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.
When you start your liveProject, you get full access to the following books for 90 days.
This liveProject is for Python programmers interested in training text generation. To begin this liveProject, you will need to be familiar with:
- Intermediate Python, with basics of NumPy and pandas
- Basics of Jupyter Notebook
- Basics of Google Colab notebooks
- Basics of TensorFlow
- 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
- You choose the schedule and decide how much time to invest as you build your project.
- Project roadmap
- Each project is divided into several achievable steps.
- Get Help
- While within the liveProject platform, get help from other participants and our expert mentors.
- Compare with others
- For each step, compare your deliverable to the solutions by the author and other participants.
- 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.