Description: Supervised Sequence Labelling with Recurrent Neural Networks by Alex Graves Estimated delivery 3-12 business days Format Hardcover Condition Brand New Description Supervised sequence labelling is a vital area of machine learning, encompassing tasks such as speech, handwriting and gesture recognition, protein secondary structure prediction and part-of-speech tagging. Publisher Description Supervised sequence labelling is a vital area of machine learning, encompassing tasks such as speech, handwriting and gesture recognition, protein secondary structure prediction and part-of-speech tagging. Recurrent neural networks are powerful sequence learning tools—robust to input noise and distortion, able to exploit long-range contextual information—that would seem ideally suited to such problems. However their role in large-scale sequence labelling systems has so far been auxiliary. The goal of this book is a complete framework for classifying and transcribing sequential data with recurrent neural networks only. Three main innovations are introduced in order to realise this goal. Firstly, the connectionist temporal classification output layer allows the framework to be trained with unsegmented target sequences, such as phoneme-level speech transcriptions; this is in contrast to previous connectionist approaches, which were dependent on error-prone prior segmentation. Secondly, multidimensional recurrent neural networks extend the framework in a natural way to data with more than one spatio-temporal dimension, such as images and videos. Thirdly, the use of hierarchical subsampling makes it feasible to apply the framework to very large or high resolution sequences, such as raw audio or video. Experimental validation is provided by state-of-the-art results in speech and handwriting recognition. Details ISBN 3642247962 ISBN-13 9783642247965 Title Supervised Sequence Labelling with Recurrent Neural Networks Author Alex Graves Format Hardcover Year 2012 Pages 146 Edition 2012th Publisher Springer-Verlag Berlin and Heidelberg GmbH & Co. KG GE_Item_ID:137566502; About Us Grand Eagle Retail is the ideal place for all your shopping needs! With fast shipping, low prices, friendly service and over 1,000,000 in stock items - you're bound to find what you want, at a price you'll love! Shipping & Delivery Times Shipping is FREE to any address in USA. Please view eBay estimated delivery times at the top of the listing. Deliveries are made by either USPS or Courier. We are unable to deliver faster than stated. International deliveries will take 1-6 weeks. NOTE: We are unable to offer combined shipping for multiple items purchased. This is because our items are shipped from different locations. Returns If you wish to return an item, please consult our Returns Policy as below: Please contact Customer Services and request "Return Authorisation" before you send your item back to us. Unauthorised returns will not be accepted. Returns must be postmarked within 4 business days of authorisation and must be in resellable condition. Returns are shipped at the customer's risk. We cannot take responsibility for items which are lost or damaged in transit. For purchases where a shipping charge was paid, there will be no refund of the original shipping charge. Additional Questions If you have any questions please feel free to Contact Us. Categories Baby Books Electronics Fashion Games Health & Beauty Home, Garden & Pets Movies Music Sports & Outdoors Toys
Price: 218.07 USD
Location: Fairfield, Ohio
End Time: 2024-10-22T02:08:52.000Z
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ISBN-13: 9783642247965
Book Title: Supervised Sequence Labelling with Recurrent Neural Networks
Number of Pages: Xiv, 146 Pages
Language: English
Publication Name: Supervised Sequence Labelling with Recurrent Neural Networks
Publisher: Springer Berlin / Heidelberg
Subject: Engineering (General), Intelligence (Ai) & Semantics, Neural Networks, Computer Vision & Pattern Recognition
Publication Year: 2012
Item Weight: 14.5 Oz
Type: Textbook
Author: Alex Graves
Subject Area: Computers, Technology & Engineering
Item Length: 9.3 in
Series: Studies in Computational Intelligence Ser.
Item Width: 6.1 in
Format: Hardcover
