Machine Learning for Cyber Agents: Attack and Defence, Abaimov Stanislav, Martellini Maurizio
Автор: Strang Gilbert Название: Linear Algebra and Learning from Data ISBN: 0692196382 ISBN-13(EAN): 9780692196380 Издательство: Cambridge Academ Рейтинг: Цена: 66520.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Linear algebra and the foundations of deep learning, together at last! From Professor Gilbert Strang, acclaimed author of Introduction to Linear Algebra, comes Linear Algebra and Learning from Data, the first textbook that teaches linear algebra together with deep learning and neural nets. This readable yet rigorous textbook contains a complete course in the linear algebra and related mathematics that students need to know to get to grips with learning from data. Included are: the four fundamental subspaces, singular value decompositions, special matrices, large matrix computation techniques, compressed sensing, probability and statistics, optimization, the architecture of neural nets, stochastic gradient descent and backpropagation.
Автор: Kevin Murphy Название: Machine Learning ISBN: 0262018020 ISBN-13(EAN): 9780262018029 Издательство: MIT Press Рейтинг: Цена: 124150.00 T Наличие на складе: Невозможна поставка. Описание:
A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.
Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.
The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package -- PMTK (probabilistic modeling toolkit) -- that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.
Автор: Misra, Siddharth Название: Machine Learning for Subsurface Characterization ISBN: 0128177365 ISBN-13(EAN): 9780128177365 Издательство: Elsevier Science Рейтинг: Цена: 123520.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
To continue to meet demand while keeping costs down, petroleum and reservoir engineers know it is critical to utilize their asset's data through more complex modeling methods, and machine learning and data analytics is the known alternative approach to accurately represent the complexity of fluid-filled rocks. With a lack of training resources available, Machine Learning for Subsurface Characterization focuses on the development and application of neural networks, deep learning, unsupervised learning, reinforcement learning, and clustering methods for subsurface characterization under constraints. Such constraints are encountered during subsurface engineering operations due to financial, operational, regulatory, risk, technological, and environmental challenges.
This reference teaches how to do more with less. Used to develop tools and techniques of data-driven predictive modelling and machine learning for subsurface engineering and science, engineers will be introduced to methods of generating subsurface signals and analyzing the complex relationships within various subsurface signals using machine learning. Algorithmic procedures in MATLAB, R, PYTHON, and TENSORFLOW are displayed in text and through online instructional video to assist training and learning. Field cases are also presented to understand real-world applications, with a particular focus on examples involving shale reservoirs.
Explaining the concept of machine learning, advantages to the industry, and applications applied to complex subsurface rocks, Machine Learning for Subsurface Characterization delivers a missing piece to the reservoir engineer's toolbox needed to support today's complex operations.
Focus on applying predictive modelling and machine learning from real case studies and Q&A sessions at the end of each chapter
Learn how to develop codes such as MATLAB, PYTHON, R, and TENSORFLOW with step-by-step guides included
Visually learn code development with video demonstrations included
Автор: Raschka, Sebastian Mirjalili, Vahid Название: Python machine learning - ISBN: 1787125939 ISBN-13(EAN): 9781787125933 Издательство: Неизвестно Цена: 53940.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This second edition of Python Machine Learning by Sebastian Raschka is for developers and data scientists looking for a practical approach to machine learning and deep learning. In this updated edition, you`ll explore the machine learning process using Python and the latest open source technologies, including scikit-learn and TensorFlow 1.x.
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data.
The updated edition of this practical book uses concrete examples, minimal theory, and three production-ready Python frameworks--scikit-learn, Keras, and TensorFlow--to help you gain an intuitive understanding of the concepts and tools for building intelligent systems. You'll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you've learned, all you need is programming experience to get started.
Автор: Li, Chong Название: Reinforcement Learning for Cyber-Physical Systems with Cybersecurity Case Studies ISBN: 1138543535 ISBN-13(EAN): 9781138543539 Издательство: Taylor&Francis Рейтинг: Цена: 84710.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book introduces reinforcement learning, and provides novel ideas and use cases to demonstrate the benefits of using reinforcement learning for Cyber Physical Systems. Two important case studies on applying reinforcement learning to cybersecurity problems are included.
