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Modern Time Series Forecasting with Python: Industry-ready machine learning and deep learning time series analysis with PyTorch and pandas
89% of respondents would recommend this to a friend
TWD 2680
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This practical, hands-on guide empowers you to build and deploy powerful time series forecasting models.
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What Stands Out
產品詳情
| Publisher | Packt Publishing |
| Publication date | 31 Oct. 2024 |
| Edition | 2nd |
| Language | English |
| Print length | 658 pages |
| ISBN-10 | 1835883184 |
| ISBN-13 | 978-1835883181 |
| Item weight | 1.12 kg |
| Dimensions | 19.05 x 3.78 x 23.5 cm |
Who Should Buy?
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Data Scientists
Ideal for data scientists looking to enhance their skills in time series analysis with advanced machine learning techniques.
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Machine Learning Engineers
Perfect for professionals seeking to apply deep learning methodologies to time series forecasting projects in various industries.
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Students and Learners
Beneficial for students studying data science who want practical knowledge of time series analysis using Python, PyTorch, and pandas.
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Beginners
Not suitable for complete beginners, as prior knowledge of Python and basic statistics is typically needed to grasp concepts.
產品描述
Modern Time Series Forecasting with Python: Industry-ready machine learning and deep learning time series analysis with PyTorch and pandas
客戶問題與解答
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問題:
What prior knowledge is needed to use this book?
Answer: A basic understanding of statistics and Python programming is recommended. -
問題:
Is this book suitable for beginners?
Answer: Yes, it starts with fundamental concepts before progressing to advanced topics. -
問題:
Can I use this book for practical applications in my job?
Answer: Absolutely, it provides industry-ready techniques and strategies for real-world forecasting problems.
Higher Education Editorial Review
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特色和優勢
- Learn traditional and advanced techniques for time series forecasting.
- Hands-on practical examples to improve forecasting accuracy.
- Includes free eBook with print or Kindle purchase.
- Covers deep learning models including RNNs and transformers.
- Ideal for professionals and students in various industries.
- New edition features enhancements in transformer architectures and probabilistic forecasting.
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