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The Press We Deserve

The Press We Deserve

Originally published in 1970. This book takes an extremely critical look at the British Press and explodes complacently held views of the time about the merits and virtues of British newspapers. From its polemical introduction by the editor it continues with chapters by a strong host of contributors to set the press in historical context consider Fleet Street’s methods and look at the effect of advertising. Particular aspects investigated are that of women’s journalism sport financial journalism and reviewing. Final chapters look at the underground fringe press provincial papers and a comparison with other countries’ press. | The Press We Deserve

GBP 31.99
1

The Writing Machine A History of the Typewriter

The Writing Machine A History of the Typewriter

First Published in 1973 The Writing Machine presents a comprehensive history of the typewriter. Michael Adler not only investigated the history of the machine but also started collecting typewriters because of the difficulty of discovering what these old machines looked like. Then he found there were other collectors all over the world who supplied him with such a wealth of data that he had eventually to limit the scope of his ‘history’. There are hundreds and hundreds of makes and models of ‘conventional’ front-stroke type bar machines with four-row keyboards but they were virtually all the same. It is the unconventional ones that are interesting and it is on these that the author concentrates. The book is amusing as well as informative and it ends with a complete catalogue of ‘unconventional’ typewriters manufactured up to the 1930s when the ‘conventional’ machine had become universal. This book is a must read for anyone interested to learn about the writing machine. | The Writing Machine A History of the Typewriter

GBP 105.00
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Hands-On Machine Learning with R

Hands-On Machine Learning with R

Hands-on Machine Learning with R provides a practical and applied approach to learning and developing intuition into today’s most popular machine learning methods. This book serves as a practitioner’s guide to the machine learning process and is meant to help the reader learn to apply the machine learning stack within R which includes using various R packages such as glmnet h2o ranger xgboost keras and others to effectively model and gain insight from their data. The book favors a hands-on approach providing an intuitive understanding of machine learning concepts through concrete examples and just a little bit of theory. Throughout this book the reader will be exposed to the entire machine learning process including feature engineering resampling hyperparameter tuning model evaluation and interpretation. The reader will be exposed to powerful algorithms such as regularized regression random forests gradient boosting machines deep learning generalized low rank models and more! By favoring a hands-on approach and using real word data the reader will gain an intuitive understanding of the architectures and engines that drive these algorithms and packages understand when and how to tune the various hyperparameters and be able to interpret model results. By the end of this book the reader should have a firm grasp of R’s machine learning stack and be able to implement a systematic approach for producing high quality modeling results. Features: · Offers a practical and applied introduction to the most popular machine learning methods. · Topics covered include feature engineering resampling deep learning and more. · Uses a hands-on approach and real world data.

GBP 82.99
1

Machine Learning for Decision Sciences with Case Studies in Python

Machine Learning for Decision Sciences with Case Studies in Python

This book provides a detailed description of machine learning algorithms in data analytics data science life cycle Python for machine learning linear regression logistic regression and so forth. It addresses the concepts of machine learning in a practical sense providing complete code and implementation for real-world examples in electrical oil and gas e-commerce and hi-tech industries. The focus is on Python programming for machine learning and patterns involved in decision science for handling data. Features: Explains the basic concepts of Python and its role in machine learning. Provides comprehensive coverage of feature engineering including real-time case studies. Perceives the structural patterns with reference to data science and statistics and analytics. Includes machine learning-based structured exercises. Appreciates different algorithmic concepts of machine learning including unsupervised supervised and reinforcement learning. This book is aimed at researchers professionals and graduate students in data science machine learning computer science and electrical and computer engineering. | Machine Learning for Decision Sciences with Case Studies in Python

GBP 160.00
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Racism and the Press

Racism and the Press

Originally published in 1991. This book presents the results of an interdisciplinary study of the press coverage of ethnic affairs. Examples are drawn mainly from British and Dutch newspapers but data from other countries are also reviewed. Besides providing the reader with a thorough content analysis of the material the book is the first to introduce a detailed discourse analytical approach to the study of the ways in which ethnic minorities are portrayed in the press. The approach focuses on the topics overall news report schemata local meanings style and rhetoric of news reports. Highly original accomplished and penetrating the book is the fruit of a decade of research into the question of racism and the press important for ethnic studies mass communication and media studies sociology and linguistics.

