gauravtanwar03 / fsdl-text-recognizer-2021-labs

Complete deep learning project developed in Full Stack Deep Learning, Spring 2021

Full Stack Deep Learning Spring 2021 Labs

Welcome!

As part of Full Stack Deep Learning 2021, we will incrementally develop a complete deep learning codebase to understand the content of handwritten paragraphs.

We will use the modern stack of PyTorch and PyTorch-Ligtning

We will use the main workhorses of DL today: CNNs, RNNs, and Transformers

We will manage our experiments using what we believe to be the best tool for the job: Weights & Biases

We will set up continuous integration system for our codebase using CircleCI

We will package up the prediction system as a REST API using FastAPI, and deploy it as a Docker container on AWS Lambda.

We will set up monitoring that alerts us when the incoming data distribution changes.

Sequence:

• Lab Setup: Set up our computing environment.
• Lab 1: Intro: Formulate problem, structure codebase, train an MLP for MNIST.
• Lab 2: CNNs: Introduce EMNIST, generate synthetic handwritten lines, and train CNNs.
• Lab 3: RNNs: Using CNN + LSTM with CTC loss for line text recognition.
• Lab 4: Transformers: Using Transformers for line text recognition.
• Lab 5: Experiment Management: Real handwriting data, Weights & Biases, and hyperparameter sweeps.
• Lab 6: Data Labeling: Label our own handwriting data and properly store it.
• Lab 7: Paragraph Recognition: Train and evaluate whole-paragraph recognition.
• Lab 8: Continuous Integration: Add continuous linting and testing of our code.
• Lab 9: Deployment: Run as a REST API locally, then in Docker, then put in production using AWS Lambda.
• Lab 10: Monitoring: Set up monitoring that alerts us when the incoming data distribution changes.