Full Stack Deep Learning Labs
Project developed during lab sessions of the Full Stack Deep Learning Bootcamp.
- We will build a handwriting recognition system from scratch, and deploy it as a web service.
- Uses Keras, but designed to be modular, hackable, and scalable
- Provides code for training models in parallel and store evaluation in Weights & Biases
- We will set up continuous integration system for our codebase, which will check functionality of code and evaluate the model about to be deployed.
- We will package up the prediction system as a REST API, deployable as a Docker container.
- We will deploy the prediction system as a serverless function to Amazon Lambda.
- Lastly, we will set up monitoring that alerts us when the incoming data distribution changes.
Schedule for the November 2019 Bootcamp
- First session (90 min)
- Setup (10 min): Get set up with jupyterhub.
- Introduction to problem and project structure (20 min).
- Gather handwriting data (10 min).
- Lab 1 (20 min): Introduce EMNIST. Training code details. Train & evaluate character prediction baselines.
- Lab 2 (30 min): Introduce EMNIST Lines. Overview of CTC loss and model architecture. Train our model on EMNIST Lines.
- Second session (60 min)
- Third session (90 min)
- Fourth session (75 min)