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Martin O'Hanlon,
Ross Exton,
Michael Conterio,
Sam Isaacs,
Rebecca Franks,
Alex Parry,
Diane Dowling,
and
Matt Hogan
Topics Covered
- Units of information
- Character encoding
- Representing images
- Representing sound
- Data compression
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Provides a comprehensive introduction to data compression, covering the fundamentals of the field and a wide range of compression techniques. It is written in a clear and concise style, and includes numerous examples and exercises.
Comprehensive guide to data compression, covering both the theoretical foundations and the practical implementation of compression algorithms. It is written in a clear and concise style, and includes numerous examples and exercises.
Provides a theoretical foundation for data compression, covering the mathematical principles behind compression algorithms. It is written in a clear and concise style, and includes numerous examples and exercises.
Provides a comprehensive introduction to data compression, covering both the theoretical foundations and the practical implementation of compression algorithms. It is written in a clear and concise style, and includes numerous examples and exercises.
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Provides a comprehensive overview of data compression algorithms and techniques, covering a wide range of compression techniques and their applications. It is written in a clear and concise style, and includes numerous examples and exercises.
Provides a comprehensive overview of data compression from a theoretical perspective, covering a wide range of compression techniques and their applications. It is written in a clear and concise style, and includes numerous examples and exercises.
Provides a comprehensive overview of data compression from a professional perspective, covering a wide range of compression techniques and their applications. It is written in a clear and concise style, and includes numerous examples and exercises.
Provides a comprehensive overview of data compression from a reference perspective, covering a wide range of compression techniques and their applications. It is written in a clear and concise style, and includes numerous examples and exercises.
Practical guide to using Python for basic automation tasks, providing a gentle introduction to Python's core concepts and its practical applications.
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Comprehensive guide to the basics of Python programming, covering data types, control flow, functions, object-oriented programming, and debugging.
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Uses a creative and engaging approach to teach Python concepts, with a focus on visual learning and hands-on exercises.
Comprehensive guide to Python's data analysis ecosystem, including NumPy, Pandas, and Matplotlib, with a focus on practical applications.
Comprehensive guide to machine learning using Python, covering supervised and unsupervised learning, model evaluation, and deployment.
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Comprehensive guide to deep learning using Python, covering neural networks, convolutional neural networks, and recurrent neural networks.
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Concise and comprehensive reference to the Python language, covering syntax, built-in functions and objects, and advanced topics.
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Deep dive into Python's advanced features, covering metaprogramming, decorators, generators, and concurrency.
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Collection of practical recipes and solutions for common Python programming tasks, covering a wide range of topics.
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Is an introductory text to Python programming, with a focus on computational thinking and problem-solving.
Comprehensive guide to the Python Standard Library, covering its vast collection of modules and their applications.
Practical guide to testing Python code using the pytest framework, covering unit testing, integration testing, and end-to-end testing.
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