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Fix links in the readme file #16

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Fix links in the readme file #16

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16 changes: 8 additions & 8 deletions readme.md
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Expand Up @@ -18,15 +18,15 @@ Project developed during lab sessions of the [Full Stack Deep Learning Bootcamp]
- [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.
- [Lab 1](lab1) (20 min): Introduce EMNIST. Training code details. Train & evaluate character prediction baselines.
- [Lab 2](lab2) (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).
- [Lab 3](lab3) (40 min): Weights & Biases + parallel experiments
- [Lab 4](lab4) (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.
- [Lab 5](lab5) (45 min) Train & evaluate line detection model.
- [Lab 6](lab6) (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.
- [Lab 7](lab7) (15 min) Add continuous integration that runs linting and tests on our codebase.
- [Lab 8](lab8) (60 min) Deploy the trained model to the web using AWS Lambda.