- 2 Discrete Logarithms and Diffie-Hellman.- 3 Integer Factorization and RSA.- 4 Digital Signatures.- 5 Combinatorics, Probability, and Information Theory.- 6 Elliptic Curves and Cryptography.- 7 Lattices and Cryptography.- 8 Additional Topics in Cryptography.- List of Notation.- References.- Index.Trade Review“This book explains the mathematical foundations of public key cryptography in a mathematically correct and thorough way without omitting important practicalities. … I would like to emphasize that the book is very well written and quite clear. Topics are well motivated, and there are a good number of examples and nicely chosen exercises. To me, this book is still the first-choice introduction to public-key cryptography.” (Klaus Galensa, Computing Reviews, March, 2015)“This is a text for an upper undergraduate/lower graduate course in mathematical cryptography. … It is very well written and quite clear. Topics are well-motivated, and there are a good number of examples and nicely chosen exercises. … An instructor of a fairly sophisticated undergraduate course in cryptography who wants to emphasize public key cryptography should definitely take a look at this book.” (Mark Hunacek, MAA Reviews, October, 2014)Table of ContentsPreface.- Introduction.- 1 An Introduction to Cryptography.- 2 Discrete Logarithms and Diffie-Hellman.- 3 Integer Factorization and RSA.- 4 Digital Signatures.- 5 Combinatorics, Probability, and Information Theory.- 6 Elliptic Curves and Cryptography.- 7 Lattices and Cryptography.- 8 Additional Topics in Cryptography.- List of Notation.- References.- Index.
£56.69
De Gruyter Practical AI for Business Leaders Product
Book SynopsisMost economists agree that AI is a general purpose technology (GPT) like the steam engine, electricity, and the computer. AI will drive innovation in all sectors of the economy for the foreseeable future. Practical AI for Business Leaders, Product Managers, and Entrepreneurs is a technical guidebook for the business leader or anyone responsible for leading AI-related initiatives in their organization. The book can also be used as a foundation to explore the ethical implications of AI. Authors Alfred Essa and Shirin Mojarad provide a gentle introduction to foundational topics in AI. Each topic is framed as a triad: concept, theory, and practice. The concept chapters develop the intuition, culminating in a practical case study. The theory chapters reveal the underlying technical machinery. The practice chapters provide code in Python to implement the models discussed in the case study. With this book, readers will learn: The technical foundations of machine learning and deep leaTable of Contents Introduction What is AI and why it is at the center of major business transformation? How is it related to machine learning? What is deep learning, and how is it related to ML? Why is it important? How the book is organized Who is the audience? Section 1: Machine Learning Chapter 1.1, introduction, machine learning, different types of machine learning Chapter 1.2, Machine Learning Technical Overview Chapter 1.3, Hands-On Machine Learning with Scikit Learn Chapter 1.4, Advanced Topics/flavors of Machine learning Appendix: mathematical interlude Section 2: Deep Learning Chapter 2.1, introduction (what is it, why is it important) Chapter 2.2, Deep Learning Technical Overview Chapter 2.3, Hands-On Deep Learning with Keras Chapter 2.4, Advanced Topics/flavors of deep learning Appendix: mathematical interlude Section 3: Putting AI into Practice: Innovation Framework Chapter 3.1: Diffusion and Dynamics of Innovation Chapter 3.2: Managing an Innovation Portfolio
£40.95
BCS Learning & Development Limited Principles of Data Management: Facilitating
Book SynopsisData is a valuable corporate asset and its effective management is vital to an organisation’s success and survival. With this book you will learn to master the key principles of data management and use them to implement best practices in your organization. This professional guide covers all the key areas of data management, including database development and corporate data modelling. It is business-focused, providing the knowledge and techniques required to successfully implement the data management function. This fully updated new edition provides new chapters on the most important data topics such as big data, artificial intelligence, linked data and concept systems. Principles of Data Management is fully aligned with syllabus for the BCS Professional Certificate in Data Management Essentials, making this the go-to text to unlocking the value of your data. Ideal for business managers and all involved in the development of information systems as well as data management professionals Comprehensive and descriptive view of data management Suitable for all levels, from beginners to advanced learners Must-read for anyone involved in the development of systems to manage data Trade ReviewThis book is an excellent guide to understanding data management theory and techniques. It works at all levels: from beginner to advanced, and from reference source to the practicalities of implementation. I would highly recommend to anyone wanting to get to grips with data management, regardless of experience in the field. -- Ian Wallis, Managing Director, Data Strategists LtdKeith has developed a broad and thorough understanding of all aspects of data management over many years, so is without doubt one of the authorities on data management. This updated book includes reference to a number of new techniques as well as refining existing guidance on data modelling and database structures. Keith clearly explains both the importance of planning and analysis of databases and repositories and an explanation of key techniques to achieve this. A ‘must buy’ for the bookshelf of any data management practitioner. -- Julian Schwarzenbach, Chair of the BCS Data Management Specialist GroupThis book provides a comprehensive and descriptive view of data management within a database setting. This is a must read