Assignment : Deep Learning In this assignment, you'll need amazon_review_500.csv for this assignment. This csv file has two columns as follows. The label column provides polarity sentiment, either positive or negative label text 2 I must admit that I'm addicted to "Version 2.0... 1 I think it's such a shame that an enormous tal... 2 The Sunsout No Room at The Inn Puzzle has oddl... ... ... Q1: Train a CNN classification model Create a function sentiment_cnn( ) to detect sentiment as follows: the input parameter is the full filename path to amazon_review_500.csv convert the text into padded sequences of numbers (see Exercise 5.2) hold 20% of the data for testing carefully select hyperparameters: max number of words for embedding layer, input sentence length, filters, the number of filters, batch size, and epoch etc. create a CNN model with the training data print out accuracy, precision, recall calculated from testing data. Your precision_macro, recall_macro, and accurracy should be all about 70%. If your result is much lower than that (e.g. below 67%), you need to tune the hyperparameters Also note that the label in the dataset is either 1 or 2. Your binary prediction out of CNN is either 0 or 1. Conversion is needed in order to compare predictions with actual labels This function has no return. Besides your code, also provide a pdf document showing the following How you choose the hyperparameters Screenshots of model trainning history Testing accuracy, precision, recall Q2 Improve the performance of CNN model Create a function improved_sentiment_cnn( ) to detect sentiment with improved accuracy You still need to train a CNN model You can apply different techniques, e.g. map words to pretrained word vectors e.g. from Google (https://drive.google.com/file/d/0B7XkCwpI5KDYNlNUTTlSS21pQmM/edit? usp=sharing (https://drive.google.com/file/d/0B7XkCwpI5KDYNlNUTTlSS21pQmM/edit? usp=sharing)) or e.g. from spacy package (https://spacy.io/usage/vectors-similarity (https://spacy.io/usage/vectors-similarity)) e.g. create your own pretrained word vectors using other review documents you can find add additional features etc. Your taraget is to improve the accuracy by about 5% from the model you created in Q1. For fair comparison, make sure you use the same datasets for training/testing. This function has no return. Please provide a pdf document showing the following Screenshots of model trainning history Testing accuracy, precision, recall Your analysis about what technique contributes to the performance improvement why this technique is useful In [ ]: