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Results 51-60 of 270 (Search time: 0.079 seconds).
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  • Sách/Book

  • Multi-Objective Optimization using Artificial Intelligence Techniques

    • Authors: Seyedali Mirjalili (2020)

  • This book focuses on the most well-regarded and recent nature-inspired algorithms capable of solving optimization problems with multiple objectives. Firstly, it provides preliminaries and essential definitions in multi-objective problems and different paradigms to solve them.
  • 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

  • Elementary Differential Equations and Boundary Value Problems

    • Authors: William E. Boyce (2020)

  • The authors have sought to combine a sound and accurate (but not abstract) exposition of the elementary theory of differential equations with considerable material on methods of solution, analysis, and approximation that have proved useful in a wide variety of applications.
  • 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

  • Data structure and algorithms using C++ : a practical implementation

    • Authors: Sachi Nandan Mohanty (2021)

  • This book is intended to flow from the basic concepts of C++ to technicalities of the programming language, its approach and debugging. The chapters of the book flow with the formulation of the problem, it's designing, finding the step-by-step solution procedure along with its compilation, debugging and execution with the output.
  • Sách/Book

  • Concise Introduction to Basic Real Analysis

    • Authors: Hemen Dutta (2021)

  • The book is ideal for students, instructors, as well as those doing research in areas requiring a basic knowledge of Real Analysis. Those more advanced in the field will also find the book useful to refresh their knowledge of the topic.