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Engineering Dependable and Secure Machine Learning Systems

This book constitutes the revised selected papers of the Third International Workshop on Engineering Dependable and Secure Machine Learning;

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Machine Learning and Systems Engineering

A large international conference on Advances in Machine Learning and Systems Engineering was held in UC Berkeley, California, USA, October;

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Building Secure And High-performance Software Systems

Designing reliable, complex and dependable software systems is a continuous challenge to the software engineering community. The;

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Dependable Computing Systems

A team of recognized experts leads the way to dependable computing systems. With computers and networks pervading every aspect of daily;

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Dependable Software Engineering. Theories, Tools, and Applications

This book constitutes the proceedings of the 5th International Symposium on Dependable Software Engineering: Theories, Tools, and;

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Intelligent, Secure, and Dependable Systems in Distributed and Cloud Environments

This book constitutes the refereed proceedings of the First International Conference on Intelligent, Secure, and Dependable Systems;

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Cyber Security Cryptography and Machine Learning

This book constitutes the proceedings of the first International Symposium on Cyber Security Cryptography and Machine Learning, held;

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Advances in Machine Learning Applications in Software Engineering

of developing and maintaining larger and complex software systems in a dynamic and changing environment, machine learning methods have been playing an;

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Machine Learning Technologies and Applications

This book comprises the best deliberations with the theme Machine Learning Technologies and Applications in the International Conference;

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Fundamentals of Dependable Computing for Software Engineers

describes a comprehensive dependability-engineering process and explains the roles of software and software engineers in computer system;

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Fundamentals of Dependable Computing for Software Engineers

describes a comprehensive dependability-engineering process and explains the roles of software and software engineers in computer system;

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A Primer on Machine Learning Applications in Civil Engineering

Machine learning has undergone rapid growth in diversification and practicality, and the repertoire of techniques has evolved and expanded;

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Advances in Cybernetics Cognition and Machine Learning for Communication Techn

This book highlights recent advances in Cybernetics, Machine Learning and Cognitive Science applied to Communications Engineering and;

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Official Google Cloud Certified Professional Data Engineer Study Guide

exam objectives and review questions, plus the online learning environment includes additional complete practice tests. Written by;

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Engineering Artificially Intelligent Systems

Many current AI and machine learning algorithms and data and information fusion processes attempt in software to estimate situations in our;

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Dependable Software Systems Engineering

and safe.This book presents papers from the NATO Advanced Study Institute Summer School Dependable Software Systems Engineering, held;

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Machine Learning for Biometrics

Machine Learning for Biometrics: Concepts, Algorithms and Applications highlights the fundamental concepts of machine learning;

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Knowledge Engineering, Machine Learning and Lattice Computing with Applications

selected from 130 submissions. The papers are organized in topical sections on bioinspired and machine learning methods, machine learning;

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Machine Learning and Knowledge Discovery for Engineering Systems Health Management

Machine Learning and Knowledge Discovery for Engineering Systems Health Management presents state-of-the-art tools and techniques for;

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Machine Learning with PySpark

Build machine learning models, natural language processing applications, and recommender systems with PySpark to solve various business;

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Dependable Software Engineering: Theories, Tools, and Applications

, and Applications, SETTA 2015, held in Nanjing, China, in November 2015. The 20 full papers presented together with 3 invited talks were;

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Mastering Machine Learning for Penetration Testing

Become a master at penetration testing using machine learning with Python Key Features Identify ambiguities and breach intelligent security;

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Progress in Systems Engineering

intelligence, signal processing, decision support, pattern recognition and machine learning, information and communication technologies, image;

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Machine Learning Engineering with Python

Supercharge the value of your machine learning models by building scalable and robust solutions that can serve them in production;

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Engineering Applications of Neural Networks

and selected from 39 submissions. The papers are organized in topical sections on activity recognition, deep learning, extreme learning;

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Data-Driven Science and Engineering

intersection of data-driven methods, machine learning, applied optimization, and classical fields of engineering mathematics and mathematical physics;

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Advances in Systems Engineering

in systems engineering, it covers a variety of topics, such as analog and digital hardware systems, artificial intelligence and machine learning;

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