ELEC 4806/5806 Introduction to Deep Learning and PyTorch/TensorFlow Lab 6 LSTM -Twitter Emoji Prediction Model 1. Experiment goal: Understand RNN • Understand LSTM. • • Understand data preprocessing and cleaning. For this lab only use framework PyTorch to demonstrate your logical understanding of the concept. 2. Introduction: Theory Problem: Long short-term memory (LSTM) is a variation of an RNN model. An RNN can only memorize short-term information, but LSTM can handle long time-series data. Code Problem: In this lab, you will implement LSTM using glove word embedding(glove. 6B.100d.txt ) algorithm with mapping .csv, train.csv,test.csv and Outformat.csv Please refer to the LSTM modules to complete this lab. But you need to pay attention to the following: • Import the libraries. • Import the input files. mapping = pd.read_csv("/Mapping.csv") train = pd.read_csv("/Train.csv") test = pd.read_csv("/Test.csv") out = pd.read_csv("/OutputFormat.csv") • train data. • Test data • • Plagiarizing code directly from other websites is not allowed. Please try to use Google Colab to complete this Lab. Without GPU, the training process may become unacceptably long. Submission requirements (all team members need to submit): 1. Please submit the .ipynb file and make sure i can easily run your code. If you use google colab, you can download the .ipynb file by clicking “File→Download→Download .ipynb". Please include the names of all team members in the file name. 2. Please submit a pdf version as well. You can use this website (https://htmtopdf.herokuapp.com/ipynbviewer/) to convert your .ipynb file to pdf. 3. Please record a video to demo your work. Especially the code problem 4. When you are recording a video, if you just read the comments you wrote in the code, or read according to a script, then you will not be able to prove that the work was done independently by you. Therefore, the lab will be judged as plagiarism.

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ELEC 4806/5806 Introduction to Deep Learning and PyTorch/TensorFlow Lab 6 LSTM -Twitter Emoji Prediction Model 1. Experiment goal: Understand RNN • Understand LSTM. • • Understand data preprocessing and cleaning. For this lab only use framework PyTorch to demonstrate your logical understanding of the concept. 2. Intro