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Results 11-20 of 27 (Search time: 0.053 seconds).
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  • Sách/Book

  • Deep learning with PyTorch

    • Authors: Eli Stevens (2020)

  • Deep Learning with PyTorch teaches you to create deep learning and neural network systems with PyTorch. This practical book gets you to work right away building a tumor image classifier from scratch. After covering the basics, you’ll learn best practices for the entire deep learning pipeline, tackling advanced projects as your PyTorch skills become more sophisticated.
  • Sách/Book

  • Machine Learning Engineering

    • Authors: Andriy Burkov (2020)

  • The Machine Learning Engineering for Production (MLOps) Specialization covers how to conceptualize, build, and maintain integrated systems that continuously operate in production. In striking contrast with standard machine learning modeling, production systems need to handle relentless evolving data. Moreover, the production system must run non-stop at the minimum cost while producing the maximum performance. In this Specialization, you will learn how to use well-established tools and methodologies for doing all of this effectively and efficiently.
  • Sách/Book

  • Univariate, bivariate, and multivariate statistics using R : quantitative tools for data analysis and data science

    • Authors: Daniel J. Denis (2020)

  • This book provides a user-friendly and practical guide on R, with emphasis on covering a broader range of statistical methods than previous books on R. This is a "how to" book and will be of use to undergraduates and graduate students along with researchers and professionals who require a quick go-to source to help them perform essential statistical analyses and data management tasks in R. The book only assumes minimal prior knowledge of statistics, providing readers with the tools they need right now to help them understand and interpret their data analyses. This book covers univariate, bivariate, and multivariate statistical methods, as well as some nonparametric tests.
  • Sách/Book

  • Artificial Intelligence with Python : Your complete guide to building intelligent apps using Python 3.x

    • Authors: Alberto Artasanchez (2020)

  • Book DescriptionArtificial Intelligence with Python, Second Edition is an updated and expanded version of the bestselling guide to artificial intelligence using the latest version of Python 3.x. Not only does it provide you an introduction to artificial intelligence, this new edition goes further by giving you the tools you need to explore the amazing world of intelligent apps and create your own applications.
  • 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

  • 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.