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Automated Machine Learning: Hyperparameter optimization, neural architecture search, and algorithm selection with cloud platforms
90% of respondents would recommend this to a friend
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By the end of this machine learning book, you'll be able to build and deploy AutoML models that are not only accurate, but also increase productivity, allow interoperability, and minimize feature engineering tasks.
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Detalji o proizvodu
- Automate machine learning tasks with AutoML
- Learn how to optimize hyperparameters and neural architecture
- Implement AutoML using open source tools or cloud platforms
- Reduce human errors and save time in machine learning models
- Make machine learning accessible for all users
- Increase productivity and minimize feature engineering tasks
| 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.
OPIS PROIZVODA
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" serves as an invaluable resource for beginners and intermediate users who are eager to explore the rapidly evolving field of automated machine learning (AutoML). The manuscript exhibits a well-organized approach, delving into both open-source and commercially available AutoML options from major cloud platforms including Azure, AWS, and Google Cloud. It provides a comprehensive overview of the state of the AutoML ecosystem, illustrating its growing relevance in modern AI applications. Readers commend the book for its breadth of knowledge and systematic structure, which covers the essentials of AutoML without diving deeply into the basics of machine learning, Python programming, or cloud environments. This makes it particularly beneficial for those with some foundational understanding who wish to expedite their learning process and automate tedious tasks in their machine learning workflows. Key strengths of the book include its practical, hands-on examples using popular datasets, which provide users concrete instances of how to implement AutoML systems. Furthermore, the inclusion of various frameworks, process flow diagrams, and tables enhance the comparative analysis of different AutoML options, clarifying the complexities of the subject matter. While the book shines in many respects, it is noted that the examples are mostly centered around simpler datasets, and readers would appreciate more challenging use cases to enrich their learning experience. Additionally, the reliance on screenshots, although helpful, poses a challenge as readers voice concern regarding their longevity and potential obsolescence as cloud platforms evolve. Overall, "Automated Machine Learning" is highly recommended for AI practitioners looking to embrace automation in their machine learning processes while providing a thorough exploration of available tools and frameworks. **
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Pros
- Comprehensive overview of AutoML options across major cloud platforms and open-source systems.
- Well-structured approach that aids beginners in grasping complex concepts.
- Hands-on examples with practical applications using popular datasets.
- Visual aids enhance understanding and facilitate comparison of AutoML options.
- Engaging writing style with humorous elements makes for enjoyable reading.
Cons
- Focus on simpler datasets may limit exposure to more complex use cases.
Product Price History
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Karakteristike i prednosti
- Learn AutoML using OSS, Azure, AWS, GCP, or any platform of your choice
- Reduce human errors in machine learning models
- Make machine learning accessible for all users to promote decentralized processes
- Implement AutoML in enterprise cloud to deploy ML models and pipelines
- Automate data science modeling tasks to implement ML solutions easily and focus on more complex problems
- Requires beginner-level knowledge of building ML models
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