Tweet Emotion Recognition with TensorFlow

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在此免费指导项目中,您将:

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

在面试中展现此实践经验

2 hours
中级
无需下载
分屏视频
英语(English)
仅限桌面

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.

必备条件

Prior programming experience in Python, familiarity with TensorFlow, theoretical understanding of recurrent neural networks.

您要培养的技能

  • Natural Language Processing

  • Deep Learning

  • Machine Learning

  • Tensorflow

  • keras

分步进行学习

在与您的工作区一起在分屏中播放的视频中,您的授课教师将指导您完成每个步骤:

  1. Introduction

  2. Setup and Imports

  3. Importing Data

  4. Tokenizer

  5. Padding and Truncating Sequences

  6. Preparing Labels

  7. Creating and Training RNN Model

  8. Model Evaluation

指导项目工作原理

您的工作空间就是浏览器中的云桌面,无需下载

在分屏视频中,您的授课教师会为您提供分步指导

授课教师

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您可以直接在浏览器中于分屏环境下完成任务,以此从做中学。在屏幕的左侧,您将在工作空间中完成任务。在屏幕的右侧,您将看到有授课教师逐步指导您完成项目。