Machine learning

This SpringerBrief presents the underlying principles of machine learning and how to deploy various deep learning tools and techniques to tackle and solve certain challenges facing the cybersecurity industry.By implementing innovative deep learning solutions, cybersecurity researchers, students and practitioners can analyze patterns and learn how to prevent cyber-attacks and respond to changing malware behavior. The knowledge and tools introduced in this brief can also assist cybersecurity teams to become more proactive in preventing threats and responding to active attacks in real time. It can reduce the amount of time spent on routine tasks and enable organizations to use their resources more strategically. In short, the knowledge and techniques provided in this brief can help make cybersecurity simpler, more proactive, less expensive and far more effectiveAdvanced-level students in computer science studying machine learning with a cybersecurity focus will find this SpringerBrief useful as a study guide. Researchers and cybersecurity professionals focusing on the application of machine learning tools and techniques to the cybersecurity domain will also want to purchase this SpringerBrief.

An Introduction to the Machine Learning Empowered Intelligent Data Center NetworkingFundamentals of Machine Learning in Data Center Networks. This book reviews the common learning paradigms that are widely used in data centernetworks, and offers an introduction to data collection and data processing in data centers. Additionally, it proposes a multi-dimensional and multi-perspective solution quality assessment system called REBEL-3S. The book offers readers a solid foundation for conducting research in the field of AI-assisted data center networks.Comprehensive Survey of AI-assisted Intelligent Data Center Networks. This book comprehensively investigates the peer-reviewed literature published in recent years. The wide range of machine learning techniques is fully reflected to allow fair comparisons. In addition, the book provides in-depth analysis and enlightening discussions on the effectiveness of AI in DCNs from various perspectives, covering flow prediction, flow classification, load balancing, resource management, energy management, routing optimization, congestion control, fault management, and network security.Provides a Broad Overview with Key Insights. This book introduces several novel intelligent networking concepts pioneered by real-world industries, such as Knowledge Defined Networks, Self-Driving Networks, Intent-driven Networks and Intent-based Networks. Moreover, it shares unique insights into the technological evolution of the fusion of artificial intelligence and data center networks, together with selected challenges and future research opportunities.

This book will teach you how to build recommender systems with machine learning algorithms using Python. Recommender systems have become an essential part of every internet-based business today.You'll start by learning basic concepts of recommender systems, with an overview of different types of recommender engines and how they function. Next, you will see how to build recommender systems with traditional algorithms such as market basket analysis and content- and knowledge-based recommender systems with NLP. The authors then demonstrate techniques such as collaborative filtering using matrix factorization and hybrid recommender systems that incorporate both content-based and collaborative filtering techniques. This is followed by a tutorial on building machine learning-based recommender systems using clustering and classification algorithms like K-means and random forest. The last chapters cover NLP, deep learning, and graph-based techniques to build a recommender engine. Each chapter includes data preparation, multiple ways to evaluate and optimize the recommender systems, supporting examples, and illustrations.By the end of this book, you will understand and be able to build recommender systems with various tools and techniques with machine learning, deep learning, and graph-based algorithms.What You Will LearnUnderstand and implement different recommender systems techniques with PythonEmploy popular methods like content- and knowledge-based, collaborative filtering, market basket analysis, and matrix factorization Build hybrid recommender systems that incorporate both content-based and collaborative filteringLeverage machine learning, NLP, and deep learning for building recommender systemsWho This Book Is ForData scientists, machine learning engineers, and Python programmers interested in building and implementing recommender systems to solve problems.

Machine Learning Applications in Subsurface Energy Resource Management presents a current snapshot of the state of the art and future outlook for machine learning applications in subsurface energy resource management (e.g., oil and gas, geologic carbon sequestration, geothermal energy).

This book provides an introduction to the most popular methods in machine learning. It covers regression including regularization, tree-based methods including Random Forests and Boosted Trees, Artificial Neural Networks including CNNs, reinforcement learning, and unsupervised learning focused on clustering.

The text discusses models related to smart automation in the manufacturing processes and early warning detection systems using machine intelligence. It will serve as an ideal reference text for graduate students and academic researchers in diverse engineering fields such as electrical, electronics and communication.

This book focuses on feature extraction methods for optical geospatial data using Machine Learning (ML). It is a practical guide for professionals and graduate students starting a career in information extraction. It explains spatial feature extraction in an easy-to-understand way and includes real case studies.

