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  • Sách/Book

  • Elementary linear algebra : applications version

    • Authors: Howard Anton (2013)

  • Elementary Linear Algebra 11th edition gives an elementary treatment of linear algebra that is suitable for a first course for undergraduate students. The aim is to present the fundamentals of linear algebra in the clearest possible way; pedagogy is the main consideration. Calculus is not a prerequisite, but there are clearly labeled exercises and examples (which can be omitted without loss of continuity) for students who have studied calculus
  • Sách/Book

  • Declarative Logic Programming: Theory, Systems, and Applications

    • Authors: Michael Kifer (2018)

  • The goal of this book is to help fill in the void in the literature with state-of-the-art surveys on key aspects of LP. Much attention was paid to making these surveys accessible to researchers, practitioners, and graduate students alike.
  • Sách/Book

  • Shared-Memory Parallelism Can Be Simple, Fast, and Scalable

    • Authors: Shun, Julian (2017)

  • The book starts by introducing tools and techniques for deterministic parallel programming, including means for encapsulating nondeterminism via powerful commutative building blocks, as well as a novel framework for executing sequential iterative loops in parallel, which lead to deterministic parallel algorithms that are efficient both in theory and in practice. The book then introduces Ligra, the first high-level shared-memory framework for parallel graph traversal algorithms. The framework enables short and concise implementations that deliver performance competitive with that of highly optimized code and up to orders of magnitude faster than previous systems designed for distributed memory. Finally, the book bridges the gap between theory and practice in parallel algorithm design...
  • Sách/Book

  • Deep learning with Python

    • Authors: François Chollet. (2018)

  • This book was written for anyone who wishes to explore deep learning from scratch or broaden their understanding of deep learning. Whether you're a practicing machine-learning engineer, a software developer, or a college student, you'll find value in these pages
  • Sách/Book

  • Data Analysis, Statistics, and Probability

    • Authors: - (-)

  • Data Analysis, Statistics, and Probability introduces statistics as a problem-solving process. In this course, you will build your skills through investigations of different ways to collect and represent data and to analyze and interpret variation in data. The course consists of 10 sessions, each with a half hour of video programming, problem-solving activities provided online and in a print guide, and interactive activities and demonstrations on the Web.
  • Sách/Book

  • Data analysis

    • Authors: - (-)

  • This programme includes a summary of that process and explores specific areas of data analysis that might be applicable to learners studying at undergraduate and post graduate levels. It aims to provide a definition of qualitative and quantitative data analysis and opportunities to explore photographic, video, textual and numerical data analysis through providing worked examples and further opportunities for learners to develop knowledge and skills in data analysis. The programme also aims to support the development of critical appraisal skills, thorough considering the critical review of research papers. The aims are achieved through captialising on the interactive opportunities of on-line learning.