Python machine learning programming [PyTorch&scikit-learn edition] impress top gear Tankobon Softcover – December 15, 2022
TWD 1491
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This book is irreplaceable, both as an overview of the ambitious field of machine learning and as a treasure trove of practical knowledge.
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產品詳情
| Item Weight | 1 lbs (450 grams) |
Who Should Buy?
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Beginner Data Scientists
Ideal for newcomers wishing to grasp machine learning concepts using Python libraries like PyTorch and scikit-learn.
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Students and Learners
Perfect for academic use, excellent for students needing a comprehensive guide for projects and coursework in machine learning.
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Professionals Upskilling
Beneficial for professionals seeking to enhance their skills in Python programming and machine learning techniques.
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Advanced Practitioners
Not suitable for experts who require in-depth analysis or advanced techniques beyond the foundational concepts covered.
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Complete Beginners
Users with no programming experience may struggle without initial basic knowledge of Python or coding practices.
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Casual Readers
Individuals seeking a light read or casual information may find this technical book too dense and challenging.
產品描述
Python machine learning programming [PyTorch&scikit-learn edition] impress top gear Tankobon Softcover – December 15, 2022
客戶問答
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問題:
What programming languages does this book cover?
Answer: This book focuses on Python programming, specifically using libraries like scikit-learn and PyTorch. -
問題:
Is this book suitable for beginners?
Answer: Yes, it starts with the basics of machine learning, making it accessible for beginners. -
問題:
Does the book include practical coding examples?
Answer: Absolutely, the book includes practical coding examples to help implement the concepts learned.
Sebastian Raschka , uxi (Hayden) Liu , Vahid Mirjalili Editorial Review
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TWD 1491
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特色和優勢
- Comprehensive guide covering basics to advanced machine learning techniques.
- In-depth explanations of scikit-learn and PyTorch for practical applications.
- Explores cutting-edge methods like Transformers and graph neural networks.
- Features hyperparameter tuning, ensemble learning, and regression analysis.
- Includes practical coding examples for real-world applications.
- Highly recommended by readers and industry experts for effective learning.
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