What will you Learn in the Project?
- Reading data from different text files and loading it into the dataframe
- Convert text data into vector format for training the deep learning models
- Build and train sequence-based model
- How to improve on the simple RNN-based model
- Working knowledge of Keras library
- Theoretical understanding of sequence-based models i.e. RNN, LSTM, GRU
- Google colab [Jupyter notebook] for model building
- nltk library
- Keras library for implementing sequence models
We will be performing the following tasks as part of this project:
Task-1: Import the various libraries and load the dataset into dataframe
Task-2: Convert text into numerical form for model building.
Task-3: Build the base RNN model for training.
Task-4: Train your model using split train and test data.
Task-5: Build an LSTM model and evaluate it on the test set.
Task-6: Build a Bi-directional LSTM model and evaluate on the test set.
Task-7: Build a GRU model and evaluate it on the test set.
Task-8: Compare the performance of the above models on a test set and state the best one.