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Powerful predictive systems reliant on sequential data (various NLP tasks, time series analysis, signal processing) can be created using RNNs—or Recurrent Neural Networks. By the end of this course, students will learn the advanced methods of this complex topic, acquire practical skills to implement Recurrent Neural Networks, and build deep learning models using TensorFlow in order to solve real-world problems.
Prerequisites
Data Society has designed this course for students who possess:
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a strong foundation in Python
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familiarity with common libraries: SciKit-Learn, Pandas, NumPy, and Matplotlib
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comfort building machine learning models and foundational neural network programs
Objectives
By the end of the course, students will be able to:
- Understand which mathematical concepts make RNNs a suitable architecture for solving problems that are sequential in nature
- Implement and train RNNs using TensorFlow
- Optimize RNNs to improve overall performance
