RNN_project.ipynb, my_answers.py --> both the completed notebook RNN_project.ipynb as well as all completed python functions requested in the main notebook RNN_project.ipynb (TODO items) should be copied into this python script and submitted for grading.
Step 1: Implement a function to window time series
Criteria
Meets Specifications
Window time series data.
The submission returns the proper windowed version of input time series of proper dimension listed in the notebook.
Step 2: Create a simple RNN model using keras to perform regression
Criteria
Meets Specifications
Build an RNN model to perform regression.
The submission constructs an RNN model in keras with LSTM module of dimension defined in the notebook.
Step 3: Clean up a large text corpus
Criteria
Meets Specifications
Find and remove all non-english or punctuation characters from input text data. The submission removes all non-english / non-punctuation characters.
Step 4: Implement a function to window a large text corpus
Criteria
Meets Specifications
Implement a function to window input text data
The submission returns the proper windowed version of input text of proper dimension listed in the notebook.
Step 5: Create a simple RNN model using keras to perform multiclass classification
Criteria
Meets Specifications
Build an RNN model to perform multiclass classification.
The submission constructs an RNN model in keras with LSTM module of dimension defined in the notebook.
Step 6: Generate text using a fully trained RNN model and a variety of input sequences
Criteria
Meets Specifications
Generate text using a trained RNN classifier.
The submission presents examples of generated text from a trained RNN module. The majority of this generated text should consist of real english words.