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The Emergence of a Tradition Technical Writing in the English Renaissance 1475-1640

The Emergence of a Tradition Technical Writing in the English Renaissance 1475-1640

Examining books on different topics as these appeared during the Renaissance allows us to see developments in the use of graphics the shift from orality to textuality the expansion of knowledge and rise of literacy particularly among middle-class women readers who were an important audience for many of these books. Changes in English Renaissance technical books provide a new and as yet largely unexplored means of viewing the Renaissance and the dramatic changes that emerged during the 1475-1640 period the first years of English printing. | The Emergence of a Tradition Technical Writing in the English Renaissance 1475-1640

GBP 44.99
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Applications of Artificial Intelligence (AI) and Machine Learning (ML) in the Petroleum Industry

Applications of Artificial Intelligence (AI) and Machine Learning (ML) in the Petroleum Industry

Today raw data on any industry is widely available. With the help of artificial intelligence (AI) and machine learning (ML) this data can be used to gain meaningful insights. In addition as data is the new raw material for today’s world AI and ML will be applied in every industrial sector. Industry 4. 0 mainly focuses on the automation of things. From that perspective the oil and gas industry is one of the largest industries in terms of economy and energy. Applications of Artificial Intelligence (AI) and Machine Learning (ML) in the Petroleum Industry analyzes the use of AI and ML in the oil and gas industry across all three sectors namely upstream midstream and downstream. It covers every aspect of the petroleum industry as related to the application of AI and ML ranging from exploration data management extraction processing real-time data analysis monitoring cloud-based connectivity system and conditions analysis to the final delivery of the product to the end customer while taking into account the incorporation of the safety measures for a better operation and the efficient and effective execution of operations. This book explores the variety of applications that can be integrated to support the existing petroleum and adjacent sectors to solve industry problems. It will serve as a useful guide for professionals working in the petroleum industry industrial engineers AI and ML experts and researchers as well as students.

GBP 82.99
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Machine Learning Theory to Applications

Machine Learning Theory to Applications

The book reviews core concepts of machine learning (ML) while focusing on modern applications. It is aimed at those who want to advance their understanding of ML by providing technical and practical insights. It does not use complicated mathematics to explain how to benefit from ML algorithms. Unlike the existing literature this work provides the core concepts with emphasis on fresh ideas and real application scenarios. It starts with the basic concepts of ML and extends the concepts to the different deep learning algorithms. The book provides an introduction and main elements of evaluation tools with Python and walks you through the recent applications of ML in self-driving cars cognitive decision making communication networks security and signal processing. The concept of generative networks is also presented and focuses on GANs as a tool to improve the performance of existing algorithms. In summary this book provides a comprehensive technological path from fundamental theories to the categorization of existing algorithms covers state-of-the-art practical evaluation tools and methods to empower you to use synthetic data to improve the performance of applications. | Machine Learning Theory to Applications

GBP 52.99
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Maximum Likelihood Estimation with Stata Fifth Edition

