
Deep Learning for Natural Language Processing
Solve your natural language processing problems with smart deep neural networks
Packt Publishing
Published on 29. March 2019
Book
Paperback/Softback
476 pages
978-1-83855-029-5 (ISBN)
Description
Gain knowledge of various deep neural network architectures and their application areas to conquer your NLP issues.
About This Book
* Start with the basics of NLP and its issues
* Learn ways to select the best deep neural network to solve your NLP issues
* Explore convolutional and recurrent neural networks and long short term memory networks
Who This Book Is For
If you're an aspiring data scientist looking for an introduction to deep learning in the NLP domain, this is just the course for you. To easily understand the concepts in the course, you must have strong knowledge and working experience of Python, linear algebra, and machine learning.
What You Will Learn
* Understand various pre-processing techniques for deep learning problems
* Build a vector representation of text using word2vec and Glove
* Create a named entity recognizer and parts of speech tagger with Apache openNLP
* Create a machine translation model in Keras
* Develop a text generation application using LSTM
* Build a trigger word detection application using an attention model
In Detail
Deep Learning with NLP starts by introducing you to the world of natural language processing, its applications, and its issues. After studying various neural network architectures and their specific areas of application, you will learn ways to select the best model to suit your needs.
As you advance through the modules, you'll study the convolutional, recurrent, and recursive neural networks and long short term memory networks, and then implement their models using Keras. You will get a chance to explore state-of-the-art NLP techniques, including attention model and beam search, which you will use to develop a trigger word detection application.
By the end of the course, not only will you have sound knowledge of NLP, but you will also be a pro at selecting the best text-pre processing and neural network models to solve all your NLP issues.
Gain knowledge of various deep neural network architectures and their application areas to conquer your NLP issues.
About This Book
* Start with the basics of NLP and its issues
* Learn ways to select the best deep neural network to solve your NLP issues
* Explore convolutional and recurrent neural networks and long short term memory networks
Who This Book Is For
If you're an aspiring data scientist looking for an introduction to deep learning in the NLP domain, this is just the course for you. To easily understand the concepts in the course, you must have strong knowledge and working experience of Python, linear algebra, and machine learning.
What You Will Learn
* Understand various pre-processing techniques for deep learning problems
* Build a vector representation of text using word2vec and Glove
* Create a named entity recognizer and parts of speech tagger with Apache openNLP
* Create a machine translation model in Keras
* Develop a text generation application using LSTM
* Build a trigger word detection application using an attention model
In Detail
Deep Learning with NLP starts by introducing you to the world of natural language processing, its applications, and its issues. After studying various neural network architectures and their specific areas of application, you will learn ways to select the best model to suit your needs.
As you advance through the modules, you'll study the convolutional, recurrent, and recursive neural networks and long short term memory networks, and then implement their models using Keras. You will get a chance to explore state-of-the-art NLP techniques, including attention model and beam search, which you will use to develop a trigger word detection application.
By the end of the course, not only will you have sound knowledge of NLP, but you will also be a pro at selecting the best text-pre processing and neural network models to solve all your NLP issues.
About This Book
* Start with the basics of NLP and its issues
* Learn ways to select the best deep neural network to solve your NLP issues
* Explore convolutional and recurrent neural networks and long short term memory networks
Who This Book Is For
If you're an aspiring data scientist looking for an introduction to deep learning in the NLP domain, this is just the course for you. To easily understand the concepts in the course, you must have strong knowledge and working experience of Python, linear algebra, and machine learning.
What You Will Learn
* Understand various pre-processing techniques for deep learning problems
* Build a vector representation of text using word2vec and Glove
* Create a named entity recognizer and parts of speech tagger with Apache openNLP
* Create a machine translation model in Keras
* Develop a text generation application using LSTM
* Build a trigger word detection application using an attention model
In Detail
Deep Learning with NLP starts by introducing you to the world of natural language processing, its applications, and its issues. After studying various neural network architectures and their specific areas of application, you will learn ways to select the best model to suit your needs.
As you advance through the modules, you'll study the convolutional, recurrent, and recursive neural networks and long short term memory networks, and then implement their models using Keras. You will get a chance to explore state-of-the-art NLP techniques, including attention model and beam search, which you will use to develop a trigger word detection application.
By the end of the course, not only will you have sound knowledge of NLP, but you will also be a pro at selecting the best text-pre processing and neural network models to solve all your NLP issues.
Gain knowledge of various deep neural network architectures and their application areas to conquer your NLP issues.
About This Book
* Start with the basics of NLP and its issues
* Learn ways to select the best deep neural network to solve your NLP issues
* Explore convolutional and recurrent neural networks and long short term memory networks
Who This Book Is For
If you're an aspiring data scientist looking for an introduction to deep learning in the NLP domain, this is just the course for you. To easily understand the concepts in the course, you must have strong knowledge and working experience of Python, linear algebra, and machine learning.
What You Will Learn
* Understand various pre-processing techniques for deep learning problems
* Build a vector representation of text using word2vec and Glove
* Create a named entity recognizer and parts of speech tagger with Apache openNLP
* Create a machine translation model in Keras
* Develop a text generation application using LSTM
* Build a trigger word detection application using an attention model
In Detail
Deep Learning with NLP starts by introducing you to the world of natural language processing, its applications, and its issues. After studying various neural network architectures and their specific areas of application, you will learn ways to select the best model to suit your needs.
As you advance through the modules, you'll study the convolutional, recurrent, and recursive neural networks and long short term memory networks, and then implement their models using Keras. You will get a chance to explore state-of-the-art NLP techniques, including attention model and beam search, which you will use to develop a trigger word detection application.
By the end of the course, not only will you have sound knowledge of NLP, but you will also be a pro at selecting the best text-pre processing and neural network models to solve all your NLP issues.
More details
Language
English
Place of publication
Birmingham
United Kingdom
Dimensions
Height: 235 mm
Width: 191 mm
Thickness: 21 mm
Weight
694 gr
ISBN-13
978-1-83855-029-5 (9781838550295)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions

Karthiek Reddy Bokka
Deep Learning for Natural Language Processing
Solve your natural language processing problems with smart deep neural networks
E-Book
06/2019
Packt Publishing
€27.49
Available for download
Persons
Tanuj Jain is a data scientist working at a Germany-based company. He has a master's degree in electrical engineering with a focus on statistical pattern recognition. He has been developing deep learning models and putting them in production for commercial use at his current job. Natural language processing is a special interest area for him and he has applied his know-how to classification and sentiment rating tasks. Manoveg Saxena is a data scientist with a focus on natural language processing. He has a master's degree in computer science with specialization in big data applications. He has the experience with text based deep learning problems that deal with classification related tasks.
Tanuj Jain is a data scientist working at a Germany-based company. He has a master's degree in electrical engineering with a focus on statistical pattern recognition. He has been developing deep learning models and putting them in production for commercial use at his current job. Natural language processing is a special interest area for him and he has applied his know-how to classification and sentiment rating tasks. Manoveg Saxena is a data scientist with a focus on natural language processing. He has a master's degree in computer science with specialization in big data applications. He has the experience with text based deep learning problems that deal with classification related tasks.
Tanuj Jain is a data scientist working at a Germany-based company. He has a master's degree in electrical engineering with a focus on statistical pattern recognition. He has been developing deep learning models and putting them in production for commercial use at his current job. Natural language processing is a special interest area for him and he has applied his know-how to classification and sentiment rating tasks. Manoveg Saxena is a data scientist with a focus on natural language processing. He has a master's degree in computer science with specialization in big data applications. He has the experience with text based deep learning problems that deal with classification related tasks.