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108807314
Deep Learning for Time Series Cookbook: Use PyTorch and Python recipes for forecasting, classification, and anomaly detection
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Item #:
108807314
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Unlock the potential of time series data with deep learning expertise using PyTorch and Python.
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What Stands Out
Comprehensive Recipes
Offers practical, step-by-step recipes using PyTorch specifically for time series tasks, allowing readers to quickly apply deep learning techniques to their projects without getting lost in theory.
Diverse Applications
Covers a variety of applications including forecasting, classification, and anomaly detection, making it a versatile resource for data scientists and analysts in multiple industries eager to utilize time series data.
Hands-On Learning
Encourages a hands-on approach with real-world datasets, enhancing the learning experience for readers by bridging the gap between academic concepts and practical implementation in Python.
Product Details
- Build real-world Artificial Intelligence applications with Time Series data and Deep LearningTake your deep learning skills to the next level by mastering PyTorch with tens of Python recipesSolve forecasting problems and predict the future using advanced neural network architectures in PyTorchKey FeaturesLearn how to train accurate forecasting model using neural networks and real-world time seriesBuild advanced deep neural network architectures using PyTorchTackle several time series tasks, such as forecasting, classification, hierarchical forecasting, and anomaly detectionBook DescriptionMany real-world systems are captured through the lens of time series. The analysis and forecasting of time series has thus become a key aspect of several organizations. Deep learning is the hottest Artificial Intelligence technology. It leverages large amounts of data to build intricate and accurate forecasting models.This book is a comprehensive cookbook that guides you through the development of deep learning models for time series data using PyTorch. We start from the basic concepts behind time series analysis and the PyTorch framework. Then, we dive into the details of several time series problems, including forecasting, classification, anomaly detection, and hierarchical time series forecasting. You'll learn how to tackle these tasks with a set of code recipes.By the end of this book, you'll have a solid understanding of time series data problems and how to tackle them using deep learning based on PyTorch.What you will learnUnderstand main time series analysis concepts and how to apply them using pandasLearn about PyTorch and how to use it to build deep learning modelsExplore how to transform a time series for training transformers and other advanced deep neural networksUnderstand how to deal with various time series characteristics, such as trend, seasonality, or non-constant varianceTackle different kinds of forecasting problems, involving univariate, multivariate, or hierarchical time seriesUnderstand how to apply residual and convolutional neural networks for time series classification problemsLearn how to solve time series anomaly detection problems using auto-encoders and Generative Adversarial NetworksWho This Book Is ForIf you are a machine learning enthusiast or someone who wants to learn more about building forecasting applications using deep learning, this book is for you. In order to learn from this book, you should have basic knowledge of Python and machine learning.Table of ContentsGetting Started with Time SeriesGetting Started with kerasUnivariate Time Series ForecastingAdvanced Forecasting ProblemsAdvanced Deep Learning Architectures for Time Series ForecastingProbabilistic Time Series ForecastingDeep Learning for Time Series ClassificationDeep Learning for Time Series Anomaly Detection
| Publisher | Packt Publishing |
| Publication date | 29 Mar. 2024 |
| Language | English |
| Print length | 304 pages |
| ISBN-10 | 1805129236 |
| ISBN-13 | 978-1805129233 |
| Item weight | 476 g |
| Dimensions | 19.05 x 1.57 x 23.5 cm |
Who Should Buy?
-
Data Scientists
Ideal for professionals looking to enhance their skills in time series forecasting and anomaly detection using PyTorch.
-
Machine Learning Enthusiasts
A great resource for hobbyists eager to apply deep learning techniques to real-world time series problems.
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Academics and Researchers
Beneficial for those engaged in research requiring practical applications of deep learning in time dependent data.
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Complete Beginners
Not suitable for individuals without a basic understanding of programming or machine learning principles.
Product Description
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Higher Education Editorial Review
Deep Learning For Time Series Cookbook: Use PyTorch And Python Recipes For Forecasting, Classification, And Anomaly Detection offers practical applications and recipes for utilizing deep learning techniques effectively in time series analysis. The book, published by Packt Publishing, features 304 pages of insightful content, scheduled for release on 29 Mar. 2024, making it a timely addition for enthusiasts and professionals alike. Readers will appreciate the clear layout and accessible language, as it covers essential topics like forecasting and classification using PyTorch and Python. This makes it suitable for both beginners and advanced practitioners looking to enhance their skills in anomaly detection and other critical areas of time series data processing.
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Pros
- Comprehensive recipes for hands-on learning
- Covers key topics like anomaly detection
- Accessible language for all skill levels
- Structured layout enhances learning experience
- Focus on practical applications with PyTorch
Cons
- Some users may find it too technical at times
Product Price History
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USD 82
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Features & Benefits
- Master time series analysis and modeling with deep learning.
- Utilize PyTorch to build advanced deep neural networks.
- Learn practical solutions for forecasting, classification, and anomaly detection.
- Includes free PDF eBook with purchase of print or Kindle version.
- Gain hands-on experience through easy-to-follow code recipes.
- Ideal for machine learning enthusiasts with basic Python knowledge.
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