Deep Learning Based Emotion Recognition with PyTorch and TensorFlow

In this tutorial we are going to learn how to train deep neural networks, such as recurrent neural networks (RNNs), for addressing a natural language task known as emotion recognition.

elvis
elvis
Oct 18, 2018 · 2 min read

In this tutorial we are going to learn how to train deep neural networks, such as recurrent neural networks (RNNs), for addressing a natural language task known as emotion recognition. We will cover everything you need to know to get started with NLP using deep learning frameworks such as TensorFlow and PyTorch. We will cover the common best practices, functionalities, and steps you need to understand the basics of TensorFlow’s and PyTorch’s APIs to build powerful predictive models via the computation graph. In the process of building our models, we will compare PyTorch and TensorFlow to let the learner appreciate the strengths of each tool.

Below is a clip demonstrating what the notebooks contains:

TensorFlow version (Colab Notebook | GitHub)

PyTorch version (Colab Notebook | GitHub)

One last quick thing: any sort of engagement (like follows, shares, 👏👏👏, and feedback) will make a huge difference for the future and sustainability of the dair.ai publication. So I will deeply appreciate any of that in advance.

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