John Liu – författare

The enterprise-focused framework of supply chain, which an overwhelming majority of books on supply chain management (SCM) have adopted, falls short in explaining recent developments in the real world, especially the so-called Wal-Mart model, in which a 'factory' is a virtual logistics network of multiple international manufacturing firms. The book fills the gap and examines supply chain and transport logistics. The book also includes the development of a unified methodological framework which underpins all the characteristics of the interrelationship between supply chain management and logistics. It covers many aspects of the important and innovative developments well. The book offers a unique coverage of integrated logistics of navigation, aviation and transportation. The book not only answers the urgent need for a book on supply chain management and transport logistics but also highlights the central role of supply chain logistics in the emerging fields of sustainable (green), humanitarian and maritime supply chains and the importantance of studying supply chain management together with transport logistics. It also explains the difference between supply chain logistics and manufacturing logistics. It is a useful reference for those in the industry as well as for those taking related courses.

The enterprise-focused framework of supply chain, which an overwhelming majority of books on supply chain management (SCM) have adopted, falls short in explaining recent developments in the real world, especially the so-called Wal-Mart model, in which a 'factory' is a virtual logistics network of multiple international manufacturing firms. The book fills the gap and examines supply chain and transport logistics. The book also includes the development of a unified methodological framework which underpins all the characteristics of the interrelationship between supply chain management and logistics. It covers many aspects of the important and innovative developments well. The book offers a unique coverage of integrated logistics of navigation, aviation and transportation. The book not only answers the urgent need for a book on supply chain management and transport logistics but also highlights the central role of supply chain logistics in the emerging fields of sustainable (green), humanitarian and maritime supply chains and the importantance of studying supply chain management together with transport logistics. It also explains the difference between supply chain logistics and manufacturing logistics. It is a useful reference for those in the industry as well as for those taking related courses.

This book investigates the trends and challenges that ports, logistics and supply chains have tackled in recent decades and the way forward. A new concept, port focal logistics is introduced which appreciates the efforts by previous studies in this field, but simultaneously recognize the limitations, and the need for further improvements.

This book investigates the trends and challenges that ports, logistics and supply chains have tackled in recent decades and the way forward. A new concept, port focal logistics is introduced which appreciates the efforts by previous studies in this field, but simultaneously recognize the limitations, and the need for further improvements.

This textbook explains Deep Learning Architecture, with applications to various NLP Tasks, including Document Classification, Machine Translation, Language Modeling, and Speech Recognition. With the widespread adoption of deep learning, natural language processing (NLP),and speech applications in many areas (including Finance, Healthcare, and Government) there is a growing need for one comprehensive resource that maps deep learning techniques to NLP and speech and provides insights  into  using  the  tools  and  libraries  for  real-world  applications. Deep Learning for NLP and Speech Recognition explains recent deep learning methods applicable to NLP and speech, provides state-of-the-art approaches, and offers real-world case studies with code to provide hands-on experience.  Many books focus on deep learning theory or deep learning for NLP-specific tasks while others are cookbooks for tools and libraries, but the constant flux of new algorithms, tools, frameworks, and libraries in a rapidly evolving landscape means that there are few available texts that offer the material in this book. The book is organized into three parts, aligning to different groups of readers and their expertise. The three parts are:Machine Learning, NLP, and Speech IntroductionThe first part has three chapters that introduce readers to the fields of  NLP, speech recognition,  deep learning and machine learning with basic theory and hands-on case studies using Python-based tools and libraries.Deep Learning BasicsThe five chapters in the second part introduce deep learning and various topics that are crucial for speech and text processing, including word embeddings, convolutional neural networks, recurrent neural networks and speech recognition basics. Theory, practical tips, state-of-the-art methods, experimentations and analysis in using the methods discussed in theory on real-world tasks. Advanced Deep Learning Techniques for Text and SpeechThe third part has five chapters that discuss the latest and cutting-edge research in the areas of deep learning that intersect with NLP and speech. Topics including attention mechanisms, memory augmented networks, transfer learning, multi-task learning, domain adaptation, reinforcement learning, and end-to-end deep learning for speech recognition are covered using case studies.