for anyone involved in the development of systems to manage data. This book is as useful as it is interesting. It covers everything you need to know about getting the most out of your data management processes and architecture. -- Ian Rush, Data & Process Advantage LtdTable of ContentsPart 1: Preliminaries Chapter 1 Data and the enterprise Chapter 2 Databases and their development Chapter 3 What is data management? Part 2: Data Administration Chapter 4 Corporate data modelling Chapter 5 Data definition and naming Chapter 6 Metadata Chapter 7 Data quality Chapter 8 Data accessibility Chapter 9 Master data management Part 3: Database and Repository Administration Chapter 10 Database administration Chapter 11 Repository administration Part 4: The Data Management Environment Chapter 12 The use of packaged application software Chapter 13 Distributed data and databases Chapter 14 Business intelligence Chapter 15 Object orientation Chapter 16 Multimedia Chapter 17 Integrating data and web technology Chapter 18 Linked data Chapter 19 Concept systems Chapter 20 Big data and artificial intelligence Appendices Appendix A Comparison of data modelling notations Appendix B Generic data models Appendix C HTML and XML Appendix D Techniques and skills for data management Appendix E Data strategy Appendix F International standards for data management Appendix G The BCS Data Management Essentials syllabus
£33.24
Springer Nature Switzerland AG Core Data Analysis: Summarization, Correlation,
Book SynopsisThis text examines the goals of data analysis with respect to enhancing knowledge, and identifies data summarization and correlation analysis as the core issues. Data summarization, both quantitative and categorical, is treated within the encoder-decoder paradigm bringing forward a number of mathematically supported insights into the methods and relations between them. Two Chapters describe methods for categorical summarization: partitioning, divisive clustering and separate cluster finding and another explain the methods for quantitative summarization, Principal Component Analysis and PageRank. Features:· An in-depth presentation of K-means partitioning including a corresponding Pythagorean decomposition of the data scatter. · Advice regarding such issues as clustering of categorical and mixed scale data, similarity and network data, interpretation aids, anomalous clusters, the number of clusters, etc.· Thorough attention to data-driven modelling including a number of mathematically stated relations between statistical and geometrical concepts including those between goodness-of-fit criteria for decision trees and data standardization, similarity and consensus clustering, modularity clustering and uniform partitioning.New edition highlights: · Inclusion of ranking issues such as Google PageRank, linear stratification and tied rankings median, consensus clustering, semi-average clustering, one-cluster clustering· Restructured to make the logics more straightforward and sections self-containedCore Data Analysis: Summarization, Correlation and Visualization is aimed at those who are eager to participate in developing the field as well as appealing to novices and practitioners. Trade Review“This book provides a clear overview of the data analysis process, the different types of statistical techniques employed for data analysis, and their role and purpose. … There is good use of a variety of examples to demonstrate how the different techniques are applied in practice. The book’s main purpose would be as a textbook for undergraduate students, or a reference book for data analysts.” (Mark Taylor, Computing Reviews, May 5, 2022)Table of Contents
£54.99
Springer Nature Switzerland AG Data Structures and Algorithms with Scala: A
Book SynopsisThis practically-focused textbook presents a concise tutorial on data structures and algorithms using the object-functional language Scala. The material builds upon the foundation established in the title Programming with Scala: Language Exploration by the same author, which can be treated as a companion text for those less familiar with Scala.Topics and features: discusses data structures and algorithms in the form of design patterns; covers key topics on arrays, lists, stacks, queues, hash tables, binary trees, sorting, searching, and graphs; describes examples of complete and running applications for each topic; presents a functional approach to implementations for data structures and algorithms (excepting arrays); provides numerous challenge exercises (with solutions), encouraging the reader to take existing solutions and improve upon them; offers insights from the author’s extensive industrial experience; includes a glossary, and an appendix supplying an overview of discrete mathematics.Highlighting the techniques and skills necessary to quickly derive solutions to applied problems, this accessible text will prove invaluable to time-pressured students and professional software engineers.Table of ContentsFoundational Components Fundamental Algorithms Arrays Lists Stacks Queues Hash Tables Binary Trees Sorting Searching Graphs Appendix A: Solutions for Selected Exercises Appendix B: Review of Discrete Mathematical Topics
£31.34
Springer Nature Switzerland AG Quantum Technology and Optimization Problems: First International Workshop, QTOP 2019, Munich, Germany, March 18, 2019, Proceedings
Book SynopsisThis book constitutes the refereed proceedings of the First International Workshop on Quantum Technology and Optimization Problems, QTOP 2019, held in Munich, Germany, in March 2019.The 18 full papers presented together with 1 keynote paper in this volume were carefully reviewed and selected from 21 submissions. The papers are grouped in the following topical sections: analysis of optimization problems; quantum gate algorithms; applications of quantum annealing; and foundations and quantum technologies.Table of ContentsAnalysis of Optimization Problems.- Quantum Gate Algorithms.- Applications of Quantum Annealing.- Foundations and Quantum Technologies.