Maximum Likelihood Estimation with Stata Fifth Edition

Maximum Likelihood Estimation with Stata Fifth Edition is the essential reference and guide for researchers in all disciplines who wish to write maximum likelihood (ML) estimators in Stata. Beyond providing comprehensive coverage of Stata’s commands for writing ML estimators the book presents an overview of the underpinnings of maximum likelihood and how to think about ML estimation. The fifth edition includes a new second chapter that demonstrates the easy-to-use mlexp command. This command allows you to directly specify a likelihood function and perform estimation without any programming. The core of the book focuses on Stata's ml command. It shows you how to take full advantage of ml’s noteworthy features: Linear constraints Four optimization algorithms (Newton–Raphson DFP BFGS and BHHH) Observed information matrix (OIM) variance estimator Outer product of gradients (OPG) variance estimator Huber/White/sandwich robust variance estimator Cluster–robust variance estimator Complete and automatic support for survey data analysis Direct support of evaluator functions written in Mata When appropriate options are used many of these features are provided automatically by ml and require no special programming or intervention by the researcher writing the estimator. In later chapters you will learn how to take advantage of Mata Stata's matrix programming language. For ease of programming and potential speed improvements you can write your likelihood-evaluator program in Mata and continue to use ml to control the maximization process. A new chapter in the fifth edition shows how you can use the moptimize() suite of Mata functions if you want to implement your maximum likelihood estimator entirely within Mata. In the final chapter the authors illustrate the major steps required to get from log-likelihood function to fully operational estimation command. This is done using several different models: logit and probit linear regression Weibull regression the Cox proportional hazards model random-effects regression and seemingly unrelated regression. This edition adds a new example of a bivariate Poisson model a model that is not available otherwise in Stata. The authors provide extensive advice for developing your own estimation commands. With a little care and the help of this book users will be able to write their own estimation commands-commands that look and behave just like the official estimation commands in Stata. Whether you want to fit a special ML estimator for your own research or wish to write a general-purpose ML estimator for others to use you need this book.

GBP 59.99
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Machine Learning Animated

Machine Learning Animated

The release of ChatGPT has kicked off an arms race in Machine Learning (ML) however ML has also been described as a black box and very hard to understand. Machine Learning Animated eases you into basic ML concepts and summarizes the learning process in three words: initialize adjust and repeat. This is illustrated step by step with animation to show how machines learn: from initial parameter values to adjusting each step to the final converged parameters and predictions. This book teaches readers to create their own neural networks with dense and convolutional layers and use them to make binary and multi-category classifications. Readers will learn how to build deep learning game strategies and combine this with reinforcement learning witnessing AI achieve super-human performance in Atari games such as Breakout Space Invaders Seaquest and Beam Rider. Written in a clear and concise style illustrated with animations and images this book is particularly appealing to readers with no background in computer science mathematics or statistics. Access the book's repository at: https://github. com/markhliu/MLA

GBP 74.99
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Machine Learning in Healthcare Fundamentals and Recent Applications

Machine Learning in Healthcare Fundamentals and Recent Applications

Artificial intelligence (AI) and machine learning (ML) techniques play an important role in our daily lives by enhancing predictions and decision-making for the public in several fields such as financial services real estate business consumer goods social media etc. Despite several studies that have proved the efficacy of AI/ML tools in providing improved healthcare solutions it has not gained the trust of health-care practitioners and medical scientists. This is due to poor reporting of the technology variability in medical data small datasets and lack of standard guidelines for application of AI. Therefore the development of new AI/ML tools for various domains of medicine is an ongoing field of research. Machine Learning in Healthcare: Fundamentals and Recent Applications discusses how to build various ML algorithms and how they can be applied to improve healthcare systems. Healthcare applications of AI are innumerable: medical data analysis early detection and diagnosis of disease providing objective-based evidence to reduce human errors curtailing inter- and intra-observer errors risk identification and interventions for healthcare management real-time health monitoring assisting clinicians and patients for selecting appropriate medications and evaluating drug responses. Extensive demonstrations and discussion on the various principles of machine learning and its application in healthcare is provided along with solved examples and exercises. This text is ideal for readers interested in machine learning without any background knowledge and looking to implement machine-learning models for healthcare systems. | Machine Learning in Healthcare Fundamentals and Recent Applications

GBP 82.99
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Texts from the Querelle 1521–1615 Essential Works for the Study of Early Modern Women: Series III Part Two Volume 1

Texts from the Querelle 1521–1615 Essential Works for the Study of Early Modern Women: Series III Part Two Volume 1

