Tweet Emotion Recognition with TensorFlow
Use a Tokenizer in TensorFlow
Pad and Truncate Sequences
Create and Train a Recurrent Neural Network
Use NLP and Deep Learning to perform Text Classification
Showcase this hands-on experience in an interview
In this 2-hour long guided project, we are going to create a recurrent neural network and train it on a tweet emotion dataset to learn to recognize emotions in tweets. The dataset has thousands of tweets each classified in one of 6 emotions. This is a multi class classification problem in the natural language processing domain. We will be using TensorFlow as our machine learning framework. You will need prior programming experience in Python. This is a practical, hands on guided project for learners who already have theoretical understanding of Neural Networks, recurrent neural networks, and optimization algorithms like gradient descent but want to understand how to use the Tensorflow to start performing natural language processing tasks like text classification. You should also have some basic familiarity with TensorFlow. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Prior programming experience in Python, familiarity with TensorFlow, theoretical understanding of recurrent neural networks.
Skills you will develop
Learn step-by-step
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
Introduction
Setup and Imports
Importing Data
Tokenizer
Padding and Truncating Sequences
Preparing Labels
Creating and Training RNN Model
Model Evaluation
How Guided Projects work
Your workspace is a cloud desktop right in your browser, no download required
In a split-screen video, your instructor guides you step-by-step
Reviews
TOP REVIEWS FROM TWEET EMOTION RECOGNITION WITH TENSORFLOW
One of the best course for knowledge up gradation. The Speakers explain very well.
Nice project! Easy to follow, centered on the relevant.
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