fsdl-text-recognizer-project

by full-stack-deep-learning

full-stack-deep-learning / fsdl-text-recognizer-project

The source repository is at https://github.com/full-stack-deep-learning/fsdl-text-recognizer

846 Stars 323 Forks Last release: Not found 70 Commits 0 Releases

Available items

No Items, yet!

The developer of this repository has not created any items for sale yet. Need a bug fixed? Help with integration? A different license? Create a request here:

Full Stack Deep Learning Labs

Welcome!

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)
    • Lab 3 (40 min): Weights & Biases + parallel experiments
    • Lab 4 (20 min): IAM Lines and experimentation time (hyperparameter sweeps, leave running overnight).
  • Third session (90 min)
    • Review results from the class on W&B
    • Lab 5 (45 min) Train & evaluate line detection model.
    • Lab 6 (45 min) Label handwriting data generated by the class, download and version results.
  • Fourth session (75 min)
    • Lab 7 (15 min) Add continuous integration that runs linting and tests on our codebase.
    • Lab 8 (60 min) Deploy the trained model to the web using AWS Lambda.

We use cookies. If you continue to browse the site, you agree to the use of cookies. For more information on our use of cookies please see our Privacy Policy.