Автор: Brij B. Gupta, Quan Z. Sheng Название: Machine Learning for Computer and Cyber Security ISBN: 1138587303 ISBN-13(EAN): 9781138587304 Издательство: Taylor&Francis Рейтинг: Цена: 178640.00 T Наличие на складе: Нет в наличии. Описание: This comprehensive book offers valuable insights while using a wealth of examples and illustrations to effectively demonstrate the principles, algorithms, challenges and applications of machine learning and data mining for computer and cyber security.
Автор: Iqbal Farkhund, Debbabi Mourad, Fung Benjamin C. M. Название: Machine Learning for Authorship Attribution and Cyber Forensics ISBN: 3030616746 ISBN-13(EAN): 9783030616748 Издательство: Springer Рейтинг: Цена: 149060.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: 1 CYBERSECURITY AND CYBERCRIME INVESTIGATION 1.1 CYBERSECURITY 1.2 KEY COMPONENTS TO MINIMIZING CYBERCRIMES 1.3 DAMAGE RESULTING FROM CYBERCRIME 1.4 CYBERCRIMES 1.4.1 Major Categories of Cybercrime 1.4.2 Causes of and Motivations for Cybercrime 1.5 MAJOR CHALLENGES 1.5.1 Hacker Tools and Exploit Kits 1.5.2 Universal Access 291.5.3 Online Anonymity 1.5.4 Organized Crime 301.5.5 Nation State Threat Actors 311.6 CYBERCRIME INVESTIGATION 322 MACHINE LEARNING FRAMEWORK FOR MESSAGING FORENSICS 342.1 SOURCES OF CYBERCRIMES 362.2 FEW ANALYSIS TOOLS AND TECHNIQUES 382.3 PROPOSED FRAMEWORK FOR CYBERCRIMES INVESTIGATION 392.4 AUTHORSHIP ANALYSIS 412.5 INTRODUCTION TO CRIMINAL INFORMATION MINING 432.5.1 Existing Criminal Information Mining Approaches 442.5.2 WordNet-based Criminal Information Mining 472.6 WEKA 483 HEADER-LEVEL INVESTIGATION AND ANALYZING NETWORK INFORMATION 503.1 STATISTICAL EVALUATION 523.2 TEMPORAL ANALYSIS 533.3 GEOGRAPHICAL LOCALIZATION 533.4 SOCIAL NETWORK ANALYSIS 553.5 CLASSIFICATION 563.6 CLUSTERING 584 AUTHORSHIP ANALYSIS APPROACHES 594.1 HISTORICAL PERSPECTIVE 594.2 ONLINE ANONYMITY AND AUTHORSHIP ANALYSIS 604.3 STYLOMETRIC FEATURES 614.4 AUTHORSHIP ANALYSIS METHODS 634.4.1 Statistical Analysis Methods 644.4.2 Machine Learning Methods 644.4.1 Classification Method Fundamentals 664.5 AUTHORSHIP ATTRIBUTION 674.6 AUTHORSHIP CHARACTERIZATION 694.7 AUTHORSHIP VERIFICATION 704.8 LIMITATIONS OF EXISTING AUTHORSHIP TECHNIQUES 725 AUTHORSHIP ANALYSIS - WRITEPRINT MINING FOR AUTHORSHIP ATTRIBUTION 745.1 AUTHORSHIP ATTRIBUTION PROBLEM 785.1.1 Attribution without Stylistic Variation 795.1.2 Attribution with Stylistic Variation 795.2 BUILDING BLOCKS OF THE PROPOSED APPROACH 805.3 WRITEPRINT 875.4 PROPOSED APPROACHES 875.4.1 AuthorMiner1: Attribution without Stylistic Variation 885.4.2 AuthorMiner2: Attribution with Stylistic Variation 926 AUTHORSHIP ATTRIBUTION WITH FEW TRAINING SAMPLES 976.1 PROBLEM STATEMENT AND FUNDAMENTALS 1006.2 PROPOSED APPROACH 1016.2.1 Preprocessing 1016.2.2 Clustering by Stylometric Features 1026.2.3 Frequent Stylometric Pattern Mining 1046.2.4 Writeprint Mining 1056.2.5 Identifying Author 1066.3 EXPERIMENTS AND DISCUSSION 1067 AUTHORSHIP CHARACTERIZATION 1137.1 PROPOSED APPROACH 1157.1.1 Clustering Anonymous Messages 1167.1.2 Extracting Writeprints from Sample Messages 1167.1.3 Identifying Author Characteristics 1167.2 EXPERIMENTS AND DISCUSSION 1178 AUTHORSHIP VERIFICATION 1208.1 PROBLEM STATEMENT 1238.2 PROPOSED APPROACH 1258.2.1 Verification by Classification 1268.2.2 Verification by Regression 1268.3 EXPERIMENTS AND DISCUSSION 1278.3.1 Verification by Classification. 1288.3.2 Verification by Regression 1289 AUTHORSHIP ATTRIBUTION USING CUSTOMIZED ASSOCIATIVE CLASSIFICATION 1319.1 PROBLEM STATEMENT 1329.1.1 Extracting Stylometric Features 1329.1.2 Associative Classification Writeprint 1339.1.3 Refined Problem Statement 1369.2 CLASSIFICATION BY MULTIPLE ASSOCIATION RULE FOR AUTHORSHIP ANALYSIS 1379.2.1 Mining Class Association Rules 1379.2.2 Pruning Class Association Rules 1399.2.3 Auth