The juxtaposition of "machine learning" and "pure mathematics and theoretical physics" may first appear as contradictory in terms. The rigours of proofs and derivations in the latter seem to reside in a different world from the randomness of data and statistics in the former. Yet, an often under-appreciated component of mathematical discovery, typically not presented in a final draft, is experimentation: both with ideas and with mathematical data. Think of the teenage Gauss, who conjectured the Prime Number Theorem by plotting the prime-counting function, many decades before complex analysis was formalized to offer a proof.Can modern technology in part mimic Gauss's intuition? The past five years saw an explosion of activity in using AI to assist the human mind in uncovering new mathematics: finding patterns, accelerating computations, and raising conjectures via the machine learning of pure, noiseless data. The aim of this book, a first of its kind, is to collect research and survey articles from experts in this emerging dialogue between theoretical mathematics and machine learning. It does not dwell on the well-known multitude of mathematical techniques in deep learning, but focuses on the reverse relationship: how machine learning helps with mathematics. Taking a panoramic approach, the topics range from combinatorics to number theory, and from geometry to quantum field theory and string theory. Aimed at PhD students as well as seasoned researchers, each self-contained chapter offers a glimpse of an exciting future of this symbiosis.

Bayesian Optimization in Action teaches you how to build Bayesian Optimisation systems from the ground up. This book transforms state-of-the-art research into usable techniques that you can easily put into practice -- all fully illustrated with useful code samples. You will hone your understanding of Bayesian Optimisation through engaging examples -- from forecasting the weather to finding the optimal amount of sugar for coffee and even deciding if someone is psychic! Along the way, you will explore scenarios with multiple objectives, when each decision has its own cost, and when feedback is in the form of pairwise comparisons. With this collection of techniques, you will be ready to find the optimal solution for everything -- from transport and logistics to cancer treatments. About the reader For machine learning practitioners who are confident in math and statistics.

Education has always been one of the cornerstones for societal evolution and economic growth. We are currently witnessing a significant transformation in the development of education and especially post-secondary education.The use of technology impacts the way educational content is presented and acquired in many areas. The designs of immersive educational worlds and the combination of rational and emotional educational experiences that cannot be designed in the same way in the traditional classroom will come increasingly into focus.Seen in this way the book also contributes to generalize the experience of the COVID-19 crisis and its impact to quality of learning and education.Scientifically based statements as well as excellent experiences (best practice) are necessary. This book contains scientific papers in the fields of: The future of learning Eruptive technologies in learningPedagogy of online learning Deep learning vs machine learning: opportunities and challengesReimagining and rapid transition of learningInterested readership includes policymakers, academics, educators, researchers in pedagogy and learning theory, schoolteachers, learning industry, further and continuing education lecturers, etc.

This book explores the application of data mining and machine learning techniques in studying the activity pattern, decision-making skills, misconducts, and actions resulting in the intervention of VAR in European soccer leagues referees. The game of soccer at the elite level is characterised by intense competitions, a high level of intensity, technical, and tactical skills coupled with a long duration of play. Referees are required to officiate the game and deliver correct and indisputable decisions throughout the duration of play. The increase in the spatial and temporal task demands of the game necessitates that the referees must respond and cope with the physiological and psychological loads inherent in the game. The referees are also required to deliver an accurate decision and uphold the rules and regulations of the game during a match. These demands and attributes make the work of referees highly complex. The increasing pace and complexity of the game resulted in the introduction of the Video Assistant Referee (VAR) to assist and improve the decision-making of on-field referees. Despite the integration of VAR into the current refereeing system, the performances of the referees are yet to be error-free. Machine learning coupled with data mining techniques has shown to be vital in providing insights from a large dataset which could be used to draw important inferences that can aid decision-making for diagnostics purposes and overall performance improvement.A total of 6232 matches from 5 consecutive seasons officiated across the English Premier League, Spanish LaLiga, Italian Serie A as well as the German Bundesliga was studied. It is envisioned that the findings in this book could be useful in recognising the activity pattern of top-class referees, that is non-trivial for the stakeholders in devising strategies to further enhance the performances of referees as well as empower talent identification experts with pertinent information for mapping out future high-performance referees.

Get a hands-on introduction to Transformer architecture using the Hugging Face library. This book explains how Transformers are changing the AI domain, particularly in the area of natural language processing.This book covers Transformer architecture and its relevance in natural language processing (NLP). It starts with an introduction to NLP and a progression of language models from n-grams to a Transformer-based architecture. Next, it offers some basic Transformers examples using the Google colab engine. Then, it introduces the Hugging Face ecosystem and the different libraries and models provided by it. Moving forward, it explains language models such as Google BERT with some examples before providing a deep dive into Hugging Face API using different language models to address tasks such as sentence classification, sentiment analysis, summarization, and text generation.After completing Introduction to Transformers for NLP, you will understand Transformer concepts and be able to solve problems using the Hugging Face library.What You Will LearnUnderstand language models and their importance in NLP and NLU (Natural Language Understanding)Master Transformer architecture through practical examplesUse the Hugging Face library in Transformer-based language modelsCreate a simple code generator in Python based on Transformer architectureWho This Book Is ForData Scientists and software developers interested in developing their skills in NLP and NLU (Natural Language Understanding)

