docs: Clean up whitespace and formatting in module READMEs

- Fixed trailing whitespace in several module README files
- Ensures consistent formatting across all documentation

modules/source/02_tensor/README.md

@@ -75,7 +75,7 @@ jupyter notebook tensor_dev.ipynb
 
 ### Step-by-Step Implementation
 1. **Basic Tensor class** - Constructor and properties
-2. **Shape management** - Understanding tensor dimensions  
+2. **Shape management** - Understanding tensor dimensions
 3. **Arithmetic operations** - Addition, multiplication, etc.
 4. **Utility methods** - Reshape, transpose, sum, mean
 5. **Error handling** - Robust edge case management
@@ -95,7 +95,7 @@ print(f"Sum: {x.sum()}")    # Should be 10.0
 # Export your tensor implementation
 tito export
 
-# Test your implementation  
+# Test your implementation
 tito test --module tensor
 ```
 

modules/source/03_activations/README.md

@@ -66,13 +66,13 @@ output = tanh(Tensor([0, 1, -1]))         # [0, 0.76, -0.76]
 ### Prerequisites
 Ensure you have completed the tensor module and understand basic tensor operations:
 
-```bash
+   ```bash
 # Activate TinyTorch environment
-source bin/activate-tinytorch.sh
+   source bin/activate-tinytorch.sh
 
 # Verify tensor module is working
 tito test --module tensor
-```
+   ```
 
 ### Development Workflow
 1. **Open the development file**: `modules/source/03_activations/activations_dev.py`
@@ -86,9 +86,9 @@ tito test --module tensor
 ### Comprehensive Test Suite
 Run the full test suite to verify mathematical correctness:
 
-```bash
+   ```bash
 # TinyTorch CLI (recommended)
-tito test --module activations
+   tito test --module activations
 
 # Direct pytest execution
 python -m pytest tests/ -k activations -v
@@ -114,7 +114,7 @@ The module includes comprehensive educational feedback:
 # Visual feedback with plotting
 📊 Plotting ReLU behavior across range [-5, 5]...
 📈 Function visualization shows expected behavior
-```
+   ```
 
 ### Manual Testing Examples
 ```python

modules/source/07_dataloader/README.md

@@ -51,10 +51,10 @@ for batch_images, batch_labels in train_loader:
 ```python
 # Flexible interface supporting multiple datasets
 class Dataset:
-    def __getitem__(self, index): 
+    def __getitem__(self, index):
         # Return (data, label) for any dataset type
         pass
-    def __len__(self): 
+    def __len__(self):
         # Enable len() and iteration
         pass
 

modules/source/08_autograd/README.md

@@ -94,15 +94,15 @@ print(f"Complex gradient dy: {y.grad}")
 ### Prerequisites
 Ensure you understand the mathematical building blocks:
 
-```bash
+   ```bash
 # Activate TinyTorch environment
-source bin/activate-tinytorch.sh
+   source bin/activate-tinytorch.sh
 
 # Verify prerequisite modules
 tito test --module tensor
 tito test --module activations
 tito test --module layers
-```
+   ```
 
 ### Development Workflow
 1. **Open the development file**: `modules/source/08_autograd/autograd_dev.py`

modules/source/10_training/README.md

@@ -1,7 +1,7 @@
 # 🔥 Module: Training
 
 ## 📊 Module Info
-- **Difficulty**: ⭐⭐⭐⭐⭐ Expert
+- **Difficulty**: ⭐⭐⭐⭐ Expert
 - **Time Estimate**: 8-10 hours
 - **Prerequisites**: Tensor, Activations, Layers, Networks, DataLoader, Autograd, Optimizers modules
 - **Next Steps**: Compression, Kernels, Benchmarking, MLOps modules
@@ -38,7 +38,7 @@ from tinytorch.core.metrics import Accuracy
 # Define complete model architecture
 model = Sequential([
     Dense(784, 128), ReLU(),
-    Dense(128, 64), ReLU(), 
+    Dense(128, 64), ReLU(),
     Dense(64, 10), Softmax()
 ])
 

modules/source/11_compression/README.md

@@ -1,7 +1,7 @@
 # 🔥 Module: Compression
 
 ## 📊 Module Info
-- **Difficulty**: ⭐⭐⭐⭐⭐ Expert
+- **Difficulty**: ⭐⭐⭐⭐ Expert
 - **Time Estimate**: 8-10 hours
 - **Prerequisites**: Networks, Training modules
 - **Next Steps**: Kernels, MLOps modules

modules/source/12_kernels/README.md

@@ -1,7 +1,7 @@
 # 🔥 Module: Kernels
 
 ## 📊 Module Info
-- **Difficulty**: ⭐⭐⭐⭐⭐ Expert
+- **Difficulty**: ⭐⭐⭐⭐ Expert
 - **Time Estimate**: 8-10 hours
 - **Prerequisites**: All previous modules (01-11), especially Compression
 - **Next Steps**: Benchmarking, MLOps modules

modules/source/14_mlops/README.md

@@ -1,8 +1,8 @@
 # 🔥 Module: MLOps
 
 ## 📊 Module Info
-- **Difficulty**: ⭐⭐⭐⭐⭐ Expert
-- **Time Estimate**: 10-12 hours
+- **Difficulty**: ⭐⭐⭐⭐ Expert
+- **Time Estimate**: 8-10 hours
 - **Prerequisites**: All previous modules (01-13) - Complete TinyTorch ecosystem
 - **Next Steps**: **🎓 Course completion** - Deploy your complete ML system!