【Kinoppy】

「人工知能とデータサイエンス 2019/2020」(電子洋書)ご案内
Part 2.

人工知能とデータサイエンスの活用は、産官学共通の課題となっています。人口縮小とビッグデータの挑戦を見据えて、膨大な情報を効率的にフィルタリングし、解析し、可視化し、有意義な研究や意思決定、生産に結びつけていくために、どのようなリテラシーが求められているでしょうか?
最近一年間の新刊・好評書目の中から、電子書籍でご利用いただけるタイトルをピックアップしてご案内します。

Part 2. の今回は機械学習・深層学習・ビッグデータ&データマイニング編(37点)です。

Part 1.のリストはこちら

【2019年10月25日更新】

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1.機械学習・深層学習

商品詳細へニューラルネットワークと深層学習(テキスト)
Neural Networks and Deep Learning : A Textbook

Aggarwal, Charu C.
紙版刊行: 2018/08
Springer




This book covers both classical and modern models in deep learning. The chapters of this book span three categories: the basics of neural networks; fundamentals of neural networks; advanced topics in neural networks.

商品詳細へサイバーセキュリティのための深層学習の応用
Deep Learning Applications for Cyber Security (Advanced Sciences and Technologies for Security Applications)

Alazab, Mamoun/ Tang, MingJian (EDT)
紙版刊行: 2019/06
Springer




Provides wide coverage of popular Deep Learning tools and frameworks enabling the readers to quickly develop workable and advanced prototypes.

商品詳細へ機械学習への経験的アプローチ
Empirical Approach to Machine Learning (Studies in Computational Intelligence) Vol. 800

Angelov, Plamen P./ Gu, X.
紙版刊行: 2018/11
Springer




This book provides a 'one-stop source' for all readers who are interested in a new, empirical approach to machine learning that, unlike traditional methods, successfully addresses the demands of today's data-driven world.

商品詳細へ深層学習応用ハンドブック
Handbook of Deep Learning Applications (Smart Innovation, Systems and Technologies) Vol. 136

Balas, Valentina/ Roy, Sanjiban Sekhar/ Sharma, Dharmendra (EDT)
紙版刊行: 2019/03
Springer




This book presents a broad range of deep-learning applications related to vision, natural language processing, gene expression, arbitrary object recognition, driverless cars, semantic image segmentation, deep visual residual abstraction, brain-computer interfaces, etc.

商品詳細へデータサイエンスのための教師あり(なし)学習
Supervised and Unsupervised Learning for Data Science (Unsupervised and Semi-Supervised Learning)

Berry, Michael W./ Mohamed, Azlinah/ Yap, Bee Wah (EDT)
紙版刊行: 2019/09
Springer




This book covers the state of the art in learning algorithms with an inclusion of semi-supervised methods to provide a broad scope of clustering and classification solutions for big data applications.

商品詳細へ混合モデルと応用
Mixture Models and Applications (Unsupervised and Semi-Supervised Learning)

Bouguila, Nizar/ Fan, Wentao (EDT)
紙版刊行: 2019/09
Springer




Reports advances on classic problems in mixture modeling such as paramenter estimation, model selection, and feature selection.

商品詳細へ両手ロボットのスキル強化学習
Reinforcement Learning of Bimanual Robot Skills (Springer Tracts in Advanced Robotics) Vol. 134

Colomé, Adrià / Torras, Carme
紙版刊行: 2019/09
Springer




This book tackles all the stages and mechanisms involved in the learning of manipulation tasks by bimanual robots in unstructured settings, as it can be the task of folding clothes. The first part describes how to build an integrated system, capable of properly handling the kinematics and dynamics of the robot along the learning process.

商品詳細へ辞書学習アルゴリズムと応用
Dictionary Learning Algorithms and Applications

Dumitrescu, Bogdan/ Irofti, Paul
紙版刊行: 2018/05
Springer




This book covers all the relevant dictionary learning algorithms, presenting them in full detail and showing their distinct characteristics while also revealing the similarities. It gives implementation tricks that are often ignored but that are crucial for a successful program.

