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Nº de artículo:
32273991
Hands-On Machine Learning with C++: Build, train, and deploy end-to-end machine learning and deep learning pipelines
89% of respondents would recommend this to a friend
Nº de artículo:
32273991
USD 79
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What Stands Out
End-to-End Pipeline
Offers comprehensive guidance on building, training, and deploying machine learning models, ensuring users can implement complete solutions from start to finish.
C++ Focus
Utilizes C++ for performance-driven applications, making it ideal for software engineers looking to integrate machine learning into high-performance systems.
Practical Learning
Emphasizes hands-on projects to enhance understanding, allowing readers to apply concepts in real-world scenarios effectively.
Detalles de producto
| Publisher | Packt Publishing |
| Publication date | May 15, 2020 |
| Language | English |
| Print length | 530 pages |
| ISBN-10 | 1789955335 |
| ISBN-13 | 978-1789955330 |
| Item Weight | 2.21 pounds (1 kg) |
| Dimensions | 7.5 x 1.2 x 9.25 inches (19.1 x 3 x 23.5 cm) |
Who Should Buy?
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Beginner Developers
New developers can learn machine learning concepts effectively through practical examples and hands-on projects with C++.
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Data Scientists
Data scientists looking to implement machine learning models in production will benefit from end-to-end project coverage.
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C++ Enthusiasts
C++ programmers eager to apply their skills to machine learning will find this book particularly useful and engaging.
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Non-Technical Users
Users without a programming background may struggle with the technical content and programming requirements of the book.
DESCRIPCIÓN DEL PRODUCTO
Hands-On Machine Learning with C++: Build, train, and deploy end-to-end machine learning and deep learning pipelines
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Natural Language Processing Editorial Review
Hands-On Machine Learning with C++: Build, train, and deploy end-to-end machine learning and deep learning pipelines is a comprehensive guide tailored for C++ programmers looking to delve into the realm of machine learning. The author's approach combines practical examples using real csv data, which enhances the learning experience by allowing immediate application of concepts. The book focuses on essential topics such as supervised and unsupervised learning methods, and also provides advanced examples and deployment challenges. Despite the steep learning curve associated with C++17, readers find the organization and depth of content to be highly valuable for bridging the gap between traditional C++ programming and modern machine learning practices.
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ventajas
- Includes practical, real-world coding examples
- Covers essential supervised and unsupervised learning
- Well-organized with useful further reading sections
- Allows C++ developers to leverage ML techniques
- Explains deployment challenges effectively
Contras
- A learning curve may arise from C++17 code usage
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