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  • Sách/Book

  • Machine Learning and Artificial Intelligence

    • Authors: Ameet V Joshi (2020)

  • This book provides comprehensive coverage of combined Artificial Intelligence (AI) and Machine Learning (ML) theory and applications. Rather than looking at the field from only a theoretical or only a practical perspective, this book unifies both perspectives to give holistic understanding. The first part introduces the concepts of AI and ML and their origin and current state.
  • Sách/Book

  • The Handbook of Multimodal-Multisensor Interfaces

    • Authors: Sharon Oviatt (2018)

  • This second volume of the handbook begins with multimodal signal processing, architectures, and machine learning. It includes recent deep learning approaches for processing multisensorial and multimodal user data and interaction, as well as context-sensitivity. A further highlight is processing of information about users' states and traits, an exciting emerging capability in next-generation user interfaces.
  • Sách/Book

  • Natural language processing in action : understanding, analyzing, and generating text with Python

    • Authors: Hobson Lane (2019)

  • Modern NLP techniques based on machine learning radically improve the ability of software to recognize patterns, use context to infer meaning, and accurately discern intent from poorly-structured text. In Natural Language Processing in Action, readers explore carefully chosen examples and expand their machine's knowledge which they can then apply to a range of challenges.
  • Sách/Book

  • Artificial Intelligence Techniques for Advanced Computing Applications : Proceedings of ICACT 2020 D.

    • Authors: Jude Hemanth (2021)

  • This book features a collection of high-quality research papers presented at the International Conference on Advanced Computing Technology (ICACT 2020), held at the SRM Institute of Science and Technology, Chennai, India, on 23-24 January 2020. It covers the areas of computational intelligence, artificial intelligence, machine learning, deep learning, big data, and applications of artificial intelligence in networking, IoT and bioinformatics
  • Sách/Book

  • Software engineering and algorithms in intelligent systems Volume 1

    • Authors: Radek Silhavy (2018)

  • This book presents new software engineering approaches and methods, discussing real-world problems and exploratory research that describes novel approaches, modern design techniques, hybrid algorithms and empirical methods. This book constitutes part of the refereed proceedings of the Software Engineering and Algorithms in Intelligent Systems Section of the 7th Computer Science On-line Conference 2018 (CSOC 2018), held in April 2018.
  • Sách/Book

  • Doing data science in R

    • Authors: Mark Andrews (2021)

  • Focuses on providing practical guidance for all aspects, helping readers get to grips with the tools, software, and statistical methods needed to provide the right type and level of analysis their data requires
  • Sách/Book

  • Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits

    • Authors: Tarek Amr (2020)

  • The book begins with an explanation of machine learning concepts and fundamentals, and strikes a balance between theoretical concepts and their applications. Each chapter covers a different set of algorithms, and shows you how to use them to solve real-life problems.
  • Sách/Book

  • Artificial Intelligence in a Throughput Model : Some Major Algorithms

    • Authors: Waymond Rodgers (2020)

  • This book provides an overview of the existing biometric technologies, decision-making algorithms and the growth opportunity in biometrics. The book proposes a throughput model, which draws on computer science, economics and psychology to model perceptual, informational sources, judgmental processes and decision choice algorithms
  • Sách/Book

  • Hands-On Gradient Boosting with XGBoost and scikit-learn

    • Authors: Corey Wade (2020)

  • The book introduces machine learning and XGBoost in scikit-learn before building up to the theory behind gradient boosting. You'll cover decision trees and analyze bagging in the machine learning context, learning hyperparameters that extend to XGBoost along the way. You'll build gradient boosting models from scratch and extend gradient boosting to big data while recognizing speed limitations using timers.