Time series forecasting using deep learning: (Record no. 5969)

MARC details
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
Holdings
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

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