Deep Learning for Natural Language Processing Creating Neural Networks with Python /
Discover the concepts of deep learning used for natural language processing (NLP), with full-fledged examples of neural network models such as recurrent neural networks, long short-term memory networks, and sequence-2-sequence models. You'll start by covering the mathematical prerequisites and...
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| Main Author: | |
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| Format: | Electronic eBook |
| Language: | English |
| Published: |
Berkeley, CA :
Apress,
2018.
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| Edition: | 1st ed. 2018. |
| Subjects: | |
| ISBN: | 9781484236857 |
| Online Access: |
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| 008 | 180626s2018 xxu| s |||| 0|eng d | ||
| 020 | |a 9781484236857 | ||
| 024 | 7 | |a 10.1007/978-1-4842-3685-7 |2 doi | |
| 035 | |a CVTIDW08276 | ||
| 040 | |a Springer-Nature |b eng |c CVTISR |e AACR2 | ||
| 041 | |a eng | ||
| 100 | 1 | |a Goyal, Palash. |4 aut | |
| 245 | 1 | 0 | |a Deep Learning for Natural Language Processing |h [electronic resource] : |b Creating Neural Networks with Python / |c by Palash Goyal, Sumit Pandey, Karan Jain. |
| 250 | |a 1st ed. 2018. | ||
| 260 | 1 | |a Berkeley, CA : |b Apress, |c 2018. | |
| 300 | |a XVII, 277 p. 99 illus., 2 illus. in color. |b online resource. | ||
| 500 | |a Professional and Applied Computing | ||
| 505 | 0 | |a Chapter 1: Introduction to NLP and Deep Learning -- Chapter 2: Word Vector representations -- Chapter 3: Unfolding Recurrent Neural Networks -- Chapter 4: Developing a Chatbot -- Chapter 5: Research Paper Implementation: Sentiment Classification. | |
| 516 | |a text file PDF | ||
| 520 | |a Discover the concepts of deep learning used for natural language processing (NLP), with full-fledged examples of neural network models such as recurrent neural networks, long short-term memory networks, and sequence-2-sequence models. You'll start by covering the mathematical prerequisites and the fundamentals of deep learning and NLP with practical examples. The first three chapters of the book cover the basics of NLP, starting with word-vector representation before moving onto advanced algorithms. The final chapters focus entirely on implementation, and deal with sophisticated architectures such as RNN, LSTM, and Seq2seq, using Python tools: TensorFlow, and Keras. Deep Learning for Natural Language Processing follows a progressive approach and combines all the knowledge you have gained to build a question-answer chatbot system. This book is a good starting point for people who want to get started in deep learning for NLP. All the code presented in the book will be available in the form of IPython notebooks and scripts, which allow you to try out the examples and extend them in interesting ways. You will: Gain the fundamentals of deep learning and its mathematical prerequisites Discover deep learning frameworks in Python Develop a chatbot Implement a research paper on sentiment classification. | ||
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 0 | |a Python (Computer program language). | |
| 650 | 0 | |a Open source software. | |
| 650 | 0 | |a Computer programming. | |
| 856 | 4 | 0 | |u http://hanproxy.cvtisr.sk/han/cvti-ebook-springer-eisbn-978-1-4842-3685-7 |y Vzdialený prístup pre registrovaných používateľov |
| 910 | |b ZE05556 | ||
| 919 | |a 978-1-4842-3685-7 | ||
| 974 | |a andrea.lebedova |f Elektronické zdroje | ||
| 992 | |a SUD | ||
| 999 | |c 274511 |d 274511 | ||