Found insideThis volume constitutes the refereed proceedings of the 14th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2019, held in León, Spain, in September 2019. Intended to anyone interested in numerical computing and data science: students, researchers, teachers, engineers, analysts, hobbyists. Found inside – Page iThis book constitutes the refereed proceedings of the 40th European Conference on IR Research, ECIR 2018, held in Grenoble, France, in March 2018. This book provides an introduction to artificial intelligence (AI) and deep learning (DL), IBM PowerAI, and components of IBM PowerAI, deploying IBM PowerAI, guidelines for working with data and creating models, an introduction to IBM ... This book describes, in easy-to-understand language, the latest and most fascinating findings of mathematicians and computer scientists in the field of Pi. Attention is focused on new methods of high-speed computation. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students. Found insideIf you are running more than just a few containers or want automated management of your containers, you need Kubernetes. This book focuses on helping you master the advanced management of Kubernetes clusters. Found insideThe main challenge is how to transform data into actionable knowledge. In this book you will learn all the important Machine Learning algorithms that are commonly used in the field of data science. Found insideIt focuses on creating cloud native applications using the latest version of IBM WebSphere® Application Server Liberty, IBM Bluemix® and other Open Source Frameworks in the Microservices ecosystem to highlight Microservices best practices ... Beginners and hobbyists can jump right in to creating AI projects with the Raspberry PI using this book. Found insideThis book constitutes the proceedings of the 5th Latin American Conference, CARLA 2018, held in Bucaramanga, Colombia, in September 2018. The 24 papers presented in this volume were carefully reviewed and selected from 38 submissions. Found insideThis book sets an ambitious goal: to shift the development of language processing systems to a much more automated setting than previous works. Found insideStyle and approach This highly practical book will show you how to implement Artificial Intelligence. The book provides multiple examples enabling you to create smart applications to meet the needs of your organization. Found insideCompletely updated and revised edition of the bestselling guide to artificial intelligence, updated to Python 3.8, with seven new chapters that cover RNNs, AI and Big Data, fundamental use cases, machine learning data pipelines, chatbots, ... Many patterns are also backed by concrete code examples. This book is ideal for developers already familiar with basic Kubernetes concepts who want to learn common cloud native patterns. Found insideEach chapter provides a comprehensive summary of their subject's current research directions. This book hence can equally be useful for students and researchers. Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics About This Book Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization Learn ... This book is actually two books in one. The first section is a short tutorial on developing enterprise applications, which you can read from start to finish to understand the scope of the book's lessons. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. Identification of the gene involved in 4q25-linked Axenfeld-Rieger syndrome, PITX2 / Elena V. Semina -- Winged helix/forkhead transcription factors and Rieger syndrome / Darryl Y. Nishimura and Ruth E. Swiderski -- Rieger syndrome and PAX6 ... Found insideThis hands-on guide uses Julia 1.0 to walk you through programming one step at a time, beginning with basic programming concepts before moving on to more advanced capabilities, such as creating new types and multiple dispatch. This book examines and explains a variety of scientific programming models (programming models relevant to scientists) with an emphasis on how programming constructs map to different parts of the computer's architecture. Found insideThis book covers a large number, including the IPython Notebook, pandas, scikit-learn and NLTK. Each chapter of this book introduces you to new algorithms and techniques. Found insideThe Python ecosystem with scikit-learn and pandas is required for operational machine learning. Whether you are trying to build dynamic network models or forecast real-world behavior, this book illustrates how graph algorithms deliver value—from finding vulnerabilities and bottlenecks to detecting communities and improving machine ... This book covers all the libraries in Spark ecosystem: Spark Core, Spark SQL, Spark Streaming, Spark ML, and Spark GraphX. This book is neither a textbook in numerical methods, a comprehensive introductory book to Julia programming, a textbook on numerical optimization, a complete manual of optimization solvers, nor an introductory book to computational science ... Found inside – Page iiThis book: Provides complete coverage of the major concepts and techniques of natural language processing (NLP) and text analytics Includes practical real-world examples of techniques for implementation, such as building a text ... Found insideUsing clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will discover the importance of statistical methods to machine learning, summary stats, hypothesis testing, nonparametric stats, resampling methods, ... Found inside – Page iWho This Book Is For IT professionals, analysts, developers, data scientists, engineers, graduate students Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. This second edition of a well-received text, with 20 new chapters, presents a coherent and unified repository of recommender systems’ major concepts, theories, methodologies, trends, and challenges. This book covers: Supervised learning regression-based models for trading strategies, derivative pricing, and portfolio management Supervised learning classification-based models for credit default risk prediction, fraud detection, and ... This book is intended for the budding data scientist or quantitative analyst with only a basic exposure to R and statistics. This book provides a comprehensive introduction to the basic concepts, models, and applications of graph neural networks. Found insideWith this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas ... The book then discusses SSL applications and offers guidelines for SSLpractitioners by analyzing the results of extensive benchmark experiments. Finally, the book looksat interesting directions for SSL research. Found insideThe purpose of this book is two-fold, we focus on detailed coverage of deep learning and transfer learning, comparing and contrasting the two with easy-to-follow concepts and examples. Found insideThis book constitutes the proceedings of the 24th International Symposium on Foundations of Intelligent Systems, ISMIS 2018, held in Limassol, Cyprus, in October 2018. This book is for programmers, scientists, and engineers who have knowledge of the Python language and know the basics of data science. It is for those who wish to learn different data analysis methods using Python and its libraries. This is a textbook that teaches the bridging topics between numerical analysis, parallel computing, code performance, large scale applications. The first book of its kind dedicated to the challenge of person re-identification, this text provides an in-depth, multidisciplinary discussion of recent developments and state-of-the-art methods. Found insideThis book is divided into four sections: Introduction—Learn what site reliability engineering is and why it differs from conventional IT industry practices Principles—Examine the patterns, behaviors, and areas of concern that influence ... Found insideThis book gives you hands-on experience with the most popular Python data science libraries, Scikit-learn and StatsModels. After reading this book, you’ll have the solid foundation you need to start a career in data science. Found inside – Page iThe second edition of this book will show you how to use the latest state-of-the-art frameworks in NLP, coupled with Machine Learning and Deep Learning to solve real-world case studies leveraging the power of Python. Found insideExploit the power of data in your business by building advanced predictive modeling applications with Python About This Book Master open source Python tools to build sophisticated predictive models Learn to identify the right machine ... 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