Misogyny and its opposite philogyny have been perennial topics in Western literature from its earliest days to the present day but only at certain historic periods have pro-woman authors challenged fundamental negative assumptions about women by engaging in formal debate with misogynists and juxtaposing these two attitudes toward women in pairs or series of texts devoted exclusively to discussing womankind. This dialectic of attack on and defence of the female sex known as the querelle des femmes (debate about women) was especially popular among authors and readers during the sixteenth and earlier seventeenth centuries in England. At least 36 texts exclusively devoted to attacking and/or defending women were published in the hundred years between 1540 and 1640. The works included in these two volumes exemplify the content and the methods of debate in England during those two centuries. Volume one includes texts from 1521 through to 1615. | Texts from the Querelle 1521–1615 Essential Works for the Study of Early Modern Women: Series III Part Two Volume 1

GBP 44.99
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Machine Learning for Factor Investing: R Version

Machine Learning for Factor Investing: R Version

Machine learning (ML) is progressively reshaping the fields of quantitative finance and algorithmic trading. ML tools are increasingly adopted by hedge funds and asset managers notably for alpha signal generation and stocks selection. The technicality of the subject can make it hard for non-specialists to join the bandwagon as the jargon and coding requirements may seem out of reach. Machine Learning for Factor Investing: R Version bridges this gap. It provides a comprehensive tour of modern ML-based investment strategies that rely on firm characteristics. The book covers a wide array of subjects which range from economic rationales to rigorous portfolio back-testing and encompass both data processing and model interpretability. Common supervised learning algorithms such as tree models and neural networks are explained in the context of style investing and the reader can also dig into more complex techniques like autoencoder asset returns Bayesian additive trees and causal models. All topics are illustrated with self-contained R code samples and snippets that are applied to a large public dataset that contains over 90 predictors. The material along with the content of the book is available online so that readers can reproduce and enhance the examples at their convenience. If you have even a basic knowledge of quantitative finance this combination of theoretical concepts and practical illustrations will help you learn quickly and deepen your financial and technical expertise.

GBP 66.99
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Machine Learning for Factor Investing Python Version

Machine Learning for Factor Investing Python Version

Machine learning (ML) is progressively reshaping the fields of quantitative finance and algorithmic trading. ML tools are increasingly adopted by hedge funds and asset managers notably for alpha signal generation and stocks selection. The technicality of the subject can make it hard for non-specialists to join the bandwagon as the jargon and coding requirements may seem out-of-reach. Machine learning for factor investing: Python version bridges this gap. It provides a comprehensive tour of modern ML-based investment strategies that rely on firm characteristics. The book covers a wide array of subjects which range from economic rationales to rigorous portfolio back-testing and encompass both data processing and model interpretability. Common supervised learning algorithms such as tree models and neural networks are explained in the context of style investing and the reader can also dig into more complex techniques like autoencoder asset returns Bayesian additive trees and causal models. All topics are illustrated with self-contained Python code samples and snippets that are applied to a large public dataset that contains over 90 predictors. The material is available online so that readers can reproduce and enhance the examples at their convenience. If you have even a basic knowledge of quantitative finance this combination of theoretical concepts and practical illustrations will help you learn quickly and deepen your financial and technical expertise. | Machine Learning for Factor Investing Python Version

GBP 66.99
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Operational Auditing Principles and Techniques for a Changing World

Operational Auditing Principles and Techniques for a Changing World

Operational Auditing: Principles and Techniques for a Changing World 2nd edition explains the proven approaches and essential procedures to perform risk-based operational audits. It shows how to effectively evaluate the relevant dynamics associated with programs and processes including operational strategic technological financial and compliance objectives and risks. This book merges traditional internal audit concepts and practices with contemporary quality control methodologies tips tools and techniques. It explains how internal auditors can perform operational audits that result in meaningful findings and useful recommendations to help organizations meet objectives and improve the perception of internal auditors as high-value contributors appropriate change agents and trusted advisors. The 2nd edition introduces or expands the previous coverage of: • Control self-assessments. • The 7 Es framework for operational quality. • Linkages to ISO 9000. • Flowcharting techniques and value-stream analysis • Continuous monitoring. • The use of Key Performance Indicators (KPIs) and Key Risk Indicators (KRIs). • Robotic process automation (RPA) artificial intelligence (AI) and machine learning (ML); and • Adds a new chapter that will examine the role of organizational structure and its impact on effective communications task allocation coordination and operational resiliency to more effectively respond to market demands. | Operational Auditing Principles and Techniques for a Changing World

