https://github.com/trainingbypackt/deep-learning-for-natural-language-processing

Solve your natural language processing problems with smart deep neural networks
https://github.com/trainingbypackt/deep-learning-for-natural-language-processing

deeplearning glove gru keras language lstm namedentityrecognizer natural nlp nlp-library nlp-machine-learning partsofspeechtagger textpreprocessing word2vec

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Solve your natural language processing problems with smart deep neural networks

• Host: GitHub
• URL: https://github.com/trainingbypackt/deep-learning-for-natural-language-processing
• Owner: TrainingByPackt
• License: mit
• Created: 2019-02-13T05:04:17.000Z (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2019-06-03T10:19:46.000Z (over 5 years ago)
• Last Synced: 2023-03-07T23:23:16.443Z (almost 2 years ago)
• Topics: deeplearning, glove, gru, keras, language, lstm, namedentityrecognizer, natural, nlp, nlp-library, nlp-machine-learning, partsofspeechtagger, textpreprocessing, word2vec
• Language: Jupyter Notebook
• Size: 44 MB
• Stars: 28
• Watchers: 7
• Forks: 50
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Deep Learning for Natural Language Processing

Applying deep learning approaches to various NLP tasks can take your computational algorithms to a completely new level in terms of speed and accuracy. Deep Learning for Natural Language Processing starts off by highlighting the basic building blocks of the natural language processing domain. The book goes on to introduce the problems that you can solve using state-of-the-art neural network models. After this, delving into the various neural network architectures and their specific areas of application will help you to understand how to select the best model to suit your needs.

As you advance through this deep learning book, you’ll study convolutional, recurrent, and recursive neural networks, in addition to covering long short-term memory networks (LSTM). Understanding these networks will help you to implement their models using Keras. In the later chapters, you will be able to develop a trigger word detection application using NLP techniques such as attention model and beam search.

By the end of this book, you will not only have sound knowledge of natural language processing but also be able to select the best text pre-processing and neural network models to solve a number of NLP issues.

## 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

Build a machine translation model in Keras

Develop a text generation application using LSTM

Build a trigger word detection application using an attention model

## Hardware Requirements

For the optimal student experience, we recommend the following hardware

configuration:

* Processor: Intel Core i5 or equivalent

* Memory: 4 GB RAM

* Storage: 5 GB available space

## Software Requirements

We also recommend that you have the following software installed in advance:

* OS: Windows 7 SP1 64-bit, Windows 8.1 64-bit or Windows 10 64-bit, Linux

(Ubuntu, Debian, Red Hat, or Suse), or the latest version of OS X

* Python (3.6.5 or later, preferably 3.7; available through https://www.python.org/downloads/release/python-371/)

* Jupyter (go to https://jupyter.org/install and follow the instructions to install).Alternatively, you can use Anaconda to install Jupyter.

* Keras (https://keras.io/#installation)

* Google Colab: It is a free Jupyter notebook environment and runs on cloud infrastructure. It is highly recommended as it requires no setup and has pre-installed popular Python packages and libraries (https://colab.research.google.com/notebooks/welcome.ipynb)