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Advanced Natural Language Processing with TensorFlow 2

You're reading from   Advanced Natural Language Processing with TensorFlow 2 Build effective real-world NLP applications using NER, RNNs, seq2seq models, Transformers, and more

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Product type Paperback
Published in Feb 2021
Publisher Packt
ISBN-13 9781800200937
Length 380 pages
Edition 1st Edition
Languages
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Authors (2):
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Tony Mullen Tony Mullen
Author Profile Icon Tony Mullen
Tony Mullen
Ashish Bansal Ashish Bansal
Author Profile Icon Ashish Bansal
Ashish Bansal
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Toc

Table of Contents (13) Chapters Close

Preface 1. Essentials of NLP 2. Understanding Sentiment in Natural Language with BiLSTMs FREE CHAPTER 3. Named Entity Recognition (NER) with BiLSTMs, CRFs, and Viterbi Decoding 4. Transfer Learning with BERT 5. Generating Text with RNNs and GPT-2 6. Text Summarization with Seq2seq Attention and Transformer Networks 7. Multi-Modal Networks and Image Captioning with ResNets and Transformer Networks 8. Weakly Supervised Learning for Classification with Snorkel 9. Building Conversational AI Applications with Deep Learning 10. Installation and Setup Instructions for Code 11. Other Books You May Enjoy
12. Index

GitHub location

The code for this book is located in the following public GitHub repository:

https://github.com/PacktPublishing/Advanced-Natural-Language-Processing-with-TensorFlow-2

Please clone this repository to access all the code for the book. Please note that seminal papers for each of the chapters are included in the GitHub repository inside each chapter's directory.

Now, the common steps to set up the conda environment are explained below:

  • Step 1: Create a new conda environment with Python 3.7.5:
    $ conda create -n tf24nlp python==3.7.5
    

    The environment is named tf24nlp but feel free to use your own name and make sure you use that in the following steps. I like to prefix my environment names with the version of TensorFlow being used and I suffix a "g" if that environment has a GPU version of the library. As you can probably infer, we are going to use TensorFlow 2.4.

  • Step 2: Activate the environment and...
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