商品詳細へ機械学習:統計的学習理論への実践的アプローチ(テキスト)
Machine Learning : A Practical Approach on the Statistical Learning Theory

Fernandes de Mello, Rodrigo/ Antonelli Ponti, Moacir
紙版刊行: 2018/08
Springer




This book presents the Statistical Learning Theory in a detailed and easy to understand way, by using practical examples, algorithms and source codes. It can be used as a textbook in graduation or undergraduation courses, for self-learners, or as reference with respect to the main theoretical concepts of Machine Learning. Fundamental concepts of Linear Algebra and Optimization applied to Machine Learning are provided, as well as source codes in R, making the book as self-contained as possible.

商品詳細へ応用機械学習(テキスト)
Applied Machine Learning

Forsyth, David
紙版刊行: 2019/07
Springer




Covers the ideas in machine learning that everyone going to use learning tools should know, whatever their chosen specialty or career.

商品詳細へRによる深層学習入門
Deep Learning with R

Ghatak, Abhijit
紙版刊行: 2019/05
Springer




This introduces deep learning and enables the reader to create applications on computer vision, natural language processing and transfer learning.

商品詳細へ伊庭斉志著/機械学習と深層ニューラルネットワークへの進化的アプローチ
Evolutionary Approach to Machine Learning and Deep Neural Networks : Neuro-Evolution and Gene Regulatory Networks

Iba, Hitoshi
紙版刊行: 2018/06
Springer




This book provides theoretical and practical knowledge about a methodology for evolutionary algorithm-based search strategy with the integration of several machine learning and deep learning techniques.

商品詳細へ自然言語処理と音声認識のための深層学習入門
Deep Learning for NLP and Speech Recognition

Kamath, Uday/ Liu, John/ Whitaker, Jimmy
紙版刊行: 2019/05
Springer




This textbook explains Deep Learning Architecture, with applications to various NLP Tasks, including Document Classification, Machine Translation, Language Modeling, and Speech Recognition.

商品詳細へPython機械学習
Python Machine Learning

Lee, Wei-Meng
紙版刊行: 2019/05
Wiley




This code-intensive book encourages readers to try out various examples of statistics and programming knowledge which are designed to be compact, yet easy to follow and understand. Readers will get started by following fundamental topics such as an introduction to Machine Learning and Data Science.

商品詳細へサイバーフィジカル・システムのための強化学習
Reinforcement Learning for Cyber-Physical Systems : With Cybersecurity Case Studies

Li, Chong/ Qiu, Meikang
紙版刊行: 2019/02
Chapman & Hall




This book is an ideal text for graduate students or junior/senior undergraduates in the fields of science, engineering, computer science, or applied mathematics. It would also prove useful to researchers and engineers interested in cybersecurity, RL, and CPS.

商品詳細へ埋め込み深層学習
Embedded Deep Learning : Algorithms, Architectures and Circuits for Always-on Neural Network Processing

Moons, Bert/ Bankman, Daniel/ Verhelst, Marian
紙版刊行: 2018/10
Springer




Discusses the optimization of neural networks for embedded deployment on all levels of the design hierarchy - applications, algorithms, hardware architectures, and circuits - supported by real silicon prototypes.

商品詳細へ中島伸一(共)著/変分ベイズ学習理論
Variational Bayesian Learning Theory

Nakajima, Shinichi/ Watanabe, Kazuho/ Sugiyama, Masashi
紙版刊行: 2019/07
Cambridge Univ Pr




Designed for researchers and graduate students in machine learning, this book introduces the theory of variational Bayesian learning, a popular machine learning method, and suggest how to make use of it in practice. Detailed derivations allow readers to follow along without prior knowledge of the specific mathematical techniques.

商品詳細へ機械学習入門
An Introduction to Machine Learning

Rebala, Gopinath/ Ravi, Ajay/ Churiwala, Sanjay
紙版刊行: 2019/05
Springer




This book provides a thorough conceptual understanding of Machine Learning techniques and algorithms. Many of the mathematical concepts are explained in an intuitive manner.

商品詳細へ深層強化学習
Deep Reinforcement Learning : Frontiers of Artificial Intelligence

Sewak, Mohit
紙版刊行: 2019/08
Springer




Allows readers to gain an understanding of algorithms such as TD Learning and Deep Q Learning, and Asynchronous Advantage Actor-Critic Models.

