Deep Learning for Natural Language Processing

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.