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Advanced Deep Learning with Python

You're reading from   Advanced Deep Learning with Python Design and implement advanced next-generation AI solutions using TensorFlow and PyTorch

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Product type Paperback
Published in Dec 2019
Publisher Packt
ISBN-13 9781789956177
Length 468 pages
Edition 1st Edition
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Author (1):
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Ivan Vasilev Ivan Vasilev
Author Profile Icon Ivan Vasilev
Ivan Vasilev
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Toc

Table of Contents (17) Chapters Close

Preface 1. Section 1: Core Concepts
2. The Nuts and Bolts of Neural Networks FREE CHAPTER 3. Section 2: Computer Vision
4. Understanding Convolutional Networks 5. Advanced Convolutional Networks 6. Object Detection and Image Segmentation 7. Generative Models 8. Section 3: Natural Language and Sequence Processing
9. Language Modeling 10. Understanding Recurrent Networks 11. Sequence-to-Sequence Models and Attention 12. Section 4: A Look to the Future
13. Emerging Neural Network Designs 14. Meta Learning 15. Deep Learning for Autonomous Vehicles 16. Other Books You May Enjoy

Sequence-to-Sequence Models and Attention

In Chapter 7, Understanding Recurrent Networks, we outlined several types of recurrent models, depending on the input-output combinations. One of them is indirect many-to-many or sequence-to-sequence (seq2seq), where an input sequence is transformed into another, different output sequence, not necessarily with the same length as the input. Machine translation is the most popular type of seq2seq task. The input sequences are the words of a sentence in one language and the output sequences are the words of the same sentence translated into another language. For example, we can translate the English sequence tourist attraction to the German touristenattraktion. Not only is the output sentence a different length, but there is no direct correspondence between the elements of the input and output sequences. In particular, one output element...

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