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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.
Lab materials for the Full Stack Deep Learning Course
| Date | Stars |
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| 2026-07-31 | 1222 |
| 2026-08-04 | 1223 |
| 2026-08-06 | 1223 |
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# 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.
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:5a79bd4738061048, topic:deep-learning
