Neural Networks and Deep Learning : A Textbook
Aggarwal, Charu C.
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.
Handbook of Deep Learning Applications （Smart Innovation, Systems and Technologies） Vol. 136
Balas, Valentina/ Roy, Sanjiban Sekhar/ Sharma, Dharmendra （EDT）
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.
Reinforcement Learning of Bimanual Robot Skills （Springer Tracts in Advanced Robotics） Vol. 134
Colomé, Adrià / Torras, Carme
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.
Dictionary Learning Algorithms and Applications
Dumitrescu, Bogdan/ Irofti, Paul
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.
Machine Learning : A Practical Approach on the Statistical Learning Theory
Fernandes de Mello, Rodrigo/ Antonelli Ponti, Moacir
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.
Applied Machine Learning
Covers the ideas in machine learning that everyone going to use learning tools should know, whatever their chosen specialty or career.
Deep Learning with R
This introduces deep learning and enables the reader to create applications on computer vision, natural language processing and transfer learning.
Deep Learning for NLP and Speech Recognition
Kamath, Uday/ Liu, John/ Whitaker, Jimmy
This textbook explains Deep Learning Architecture, with applications to various NLP Tasks, including Document Classification, Machine Translation, Language Modeling, and Speech Recognition.
Python Machine Learning
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.
Reinforcement Learning for Cyber-Physical Systems : With Cybersecurity Case Studies
Li, Chong/ Qiu, Meikang
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.
Variational Bayesian Learning Theory
Nakajima, Shinichi/ Watanabe, Kazuho/ Sugiyama, Masashi
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.
An Introduction to Machine Learning
Rebala, Gopinath/ Ravi, Ajay/ Churiwala, Sanjay
This book provides a thorough conceptual understanding of Machine Learning techniques and algorithms. Many of the mathematical concepts are explained in an intuitive manner.
Multimodal Scene Understanding : Algorithms, Applications and Deep Learning
Yang, Michael/ Rosenhahn, Bodo/ Murino, Vittorio （EDT）
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.
Mobile Data Mining and Applications （Information Fusion and Data Science）
Jiang, Hao/ Chen, Qimei/ Zeng, Yuanyuan
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.
Handbook of IoT and Big Data （Science, Technology, and Management）
Solanki, Vijender Kumar/ Daz, Vicente Garca/ Davim, J. Pau （EDT）
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.
Social Media Data Mining and Analytics
Szabo, Gabor/ Boykin, Oscar
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.
Statistics : An Introduction Using R 2ND
Crawley, Michael J.
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.
Causal Inference for Statistics, Social, and Biomedical Sciences : An Introduction
Imbens, Guido W./ Rubin, Donald B.
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.