£58.49
Springer Nature Switzerland AG Quality, Reliability, Security and Robustness in Heterogeneous Systems: 14th EAI International Conference, Qshine 2018, Ho Chi Minh City, Vietnam, December 3–4, 2018, Proceedings
Book SynopsisThis book constitutes the refereed post-conference proceedings of the 14th EAI International Conference on Quality, Reliability, Security and Robustness in Heterogeneous Networks, QShine 2018, held in Ho Chi Minh City, Vietnam, in December 2018. The 13 revised full papers were carefully reviewed and selected from 28 submissions. The papers are organized thematically in tracks, starting with security and privacy, telecommunication systems and networks, networks and applications.Table of ContentsImproving Privacy for GeoIP DNS Traffic.- Deep Reinforcement Learning based QoS-aware Routing in Knowledge-defined networking.- 3 Throughput optimization for multirate multicasting through association control in IEEE 802.11 WLAN.- An NS-3 MPTCP Implementation.- A Novel Security Framework for Industrial IoT based on ISA 100.11a.- Social-aware Caching and Resource Sharing Optimization for Video Delivering in 5G Networks.- Energy Efficiency in QoS Constrained 60 GHz Millimeter-Wave Ultra-dense Networks.- Priority-based Device Discovery in Public Safety D2D Networks with Full Duplexing.- Modified Direct Method for Point-to-Point Blocking.- Probability in Multi-service Switching Networks with Resource Allocation Control.- Inconsistencies among Spectral Robustness Metrics.- QoS criteria for energy-aware switching networks.- Modelling Overflow Systems with Queuing in Primary.- Exploring YouTube’s CDN Heterogeneity.
£37.99
Springer Nature Switzerland AG Computational Intelligence in Music, Sound, Art and Design: 8th International Conference, EvoMUSART 2019, Held as Part of EvoStar 2019, Leipzig, Germany, April 24–26, 2019, Proceedings
Book SynopsisThis book constitutes the refereed proceedings of the 8th International Conference on Evolutionary Computation in Combinatorial Optimization, EvoMUSART 2019, held in Leipzig, Germany, in April 2019, co-located with the Evo*2019 events EuroGP, EvoCOP and EvoApplications. The 16 revised full papers presented were carefully reviewed and selected from 24 submissions. The papers cover a wide range of topics and application areas, including: visual art and music generation, analysis, and interpretation; sound synthesis; architecture; video; poetry; design; and other creative tasks.Table of ContentsDeep Learning Concepts for Evolutionary Art.- Adversarial Evolution and Deep Learning – How Does An Artist Play with Our Visual System.- Autonomy, Authenticity, Authorship and Intention in Computer Generated Art.- Camera Obscurer: Generative Art for Design Inspiration.- Swarm-Based Identification of Animation Key Points from 2D-medialness Maps.- Paintings, Polygons and Plant Propagation.- Evolutionary Games for Audiovisual Works: Exploring the Demographic Prisoner's Dilemma.- Emojinating: Evolving Emoji Blends.- Automatically Generating Engaging Presentation Slide Decks.- Tired of choosing? Just Add Structure and Virtual Reality.- EvoChef: Show Me What to Cook! Artificial Evolution of Culinary Arts.- Comparing Models for Harmony Prediction in An Interactive Audio Looper.- Stochastic Synthesizer Patch Exploration in Edisyn.- Evolutionary Multi-Objective Training Set Selection of Data Instances and Augmentations for Vocal Detection.- Automatic Jazz Melody Composition Through a Learning-Based Genetic Algorithm.- Exploring Transfer Functions in Evolved CTRNNs for Music Generation.