Автор: J?rgen Beyerer; Oliver Niggemann; Christian K?hner Название: Machine Learning for Cyber Physical Systems ISBN: 3662538059 ISBN-13(EAN): 9783662538050 Издательство: Springer Рейтинг: Цена: 158380.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: The work presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It contains some selected papers from the international Conference ML4CPS – Machine Learning for Cyber Physical Systems, which was held in Karlsruhe, September 29th, 2016. Cyber Physical Systems are characterized by their ability to adapt and to learn: They analyze their environment and, based on observations, they learn patterns, correlations and predictive models. Typical applications are condition monitoring, predictive maintenance, image processing and diagnosis. Machine Learning is the key technology for these developments.
Автор: Oliver Niggemann; J?rgen Beyerer Название: Machine Learning for Cyber Physical Systems ISBN: 3662488361 ISBN-13(EAN): 9783662488362 Издательство: Springer Рейтинг: Цена: 130610.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Development of a Cyber-Physical System based on selective dynamic Gaussian naive Bayes model for a self-predict laser surface heat treatment processcontrol.- Evidence Grid Based Information Fusion for Semantic Classifiers in Dynamic Sensor Networks.- Forecasting Cellular Connectivity for Cyber-Physical Systems: A Machine Learning Approach.- Towards Optimized Machine Operations by Cloud Integrated Condition Estimation.- Prognostics Health Management System based on Hybrid Model to Predict Failures of a Planetary Gear Transmission.- Evaluation of Model-Based Condition Monitoring Systems in Industrial Application Cases.- Towards a novel learning assistant for networked automation systems.- Effcient Image Processing System for an Industrial Machine Learning Task.- Efficient engineering in special purpose machinery through automated control code synthesis based on a functional categorisation.- Geo-Distributed Analytics for the Internet of Things.- Implementation and Comparison of Cluster-Based PSO Extensions in Hybrid Settings with Efficient Approximation.- Machine-specifc Approach for Automatic Classifcation of Cutting Process Efficiency.- Meta-analysis of Maintenance Knowledge Assets Towards Predictive Cost Controlling of Cyber Physical Production Systems.- Towards Autonomously Navigating and Cooperating Vehicles in Cyber-Physical Production Systems.
Автор: Jeffrey J. P. Tsai; Philip S. Yu Название: Machine Learning in Cyber Trust ISBN: 1441946985 ISBN-13(EAN): 9781441946980 Издательство: Springer Рейтинг: Цена: 144410.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: In cyber-based systems, tasks can be formulated as learning problems and approached as machine-learning algorithms. This book covers applications of machine-learning methods in reliability, security, performance and privacy issues in cyber space.
Автор: Dinur Название: Cyber Security Cryptography and Machine Learning ISBN: 3319941461 ISBN-13(EAN): 9783319941462 Издательство: Springer Рейтинг: Цена: 46570.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book constitutes the refereed proceedings of the Second International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2018, held in Beer-Sheva, Israel, in June 2018. The 16 full and 6 short papers presented in this volume were carefully reviewed and selected from 44 submissions.
Казахстан, 010000 г. Астана, проспект Туран 43/5, НП2 (офис 2) ТОО "Логобук" Тел:+7 707 857-29-98 ,+7(7172) 65-23-70 www.logobook.kz