GBP 64.99
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Artificial Intelligence in Commercial Aviation Use Cases and Emerging Strategies

Artificial Intelligence in Commercial Aviation Use Cases and Emerging Strategies

This book is a must read for aviation managers and all stakeholders that are interested in improving the business performance of airlines. In this book the first of its kind on AI in Commercial Aviation the author outlines how Machine Learning and AI are accelerating and improving the performance of airlines. Moreover the author shares insights into many new use cases that emerging technology can deliver. He tackles all crucial functions from air navigation flight operations to sales distribution cargo retailing and commercial optimization. He then looks forward to blockchain and the metaverse and its opportunities. With connected devices and the Internet of Everything (IoE) airlines can become retailers sell deliver and service holistic experiences tailored to individuals in real time. This requires airlines to modernize processes and practices supported by decision intelligence (AI) that ingests sophisticated insights and executes service automation in real time. Transforming airlines from a production to a services-based execution also requires departments to be aligned along overriding customer experience and profitability goals. The book demonstrates how AI can be deployed to redesign airline organization as well. The author also describes the next wave of business transformation around the integration of commercial functions using Composite AI at enterprise level. With his holistic understanding and experience in the airline industry the author provides valuable insights and helps managers understand how to embrace ML and AI and contribute to future commercial aviation and cargo success. | Artificial Intelligence in Commercial Aviation Use Cases and Emerging Strategies

GBP 45.00
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Deep Learning in Biomedical and Health Informatics Current Applications and Possibilities

Deep Learning in Biomedical and Health Informatics Current Applications and Possibilities

This book provides a proficient guide on the relationship between Artificial Intelligence (AI) and healthcare and how AI is changing all aspects of the healthcare industry. It also covers how deep learning will help in diagnosis and the prediction of disease spread. The editors present a comprehensive review of research applying deep learning in health informatics in the fields of medical imaging electronic health records genomics and sensing and highlights various challenges in applying deep learning in health care. This book also includes applications and case studies across all areas of AI in healthcare data. The editors also aim to provide new theories techniques developments and applications of deep learning and to solve emerging problems in healthcare and other domains. This book is intended for computer scientists biomedical engineers and healthcare professionals researching and developing deep learning techniques. In short the volume : Discusses the relationship between AI and healthcare and how AI is changing the health care industry. Considers uses of deep learning in diagnosis and prediction of disease spread. Presents a comprehensive review of research applying deep learning in health informatics across multiple fields. Highlights challenges in applying deep learning in the field. Promotes research in ddeep llearning application in understanding the biomedical process. Dr. . M. A. Jabbar is a professor and Head of the Department AI&ML Vardhaman College of Engineering Hyderabad Telangana India. Prof. (Dr. ) Ajith Abraham is the Director of Machine Intelligence Research Labs (MIR Labs) Auburn Washington USA. Dr. . Onur Dogan is an assistant professor at İzmir Bakırçay University Turkey. Prof. Dr. Ana Madureira is the Director of The Interdisciplinary Studies Research Center at Instituto Superior de Engenharia do Porto (ISEP) Portugal. Dr. . Sanju Tiwari is a senior researcher at Universidad Autonoma de Tamaulipas Mexico. | Deep Learning in Biomedical and Health Informatics Current Applications and Possibilities

GBP 44.99
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Introduction to Machine Learning with Applications in Information Security

