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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 success of the Wal-Mart model
rests on dynamic innovations in two key dimensions, namely,
all-mode logistics service facilitation and industrial organization
of supply chains, on which existing SCM textbooks have little
coverage. For example, managing transport utility and facility,
such as seaports and airports, has become expected parts of
logistics and SCM, especially in an international orientation;
which, however, are seldom covered in the textbooks on SCM and
logistics. Supply chain and transport logistics as termed in this
book is precisely based on this intriguing interrelationship,
referring to supply-chain centered logistics of enterprise-crossing
characteristics, including both service facilitation and industrial
organization (IO) aspects of logistics. This book also includes the
development of a unified methodological framework which underpins
all the characteristics of the intriguing 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 importance of studying supply chain
management together with transport logistics. It also explains the
difference between supply chain
Tough Test Questions? Missed Lectures? Not Enough Time? Textbook
too pricey? Fortunately, there's Schaum's. This all-in-one-package
includes more than 2,400 fully solved problems, examples, and
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you will have access to the revised online Schaum's.com website--
it's just like having your own virtual tutor! You'll find
everything you need to build confidence, skills, and knowledge for
the highest score possible. More than 40 million students have
trusted Schaum's to help them succeed in the classroom and on
exams. Schaum's is the key to faster learning and higher grades in
every subject. Each Outline presents all the essential course
information in an easy-to-follow, topic-by-topic format. Helpful
tables and illustrations increase your understanding of the subject
at hand. Schaum's Outline of Mathematical Handbook of Formulas and
Tables, Fifth Edition features: * More than 2,400 formulas and
tables * Clear explanations for all mathematical formulas and
procedures * Formulas and tables for elementary to advanced topics
* A complete index to all topics * Access to revised Schaums.com
website
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 title 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.
A collection of standard and cutting-edge techniques for using
Xenopus oocytes and oocytes/egg extracts to reconstitute biological
and cellular processes. These readily reproducible methods take
advantage of the oocyte's impressive protein abundance, its
striking protein translation capacity, and its breathtaking
possibilities for the assembly of infectious viral particles by
single cell injection of multiple RNAs. The authors focus on the
versatility of frog oocytes and egg extracts in cell biology and
signal transduction, and cover all the major uses of
oocytes/extracts as experimental models.
A collection of standard and cutting-edge techniques for using
Xenopus oocytes and oocytes/egg extracts to reconstitute biological
and cellular processes. These readily reproducible methods take
advantage of the oocyte's impressive protein abundance, its
striking protein translation capacity, and its breathtaking
possibilities for the assembly of infectious viral particles by
single cell injection of multiple RNAs. The authors focus on the
versatility of frog oocytes and egg extracts in cell biology and
signal transduction, and cover all the major uses of
oocytes/extracts as experimental models.
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 Introduction The 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 Basics The 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 Speech The 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, GMU This 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, NYU This 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 of Explainable AI-XAI Group
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, GMU This 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, NYU This 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 of Explainable AI-XAI Group
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 success of the Wal-Mart model
rests on dynamic innovations in two key dimensions, namely,
all-mode logistics service facilitation and industrial organization
of supply chains, on which existing SCM textbooks have little
coverage. For example, managing transport utility and facility,
such as seaports and airports, has become expected parts of
logistics and SCM, especially in an international orientation;
which, however, are seldom covered in the textbooks on SCM and
logistics. Supply chain and transport logistics as termed in this
book is precisely based on this intriguing interrelationship,
referring to supply-chain centered logistics of enterprise-crossing
characteristics, including both service facilitation and industrial
organization (IO) aspects of logistics. This book also includes the
development of a unified methodological framework which underpins
all the characteristics of the intriguing 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 importance of studying supply chain
management together with transport logistics. It also explains the
difference between supply chain
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 Introduction The 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 Basics The 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 Speech The 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.
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