Deep Learning for Time Series Cookbook. (Record no. 15993)
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| fixed length control field | 03599nam a2200277uu 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250710182906.0 |
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| fixed length control field | 250616s||||||||||||||||o||||||||||| |d |
| 024 80 - OTHER STANDARD IDENTIFIER | |
| Standard number or code | 9781805122739 |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | PACKT |
| Transcribing agency | PACKT |
| 041 ## - LANGUAGE CODE | |
| Language code of text/sound track or separate title | en |
| 044 ## - COUNTRY OF PUBLISHING/PRODUCING ENTITY CODE | |
| MARC country code | GB |
| 100 0# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Vitor Cerqueira |
| Relator term | author. |
| 245 00 - TITLE STATEMENT | |
| Title | Deep Learning for Time Series Cookbook. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | GB: |
| Name of publisher, distributor, etc. | Packt, |
| Date of publication, distribution, etc. | 2024-03-29. |
| 263 ## - PROJECTED PUBLICATION DATE | |
| Projected publication date | 2024-03-29 |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | GB: |
| Name of producer, publisher, distributor, manufacturer | Packt, |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 274. |
| 377 ## - ASSOCIATED LANGUAGE | |
| Language code | en |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | <p><b>Learn how to deal with time series data and how to model it using deep learning and take your skills to the next level by mastering PyTorch using different Python recipes</b></p><h4>Key Features</h4><ul><li>Learn the fundamentals of time series analysis and how to model time series data using deep learning</li><li>Explore the world of deep learning with PyTorch and build advanced deep neural networks</li><li>Gain expertise in tackling time series problems, from forecasting future trends to classifying patterns and anomaly detection</li><li>Purchase of the print or Kindle book includes a free PDF eBook</li></ul><h4>Book Description</h4>Most organizations exhibit a time-dependent structure in their processes, including fields such as finance. By leveraging time series analysis and forecasting, these organizations can make informed decisions and optimize their performance. Accurate forecasts help reduce uncertainty and enable better planning of operations. Unlike traditional approaches to forecasting, deep learning can process large amounts of data and help derive complex patterns. Despite its increasing relevance, getting the most out of deep learning requires significant technical expertise. This book guides you through applying deep learning to time series data with the help of easy-to-follow code recipes. You'll cover time series problems, such as forecasting, anomaly detection, and classification. This deep learning book will also show you how to solve these problems using different deep neural network architectures, including convolutional neural networks (CNNs) or transformers. As you progress, you'll use PyTorch, a popular deep learning framework based on Python to build production-ready prediction solutions. By the end of this book, you'll have learned how to solve different time series tasks with deep learning using the PyTorch ecosystem.<h4>What you will learn</h4><ul><li>Grasp the core of time series analysis and unleash its power using Python</li><li>Understand PyTorch and how to use it to build deep learning models</li><li>Discover how to transform a time series for training transformers</li><li>Understand how to deal with various time series characteristics</li><li>Tackle forecasting problems, involving univariate or multivariate data</li><li>Master time series classification with residual and convolutional neural networks</li><li>Get up to speed with solving time series anomaly detection problems using autoencoders and generative adversarial networks (GANs)</li></ul><h4>Who this book is for</h4>If you're a machine learning enthusiast or someone who wants to learn more about building forecasting applications using deep learning, this book is for you. Basic knowledge of Python programming and machine learning is required to get the most out of this book. |
| 538 ## - SYSTEM DETAILS NOTE | |
| System details note | Data in extended ASCII character set. |
| 538 ## - SYSTEM DETAILS NOTE | |
| System details note | Mode of access: Internet. |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Luís Roque |
| Relator term | author. |
| 710 2# - ADDED ENTRY--CORPORATE NAME | |
| Corporate name or jurisdiction name as entry element | PACKT |
| 773 0# - HOST ITEM ENTRY | |
| Title | Deep Learning for Time Series Cookbook |
| Place, publisher, and date of publication | GB,Packt,2024-03-29 |
| Physical description | 274 |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="https://learning.packt.com/product/470885">https://learning.packt.com/product/470885</a> |
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