Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Complete deep learning project developed in Full Stack Deep Learning, Spring 2021
| Date | Stars |
|---|---|
| 2026-07-31 | 449 |
| 2026-08-01 | 449 |
| 2026-08-06 | 449 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# 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](setup/readme.md): Set up our computing environment. - [Lab 1: Intro](lab1/readme.md): Formulate problem, structure codebase, train an MLP for MNIST. - [Lab 2: CNNs](lab2/readme.md): Introduce EMNIST, generate synthetic handwritten lines, and train CNNs. - [Lab 3: RNNs](lab3/readme.md): Using CNN + LSTM with CTC loss for line text recognition. - [Lab 4: Transformers](lab4/readme.md): Using Transformers for line text recognition. - [Lab 5: Experiment Management](lab5/readme.md): Real handwriting data, Weights & Biases, and hyperparameter sweeps. - [Lab 6: Data Labeling](lab6/readme.md): Label our own handwriting data and properly store it. - [Lab 7: Paragraph Recognition](lab7/readme.md): Train and evaluate whole-paragraph recognition. - [Lab 8: Continuous Integration](lab8/readme.md): Add continuous linting and testing of our code. - [Lab 9: Deployment](lab9/readme.md): Run as a REST API locally, then in Docker, then put in production using AWS Lambda. - [Lab 10: Monitoring](lab10/readme.md): Set up monitoring that alerts us when the incoming data distribution changes.
Excerpt of 1,853 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:5bc2eb0e741658b1, llm:Repository title and description: 'fsdl-text-recognizer-2021-labs' — Complete deep learning project developed in Full Stack Deep Learning, Spring 2021 (text recognizer)
matched fp:5bc2eb0e741658b1, llm:Repository title and description: 'fsdl-text-recognizer-2021-labs' — Complete deep learning project developed in Full Stack Deep Learning, Spring 2021 (text recognizer)
matched fp:5bc2eb0e741658b1, llm:Repository title and description: 'fsdl-text-recognizer-2021-labs' — Complete deep learning project developed in Full Stack Deep Learning, Spring 2021 (text recognizer)