Introduction to Machine Learning with Applications in Information Security

Introduction to Machine Learning with Applications in Information Security Second Edition provides a classroom-tested introduction to a wide variety of machine learning and deep learning algorithms and techniques reinforced via realistic applications. The book is accessible and doesn’t prove theorems or dwell on mathematical theory. The goal is to present topics at an intuitive level with just enough detail to clarify the underlying concepts. The book covers core classic machine learning topics in depth including Hidden Markov Models (HMM) Support Vector Machines (SVM) and clustering. Additional machine learning topics include k-Nearest Neighbor (k-NN) boosting Random Forests and Linear Discriminant Analysis (LDA). The fundamental deep learning topics of backpropagation Convolutional Neural Networks (CNN) Multilayer Perceptrons (MLP) and Recurrent Neural Networks (RNN) are covered in depth. A broad range of advanced deep learning architectures are also presented including Long Short-Term Memory (LSTM) Generative Adversarial Networks (GAN) Extreme Learning Machines (ELM) Residual Networks (ResNet) Deep Belief Networks (DBN) Bidirectional Encoder Representations from Transformers (BERT) and Word2Vec. Finally several cutting-edge deep learning topics are discussed including dropout regularization attention explainability and adversarial attacks. Most of the examples in the book are drawn from the field of information security with many of the machine learning and deep learning applications focused on malware. The applications presented serve to demystify the topics by illustrating the use of various learning techniques in straightforward scenarios. Some of the exercises in this book require programming and elementary computing concepts are assumed in a few of the application sections. However anyone with a modest amount of computing experience should have no trouble with this aspect of the book. Instructor resources including PowerPoint slides lecture videos and other relevant material are provided on an accompanying website: http://www. cs. sjsu. edu/~stamp/ML/.

GBP 62.99
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Information Technology and Organizational Learning Managing Behavioral Change in the Digital Age

Information Technology and Organizational Learning Managing Behavioral Change in the Digital Age

Because digital and information technology (IT) has become a more significant part of strategic advantage and workplace operations information systems personnel have become key to the success of corporate enterprises particularly with the pursuit of becoming more digital. This book focuses on the vital role that technology must play in the course of organizational development and learning and on the growing need to integrate technology particularly digital technology fully into the culture of all organizations. Fundamentally this fourth edition takes an even stronger position than the previous editions that organizational learning is crucial to the success of what has been coined digital transformation. Companies are struggling to understand what it means to be digital. Their technology personnel go far beyond the traditional IT staff into areas such as artificial intelligence (AI) machine learning (ML) and natural language processing (NL). These three functions now fall under the auspices of data science which is now at the center of allowing companies to become more data dominant as is necessary for survival. While traditional IT personnel have long been criticized for their inability to function as part of the business they are now vital to assist in the leadership of digital transformation. It could be a costly error to underestimate the technical skills needed by IT staff to ensure successful digital transformation. In fact subsequent chapters will highlight the technical challenges needed to build new architectures based on 5G blockchain cloud computing and eventually quantum processing. The challenge then is to integrate business and technical IT staff via cultural assimilation and to strategically integrate advanced computing architectures. This fourth edition includes new topics such as the future of work that addresses the challenges of assimilating multiple generations of employees and how to establish working cultures that are more resilient and adaptive and can be configured as a platform driven by data assets. | Information Technology and Organizational Learning Managing Behavioral Change in the Digital Age

GBP 44.99
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Linear Models with Python