GBP 38.99
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Machine Learning for Managers

Machine Learning for Managers

Machine learning can help managers make better predictions automate complex tasks and improve business operations. Managers who are familiar with machine learning are better placed to navigate the increasingly digital world we live in. There is a view that machine learning is a highly technical subject that can only be understood by specialists. However many of the ideas that underpin machine learning are straightforward and accessible to anyone with a bit of curiosity. This book is for managers who want to understand what machine learning is about but who lack a technical background in computer science statistics or math. The book describes in plain language what machine learning is and how it works. In addition it explains how to manage machine learning projects within an organization. This book should appeal to anyone that wants to learn more about using machine learning to drive value in real-world organizations. | Machine Learning for Managers

GBP 29.99
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Introduction to IoT with Machine Learning and Image Processing using Raspberry Pi

Introduction to IoT with Machine Learning and Image Processing using Raspberry Pi

Machine Learning a branch of Artificial Intelligence is influencing the society industry and academia at large. The adaptability of Python programming language to Machine Learning has increased its popularity further. Another technology on the horizon is Internet of Things (IoT). The present book tries to address IoT Python and Machine Learning along with a small introduction to Image Processing. If you are a novice programmer or have just started exploring IoT or Machine Learning with Python then this book is for you. Features: Raspberry Pi as IoT is described along with the procedure for installation and configuration. A simple introduction to Python Programming Language along with its popular library packages like NumPy Pandas SciPy and Matplotlib are dealt in an exhaustive manner along with relevant examples. Machine Learning along with Python Scikit-Learn library is explained to audience with an emphasis on supervised learning and classification. Image processing on IoT is introduced to the audience who love to apply Machine Learning algorithms to Images The book follows hands-on approach and provide a huge collection of Python programs. | Introduction to IoT with Machine Learning and Image Processing using Raspberry Pi

GBP 44.99
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Text Mining with Machine Learning Principles and Techniques

Text Mining with Machine Learning Principles and Techniques

This book provides a perspective on the application of machine learning-based methods in knowledge discovery from natural languages texts. By analysing various data sets conclusions which are not normally evident emerge and can be used for various purposes and applications. The book provides explanations of principles of time-proven machine learning algorithms applied in text mining together with step-by-step demonstrations of how to reveal the semantic contents in real-world datasets using the popular R-language with its implemented machine learning algorithms. The book is not only aimed at IT specialists but is meant for a wider audience that needs to process big sets of text documents and has basic knowledge of the subject e. g. e-mail service providers online shoppers librarians etc. The book starts with an introduction to text-based natural language data processing and its goals and problems. It focuses on machine learning presenting various algorithms with their use and possibilities and reviews the positives and negatives. Beginning with the initial data pre-processing a reader can follow the steps provided in the R-language including the subsuming of various available plug-ins into the resulting software tool. A big advantage is that R also contains many libraries implementing machine learning algorithms so a reader can concentrate on the principal target without the need to implement the details of the algorithms her- or himself. To make sense of the results the book also provides explanations of the algorithms which supports the final evaluation and interpretation of the results. The examples are demonstrated using realworld data from commonly accessible Internet sources. | Text Mining with Machine Learning Principles and Techniques

GBP 44.99
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A Narrative History of the American Press

A Narrative History of the American Press

Beginning with the American Revolution and spanning over two hundred years of American journalism A Narrative History of the American Press provides an overview of the events institutions and people who have shaped the press from the creation of the First Amendment to today. Gregory A. Borchard’s introductory text helps readers develop an understanding of the role of the press in both the U. S. and world history and how American culture has shaped—and been shaped by—the role of journalism in everyday life. The text along with a rich array of supplemental materials available online provides students with the tools used by both reporters and historians to understand the present through the past allowing readers to use the history of journalism as a lens for implementing their own storytelling reporting and critical analysis skills.

