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.
| Lab | Colab | Video |
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| Lab Overview | |
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| Lab 01: Deep Neural Networks in PyTorch | |
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| Lab 02a: PyTorch Lightning | |
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| Lab 02b: Training a CNN on Synthetic Handwriting Data | |
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| Lab 03: Transformers and Paragraphs | |
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| Lab 04: Experiment Tracking | |
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| Lab 05: Troubleshooting & Testing | |
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| Lab 06: Data Annotation | |
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| Lab 07: Deployment | |
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| Lab 08: Monitoring | |