What is the primary focus of 'Modern Data Mining Algorithms in C++ and CUDA C'?

The book primarily focuses on advanced data mining techniques using C++ and CUDA C to optimize feature extraction and selection algorithms. It delves into recent developments in these areas, detailing how they can efficiently handle large datasets. This is particularly beneficial for data scientists and developers who require robust solutions for high-performance data analysis and machine learning tasks, especially in environments that demand quick processing and iterative computations.

Who is the target audience for this book?

The target audience for this book includes data scientists, software engineers, and researchers in the field of data mining and machine learning. It is designed for professionals who have a fundamental understanding of programming in C++ and CUDA C and wish to apply these skills to data science problems. The book serves as a comprehensive resource for those seeking practical insights and cutting-edge techniques for feature extraction and selection, crucial for developing effective models.

What programming languages are emphasized in the book?

The book emphasizes C++ and CUDA C as the primary programming languages for implementing modern data mining algorithms. C++ provides a powerful foundation for system-level programming and performance optimization, while CUDA C offers parallel computing capabilities that significantly enhance data processing speed by enabling developers to run algorithms on NVIDIA GPUs. This combination is essential for handling large-scale data efficiently in real-time applications.

Are there any practical examples included in this book?

Yes, the book includes a variety of practical examples that illustrate the implementation of data mining algorithms using C++ and CUDA C. These examples demonstrate real-world applications in feature extraction and selection, allowing readers to understand how to apply theoretical concepts into practice. This hands-on approach helps bridge the gap between theory and application, making it easier to grasp complex algorithms and facilitate their usage in actual data science projects.

How does CUDA C enhance data mining capabilities?

CUDA C enhances data mining capabilities by allowing developers to leverage the parallel processing power of NVIDIA GPUs. This is especially beneficial for executing large-scale data mining tasks more quickly compared to traditional CPU processing. By employing CUDA C, algorithms can efficiently perform operations on large datasets, making it ideal for tasks involving deep learning, where processing speed and efficiency are paramount for model training and evaluation.

What are some key topics covered in the book?

Key topics covered in the book include advanced feature extraction methods, selection algorithms, and implementations of machine learning techniques in C++ and CUDA C. The discussions also extend to scalability challenges in data science and solutions through algorithm optimization and parallel computing. By focusing on recent developments, the book ensures that readers are well-equipped with current trends and techniques in the evolving field of data mining.

Can beginners benefit from this book?

While the book is primarily aimed at individuals with a foundational knowledge of C++ and CUDA C, motivated beginners can also benefit from it. Readers are encouraged to familiarize themselves with the basic concepts of programming and data mining. The clear explanations and structured approach in the book facilitate understanding, making it a valuable resource for beginners seeking to advance their expertise in data mining and its applications in data science.

How does feature extraction improve data analysis?

Feature extraction improves data analysis by transforming raw data into a structured format that highlights relevant information, minimizing noise, and reducing dimensionality. This process enhances the performance of machine learning algorithms by providing them with essential features that enable better decision-making. Effective feature extraction leads to more accurate predictive models and insights, particularly in complex datasets, making it a fundamental aspect of successful data science endeavors.

What advancements in data mining does this book discuss?

The book discusses significant advancements in data mining, particularly focusing on the latest algorithms for feature extraction and selection. It places a strong emphasis on methodologies developed for high-performance computing, utilizing C++ and CUDA C to maximize efficiency and scalability. These advancements enable data scientists to tackle increasingly complex data challenges and extract valuable insights more effectively, promoting innovation in the field of data science.

Where can I buy 'Modern Data Mining Algorithms in C++ and CUDA C'?

You can purchase 'Modern Data Mining Algorithms in C++ and CUDA C' on Ubuy, which is a reliable e-commerce platform in Hong Kong. Ubuy offers a wide selection of books, including this specialized title, ensuring you can easily obtain the resources you need for your data science journey. Whether you are looking to enhance your programming skills or dive deeper into data mining algorithms, Ubuy is your go-to destination.

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Modern Data Mining Algorithms in C++ and CUDA C: Recent Developments in Feature Extraction and Selection Algorithms for Data Science

Discover a variety of data-mining algorithms that are useful for selecting small sets of important features from among unwieldy masses of candidates or extracting useful features from measured variables.

物品 #: 47709520

Modern Data Mining Algorithms in C++ and CUDA C: Recent Developments in Feature Extraction and Selection Algorithms for Data Science

物品 #: 47709520

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Discover a variety of data-mining algorithms that are useful for selecting small sets of important features from among unwieldy masses of candidates or extracting useful features from measured variables.

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What Stands Out

Comprehensive Coverage

Offers in-depth exploration of modern data mining algorithms, enhancing the reader's understanding of feature extraction and selection tailored for data science applications.

C++ and CUDA Integration

Integrates C++ and CUDA C for accelerated performance, allowing developers to leverage powerful parallel computing for efficient data processing and algorithm execution.