£44.99
Springer Nature Switzerland AG Theory of Information and its Value
Book SynopsisThis English version of Ruslan L. Stratonovich’s Theory of Information (1975) builds on theory and provides methods, techniques, and concepts toward utilizing critical applications. Unifying theories of information, optimization, and statistical physics, the value of information theory has gained recognition in data science, machine learning, and artificial intelligence. With the emergence of a data-driven economy, progress in machine learning, artificial intelligence algorithms, and increased computational resources, the need for comprehending information is essential. This book is even more relevant today than when it was first published in 1975. It extends the classic work of R.L. Stratonovich, one of the original developers of the symmetrized version of stochastic calculus and filtering theory, to name just two topics.Each chapter begins with basic, fundamental ideas, supported by clear examples; the material then advances to great detail and depth. The reader is not required to be familiar with the more difficult and specific material. Rather, the treasure trove of examples of stochastic processes and problems makes this book accessible to a wide readership of researchers, postgraduates, and undergraduate students in mathematics, engineering, physics and computer science who are specializing in information theory, data analysis, or machine learning.Trade Review“The book could be useful in advanced graduate courses with students, who are not afraid of integrals and probabilities.” (Jaak Henno, zbMATH 1454.94002, 2021)Table of Contents
£89.99
Springer Nature Switzerland AG Analysis of Experimental Algorithms: Special Event, SEA² 2019, Kalamata, Greece, June 24-29, 2019, Revised Selected Papers
Book SynopsisThis book constitutes the refereed post-conference proceedings of the Special Event on the Analysis of Experimental Algorithms, SEA² 2019, held in Kalamata, Greece, in June 2019.The 35 revised full papers presented were carefully reviewed and selected from 45 submissions. The papers cover a wide range of topics in both computer science and operations research/mathematical programming. They focus on the role of experimentation and engineering techniques in the design and evaluation of algorithms, data structures, and computational optimization methods.
£62.99
Springer Nature Switzerland AG Algorithm Portfolios: Advances, Applications, and
Book SynopsisThis book covers algorithm portfolios, multi-method schemes that harness optimization algorithms into a joint framework to solve optimization problems. It is expected to be a primary reference point for researchers and doctoral students in relevant domains that seek a quick exposure to the field. The presentation focuses primarily on the applicability of the methods and the non-expert reader will find this book useful for starting designing and implementing algorithm portfolios. The book familiarizes the reader with algorithm portfolios through current advances, applications, and open problems. Fundamental issues in building effective and efficient algorithm portfolios such as selection of constituent algorithms, allocation of computational resources, interaction between algorithms and parallelism vs. sequential implementations are discussed. Several new applications are analyzed and insights on the underlying algorithmic designs are provided. Future directions, new challenges, and open problems in the design of algorithm portfolios and applications are explored to further motivate research in this field.Table of Contents1. Metaheuristic optimization algorithms.- 2. Algorithm portfolios.- 3. Selection of constituent algorithms.- 4. Allocation of computation resources.- 5. Sequential and parallel models.- 6. Recent applications.- 7. Epilogue.- References.
£49.49
Springer Nature Switzerland AG Algorithms on Trees and Graphs: With Python Code
Book SynopsisGraph algorithms is a well-established subject in mathematics and computer science. Beyond classical application fields, such as approximation, combinatorial optimization, graphics, and operations research, graph algorithms have recently attracted increased attention from computational molecular biology and computational chemistry. Centered around the fundamental issue of graph isomorphism, this text goes beyond classical graph problems of shortest paths, spanning trees, flows in networks, and matchings in bipartite graphs. Advanced algorithmic results and techniques of practical relevance are presented in a coherent and consolidated way. This book introduces graph algorithms on an intuitive basis followed by a detailed exposition in a literate programming style, with correctness proofs as well as worst-case analyses. Furthermore, full C++ implementations of all algorithms presented are given using the LEDA library of efficient data structures and algorithms.Table of Contents1. Introduction.- 2. Algorithmic Techniques.- 3. Tree Traversal.- 4. Tree Isomorphism.- 5. Graph Traversal.- 6. Clique, Independent Set, and Vertex Cover.- 7. Graph Isomorphism.
