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  • Book

  • Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow

    • Authors: Raschka, Sebastian (2017)

  • Python Machine Learning Second Edition now includes the popular TensorFlow 1.x deep learning library. The scikit-learn code has also been fully updated to v0.18.1 to include improvements and additions to this versatile machine learning library. Sebastian Raschka and Vahid Mirjalili's unique insight and expertise introduce you to machine learning and deep learning algorithms from scratch, and show you how to apply them to practical industry challenges using realistic and interesting examples. By the end of the book, you'll be ready to meet the new data analysis opportunities.
  • Book

  • Building the data warehouse

    • Authors: W.H. Inmon (2005)

  • The new edition of the classic bestseller that launched the data warehousing industry covers new approaches and technologies, many of which have been pioneered by Inmon himself In addition to explaining the fundamentals of data warehouse systems, the book covers new topics such as methods for handling unstructured data in a data warehouse and storing data across multiple storage media Discusses the pros and cons of relational versus multidimensional design and how to measure return on investment in planning data warehouse projects Covers advanced topics, including data monitoring and testing
  • Book

  • Testing and quality assurance for component-based software

    • Authors: Jerry Zeyu Gao (2003)

  • Presenting the state-of-the-art in component-based software testing, this new, cutting-edge resource offers you an in-depth understanding of the current issues, challenges, needs, and solutions in this critical area. The book discusses the very latest advances in component-based testing and quality assurance in an accessible tutorial format, making the material easy to comprehend and benefit from no matter what your professional level. The book clearly explains what component-based software testing is, why it is important, and how it differs from traditional software testing. From an introduction to software components, testing component-based software, and validation methods for software components, to performance testing and measurement, standards and certification, and verificati...
  • Book

  • Big data : principles and best practices of scalable real-time data systems

    • Authors: Nathan Marz (2015)

  • Services like social networks, web analytics, and intelligent e-commerce often need to manage data at a scale too big for a traditional database. As scale and demand increase, so does Complexity. Fortunately, scalability and simplicity are not mutually exclusive- rather than using some trendy technology, a different approach is needed. Big data systems use many machines working in parallel to store and process data, which introduces fundamental challenges unfamiliar to most developers. Big Data shows how to build these systems using an architecture that takes advantage of clustered hardware along with new tools designed specifically to capture and analyze web-scale data. It describes a scalable, easy to understand approach to big data systems that can be built and run by a small tea...
  • Book

  • Android : a programmer's guide

    • Authors: J.F. DiMarzio (2008)

  • Build compelling Java-based mobile applications using the Android SDK and the Eclipse open-source software development platform. Android: A Programmer's Guide shows you, step-by-step, how to download and set up all of the necessary tools, build and tune dynamic Android programs, and debug your results. Discover how to provide web and chat functions, interact with the phone dialer and GPS devices, and access the latest Google services. You'll also learn how to create custom Content Providers and database-enable your applications using SQL
  • Book

  • An Introduction to Machine Learning

    • Authors: Miroslav Kubat (2017)

  • This revised edition contains three entirely new chapters on critical topics regarding the pragmatic application of machine learning in industry. The chapters examine multi-label domains, unsupervised learning and its use in deep learning, and logical approaches to induction. Numerous chapters have been expanded, and the presentation of the material has been enhanced. The book contains many new exercises, numerous solved examples, thought-provoking experiments, and computer assignments for independent work.
  • Book

  • Introduction To Machine Learning With Python

    • Authors: C. Müller, Andreas (2017)

  • You’ll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Müller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book.