商品詳細へ機械学習のパラダイム
Machine Learning Paradigms : Applications of Learning and Analytics in Intelligent Systems (Learning and Analytics in Intelligent Systems) Vol. 1

Tsihrintzis, George A./ Virvou, Maria/ Sakkopoulos, Evangelos (EDT)
紙版刊行: 2019/06
Springer




This book is the inaugural volume in the new Springer series on Learning and Analytics in Intelligent Systems. Accordingly, the new series encourages an integrated approach to themes and topics in these disciplines, which will result in significant cross-fertilization, research advances and new knowledge creation.

商品詳細へスパース・低ランクモデリングを通じたディープラーニング
Deep Learning through Sparse and Low-rank Modeling (Computer Vision and Pattern Recognition)

Wang, Zhangyang/ Fu, Yun/ Huang, Thomas S.
紙版刊行: 2019/04
Academic Pr




Shows how the structure and algorithms of sparse and low-rank methods improves the performance and interpretability of Deep Learning models.

商品詳細へマルチモーダル場面理解:アルゴリズム・応用・深層学習
Multimodal Scene Understanding : Algorithms, Applications and Deep Learning

Yang, Michael/ Rosenhahn, Bodo/ Murino, Vittorio (EDT)
紙版刊行: 2019/07
Academic Pr




The book is ideal for researchers from the fields of computer vision, remote sensing, robotics, and photogrammetry, helping to foster interdisciplinary interaction and collaboration between them.

商品詳細へデータマイニングとマシンラーニングにおける自然界に触発された演算法
Nature-Inspired Computation in Data Mining and Machine Learning (Studies in Computational Intelligence) Vol. 855

Yang, Xin-She/ He, Xing-Shi (EDT)
紙版刊行: 2019/10
Springer




This book reviews the latest developments in nature-inspired computation, with a focus on the cross-disciplinary applications in data mining and machine learning.

2.ビッグデータ&データマイニング

商品詳細へサイバーフィジカル・システムのためのビッグデータ解析
Big Data Analytics for Cyber-Physical Systems : Machine Learning for the Internet of Things

Dartmann, Guido/ Song, Houbing/ Schmeink, Anke (EDT)
紙版刊行: 2019/07
Elsevier Science Ltd




Examines sensor signal processing, IoT gateways, optimization and decision-making, intelligent mobility, and the implementation of machine learning algorithms in embedded systems.

商品詳細へビッグデータ解析ハンドブック
Handbook of Big Data Analytics (Springer Handbooks of Computational Statistics)

Härdle, Wolfgang Karl/ Lu, Henry Horng-Shing/ Shen, Xiaotong (EDT)
紙版刊行: 2018/09
Springer




Addressing a broad range of big data analytics in cross-disciplinary applications, this essential handbook focuses on the statistical prospects offered by recent developments in this field.

商品詳細へモバイル・データマイニング
Mobile Data Mining and Applications (Information Fusion and Data Science)

Jiang, Hao/ Chen, Qimei/ Zeng, Yuanyuan
紙版刊行: 2019/05
Springer




Usage of mobile data mining is addressed in the context of three applications: wireless communication optimization, applications of mobile data mining on the cellular networks of the future, and how mobile data shapes future cities.In the discussion of wireless communication optimization, both licensed and unlicensed spectra are exploited.

商品詳細へ大規模データマイニングにおける進化的決定木
Evolutionary Decision Trees in Large-Scale Data Mining (Studies in Big Data)

Kretowski, Marek
紙版刊行: 2019/08
Springer




This book presents a unified framework, based on specialized evolutionary algorithms, for the global induction of various types of classification and regression trees from data.

商品詳細へウェブ・ライブラリアン概論
Concepts and Methods for a Librarian of the Web (Studies in Big Data)

Kubek, Mario
紙版刊行: 2019/08
Springer




Presents novel, librarian-inspired approaches, methods, and technical solutions to decentral search for text documents in the WWW using peer-to-peer (P2P) technology.

