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Modern Time Series Forecasting with Python: Industry-ready machine learning and deep learning time series analysis with PyTorch and pandas Paperback
AMD 31387
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This practical, hands-on guide empowers you to build and deploy powerful time series forecasting models.
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Информация о продукте
| Item Weight | 1.2 lbs (540 grams) |
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.
ОПИСАНИЕ ТОВАРА
Modern Time Series Forecasting with Python: Industry-ready machine learning and deep learning time series analysis with PyTorch and pandas Paperback – 31 Oct. 2024
Вопросы и ответы клиентов
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вопрос:
What prior knowledge is needed to use this book?
отвечать: A basic understanding of statistics and Python programming is recommended. -
вопрос:
Is this book suitable for beginners?
отвечать: Yes, it starts with fundamental concepts before progressing to advanced topics. -
вопрос:
Can I use this book for practical applications in my job?
отвечать: Absolutely, it provides industry-ready techniques and strategies for real-world forecasting problems.
Higher Education Editorial Review
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Особенности и преимущества
- 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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