£71.24
Springer Nature Switzerland AG A Quantum Computation Workbook
Book SynopsisTeaching quantum computation and information is notoriously difficult, because it requires covering subjects from various fields of science, organizing these subjects consistently in a unified way despite their tendency to favor their specific languages, and overcoming the subjects’ abstract and theoretical natures, which offer few examples of actual realizations. In this book, we have organized all the subjects required to understand the principles of quantum computation and information processing in a manner suited to physics, mathematics, and engineering courses as early as undergraduate studies.In addition, we provide a supporting package of quantum simulation software from Wolfram Mathematica, specialists in symbolic calculation software. Throughout the book’s main text, demonstrations are provided that use the software package, allowing the students to deepen their understanding of each subject through self-practice. Readers can change the code so as to experiment with their own ideas and contemplate possible applications. The information in this book reflects many years of experience teaching quantum computation and information. The quantum simulation-based demonstrations and the unified organization of the subjects are both time-tested and have received very positive responses from the students who have experienced them.Trade Review“The book provides an extensive bibliography and index. … this volume is well suited for a advanced graduate or first-year PhD course in quantum mechanics, with ample time available for self-study.” (L.-F. Pau, Computing Reviews, January 30, 2023)Table of Contents1 The Postulates of Quantum Mechanics.- 2 Virtual Realization of Quantum Computers.- 3 Quantum Computation: Overview.- 4 Quantum Algorithms: Introduction.- 5 Quantum Information: Introduction.- 6 Quantum Error Correction Codes: Introduction.- Appendix A Linear Algebra.- Appendix B Mathematica Application Q3.- References.
£44.99
Springer Nature Switzerland AG Emerging Technology Trends in Internet of Things and Computing: First International Conference, TIOTC 2021, Erbil, Iraq, June 6–8, 2021, Revised Selected Papers
Book SynopsisThis volume constitutes selected papers presented at the First International Conference on Emerging Technology Trends in IoT and Computing, TIOTC 2021, held in Erbil, Iraq, in June 2021. The 26 full papers were thoroughly reviewed and selected from 182 submissions. The papers are organized in the following topical sections: Internet of Things (IOT): services and applications; Internet of Things (IOT) in healthcare industry; IOT in networks, communications and distributed computing; real world application fields in information science and technology.Table of ContentsInternet of Things (IOT): Services and Applications.- Internet of Things (IOT) in Healthcare Industry.- IOT in Networks, Communications and Distributed Computing.- Real World Application Fields in information Science and Technology.
£62.99
Springer International Publishing AG Similarity Joins in Relational Database Systems
Book SynopsisState-of-the-art database systems manage and process a variety of complex objects, including strings and trees. For such objects equality comparisons are often not meaningful and must be replaced by similarity comparisons. This book describes the concepts and techniques to incorporate similarity into database systems. We start out by discussing the properties of strings and trees, and identify the edit distance as the de facto standard for comparing complex objects. Since the edit distance is computationally expensive, token-based distances have been introduced to speed up edit distance computations. The basic idea is to decompose complex objects into sets of tokens that can be compared efficiently. Token-based distances are used to compute an approximation of the edit distance and prune expensive edit distance calculations. A key observation when computing similarity joins is that many of the object pairs, for which the similarity is computed, are very different from each other. Filters exploit this property to improve the performance of similarity joins. A filter preprocesses the input data sets and produces a set of candidate pairs. The distance function is evaluated on the candidate pairs only. We describe the essential query processing techniques for filters based on lower and upper bounds. For token equality joins we describe prefix, size, positional and partitioning filters, which can be used to avoid the computation of small intersections that are not needed since the similarity would be too low.Table of ContentsPreface.- Acknowledgments.- Introduction.- Data Types.- Edit-Based Distances.- Token-Based Distances.- Query Processing Techniques.- Filters for Token Equality Joins.- Conclusion.- Bibliography.- Authors' Biographies.- Index.