GBP 42.99
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Machine Learning Concepts Techniques and Applications

Machine Learning Concepts Techniques and Applications

Machine Learning: Concepts Techniques and Applications starts at basic conceptual level of explaining machine learning and goes on to explain the basis of machine learning algorithms. The mathematical foundations required are outlined along with their associations to machine learning. The book then goes on to describe important machine learning algorithms along with appropriate use cases. This approach enables the readers to explore the applicability of each algorithm by understanding the differences between them. A comprehensive account of various aspects of ethical machine learning has been discussed. An outline of deep learning models is also included. The use cases self-assessments exercises activities numerical problems and projects associated with each chapter aims to concretize the understanding. Features Concepts of Machine learning from basics to algorithms to implementation Comparison of Different Machine Learning Algorithms – When to use them & Why – for Application developers and Researchers Machine Learning from an Application Perspective – General & Machine learning for Healthcare Education Business Engineering Applications Ethics of machine learning including Bias Fairness Trust Responsibility Basics of Deep learning important deep learning models and applications Plenty of objective questions Use Cases Activity and Project based Learning Exercises The book aims to make the thinking of applications and problems in terms of machine learning possible for graduate students researchers and professionals so that they can formulate the problems prepare data decide features select appropriate machine learning algorithms and do appropriate performance evaluation. | Machine Learning Concepts Techniques and Applications

GBP 140.00
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Entropy Randomization in Machine Learning

Entropy Randomization in Machine Learning

Entropy Randomization in Machine Learning presents a new approach to machine learning—entropy randomization—to obtain optimal solutions under uncertainty (uncertain data and models of the objects under study). Randomized machine-learning procedures involve models with random parameters and maximum entropy estimates of the probability density functions of the model parameters under balance conditions with measured data. Optimality conditions are derived in the form of nonlinear equations with integral components. A new numerical random search method is developed for solving these equations in a probabilistic sense. Along with the theoretical foundations of randomized machine learning Entropy Randomization in Machine Learning considers several applications to binary classification modelling the dynamics of the Earth’s population predicting seasonal electric load fluctuations of power supply systems and forecasting the thermokarst lakes area in Western Siberia. Features • A systematic presentation of the randomized machine-learning problem: from data processing through structuring randomized models and algorithmic procedure to the solution of applications-relevant problems in different fields • Provides new numerical methods for random global optimization and computation of multidimensional integrals • A universal algorithm for randomized machine learning This book will appeal to undergraduates and postgraduates specializing in artificial intelligence and machine learning researchers and engineers involved in the development of applied machine learning systems and researchers of forecasting problems in various fields.

GBP 82.99
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Applied Machine Learning for Smart Data Analysis

Applied Machine Learning for Smart Data Analysis

The book focuses on how machine learning and the Internet of Things (IoT) has empowered the advancement of information driven arrangements including key concepts and advancements. Ontologies that are used in heterogeneous IoT environments have been discussed including interpretation context awareness analyzing various data sources machine learning algorithms and intelligent services and applications. Further it includes unsupervised and semi-supervised machine learning techniques with study of semantic analysis and thorough analysis of reviews. Divided into sections such as machine learning security IoT and data mining the concepts are explained with practical implementation including results. Key Features Follows an algorithmic approach for data analysis in machine learning Introduces machine learning methods in applications Address the emerging issues in computing such as deep learning machine learning Internet of Things and data analytics Focuses on machine learning techniques namely unsupervised and semi-supervised for unseen and seen data sets Case studies are covered relating to human health transportation and Internet applications | Applied Machine Learning for Smart Data Analysis

GBP 125.00
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Machine Learning for the Physical Sciences Fundamentals and Prototyping with Julia

Machine Learning for the Physical Sciences Fundamentals and Prototyping with Julia

