revised repo for Manning book Deep Learning with Structured Data https://www.manning.com/books/deep-learning-with-structured-data

This repo is a rework of the original repo for the book at https://github.com/ryanmark1867/manning (which is being kept in place for the convenience of MEAP users who have already started to use it). Improvements in this new repo include:

• rationalized file names
• simplified directory structure
• notebooks tested on Python 3.6 and Python 3.7 in free and for-fee environments
• config files used to remove hard-coded parameters
• code largely refactored to make it easier to follow and simpler to run
• incorporate TensorFlow 2.0 for model training and deployment

• data - processed datasets and pickle files for intermediate datasets
• deploy - code for deploying the trained model using Rasa and Facebook Messenger, as described in chapter 8 of the book
• deploy_web - code for deploying the trained model via a simple web page served by Flask. Description coming in chapter 8.
• models - saved trained models
• notebooks - Jupyter notebooks streetcar_data_preparation.ipynb (data preparation), streetcar_model_training.ipynb (deep learning model training) and streetcar_model_training_xgb.ipynb (XGBoost model training) along with associated class and config files
• pipelines - pickled pipeline files generated by streetcar_model_training.ipynb. Together with the trained models these pipeline files are used in the deployment.
• sql - SQL used to generate a table that can be used for the simple SQL examples in chapter 2

prepare data - steps to clean up input data to prepare it to train a deep learning model. Output is a pickled dataframe containing the cleaned up dataset.

1. update notebooks/streetcar_data_preparation_config.yml to specify name of input data (pickled_input_dataframe if load_from_scratch = False; if load_from_scratch = True then copy xls files from https://open.toronto.ca/dataset/ttc-streetcar-delay-data/ to data directory) and output dataframe
2. run notebooks/streetcar_data_preparation.ipynb

train deep learning model - steps to train a deep learning model on the cleaned up dataset from the previous step. Output is a trained model (h5 file) and two pickle files for the pipeline

1. update notebooks/streetcar_model_training_config.yml to specify input dataframe (pickled_dataframe). Set this to the filename of the dataframe output in the prepare data step
2. run notebooks/streetcar_model_training.ipynb in an env. with TensorFlow 2.0 installed

train XGBoost model - steps to train a deep learning model on the cleaned up dataset from the previous step. Output is a trained model (h5 file) and two pickle files for the pipeline

1. update notebooks/streetcar_model_training_config.yml to specify input dataframe (pickled_dataframe). Set this to the filename of the dataframe output in the prepare data step
2. run streetcar_model_training_xgb.ipynb - note that the saved XGBoost models are saved in the models directory along with the saved deep learning models

web deployment of model - steps to set up a simple web page for exercising the trained model. Uses the trained model and pipeline files from the previous step.

1. update deploy_web/deploy_web_config.yml: set pipeline1_filename, pipeline2_filename and model_filename to the names of the pipeline files and trained model file generated in the training step. Alternately, you can run the training notebook using the config file as-is to use prepared pipeline and model files already included in the repo.
2. start the Flask server deploy_web/flask_server.py by running: python flask_server.py
3. open localhost:5000 in a browser, select the details for your trip, and click on Get prediction

• https://open.toronto.ca/dataset/ttc-streetcar-delay-data/ original dataset
• https://www.kaggle.com/knowledgegrappler/a-simple-nn-solution-with-keras-0-48611-pl Kaggle submission that was used as input to creation of the Keras model used in this example