Time series forecasting using deep learning: (Record no. 5969)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 01932nam a22002177a 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20240210180223.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 240210b |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9789391392574 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 006.31 |
| Item number | GRI |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Gridin, Ivan |
| 245 ## - TITLE STATEMENT | |
| Title | Time series forecasting using deep learning: |
| Remainder of title | combining PyTorch, RNN, TCN and deep neural network models to provide production-ready prediction solutions |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Name of publisher, distributor, etc. | BPB Publications |
| Place of publication, distribution, etc. | New Delhi |
| Date of publication, distribution, etc. | 2023 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | xxiii, 289 p. |
| 365 ## - TRADE PRICE | |
| Price type code | INR |
| Price amount | 899.00 |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | This book is amid at teaching the readers how to apply the deep learning techniques to the time series forecasting challenges and how to build prediction models using PyTorch.<br/><br/>The readers will learn the fundamentals of PyTorch in the early stages of the book. Next, the time series forecasting is covered in greater depth after the programme has been developed. You will try to use machine learning to identify the patterns that can help us forecast the future results. It covers methodologies such as Recurrent Neural Network, Encoder-decoder model, and Temporal Convolutional Network, all of which are state-of-the-art neural network architectures. Furthermore, for good measure, we have also introduced the neural architecture search, which automates searching for an ideal neural network design for a certain task.<br/><br/>Finally by the end of the book, readers would be able to solve complex real-world prediction issues by applying the models and strategies learnt throughout the course of the book. This book also offers another great way of mastering deep learning and its various techniques.<br/><br/>(https://in.bpbonline.com/products/time-series-forecasting-using-deep-learning?_pos=1&_sid=5a64ea01e&_ss=r&variant=41900465946811) |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Deep learning |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Time series |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Forecasting |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Neural network model |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | Book |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Collection code | Bill No | Bill Date | Home library | Current library | Shelving location | Date acquired | Source of acquisition | Cost, normal purchase price | Total Checkouts | Full call number | Accession Number | Date last seen | Copy number | Cost, replacement price | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | IT & Decisions Sciences | TB3444 | 24-01-2024 | Indian Institute of Management LRC | Indian Institute of Management LRC | General Stacks | 02/10/2024 | Technical Bureau India Pvt. Ltd. | 624.80 | 006.31 GRI | 005757 | 02/10/2024 | 1 | 899.00 | 02/10/2024 | Book |