This book introduces reinforcement learning with mathematical theory and practical examples from quantitative finance using the TensorFlow library.Reinforcement Learning for Finance begins by describing methods for training neural networks. Next, it discusses CNN and RNN ¿ two kinds of neural networks used as deep learning networks in reinforcement learning. Further, the book dives into reinforcement learning theory, explaining the Markov decision process, value function, policy, and policy gradients, with their mathematical formulations and learning algorithms. It covers recent reinforcement learning algorithms from double deep-Q networks to twin-delayed deep deterministic policy gradients and generative adversarial networks with examples using the TensorFlow Python library. It also serves as a quick hands-on guide to TensorFlow programming, covering concepts ranging from variables and graphs to automatic differentiation, layers, models, andloss functions.After completing this book, you will understand reinforcement learning with deep q and generative adversarial networks using the TensorFlow library.What You Will LearnUnderstand the fundamentals of reinforcement learningApply reinforcement learning programming techniques to solve quantitative-finance problemsGain insight into convolutional neural networks and recurrent neural networksUnderstand the Markov decision processWho This Book Is ForData Scientists, Machine Learning engineers and Python programmers who want to apply reinforcement learning to solve problems.

This book is a guide to productionizing AI solutions using best-of-breed cloud services with workarounds to lower costs. Supplemented with step-by-step instructions covering data import through wrangling to partitioning and modeling through to inference and deployment, and augmented with plenty of Python code samples, the book has been written to accelerate the process of moving from script or notebook to app.From an initial look at the context and ecosystem of AI solutions today, the book drills down from high-level business needs into best practices, working with stakeholders, and agile team collaboration. From there yoüll explore data pipeline orchestration, machine and deep learning, including working with and finding shortcuts using artificial neural networks such as AutoML and AutoAI. Yoüll also learn about the increasing use of NoLo UIs through AI application development, industry case studies, and finally a practical guide to deploying containerized AI solutions.The book is intended for those whose role demands overcoming budgetary barriers or constraints in accessing cloud credits to undertake the often difficult process of developing and deploying an AI solution.What You Will LearnDevelop and deliver production-grade AI in one monthDeploy AI solutions at a low costWork around Big Tech dominance and develop MVPs on the cheapCreate demo-ready solutions without overly complex python scripts/notebooks Who this book is for:Data scientists and AI consultants with programming skills in Python and driven to succeed in AI.

This book illustrates how data can be useful in solving business problems. It explores various analytics techniques for using data to discover hidden patterns and relationships, predict future outcomes, optimize efficiency and improve the performance of organizations. Yoüll learn how to analyze data by applying concepts of statistics, probability theory, and linear algebra. In this new edition, both R and Python are used to demonstrate these analyses. Practical Business Analytics Using R and Python also features new chapters covering databases, SQL, Neural networks, Text Analytics, and Natural Language Processing.Part one begins with an introduction to analytics, the foundations required to perform data analytics, and explains different analytics terms and concepts such as databases and SQL, basic statistics, probability theory, and data exploration. Part two introduces predictive models using statistical machine learning and discusses concepts like regression, classification, and neural networks. Part three covers two of the most popular unsupervised learning techniques, clustering and association mining, as well as text mining and natural language processing (NLP). The book concludes with an overview of big data analytics, R and Python essentials for analytics including libraries such as pandas and NumPy.Upon completing this book, you will understand how to improve business outcomes by leveraging R and Python for data analytics.What You Will LearnMaster the mathematical foundations required for business analyticsUnderstand various analytics models and data mining techniques such as regression, supervised machine learning algorithms for modeling, unsupervised modeling techniques, and how to choose the correct algorithm for analysis in any given taskUse R and Python to develop descriptive models, predictive models, and optimize modelsInterpret and recommend actions based on analytical model outcomesWho This Book Is ForSoftware professionals and developers, managers, and executives who want to understand and learn the fundamentals of analytics using R and Python.

This book reports on cutting-edge research and developments focusing on  integrating intelligent functionalities into materials, components, systems and products. Gathering the proceedings of the 6th International Conference on System-Integrated Intelligence (SysInt 2022), held on September 7-9, in Genova, Italy, it offers a comprehensive, multidisciplinary and applied perspective on the state-of-the art and challenges in the field of intelligent, flexible and connected systems. The book covers advanced methods and applications relating to artificial, pervasive and ubiquitous intelligence, sensors, smart factory and logistics, structural health monitoring, as well as soft robotics, cognitive systems and human-machine interaction. Giving a special focus to artificial intelligence, it  extensively reports on methods and algorithms for data-driven modeling, and agent-based data processing and planning. It aims at inspiring and fostering collaboration between researchers and professionals from the different fields of  electrical, manufacturing and production engineering, and materials and computer sciences.