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  • 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

  • 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.
  • Sách/Book

  • Artificial Intelligence and Robotics

    • Authors: Huimin Lu (2021)

  • This book provides insights into research in the field of artificial intelligence in combination with robotics technologies. The integration of artificial intelligence and robotic technologies is a highly topical area for researchers and developers from academia and industry around the globe, and it is likely that artificial intelligence will become the main approach for the next generation of robotics research.