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Item #:
47529177
Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python
85% of respondents would recommend this to a friend
Item #:
47529177
QAR 273
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What Stands Out
Comprehensive Guide
Offers an in-depth exploration of both machine learning and deep learning concepts, providing readers with a thorough understanding necessary to build sophisticated models using Python.
Practical Applications
Includes hands-on projects utilizing PyTorch and Scikit-Learn, allowing readers to apply theoretical knowledge to real-world problems, enhancing learning through practice.
Accessible for All
Written for beginners and experienced developers alike, the book demystifies complex topics, making advanced concepts in machine learning approachable for all skill levels.
Product Details
| Publisher | Packt Publishing |
| Publication date | 25 Feb. 2022 |
| Language | English |
| Print length | 770 pages |
| ISBN-10 | 1801819319 |
| ISBN-13 | 978-1801819312 |
| Item weight | 1.4 kg |
| Dimensions | 19.05 x 4.45 x 23.5 cm |
Who Should Buy?
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Beginners in ML
Ideal for individuals new to machine learning, providing step-by-step guidance and foundational knowledge.
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Python Developers
Perfect for Python developers looking to enhance their skills in machine learning and deep learning frameworks.
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Data Scientists
Useful for data scientists who want to implement machine learning algorithms using PyTorch and Scikit-Learn.
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Advanced Practitioners
Not suitable for seasoned experts seeking advanced techniques or in-depth theoretical explorations of machine learning.
Product Description
Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python
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Higher Education Editorial Review
Machine Learning With PyTorch And Scikit-Learn Develop Machine Learning And Deep Learning Models With Python is a comprehensive guide designed for experienced developers and machine learning enthusiasts. This book covers a vast array of topics from the basics to nuanced depths including training sets, dimension reduction, ensemble methods, and advanced concepts like transformers and GANs. As noted in the reviews, it's packed with over 700 pages of in-depth content that serves well as a reference, highlighting the practical use of Scikit-Learn together with PyTorch to ease the learning curve for many. Readers appreciate that the code samples provided are actionable and effective, reinforcing the concepts discussed throughout the text. Furthermore, the breakdown of complex topics into manageable sections makes it ideal for those looking to deepen their understanding without feeling overwhelmed. Ideal for anyone wanting to navigate the expansive world of machine learning.
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Pros
- Comprehensive coverage of machine learning topics
- Suitable for experienced developers
- Hands-on coding samples included
- Suitable as a reference guide
- In-depth explanations of advanced techniques
Cons
- May be overwhelming due to vast content coverage
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