Machine Learning for Cyber Agents: Attack and Defence, Abaimov Stanislav, Martellini Maurizio
Автор: Kevin Murphy Название: Machine Learning ISBN: 0262018020 ISBN-13(EAN): 9780262018029 Издательство: Random House (USA) Рейтинг: Цена: 17243.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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: 18528.00 р. 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: 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: 20733.00 р. 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: 17097.00 р. 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: 18904.00 р. 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: 6097.00 р. 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.
Автор: Krishna P. Venkata, Gurumoorthy Sasikumar, Obaidat Mohammad S. Название: Social Network Forensics, Cyber Security, and Machine Learning ISBN: 9811314551 ISBN-13(EAN): 9789811314551 Издательство: Springer Рейтинг: Цена: 7317.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book discusses the issues and challenges in Online Social Networks (OSNs). It highlights various aspects of OSNs consisting of novel social network strategies and the development of services using different computing models. Moreover, the book investigates how OSNs are impacted by cutting-edge innovations.
Автор: Shlomi Dolev; Danny Hendler; Sachin Lodha; Moti Yu Название: Cyber Security Cryptography and Machine Learning ISBN: 3030209504 ISBN-13(EAN): 9783030209506 Издательство: Springer Рейтинг: Цена: 8049.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book constitutes the refereed proceedings of the Third International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2019, held in Beer-Sheva, Israel, in June 2019.The 18 full and 10 short papers presented in this volume were carefully reviewed and selected from 36 submissions. They deal with the theory, design, analysis, implementation, or application of cyber security, cryptography and machine learning systems and networks, and conceptually innovative topics in these research areas.
Описание: This three volume book set constitutes the proceedings of the Third International Conference on Machine Learning for Cyber Security, ML4CS 2020, held in Xi`an, China in October 2020.The 118 full papers and 40 short papers presented were carefully reviewed and selected from 360 submissions.
Do you feel that informatics is indispensable in today's increasingly digital world? Do you want to introduce yourself to the world of programming or cyber security but don't know where to get started? If the answer to these questions is yes, then keep reading...
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PYTHON MACHINE LEARNING: A Beginner's Guide to Python Programming for Machine Learning and Deep Learning, Data Analysis, Algorithms and Data Science with Scikit Learn, TensorFlow, PyTorch and Keras
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- The Fundamentals of Python
- Python for Machine Learning
- Data Analysis in Python
- Comparing Deep Learning and Machine Learning
- The Role of Machine Learning in the Internet of Things (IoT)
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SQL FOR BEGINNERS: A Step by Step Guide to Learn SQL Programming for Query Performance Tuning on SQL Database
Throughout these pages, you will learn:
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LINUX FOR BEGINNERS: An Introduction to the Linux Operating System for Installation, Configuration and Command Line
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HACKING WITH KALI LINUX: A Beginner's Guide to Learn Penetration Testing to Protect Your Family and Business from Cyber Attacks Building a Home Security System for Wireless Network Security
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ETHICAL HACKING: A Beginner's Guide to Computer and Wireless Networks Defense Strategies, Penetration Testing and Information Security Risk Assessment
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- Most common security tools
- The three ways to scan your system
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Автор: Raschka, Sebastian Mirjalili, Vahid Название: Python machine learning - ISBN: 1787125939 ISBN-13(EAN): 9781787125933 Издательство: Неизвестно Цена: 8458.00 р. 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.
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