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142719936
Deep Learning for Time Series Cookbook: Use PyTorch and Python recipes for forecasting classification and anomaly detection
Item #:
142719936
MXN 1426
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What Stands Out
Practical Recipes
Offers hands-on PyTorch and Python recipes, providing readers with real-world applications for tackling forecasting, classification, and anomaly detection challenges in time series data.
Expert Guidance
Written by industry experts, this book combines theoretical foundations with practical insights, enabling readers to effectively implement deep learning techniques for enhanced predictive performance.
Comprehensive Coverage
Covers a wide array of time series applications, making it suitable for data scientists and machine learning engineers seeking to deepen their understanding of deep learning in diverse contexts.
Product Details
| Herausgeber | Packt Publishing |
| Erscheinungstermin | 29. Mu00e4rz 2024 |
| Sprache | Englisch |
| Seitenzahl der Print-Ausgabe | 304 Seiten |
| ISBN-10 | 1805129236 |
| ISBN-13 | 978-1805129233 |
| Abmessungen | 19.05 x 1.57 x 23.5 cm |
Who Should Buy?
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Data Scientists
Ideal for data scientists seeking practical recipes for implementing deep learning in time series analysis using PyTorch.
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Machine Learning Enthusiasts
Perfect for those eager to learn about time series forecasting, classification, and anomaly detection with hands-on approaches.
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Python Developers
Great for Python developers looking to enhance their skills by applying deep learning techniques in time series data.
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Beginners in AI
Not suitable for absolute beginners as it assumes prior knowledge of machine learning and programming in Python.
Product Description
Deep Learning for Time Series Cookbook: Use PyTorch and Python recipes for forecasting classification and anomaly detection
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Englisch Ausgabe von Vitor Cerqueira (Autor) Editorial Review
Deep Learning For Time Series Cookbook: Use PyTorch And Python Recipes For Forecasting Classification And Anomaly Detection provides a structured approach to understanding time series analysis. With 304 pages of content, it is designed to elevate readers from basic to complex concepts, focusing on practical application with state-of-the-art models like N-BEATS and Temporal Fusion Transformers. Many users find the book invaluable for its clear examples and organized code repositories, making it suitable for those with prior experience in PyTorch looking to enhance their time series project skills. It bridges fundamental concepts with advanced techniques, encouraging readers to tackle real forecasting challenges with confidence.
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Pros
- Comprehensive overview of time series forecasting
- Well-structured with increasing complexity
- Clear examples and organized code repository
- State-of-the-art models included
- Great for experienced PyTorch users
Cons
- Not suitable for complete beginners
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MXN 1426
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