Linear Models with Python

Praise for Linear Models with R: This book is a must-have tool for anyone interested in understanding and applying linear models. The logical ordering of the chapters is well thought out and portrays Faraway’s wealth of experience in teaching and using linear models. … It lays down the material in a logical and intricate manner and makes linear modeling appealing to researchers from virtually all fields of study. Biometrical Journal Throughout it gives plenty of insight … with comments that even the seasoned practitioner will appreciate. Interspersed with R code and the output that it produces one can find many little gems of what I think is sound statistical advice well epitomized with the examples chosen…I read it with delight and think that the same will be true with anyone who is engaged in the use or teaching of linear models. Journal of the Royal Statistical Society Like its widely praised best-selling companion version Linear Models with R this book replaces R with Python to seamlessly give a coherent exposition of the practice of linear modeling. Linear Models with Python offers up-to-date insight on essential data analysis topics from estimation inference and prediction to missing data factorial models and block designs. Numerous examples illustrate how to apply the different methods using Python. Features: Python is a powerful open source programming language increasingly being used in data science machine learning and computer science. Python and R are similar but R was designed for statistics while Python is multi-talented. This version replaces R with Python to make it accessible to a greater number of users outside of statistics including those from Machine Learning. A reader coming to this book from an ML background will learn new statistical perspectives on learning from data. Topics include Model Selection Shrinkage Experiments with Blocks and Missing Data. Includes an Appendix on Python for beginners. Linear Models with Python explains how to use linear models in physical science engineering social science and business applications. It is ideal as a textbook for linear models or linear regression courses.

GBP 82.99
1

Equipment Management in the Post-Maintenance Era Advancing in the Era of Smart Machines

Equipment Management in the Post-Maintenance Era Advancing in the Era of Smart Machines

Recent advancements in information systems and computer technology have led to developments in equipment and robotic technology that have permanently changed the characteristics of manufacturing equipment. Equipment Management in the Post-Maintenance Era: Advancing in the Era of Smart Machines introduces a new way of thinking to help high-tech organizations manage an increasingly complex equipment base. It also facilitates the fundamental understanding of equipment management those in traditional industries will need to prepare for the emerging microchip era in equipment. Kern Peng shares insights gained through decades of managing equipment performance. Using a systems model to analyze equipment management he introduces alternatives in equipment management that are currently gaining momentum in high-tech industries. The book highlights the fundamental internal flaw in maintenance organizational setup presents new approaches to replace maintenance functional setup and illustrates a time-tested transformation and implementation process to help transition your organization from the maintenance era to the new post-maintenance era. Fundamentally it: Breaks down the history of equipment into five phases Provides a clear understanding of equipment management fundamentals and Introduces alternatives in equipment management beyond the mainstream principles of maintenance management. More specifically the book examines maintenance management logistics including planning and budgeting; training and people development; customer services and management; vendor management; and inventory management. Supplying a comprehensive look at the history of equipment management it analyzes current maintenance practice and details approaches that can significantly improve the effectiveness and efficiency of your equipment management well into the future. This second edition addresses the role of the development of the Internet of Things (IoT) and significant advancements in artificial intelligence (AI) and machine learning (ML) in enabling a new generation of smart machines which have in turn laid the foundation for Industry 4. 0. Equipment utilizing IoT and sensors can monitor components and allow them to be serviced at an exact time without the need for a preventive maintenance schedule. Moreover equipment replacement rarely occurs at the end of the piece of equipment’s natural life; rather replacement is driven by the introduction of new technologies and products all of which lead to less maintenance activities and reduces the importance of the traditional maintenance function. Maintenance departments today operate with fewer employees and smaller budgets. At a point when machines are smart enough to keep themselves running or equipment is rendered obsolete by better equipment in a short time such as with computers and cellphones companies do not need a maintenance department. This updated edition reiterates the importance of transitioning to the post-maintenance era to effectively manage today’s sophisticated smart yet expensive equipment. Many changes the author predicted a decade ago are accelerating in the IoT era. Equipment management is moving further away from the maintenance era and advancing deeper into the post-maintenance era. The trend for smart machines is very clear and companies that do not upgrade their equipment will lose their competitiveness. As equipment and factories become smarter companies must change their practices and organizational structures to manage the new generation of equipment for Industry 4. 0. | Equipment Management in the Post-Maintenance Era Advancing in the Era of Smart Machines

GBP 48.99
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