£26.59
Springer International Publishing AG Machine Learning Algorithms: Adversarial
Book SynopsisThis book demonstrates the optimal adversarial attacks against several important signal processing algorithms. Through presenting the optimal attacks in wireless sensor networks, array signal processing, principal component analysis, etc, the authors reveal the robustness of the signal processing algorithms against adversarial attacks. Since data quality is crucial in signal processing, the adversary that can poison the data will be a significant threat to signal processing. Therefore, it is necessary and urgent to investigate the behavior of machine learning algorithms in signal processing under adversarial attacks. The authors in this book mainly examine the adversarial robustness of three commonly used machine learning algorithms in signal processing respectively: linear regression, LASSO-based feature selection, and principal component analysis (PCA). As to linear regression, the authors derive the optimal poisoning data sample and the optimal feature modifications, and also demonstrate the effectiveness of the attack against a wireless distributed learning system. The authors further extend the linear regression to LASSO-based feature selection and study the best strategy to mislead the learning system to select the wrong features. The authors find the optimal attack strategy by solving a bi-level optimization problem and also illustrate how this attack influences array signal processing and weather data analysis. In the end, the authors consider the adversarial robustness of the subspace learning problem. The authors examine the optimal modification strategy under the energy constraints to delude the PCA-based subspace learning algorithm. This book targets researchers working in machine learning, electronic information, and information theory as well as advanced-level students studying these subjects. R&D engineers who are working in machine learning, adversarial machine learning, robust machine learning, and technical consultants working on the security and robustness of machine learning are likely to purchase this book as a reference guide.Table of ContentsChapter. 1. IntroductionChapter. 2. Optimal Feature Manipulation Attacks Against Linear RegressionChapter. 3. On the Adversarial Robustness of LASSO Based Feature SelectionChapter. 4. On the Adversarial Robustness of Subspace LearningChapter. 5. Summary and ExtensionsChapter. 6. Appendix
£87.99
Springer International Publishing AG Arithmetic of Finite Fields: 9th International Workshop, WAIFI 2022, Chengdu, China, August 29 – September 2, 2022, Revised Selected Papers
Book SynopsisThis book constitutes the thoroughly refereed post-workshop proceedings of the 8th International Workshop on the Arithmetic of Finite Field, WAIFI 2022, held in Chengdu, China, in August – September 2022.The 19 revised full papers and 3 invited talks presented were carefully reviewed and selected from 25 submissions. The papers are organized in topical sections: structures in finite fields; efficient finite field arithmetic; coding theory; cryptography; sequences.Table of ContentsStructures in Finite Fields.- On a conjecture on irreducible polynomials over finite fields with restricted coefficients.- On two applications of polynomials xk – cx – d over finite fields and more.- Efficient Finite Field Arithmetic.- Polynomial Constructions of Chudnovsky-Type Algorithms for Multiplication in Finite Fields with Linear Bilinear Complexity.- Reduction-free Multiplication for Finite Fields and Polynomial Rings.- Finite Field Arithmetic in Large Characteristic for Classical and Post-Quantum Cryptography.- Fast enumeration of superspecial hyperelliptic curves of genus 4 with automorphism group V4.- Coding theory.- Two Classes of Constacyclic Codes with Variable Parameters.- Near MDS Codes with Dimension 4 and Their Application in Locally Recoverable Codes.- Optimal possibly nonlinear 3-PIR codes of small size.- PIR codes from combinatorial structures.- The Projective General Linear Group PGL(2, 5m) and Linear Codes of Length 5m + 1.- Private Information Retrieval Schemes Using Cyclic Codes.- Two Classes of Optimal Few-Weight Codes over Fq + uFq.- Explicit Non-Malleable Codes from Bipartite Graphs.- Cryptography.- Algebraic Relation of Three MinRank Algebraic Modelings.- Decomposition of Dillon's APN permutation with efficient hardware implementation.- New Versions of Miller-loop Secured against Side-Channel Attacks.- A Class of Power Mappings with Low Boomerang Uniformity.- New Classes of Bent Functions via the Switching Method.- Sequences.- Correlation measure of binary sequence families with trace representation.- Linear complexity of generalized cyclotomic sequences with period pnqm.- On the 2-adic complexity of cyclotomic binary sequences with period p2 and 2p2.
