π₯ Full Stack Deep Learning Fall 2022 Labs
Welcome!
As part of Full Stack Deep Learning 2022, we will incrementally develop a complete deep learning codebase to create and deploy a model that understands the content of hand-written paragraphs.
For an overview of the Text Recognizer application architecture, click the badge below to open an interactive Jupyter notebook on Google Colab:
We will use the modern stack of PyTorch and PyTorch Lightning.
We will use the main workhorses of DL today: CNNs 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 a quality assurance and continuous integration system for our codebase using pre-commit and GitHub Actions.
We will package up the prediction system and deploy it as a Docker container on AWS Lambda.
We will wrap that prediction system in a frontend written in Python using Gradio.
We will set up monitoring that alerts us to potential issues in our model using Gantry.
Click the badges below to access individual lab notebooks on Colab and videos on YouTube
| Lab | Colab | Video |
|---|---|---|
| Lab Overview | |
|
| Lab 01: Deep Neural Networks in PyTorch | |
|
| Lab 02a: PyTorch Lightning | |
|
| Lab 02b: Training a CNN on Synthetic Handwriting Data | |
|
| Lab 03: Transformers and Paragraphs | |
|
| Lab 04: Experiment Tracking | |
|
| Lab 05: Troubleshooting & Testing | |
|
| Lab 06: Data Annotation | |
|
| Lab 07: Deployment | |
|
| Lab 08: Monitoring | |