https://github.com/the-full-stack/fsdl-text-recognizer-project

Lab materials for the Full Stack Deep Learning Course
https://github.com/the-full-stack/fsdl-text-recognizer-project

deep-learning

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Lab materials for the Full Stack Deep Learning Course

• Host: GitHub
• URL: https://github.com/the-full-stack/fsdl-text-recognizer-project
• Owner: the-full-stack
• Created: 2019-02-25T22:02:22.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2022-06-14T04:25:32.000Z (about 3 years ago)
• Last Synced: 2025-05-11T18:03:34.460Z (about 2 months ago)
• Topics: deep-learning
• Language: Jupyter Notebook
• Homepage: https://fullstackdeeplearning.com/course
• Size: 6.03 MB
• Stars: 1,210
• Watchers: 57
• Forks: 427
• Open Issues: 15
• Metadata Files:
  • Readme: readme.md

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README

        
# Full Stack Deep Learning Labs

Welcome!

Project developed during lab sessions of the [Full Stack Deep Learning Bootcamp](https://fullstackdeeplearning.com).

- 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](setup.md) (10 min): Get set up with jupyterhub.

  - Introduction to problem and [project structure](project_structure.md) (20 min).

  - Gather handwriting data (10 min).

  - [Lab 1](lab1.md) (20 min): Introduce EMNIST. Training code details. Train & evaluate character prediction baselines.

  - [Lab 2](lab2.md) (30 min): Introduce EMNIST Lines. Overview of CTC loss and model architecture. Train our model on EMNIST Lines.

- Second session (60 min)

  - [Lab 3](lab3.md) (40 min): Weights & Biases + parallel experiments

  - [Lab 4](lab4.md) (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](lab5.md) (45 min) Train & evaluate line detection model.

  - [Lab 6](lab6.md) (45 min) Label handwriting data generated by the class, download and version results.

- Fourth session (75 min)

  - [Lab 7](lab7.md) (15 min) Add continuous integration that runs linting and tests on our codebase.

  - [Lab 8](lab8.md) (60 min) Deploy the trained model to the web using AWS Lambda.