Machine learning is an exciting topic with a myriad of applications. However most textbooks are targeted towards computer science students. This however creates a complication for scientists across the physical sciences that also want to understand the main concepts of machine learning and look ahead to applica- tions and advancements in their fields. This textbook bridges this gap providing an introduction to the mathematical foundations for the main algorithms used in machine learning for those from the physical sciences without a formal background in computer science. It demon- strates how machine learning can be used to solve problems in physics and engineering targeting senior undergraduate and graduate students in physics and electrical engineering alongside advanced researchers. All codes are available on the author's website: C•Lab (nau. edu) They are also available on GitHub: https://github. com/StxGuy/MachineLearning Key Features: Includes detailed algorithms. Supplemented by codes in Julia: a high-performing language and one that is easy to read for those in the natural sciences. All algorithms are presented with a good mathematical background. | Machine Learning for the Physical Sciences Fundamentals and Prototyping with Julia

GBP 64.99
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Machine Learning Theory and Practice

Machine Learning Theory and Practice

Machine Learning: Theory and Practice provides an introduction to the most popular methods in machine learning. The book covers regression including regularization tree-based methods including Random Forests and Boosted Trees Artificial Neural Networks including Convolutional Neural Networks (CNNs) reinforcement learning and unsupervised learning focused on clustering. Topics are introduced in a conceptual manner along with necessary mathematical details. The explanations are lucid illustrated with figures and examples. For each machine learning method discussed the book presents appropriate libraries in the R programming language along with programming examples. Features: Provides an easy-to-read presentation of commonly used machine learning algorithms in a manner suitable for advanced undergraduate or beginning graduate students and mathematically and/or programming-oriented individuals who want to learn machine learning on their own. Covers mathematical details of the machine learning algorithms discussed to ensure firm understanding enabling further exploration Presents worked out suitable programming examples thus ensuring conceptual theoretical and practical understanding of the machine learning methods. This book is aimed primarily at introducing essential topics in Machine Learning to advanced undergraduates and beginning graduate students. The number of topics has been kept deliberately small so that it can all be covered in a semester or a quarter. The topics are covered in depth within limits of what can be taught in a short period of time. Thus the book can provide foundations that will empower a student to read advanced books and research papers. | Machine Learning Theory and Practice

GBP 110.00
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A Press Divided Newspaper Coverage of the Civil War

A Press Divided Newspaper Coverage of the Civil War

Now in paperback A Press Divided provides new insights regarding the sharp political divisions that existed among the newspapers of the Civil War era. These newspapers were divided between North and South - and also divided within the North and South. These divisions reflected and exacerbated the conflicts in political thought that caused the Civil War and the political and ideological battles within the Union and the Confederacy about how to pursue the war. In the North dissenting voices alarmed the Lincoln administration to such a degree that draconian measures were taken to suppress dissenting newspapers and editors while in the South the Confederate government held to its fundamental belief in freedom of speech and was more tolerant of political attacks in the press. This volume consists of eighteen chapters on subjects including newspaper coverage of the rise of Lincoln press reports on George Armstrong Custer Confederate women war correspondents Civil War photojournalists newspaper coverage of the Emancipation Proclamation and the suppression of the dissident press. This book tells the story of a divided press before and during the Civil War discussing the roles played by newspapers in splitting the nation newspaper coverage of the war and the responses by the Union and Confederate administrations to press criticism. | A Press Divided Newspaper Coverage of the Civil War

GBP 31.99
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The Bench Grafter's Handbook Principles & Practice

The Bench Grafter's Handbook Principles & Practice

Containing 500 full color photographs and illustrations The Bench Grafter’s Handbook: Principles and Practice presents exhaustive information on all aspects of bench grafting. It details requirements of more than 200 temperate woody plant genera covering over 2 000 species and cultivars including important ornamental temperate fruit and nut crops. The book explains the principles and practices of bench grafting new procedures to enhance grafting success and recommendations for further scientific investigation. Practical issues to aid professionals and the beginner include detailed accounts supported by pictures and diagrams of the main grafting methods knifesmanship techniques and methods of training. Provision and design now and for the future of suitable structures grafting facilities and equipment to provide ideal controlled environments for grafts are described. The book describes major grafting systems sub-cold cold warm supported warm hot-pipe and other grafting strategies. It provides details of health and safety issues; work stations seat design lighting levels; recorded output figures for various types of graft; grafting knives and tools; and methods of sharpening by hand and machine. Features: Comprehensive description pictures and diagrams of how to learn and utilize important grafting methods. Detailed information and scientific principles behind the selection specification and choice of the main graft components – the rootstock and scion. Scientific principles and practicalities of providing optimal plant material equipment facilities and environmental conditions for graft union development including addressing the problems of graft incompatibility. Discussion of the actual and potential role of bench grafting in woody plant conservation with suggestions for new initiatives. This book is intended for use by nurserymen; those involved in the upkeep of extensive plant collections; conservationists; plant scientists; lecturers in horticulture; horticultural students; and amateurs with an interest in grafting. | The Bench Grafter's Handbook Principles & Practice

GBP 77.99
1

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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Intelligent Prognostics for Engineering Systems with Machine Learning Techniques

Intelligent Prognostics for Engineering Systems with Machine Learning Techniques

The text discusses the latest data-driven physics-based and hybrid approaches employed in each stage of industrial prognostics and reliability estimation. It will be a useful text for senior undergraduate graduate students and academic researchers in areas such as industrial and production engineering electrical engineering and computer science. The book Discusses basic as well as advance research in the field of prognostics Explores integration of data collection fault detection degradation modeling and reliability prediction in one volume Covers prognostics and health management (PHM) of engineering systems Discusses latest approaches in the field of prognostics based on machine learning The text deals with tools and techniques used to predict/ extrapolate/ forecast the process behavior based on current health state assessment and future operating conditions with the help of Machine learning. It will serve as a useful reference text for senior undergraduate graduate students and academic researchers in areas such as industrial and production engineering manufacturing science electrical engineering and computer science. | Intelligent Prognostics for Engineering Systems with Machine Learning Techniques

GBP 110.00
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Proceedings of the Second International Conference on Press-in Engineering 2021 Kochi Japan

Proceedings of the Second International Conference on Press-in Engineering 2021 Kochi Japan

The Second International Conference on Press-in Engineering (ICPE) 2021 was organized by the International Press-in Association (IPA). The conference is held every three years and the main theme this time is Evolution and Social Contribution of Press-in Engineering for Infrastructure Development and Disaster Prevention and Mitigation. These proceedings contain 2 keynote lectures 3 state-of-the-art lectures and about 60 papers from more than 10 countries. This publication provides good practice guidance on the application of the press-in piling method to satisfy the requirements of geo-structures which are embedded utilizing prefabricated piles. It covers actual examples of the press-in piling method applied to various geo-structures such as temporary and permanent retaining walls cofferdams cut-off walls foundation piles etc. The content addresses the technical and construction issues relating to the selection of the appropriate type of press-in piling method in accordance with required structural design criteria and soil and working conditions. The aim of this publication is to concisely describe practical uses of the press-in piling method for project owners designers contractors academic researchers and other people in the construction industry. | Proceedings of the Second International Conference on Press-in Engineering 2021 Kochi Japan

GBP 68.99
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Introduction to Machine Learning and Bioinformatics

Introduction to Machine Learning and Bioinformatics

Lucidly Integrates Current Activities Focusing on both fundamentals and recent advances Introduction to Machine Learning and Bioinformatics presents an informative and accessible account of the ways in which these two increasingly intertwined areas relate to each other. Examines Connections between Machine Learning & Bioinformatics The book begins with a brief historical overview of the technological developments in biology. It then describes the main problems in bioinformatics and the fundamental concepts and algorithms of machine learning. After forming this foundation the authors explore how machine learning techniques apply to bioinformatics problems such as electron density map interpretation biclustering DNA sequence analysis and tumor classification. They also include exercises at the end of some chapters and offer supplementary materials on their website. Explores How Machine Learning Techniques Can Help Solve Bioinformatics Problems Shedding light on aspects of both machine learning and bioinformatics this text shows how the innovative tools and techniques of machine learning help extract knowledge from the deluge of information produced by today’s biological experiments.