商品詳細へビッグデータ、クラウドコンピューティングとデータサイエンス工学
Big Data, Cloud Computing, and Data Science Engineering (Studies in Computational Intelligence) Vol. 844

Lee, Roger (EDT)
紙版刊行: 2019/09
Springer




This edited book presents the scientific outcomes of the 4th IEEE/ACIS International Conference on Big Data, Cloud Computing, Data Science & Engineerng (BCD 2019) which was held on May 29-31, 2019 in Honolulu, Hawaii.

商品詳細へインダストリー4.0 の新パラダイム
New Paradigm of Industry 4.0 : Internet of Things , Big Data and Cyber Physical Systems (Studies in Big Data)

Patnaik, Srikanta
紙版刊行: 2019/10
Springer




Sharing new theoretical findings, tools and techniques for Industry 4.0, and covering both theoretical and application-oriented approaches, the book offers a valuable asset for newcomers to the field and practicing professionals alike.

商品詳細へIoTとビックデータ・ハンドブック
Handbook of IoT and Big Data (Science, Technology, and Management)

Solanki, Vijender Kumar/ Daz, Vicente Garca/ Davim, J. Pau (EDT)
紙版刊行: 2019/03
CRC Pr




The book is divided into 4 sections that covers IoT and technologies, the future of Big Data, algorithms, and case studies showing IoT and Big Data in various fields such as health care, manufacturing and automation.

商品詳細へソーシャルメディアのデータマイニングと解析
Social Media Data Mining and Analytics

Szabo, Gabor/ Boykin, Oscar
紙版刊行: 2018/11
Wiley




Written by a Senior Data Scientist and a Software Engineer at Twitter, this book shows analysts how to use sophisticated techniques to mine social media data, obtaining the information they need to generate amazing results for their business.

商品詳細へアプリケーションのためのマルチメディア・ビッグデータ演算
Multimedia Big Data Computing for IoT Applications : Concepts, Paradigms and Solutions (Intelligent Systems Reference Library) Vol. 163

Tanwar, Sudeep/ Tyagi, Sudhanshu/ Kumar, Neeraj (EDT)
紙版刊行: 2019/08
Springer




This book considers all aspects of managing the complexity of Multimedia Big Data Computing (MMBD) for IoT applications and develops a comprehensive taxonomy. Further, the book examines the layered architecture of MMBD computing and compares the life cycle of both big data and MMBD.

3.まずはここから! AI・データサイエンス既刊ベストセラー&ロングセラー

商品詳細へ統計学教育研究国際ハンドブック
International Handbook of Research in Statistics Education (Springer International Handbooks of Education)

Ben-Zvi, Dani/ Makar, Katie/ Garfield, Joan (EDT)
紙版刊行: 2018/01
Springer




「データサイエンス」時代に求められる統計学の教育と研究にたずさわる多数の世界的な専門家を結集して、過去・現在・未来を通して重要な論点を網羅する、包括的なリサーチマップ。不確実性、統計的推論、教師教育、カリキュラム、テクノロジーの利用、学習環境の整備、成績評価など。

商品詳細へ『統計学:Rを用いた入門書 改訂第2版』(原書)
Statistics : An Introduction Using R 2ND

Crawley, Michael J.
紙版刊行: 2014/11
Wiley




邦訳:2016年4月・共立出版 This new edition of a bestselling title offers a concise introduction to a broad array is elementary enough to appeal to a wide range of disciplines.

商品詳細へ統計学・社会科学・生医学のための因果推論入門
Causal Inference for Statistics, Social, and Biomedical Sciences : An Introduction

Imbens, Guido W./ Rubin, Donald B.
紙版刊行: 2015/05
Cambridge Univ Pr




In this groundbreaking text, two world-renowned experts lay out the assumptions needed for causal inference and describe the leading analysis methods, including matching, propensity-score methods, and instrumental variables. Many detailed applications are included, with special focus on practical aspects for the empirical researcher. Winner, 2016 PROSE Award for Textbook, Social Sciences.

商品詳細へ統計学的再考:RとStanで学ぶベイズ統計学(テキスト)
Statistical Rethinking : A Bayesian Course with Examples in R and Stan (Chapman & Hall/crc Texts in Statistical Science)

Mcelreath, Richard
紙版刊行: 2015/12
Chapman & Hall




This book provides a more elementary introduction to Bayesian analysis than the Gelman book and is more suitable for graduate students in disciplines other than statistics.