Two-class classification, or binary classification, may be the most widely applied kind of machine-learning problem. In this example, you’ll learn to classify movie reviews as positive or negative, based on the text content of the reviews.

References

[1] The IDMB raw dataset : http://mng.bz/0tIo

[2] https://github.com/pytorch/examples/blob/master/mnist/main.py

[3] https://scikit-learn.org/stable/auto_examples/linear_model/plot_sparse_logistic_regression_mnist.html#sphx-glr-auto-examples-linear-model-plot-sparse-logistic-regression-mnist-py

[4] https://towardsdatascience.com/unit-testing-and-logging-for-data-science-d7fb8fd5d217

[5] https://github.com/CoreyMSchafer/code_snippets/blob/master/Decorators/decorators.py

[6] https://scikit-learn.org/stable/auto_examples/neural_networks/plot_mnist_filters.html?highlight=mnist

[7] https://nextjournal.com/gkoehler/pytorch-mnist

IMDB dataset: a set of 50,000 highly polarized reviews from the Internet Movie Database. They’re split into 25,000 reviews for training and 25,000 reviews for testing, each set consisting of 50% negative and 50% positive reviews.

Traditional Techniques: Logistic Regression, Multilayer Perceptron

In this section, we use unittest to verify the accuracy and confusion matrix of method "Logistic Regression".

• Recurrent Neural Network: LSTM

In this project, as you see, we did:

• How to explore extensions to a baseline model to improve training and predicting capacity.

• How to use unit tests for the API and the model

• How to use unit tests for logging

• Can all of the unit tests be run with a single script and do all of the unit tests pass?

• How to monitor performance

• How to compare multiple models

• How to use visualizations for the EDA investigation