GBP 59.99
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Machine Learning in Translation

Machine Learning in Translation

Machine Learning in Translation introduces machine learning (ML) theories and technologies that are most relevant to translation processes approaching the topic from a human perspective and emphasizing that ML and ML-driven technologies are tools for humans. Providing an exploration of the common ground between human and machine learning and of the nature of translation that leverages this new dimension this book helps linguists translators and localizers better find their added value in a ML-driven translation environment. Part One explores how humans and machines approach the problem of translation in their own particular ways in terms of word embeddings chunking of larger meaning units and prediction in translation based upon the broader context. Part Two introduces key tasks including machine translation translation quality assessment and quality estimation and other Natural Language Processing (NLP) tasks in translation. Part Three focuses on the role of data in both human and machine learning processes. It proposes that a translator’s unique value lies in the capability to create manage and leverage language data in different ML tasks in the translation process. It outlines new knowledge and skills that need to be incorporated into traditional translation education in the machine learning era. The book concludes with a discussion of human-centered machine learning in translation stressing the need to empower translators with ML knowledge through communication with ML users developers and programmers and with opportunities for continuous learning. This accessible guide is designed for current and future users of ML technologies in localization workflows including students on courses in translation and localization language technology and related areas. It supports the professional development of translation practitioners so that they can fully utilize ML technologies and design their own human-centered ML-driven translation workflows and NLP tasks.

GBP 34.99
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Machine Learning for Neuroscience A Systematic Approach

Machine Learning for Neuroscience A Systematic Approach

This book addresses the growing need for machine learning and data mining in neuroscience. The book offers a basic overview of the neuroscience machine learning and the required math and programming necessary to develop reliable working models. The material is presented in a easy to follow user-friendly manner and is replete with fully working machine learning code. Machine Learning for Neuroscience: A Systematic Approach tackles the needs of neuroscience researchers and practitioners that have very little training relevant to machine learning. The first section of the book provides an overview of necessary topics in order to delve into machine learning including basic linear algebra and Python programming. The second section provides an overview of neuroscience and is directed to the computer science oriented readers. The section covers neuroanatomy and physiology cellular neuroscience neurological disorders and computational neuroscience. The third section of the book then delves into how to apply machine learning and data mining to neuroscience and provides coverage of artificial neural networks (ANN) clustering and anomaly detection. The book contains fully working code examples with downloadable working code. It also contains lab assignments and quizzes making it appropriate for use as a textbook. The primary audience is neuroscience researchers who need to delve into machine learning programmers assigned neuroscience related machine learning projects and students studying methods in computational neuroscience. | Machine Learning for Neuroscience A Systematic Approach

GBP 82.99
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Production Management Advanced Models Tools and Applications for Pull Systems

Production Management Advanced Models Tools and Applications for Pull Systems

Inventory control is an essential task in production management. An effective inventory control can significantly reduce the holding cost and hence total production cost. Selecting and implementing a suitable production control system plays an important role in inventory reduction and performance improvement of a production system. Since the introduction of Toyota’s just-in-time philosophy pull control systems have been adopted by numerous companies worldwide both in the manufacturing and service sectors. This book provides some recent developments in production management and presents modeling and analysis tools for pull production control systems. It contributes by combining theoretical findings and case study analysis results with a practical and contemporary view on how to effectively manage and control production systems. Each chapter in this book focuses on a specific topic in production control systems allowing readers to identify the chapters that relate to their interests. More specifically the book is presented in three sections. The first section focuses on the design and implementation aspects of the pull production control systems as well as performance evaluation approaches for pull systems. The second section presents a recent and comprehensive literature review. Three different case studies on implementation of pull production control systems are presented in the last section. This book can be used as an essential source for students and scholars who need to specifically study the pull control systems. Since the superiority of these systems is controversial the book can also provide an interesting and informative read for practitioners managers and employees who need to deepen their knowledge on pull production management systems. | Production Management Advanced Models Tools and Applications for Pull Systems

GBP 39.99
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