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Item #:
34827171
Automated Machine Learning: Hyperparameter optimization, neural architecture search, and algorithm selection with cloud platforms
88% of respondents would recommend this to a friend
Item #:
34827171
USD 68
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What Stands Out
Hyperparameter Optimization
Efficiently fine-tune model parameters to achieve optimal performance, reducing time spent on manual adjustments and enhancing predictive accuracy.
Neural Architecture Search
Automated exploration of neural network designs to discover innovative structures, streamlining development and improving model capability without extensive human intervention.
Cloud Integration
Seamlessly leverage cloud platforms for scalable computing power, enabling rapid experimentation and deployment of machine learning models while minimizing IT overhead.
Product Details
| Publisher | Packt Publishing |
| Publication date | February 18, 2021 |
| Language | English |
| Print length | 312 pages |
| ISBN-10 | 1800567685 |
| ISBN-13 | 978-1800567689 |
| Item Weight | 1.19 pounds (540 grams) |
| Dimensions | 7.5 x 0.71 x 9.25 inches (19.1 x 1.8 x 23.5 cm) |
Who Should Buy?
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Data Scientists
Data scientists can streamline model training processes, saving time and enhancing productivity using automated machine learning features.
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Organizations with Limited Resources
Small businesses without extensive ML expertise can use this product to implement machine learning without hiring specialists.
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Researchers
Researchers can quickly run experiments, optimizing algorithms and architectures for better performance and faster iterations.
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Beginner Users
Users lacking basic understanding of machine learning concepts may struggle to effectively leverage the product's capabilities.
Product Description
Automated Machine Learning: Hyperparameter optimization, neural architecture search, and algorithm selection with cloud platforms
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Expert Systems Editorial Review
Automated Machine Learning: Hyperparameter optimization, neural architecture search, and algorithm selection with cloud platforms offers a well-structured approach to the evolving field of automated machine learning. The book is praised for its comprehensive coverage, especially regarding popular cloud platforms such as Azure, AWS, and Google Cloud, making it an excellent resource for beginners familiar with Python and machine learning. Readers appreciate the detailed examples of automated systems like TPOT and AutoKeras, along with clear instructions for setup and implementation. However, there are concerns about the readability of some screenshots used throughout the book. Overall, this manuscript serves as an invaluable guide for practitioners wanting to deepen their understanding of AutoML in practical environments.
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Pros
- Covers essential AutoML platforms like Azure and AWS
- Well-structured with multiple practical examples
- Helpful for beginners with foundational knowledge
- Offers a comprehensive overview of open-source options
- Includes clear installation instructions and step-by-step guidance
Cons
- Some screenshots may be difficult to read
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22 October 2026 · via Trustpilot
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23 October 2026 · via Trustpilot
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