Recent Developments

Focuses on the latest advancements in data mining techniques, equipping readers with up-to-date knowledge crucial for tackling contemporary challenges in data science.

產品詳情

Discover a variety of data-mining algorithms that are useful for selecting small sets of important features from among unwieldy masses of candidates, or extracting useful features from measured variables. As a serious data miner you will often be faced with thousands of candidate features for your prediction or classification application, with most of the features being of little or no value. You’ll know that many of these features may be useful only in combination with certain other features while being practically worthless alone or in combination with most others. Some features may have enormous predictive power, but only within a small, specialized area of the feature space. The problems that plague modern data miners are endless. This book helps you solve this problem by presenting modern feature selection techniques and the code to implement them. Some of these techniques are:Forward selection component analysisLocal feature selectionLinking features and a target with a hidden Markov modelImprovements on traditional stepwise selectionNominal-to-ordinal conversionAll algorithms are intuitively justified and supported by the relevant equations and explanatory material. The author also presents and explains complete, highly commented source code. The example code is in C++ and CUDA C but Python or other code can be substituted; the algorithm is important, not the code that's used to write it. What You Will LearnCombine principal component analysis with forward and backward stepwise selection to identify a compact subset of a large collection of variables that captures the maximum possible variation within the entire set.Identify features that may have predictive power over only a small subset of the feature domain. Such features can be profitably used by modern predictive models but may be missed by other feature selection methods.Find an underlying hidden Markov model that controls the distributions of feature variables and the target simultaneously. The memory inherent in this method is especially valuable in high-noise applications such as prediction of financial markets.Improve traditional stepwise selection in three ways: examine a collection of 'best-so-far' feature sets; test candidate features for inclusion with cross validation to automatically and effectively limit model complexity; and at each step estimate the probability that our results so far could be just the product of random good luck. We also estimate the probability that the improvement obtained by adding a new variable could have been just good luck. Take a potentially valuable nominal variable (a category or class membership) that is unsuitable for input to a prediction model, and assign to each category a sensible numeric value that can be used as a model input.Who This Book Is ForIntermediate to advanced data science programmers and analysts.

Publisher	Apress
Publication date	June 6, 2020
Edition	First Edition
Language	English
Print length	237 pages
ISBN-10	1484259874
ISBN-13	978-1484259870
Item Weight	15.2 ounces (430.92 grams)
Dimensions	7.01 x 0.55 x 10 inches (17.8 x 1.4 x 25.4 cm)

Who Should Buy?

Data Scientists
Ideal for data scientists seeking to implement advanced mining algorithms using C++ and CUDA for performance improvements.
C++ Developers
C++ developers looking to expand their knowledge on data mining algorithms and their practical applications in data science.
Academics Researchers
Researchers in academia who require a deeper understanding of feature extraction and selection algorithms for their studies.

Beginners
Not suitable for beginners in programming or data mining, as it assumes a high level of prior knowledge.

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回答: 從 Ubuy 在線購物 Modern Data Mining Algorithms in C++ and CUDA C: 非常簡單。. 您只需搜索產品，在結賬時選擇運輸方式，然後將其運送到您所在的位置。
題: Modern Data Mining Algorithms in C++ and CUDA C: 可以在 Hong Kong 在線購物嗎？

回答: 是的，您可以在 Ubuy Hong Kong 以合理的價格購買該產品。. Modern Data Mining Algorithms in C++ and CUDA C: 在本地不可用，但您可以信任我們的快遞服務。
題: 下訂單後需要多長時間才能收到產品？

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Probability & Statistics Editorial Review

Modern Data Mining Algorithms in C++ and CUDA C: Recent Developments in Feature Extraction and Selection Algorithms for Data Science offers a deep dive into advanced techniques for data science and machine learning. Published by Apress, the first edition of this book is packed with 237 pages of insightful content designed for practitioners and researchers alike. The book provides modern approaches to feature extraction and selection, crucial for enhancing the performance of data mining algorithms. Readers appreciate the clarity in explaining complex concepts, making it a valuable resource for both newcomers and experienced professionals in the field of data science.

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優點

Comprehensive coverage of modern data mining techniques
Focus on both C++ and CUDA C programming languages
Clear explanations make complex topics accessible
Ideal for researchers and data science practitioners
Well-organized content enhances learning experience

缺點

Minor typographical errors found in some sections

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特色和優勢

Learn techniques like forward selection component analysis.
Combine principal component analysis with stepwise selection.
Identify features with predictive power over specific feature subsets.
Understand hidden Markov models for feature and target distributions.
Improve traditional stepwise selection methods.
Convert nominal variables for model input.

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Modern Data Mining Algorithms in C++ and CUDA C: Recent Developments in Feature Extraction and Selection Algorithms for Data Science

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產品詳情

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產品敘述

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Probability & Statistics Editorial Review

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重要資訊

HKD 539

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