Generate Text Samples

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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.

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

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