diff --git a/ai_research/labs/tf_keras.md b/ai_research/labs/tf_keras.md
index d391b83..9dfe7f9 100644
--- a/ai_research/labs/tf_keras.md
+++ b/ai_research/labs/tf_keras.md
@@ -17,30 +17,31 @@ To provide students with hands-on experience in developing, training, and evalua
    
 **Setting Up the Environment**:
     - Installing TensorFlow and Keras:
-    ```bash
+    
+```bash
     pip install tensorflow keras
-    ```
+```
 
 **Image Data Preprocessing**:
 
-    - **Step 1**: Importing Necessary Libraries:
-    ```python
+- **Step 1**: Importing Necessary Libraries:
+```python
     import tensorflow as tf
     from tensorflow.keras import datasets, layers, models
-    ```
+```
 
-    - **Step 2**: Loading and Preprocessing Image Data:
-    ```python
+- **Step 2**: Loading and Preprocessing Image Data:
+```python
     (train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data()
     
     # Normalize pixel values to be between 0 and 1
     train_images, test_images = train_images / 255.0, test_images / 255.0
-    ```
+```
 
 **Building a Convolutional Neural Network (CNN)**:
 
-    - **Step 3**: Defining the CNN Architecture:
-    ```python
+- **Step 3**: Defining the CNN Architecture:
+```python
     model = models.Sequential([
         layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)),
         layers.MaxPooling2D((2, 2)),
@@ -48,34 +49,34 @@ To provide students with hands-on experience in developing, training, and evalua
         layers.MaxPooling2D((2, 2)),
         layers.Conv2D(64, (3, 3), activation='relu')
     ])
-    ```
+```
 
-    - **Step 4**: Adding Dense Layers:
-    ```python
+- **Step 4**: Adding Dense Layers:
+```python
     model.add(layers.Flatten())
     model.add(layers.Dense(64, activation='relu'))
     model.add(layers.Dense(10))
-    ```
+```
 
 **Compiling and Training the Model**:
 
-    - **Step 5**: Compiling the Model:
-    ```python
+- **Step 5**: Compiling the Model:
+```python
     model.compile(optimizer='adam',
                   loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
                   metrics=['accuracy'])
-    ```
+```
 
-    - **Step 6**: Training the Model:
-    ```python
+- **Step 6**: Training the Model:
+```python
     history = model.fit(train_images, train_labels, epochs=10, 
                         validation_data=(test_images, test_labels))
-    ```
+```
 
 **Evaluating the Model**:
 
-    - **Step 7**: Evaluating the Model and Visualizing Results:
-    ```python
+- **Step 7**: Evaluating the Model and Visualizing Results:
+```python
     test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)
     
     import matplotlib.pyplot as plt
@@ -87,9 +88,7 @@ To provide students with hands-on experience in developing, training, and evalua
     plt.ylim([0.5, 1])
     plt.legend(loc='lower right')
     plt.show()
-    ```
-
-
+```
 
 ## **Resources**