£56.99
Springer International Publishing AG Modelling and Development of Intelligent Systems: 8th International Conference, MDIS 2022, Sibiu, Romania, October 28–30, 2022, Revised Selected Papers
Book SynopsisThis book constitutes the refereed proceedings of the 8th International Conference on Modelling and Development of Intelligent Systems, MDIS 2022, held in Sibiu, Romania, during October 28–30, 2022.The 21 papers included in this book were carefully reviewed and selected from 48 submissions. They were organized in the following topical sections as follows: intelligent systems for decision support; machine learning; mathematical models for development of intelligent systems; and modelling and optimization of dynamic systems.Table of ContentsIntelligent Systems for Decision Support.- Effective LSTM Neural Network with Adam Optimizer for Improving Frost Prediction in Agriculture Data Stream.- Gaze Tracking: A Survey of Devices, Libraries and Applications.- Group Decision-Making Involving Competence of Experts in Relation to Evaluation Criteria: Case Study for e-Commerce Platform Selection.- Transparency and Traceability for AI-based Defect Detection in PCB Production.- Tasks Management Using Modern Devices.- Machine Learning.- A Method for Target Localization by Multistatic Radars.- Intrusion Detection by XGBoost Model Tuned by Improved Social Network Search Algorithm.- Bridging the Resource Gap in Cross-lingual Embedding Space.- Classification of Microstructure Images of Metals using Transfer Learning.- Generating Jigsaw Puzzles and an AI Powered Solver.- Morphology of Convolutional Neural Network with Diagonalized Pooling.- Challenges and Opportunities in Deep Learning Driven Fashion Design and Textiles Patterns Development.- Feature Selection and Extreme Learning Machine Tuning by Hybrid Sand Cat Optimization Algorithm for Diabetes Classification.- Enriching SQL-Driven Data Exploration With Different Machine Learning Models.- Mathematical Models for Development of Intelligent Systems.- Analytical Solution of the Simplest Entropiece Inversion Problem.- Latent Semantic Structure in Malicious Programs.- Innovative Lattice Sequences Based on Component by Component Construction Method for Multidimensional Sensitivity Analysis.- On an Optimization of the Lattice Sequence for the Multidimensional Integrals Connected with Bayesian Statistics.- Modelling and Optimization of Dynamic Systems.- Numerical Optimization Identification of a Keller-Segel Model for Thermoregulation in Honey Bee Colonies in Winter.- Gradient Optimization in Reconstruction of the Diffusion Coefficient in a Time Fractional Integro-Differential Equation of Pollution in Porous Media.- Flash Flood Simulation Between Slănic and Vărbilău Rivers in Vărbilău Village, Prahova County, Romania, Using Hydraulic Modeling and GIS Techniques.
£58.49
Springer International Publishing AG Tools and Algorithms for the Construction and
Book SynopsisThis open access book constitutes the proceedings of the 29th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, TACAS 2023, which was held as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2023, during April 22-27, 2023, in Paris, France. The 56 full papers and 6 short tool demonstration papers presented in this volume were carefully reviewed and selected from 169 submissions. The proceedings also contain 1 invited talk in full paper length, 13 tool papers of the affiliated competition SV-Comp and 1 paper consisting of the competition report. TACAS is a forum for researchers, developers, and users interested in rigorously based tools and algorithms for the construction and analysis of systems. The conference aims to bridge the gaps between different communities with this common interest and to support them in their quest to improve the utility, reliability, flexibility, and efficiency of tools and algorithms for building computer-controlled systems.Table of ContentsTool Demos.- EVA: a Tool for the Compositional Verification of AUTOSAR Models.- WASIM: A Word-level Abstract Symbolic Simulation Framework for Hardware Formal Verification.- Multiparty Session Typing in Java, Deductively.- PyLTA: A Verification Tool for Parameterized Distributed Algorithms.- FuzzBtor2: A Random Generator of Word-Level Model Checking Problems in Btor2 Format.- Eclipse ESCET™: The Eclipse Supervisory Control Engineering Toolkit.- Combinatorial Optimization/Theorem Proving.- New Core-Guided and Hitting Set Algorithms for Multi-Objective Combinatorial Optimization.- Verified reductions for optimization.- Specifying and Verifying Higher-order Rust Iterators.- Extending a High-Performance Prover to Higher-Order Logic.- Tools (Regular Papers).- The WhyRel Prototype for Relational Verification of Pointer Programs.- Bridging Hardware and Software Analysis with Btor2C: A Word-Level-Circuit-to-C Converter.- CoPTIC: Constraint Programming Translated Into C.- Acacia-Bonsai: A Modern Implementation of Downset-Based LTL Realizability.