Elevator pitch¶
Audience¶
- Semi-technical
- ML background, new to deep learning
- Familiarity with Python data ecosystem (Pandas, SKLearn, numpy)
- Interested in getting started in deep learning
- Interested in quickly prototyping models
Goals¶
- Easy to use interface
- Sensible default actions
- Low barrier to entry to create Keras input / output layers
Requirements¶
Backlog¶
- Numerical inputs: Null handling, Z score normalizaiton
- Categorical inputs: Create embedding, handle unseen levels
- Boolean inputs: Handle appropriately
- Datetime: Extract categorical fields, treat as epoch time if possible.
- Test run: train on random sample of data
- Convenient interface
- Logging
- Unit tests
- Appropriate exceptions
- Pip installable
Prioritized backlog¶
- Unit tests
- Logging
- Numerical inputs: Null handling, Z score normalizaiton
- Categorical inputs: Create embedding, handle unseen levels
- Boolean inputs: Handle appropriately
- Datetime: Extract categorical fields, treat as epoch time if possible.
- Appropriate exceptions
- Pip installable
POC items¶
- Interface: Need to determine options (SKLearn transformer, custom interface, etc)
- Interface: Need to outline functionality
- Boolean: Need to determine if it’ll be handled as numerical or categorical
- Pip installable: Need to determine level of effort