This textbook explains Deep Learning Architecture, with applications to various NLP Tasks, including Document Classification, Machine Translation, Language Modeling, and Speech Recognition. With the widespread adoption of deep learning, natural language processing (NLP),and speech applications in many areas (including Finance, Healthcare, and Government) there is a growing need for one comprehensive resource that maps deep learning techniques to NLP and speech and provides insights  into  using  the  tools  and  libraries  for  real-world  applications. Deep Learning for NLP and Speech Recognition explains recent deep learning methods applicable to NLP and speech, provides state-of-the-art approaches, and offers real-world case studies with code to provide hands-on experience.  Many books focus on deep learning theory or deep learning for NLP-specific tasks while others are cookbooks for tools and libraries, but the constant flux of new algorithms, tools, frameworks, and libraries in a rapidly evolving landscape means that there are few available texts that offer the material in this book. The book is organized into three parts, aligning to different groups of readers and their expertise. The three parts are:Machine Learning, NLP, and Speech IntroductionThe first part has three chapters that introduce readers to the fields of  NLP, speech recognition,  deep learning and machine learning with basic theory and hands-on case studies using Python-based tools and libraries.Deep Learning BasicsThe five chapters in the second part introduce deep learning and various topics that are crucial for speech and text processing, including word embeddings, convolutional neural networks, recurrent neural networks and speech recognition basics. Theory, practical tips, state-of-the-art methods, experimentations and analysis in using the methods discussed in theory on real-world tasks. Advanced Deep Learning Techniques for Text and SpeechThe third part has five chapters that discuss the latest and cutting-edge research in the areas of deep learning that intersect with NLP and speech. Topics including attention mechanisms, memory augmented networks, transfer learning, multi-task learning, domain adaptation, reinforcement learning, and end-to-end deep learning for speech recognition are covered using case studies.

This book is written both for readers entering the field, and for practitioners with a background in AI and an interest in developing real-world applications. The book is a great resource for practitioners and researchers in both industry and academia, and the discussed case studies and associated material can serve as inspiration for a variety of projects and hands-on assignments in a classroom setting. I will certainly keep this book as a personal resource for the courses I teach, and strongly recommend it to my students.       --Dr. Carlotta Domeniconi, Associate Professor, Computer Science Department, GMUThis book offers a curriculum for introducing interpretability to machine learning at every stage. The authors provide compelling examples that a core teaching practice like leading interpretive discussions can be taught and learned by teachers and sustained effort. And what better way to strengthen the quality of AI and Machine learning outcomes. I hope that this book will become a primer for teachers, data Science educators, and ML developers, and together we practice the art of interpretive machine learning.--Anusha Dandapani, Chief Data and Analytics Officer, UNICC and Adjunct Faculty, NYUThis is a wonderful book! I’m pleased that the next generation of scientists will finally be able to learn this important topic. This is the first book I’ve seen that has up-to-date and well-rounded coverage. Thank you to the authors!--Dr. Cynthia Rudin, Professor of Computer Science, Electrical and Computer Engineering, Statistical Science, and Biostatistics & Bioinformatics Literature on Explainable AI has up until now been relatively scarce and featured mainly mainstream algorithms like SHAP and LIME. This book has closed this gap by providing an extremely broad review of various algorithms proposed in the scientific circles over the previous 5-10 years. This book is a great guide to anyone who is new to the field of XAI or is already familiar with the field and is willing to expand their knowledge.  A comprehensive review of the state-of-the-art Explainable AI methods starting from visualization, interpretable methods, local and global explanations, time series methods, and finishing with deep learning provides an unparalleled source of information currently unavailable anywhere else. Additionally, notebooks with vivid examples are a great supplement that makes the book even more attractive for practitioners of any level.Overall, the authors provide readers with an enormous breadth of coverage without losing sight of practical aspects, which makes this book truly unique and a great addition to the library of any data scientist.Dr. Andrey Sharapov, Product Data Scientist, Explainable AI Expert and Speaker, Founder ofExplainable AI-XAI Group