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https://github.com/the-full-stack/fsdl-text-recognizer-2022-labs
Complete deep learning project developed in Full Stack Deep Learning, 2022 edition. Generated automatically from https://github.com/full-stack-deep-learning/fsdl-text-recognizer-2022
https://github.com/the-full-stack/fsdl-text-recognizer-2022-labs
deep-neural-networks machine-learning mlops
Last synced: 1 day ago
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Complete deep learning project developed in Full Stack Deep Learning, 2022 edition. Generated automatically from https://github.com/full-stack-deep-learning/fsdl-text-recognizer-2022
- Host: GitHub
- URL: https://github.com/the-full-stack/fsdl-text-recognizer-2022-labs
- Owner: the-full-stack
- License: mit
- Created: 2022-07-13T02:45:55.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-01-17T02:25:32.000Z (12 months ago)
- Last Synced: 2025-01-09T21:40:00.596Z (3 days ago)
- Topics: deep-neural-networks, machine-learning, mlops
- Language: Jupyter Notebook
- Homepage: https://fullstackdeeplearning.com/course
- Size: 979 KB
- Stars: 459
- Watchers: 9
- Forks: 229
- Open Issues: 1
-
Metadata Files:
- Readme: readme.md
- License: LICENSE.txt
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README
# 🥞 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](https://pytorch.org/) and [PyTorch Lightning](https://www.pytorchlightning.ai/).
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](https://docs.wandb.ai/).
We will set up a quality assurance and continuous integration system for our codebase using [pre-commit](https://pre-commit.com/) and [GitHub Actions](https://docs.github.com/en/actions).
We will package up the prediction system and deploy it as a [Docker](https://docs.docker.com/) container on [AWS Lambda](https://aws.amazon.com/lambda/).
We will wrap that prediction system in a frontend written in Python using [Gradio](https://gradio.app/docs).
We will set up monitoring that alerts us to potential issues in our model using [Gantry](https://gantry.io/).
## Click the badges below to access individual lab notebooks on Colab and videos on YouTube
| Lab | Colab | Video |
| :-- | :---: | :---: |
| **Lab Overview** | [![open-in-colab]](https://fsdl.me/lab00-colab) | [![yt-logo]](https://fsdl.me/2022-lab-overview-video) |
| **Lab 01: Deep Neural Networks in PyTorch** | [![open-in-colab]](https://fsdl.me/lab01-colab) | [![yt-logo]](https://fsdl.me/2022-lab-01-video) |
| **Lab 02a: PyTorch Lightning** | [![open-in-colab]](https://fsdl.me/lab02a-colab) | [![yt-logo]](https://fsdl.me/2022-lab-02-video) |
| **Lab 02b: Training a CNN on Synthetic Handwriting Data** | [![open-in-colab]](https://fsdl.me/lab02b-colab) | [![yt-logo]](https://fsdl.me/2022-lab-02-video) |
| **Lab 03: Transformers and Paragraphs** | [![open-in-colab]](https://fsdl.me/lab03-colab) | [![yt-logo]](https://fsdl.me/2022-lab-03-video) |
| **Lab 04: Experiment Tracking** | [![open-in-colab]](https://fsdl.me/lab04-colab) | [![yt-logo]](https://fsdl.me/2022-lab-04-video) |
| **Lab 05: Troubleshooting & Testing** | [![open-in-colab]](https://fsdl.me/lab05-colab) | [![yt-logo]](https://fsdl.me/2022-lab-05-video) |
| **Lab 06: Data Annotation** | [![open-in-colab]](https://fsdl.me/lab06-colab) | [![yt-logo]](https://fsdl.me/2022-lab-06-video) |
| **Lab 07: Deployment** | [![open-in-colab]](https://fsdl.me/lab07-colab) | [![yt-logo]](https://fsdl.me/2022-lab-07-video) |
| **Lab 08: Monitoring** | [![open-in-colab]](https://fsdl.me/lab08-colab) | [![yt-logo]](https://fsdl.me/2022-lab-08-video) |
[yt-logo]: https://fsdl.me/yt-logo-badge
[open-in-colab]: https://colab.research.google.com/assets/colab-badge.svg