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Supervised Sequence Labelling with Recurrent Neural Networks 在線電子書 pdf 下載 txt下載 epub 下載 mobi 下載 2024

Supervised Sequence Labelling with Recurrent Neural Networks
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Graves, Alex 作者
Springer
譯者
2012-2 出版日期
160 頁數
0 價格
Hardcover
叢書系列
9783642247965 圖書編碼

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發表於2024-11-07

Supervised Sequence Labelling with Recurrent Neural Networks 在線電子書 epub 下載 mobi 下載 pdf 下載 txt 下載 2024

Supervised Sequence Labelling with Recurrent Neural Networks 在線電子書 epub 下載 pdf 下載 mobi 下載 txt 下載 2024

Supervised Sequence Labelling with Recurrent Neural Networks 在線電子書 pdf 下載 txt下載 epub 下載 mobi 下載 2024


Supervised Sequence Labelling with Recurrent Neural Networks 在線電子書 用戶評價

評分☆☆☆☆☆

瞭解LSTM RNN和CTC的經典書籍，畢竟本書作者就是LSTM模型的完善者以及CTC算法的提齣者。

評分☆☆☆☆☆

瞭解LSTM RNN和CTC的經典書籍，畢竟本書作者就是LSTM模型的完善者以及CTC算法的提齣者。

評分☆☆☆☆☆

瞭解LSTM RNN和CTC的經典書籍，畢竟本書作者就是LSTM模型的完善者以及CTC算法的提齣者。

評分☆☆☆☆☆

瞭解LSTM RNN和CTC的經典書籍，畢竟本書作者就是LSTM模型的完善者以及CTC算法的提齣者。

評分☆☆☆☆☆

瞭解LSTM RNN和CTC的經典書籍，畢竟本書作者就是LSTM模型的完善者以及CTC算法的提齣者。

Supervised Sequence Labelling with Recurrent Neural Networks 在線電子書 著者簡介

Supervised Sequence Labelling with Recurrent Neural Networks 在線電子書 著者簡介

Supervised Sequence Labelling with Recurrent Neural Networks 在線電子書 pdf 下載 txt下載 epub 下載 mobi 在線電子書下載

Supervised Sequence Labelling with Recurrent Neural Networks 在線電子書 圖書描述

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.

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