Classify different datasets wth keras. Perform the needed pre-processing steps for each data-set, create a model, train it while checking it with a validation set, and test it with a test set. Improve the model as needed to achieve a classification accuracy of at least 85%.
Rubric:
- The pre-processing for each data-set is correct.
- The labels are one hot encoded.
- Created a validation set for each data-set.
- Created a neural net model for each data-set.
- Used different techniques to avoid over/underfitting and improved the classification.
- Plot the history of each model: plot training and validation, loss and accuracy.
- Evaluate the model with the test set.
- Included comments as needed.
- For each model, add a cell that describes the process you followed to design, and improve the model.
Deadline: 20/02/2019 23:59 hrs