Imbalanced Text Data

Generate Text Samples you own this product

This project is part of the liveProject series Training Models on Imbalanced Text Data
prerequisites
intermediate Python • basics of NumPy, pandas, Jupyter, Google Colab notebooks, TensorFlow, and NLP
skills learned
building and training a deep learning model for text classification • using sklearn module to report model classification metrics • condition based sampling technique for NumPy array
KC Tung
1 week · 4-7 hours per week · ADVANCED
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Look inside
In this liveProject, you’ll build a deep learning model that can generate text in order to create synthetic training data. You’ll establish a data training set of positive movie reviews, and then create a model that can generate text based on the data. This approach is the basis of data augmentation.
This project is designed for learning purposes and is not a complete, production-ready application or solution.

book resources

When you start your liveProject, you get full access to the following books for 90 days.

project author

KC Tung
KC Tung is an AI architect, machine learning engineer, and data scientist who specializes in delivering AI, deep learning, and NLP models across enterprise architectures. As an AI architect at Microsoft, he helps enterprise customers with use-case driven architecture, AI/ML model development/deployment in the cloud, and technology selection and integration best suited for their requirements. He is a Microsoft certified AI engineer and data engineer. He has a PhD in molecular biophysics from the University of Texas Southwestern Medical, and has spoken at the 2018 O'Reilly AI Conference in San Francisco and the 2019 O'Reilly Tensorflow World Conference in San Jose.

prerequisites

This liveProject is for Python programmers interested in training text generation. To begin this liveProject, you will need to be familiar with:

TOOLS
  • Intermediate Python, with basics of NumPy and pandas
  • Basics of Jupyter Notebook
  • Basics of Google Colab notebooks
  • Basics of TensorFlow
TECHNIQUES
  • Basics of NLP
  • Basics of deep learning

you will learn

In this liveProject, you’ll learn how to generate synthetic training data for the purpose of data augmentation. This practice is exceedingly valuable for balancing datasets, and ensuring model accuracy with limited training materials.

  • Performing tokenization of training data
  • Manipulating text into format suitable for training a text generation model
  • Building a recurrent neural network to generate textfication metrics

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book resources
Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.

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