- Synthesis.- Computing Adequately Permissive Assumptions for Synthesis.- Verification-guided Programmatic Controller Synthesis.- Taming Large Bounds in Synthesis from Bounded-Liveness Specifications.- Lockstep Composition for Unbalanced Loops.- Synthesis of Distributed Agreement-Based Systems with Effciently Decidable Verification.- LTL Reactive Synthesis with a Few Hints.- Timed Automata Verification and Synthesis via Finite Automata Learning.- Graphs/Probabilistic Systems.- A Truly Symbolic Linear-Time Algorithm for SCC Decomposition.- Transforming quantified Boolean formulas using biclique covers.- Certificates for Probabilistic Pushdown Automata via Optimistic Value Iteration.- Probabilistic Program Verification via Inductive Synthesis of Inductive Invariants.- Runtime Monitoring/Program Analysis.- Industrial-Strength Controlled Concurrency Testing for C# Programs with Coyote.- Context-Sensitive Meta-Constraint Systems for Explainable Program Analysis.- Explainable Online Monitoring of Metric Temporal Logic.- 12th Competition on Software Verification — SV-COMP 2023.- Competition on Software Verification and Witness Validation: SV-COMP 2023.- Symbiotic-Witch 2: More Efficient Algorithm and Witness Refutation (Competition Contribution).- 2LS: Arrays and Loop Unwinding (Competition Contribution).- Bubaak: Runtime Monitoring of Program Verifiers (Competition Contribution).- EBF 4.2: Black-Box Cooperative Verification for Concurrent Programs (Competition Contribution).- Goblint: Autotuning Thread-Modular Abstract Interpretation (Competition Contribution).- Java Ranger: Supporting String and Array Operations (Competition Contribution).- Korn–Software Verification with Horn Clauses (Competition Contribution).- Mopsa-C: Modular Domains and Relational Abstract Interpretation for C Programs (Competition Contribution).- PIChecker: A POR and Interpolation based Verifier for Concurrent Programs (Competition Contribution).- Ultimate Automizer and the CommuHash Normal Form (Competition Contribution).- Ultimate Taipan and Race Detection in Ultimate (Competition Contribution).- VeriAbsL: Scalable Verification by Abstraction and Strategy Prediction (Competition Contribution).- VeriFuzz 1.4: Checking for (Non-)termination (Competition Contribution).
£31.49
Springer International Publishing AG A Guide to Graph Colouring: Algorithms and
Book SynopsisThis book treats graph colouring as an algorithmic problem, with a strong emphasis on practical applications. The author describes and analyses some of the best-known algorithms for colouring arbitrary graphs, focusing on whether these heuristics can provide optimal solutions in some cases; how they perform on graphs where the chromatic number is unknown; and whether they can produce better solutions than other algorithms for certain types of graphs, and why. The introductory chapters explain graph colouring, and bounds and constructive algorithms. The author then shows how advanced, modern techniques can be applied to classic real-world operational research problems such as seating plans, sports scheduling, and university timetabling. He includes many examples, suggestions for further reading, and historical notes, and the book is supplemented by a website with an online suite of downloadable code. The book will be of value to researchers, graduate students, and practitioners in the areas of operations research, theoretical computer science, optimization, and computational intelligence. The reader should have elementary knowledge of sets, matrices, and enumerative combinatorics.Trade Review“The book gives a comprehensive description and handling on arguably one of the most important notions of combinatorics—graph coloring. … The book is nicely written, and carries a big pile of information valuable for both users and researchers in the field.” (András Sándor Pluhár, Mathematical Reviews, February, 2017)“This well-written book will serve as a utilitarian guide to graph coloring and its practical applications. It includes many definitions, theorems, proofs, algorithms, and pointers for further reading. The book will be helpful for teaching courses on graph coloring to students of mathematics and computer science. I strongly recommend it for the intended audience.” (S. V. Nagaraj, Computing Reviews, computingreviews.com, June, 2016)“The book is a comprehensive guide to graph colouring algorithms. … The book is a nice textbook for both undergraduate and graduate students in the areas of operations research and theoretical computer science. … Finally, it is a good source of knowledge for practitioners.” (Marcin Anholcer, zbMATH 1330.05002, 2016)Table of ContentsIntroduction to Graph Colouring.- Bounds and Constructive Algorithms.- Advanced Techniques for Graph Colouring.- Algorithm Case Studies.- Applications and Extensions.- Designing Seating Plans.- Designing Sports Leagues.- Designing University Timetables.- App. A, Computing Resources.- References.- Index.
£71.99
