John Liu

A. Ng

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.

Port-Focal Logistics and Global Supply Chains

Murray R. Spiegel

Seymour Lipschutz

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 practice exercises to sharpen your problem-solving skills. Plus, 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

Schaum's Outline of Mathematical Handbook of Formulas and Tables, Fifth Edition

Murray Spiegel

More than 40 million books sold in the Schaum's Outline series!

2,400 Formulas + Tables

Schaum's Outline of Mathematical Handbook of Formulas and Tables, 4th Edition

James Whitaker

Uday Kamath

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.

John Liu

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