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Nº de artículo:
51000237
Probabilistic Deep Learning: With Python, Keras and Tensorflow Probability Paperback – 8 Feb. 2021
Nº de artículo:
51000237
CRC 43198
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Probabilistic Deep Learning: With Python, Keras and TensorFlow Probability teaches the increasingly popular probabilistic approach to deep learning that allows you to refine your results more quickly and accurately without much trial-and-error testing.
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What Stands Out
Practical Approach
Offers hands-on techniques to implement probabilistic models in Python using Keras and TensorFlow Probability, making it ideal for practitioners and students seeking practical knowledge.
Comprehensive Coverage
Covers a wide range of topics in probabilistic deep learning, ensuring readers gain a thorough understanding, from theory to application, setting it apart from other resources.
Latest Tools
Utilizes cutting-edge tools and libraries, emphasizing modern methods in machine learning that empower users to build robust models, enhancing both their learning and practical skills.
Detalles de producto
| Publisher | Manning Publications |
| Publication date | 8 Feb. 2021 |
| Edition | 1st |
| Language | English |
| Print length | 252 pages |
| ISBN-10 | 1617296074 |
| ISBN-13 | 978-1617296079 |
| Item weight | 210 g |
| Dimensions | 18.73 x 0.76 x 23.5 cm |
Who Should Buy?
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Data Scientists
Ideal for data scientists looking to enhance their model-building skills using probabilistic approaches and deep learning frameworks.
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Machine Learning Students
Perfect for students aiming to understand advanced concepts in deep learning and probabilistic modeling with practical Python applications.
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Researchers
Great resource for researchers exploring innovative probabilistic methods in machine learning and applying them to real-world scenarios.
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Beginners
Not suitable for complete beginners in programming or machine learning due to its advanced concepts and technical depth.
DESCRIPCIÓN DEL PRODUCTO
Probabilistic Deep Learning: With Python, Keras and Tensorflow Probability Paperback – 8 Feb. 2021
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Data Mining Editorial Review
The book "Probabilistic Deep Learning: With Python, Keras and Tensorflow Probability" has garnered mixed reviews from customers, reflecting a blend of appreciation and criticism that illuminate different facets of the reading experience. It is noted for being well-written and easy to follow, which makes it a suitable introduction for those new to the field. Many reviewers commend the clarity of the explanations and the inclusion of code examples and graphical representations that enhance understanding. However, some users feel the book falls short of expectations, particularly regarding depth. They argue that the initial sections serve more as an introduction to deep learning and TensorFlow rather than delving into the probabilistic aspects, which only begin around the middle of the text. As a result, readers seeking comprehensive insights into probabilistic deep learning may find the content too superficial and even question the book's overall value compared to online courses that might provide a more practical and extensive learning experience. Editing errors have also been a recurring theme in the feedback, with numerous reviewers highlighting issues such as typos and translation mistakes, which detract from the professionalism of the material. Overall, while the text offers a friendly entry point into the subject, it may not meet the expectations of seasoned practitioners or those seeking in-depth knowledge on probabilistic methods in deep learning. **
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ventajas
- Well-written and clear language.
- Engaging and easily-paced read.
- Good code examples and figures to illustrate concepts.
Contras
- Initial sections are overly basic and light on content.
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CRC 43198
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características y beneficios
- Learn probabilistic deep learning to enhance accuracy
- Use Python, Keras, and TensorFlow Probability for practical applications
- Improve network performance with the right distribution for various data types
- Discover Bayesian variants to state their own uncertainty for increased accuracy
- Build deep learning applications that handle real-world data noise and uncertainty
- Includes free eBook in PDF, Kindle, and ePub formats
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