diff --git a/readme.md b/readme.md
index b28572c..ffbf3b0 100644
--- a/readme.md
+++ b/readme.md
@@ -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.