Refactor notebook generation to use separate files for better architecture

- Restored tools/py_to_notebook.py as a focused, standalone tool
- Updated tito notebooks command to use subprocess to call the separate tool
- Maintains clean separation of concerns: tito.py for CLI orchestration, py_to_notebook.py for conversion logic
- Updated documentation to use 'tito notebooks' command instead of direct tool calls
- Benefits: easier debugging, better maintainability, focused single-responsibility modules

tinytorch/core/layers.py

@@ -1,7 +1,7 @@
 # AUTOGENERATED! DO NOT EDIT! File to edit: ../../modules/layers/layers_dev.ipynb.
 
 # %% auto 0
-__all__ = ['Dense']
+__all__ = ['Dense', 'ReLU', 'Sigmoid', 'Tanh']
 
 # %% ../../modules/layers/layers_dev.ipynb 2
 import numpy as np
@@ -10,9 +10,6 @@ import sys
 from typing import Union, Optional, Callable
 from .tensor import Tensor
 
-# Import activation functions from the activations module
-from .activations import ReLU, Sigmoid, Tanh
-
 # Import our Tensor class
 # sys.path.append('../../')
 # from modules.tensor.tensor_dev import Tensor
@@ -112,3 +109,130 @@ class Dense:
     def __call__(self, x: Tensor) -> Tensor:
         """Make layer callable: layer(x) same as layer.forward(x)"""
         return self.forward(x)
+
+# %% ../../modules/layers/layers_dev.ipynb 9
+class ReLU:
+    """
+    ReLU Activation: f(x) = max(0, x)
+    
+    The most popular activation function in deep learning.
+    Simple, effective, and computationally efficient.
+    
+    TODO: Implement ReLU activation function.
+    """
+    
+    def forward(self, x: Tensor) -> Tensor:
+        """
+        Apply ReLU: f(x) = max(0, x)
+        
+        Args:
+            x: Input tensor
+            
+        Returns:
+            Output tensor with ReLU applied element-wise
+            
+        TODO: Implement element-wise max(0, x) operation
+        """
+        raise NotImplementedError("Student implementation required")
+    
+    def __call__(self, x: Tensor) -> Tensor:
+        """Make activation callable: relu(x) same as relu.forward(x)"""
+        return self.forward(x)
+
+# %% ../../modules/layers/layers_dev.ipynb 10
+class ReLU:
+    """ReLU Activation: f(x) = max(0, x)"""
+    
+    def forward(self, x: Tensor) -> Tensor:
+        """Apply ReLU: f(x) = max(0, x)"""
+        return Tensor(np.maximum(0, x.data))
+    
+    def __call__(self, x: Tensor) -> Tensor:
+        return self.forward(x)
+
+# %% ../../modules/layers/layers_dev.ipynb 11
+class Sigmoid:
+    """
+    Sigmoid Activation: f(x) = 1 / (1 + e^(-x))
+    
+    Squashes input to range (0, 1). Often used for binary classification.
+    
+    TODO: Implement Sigmoid activation function.
+    """
+    
+    def forward(self, x: Tensor) -> Tensor:
+        """
+        Apply Sigmoid: f(x) = 1 / (1 + e^(-x))
+        
+        Args:
+            x: Input tensor
+            
+        Returns:
+            Output tensor with Sigmoid applied element-wise
+            
+        TODO: Implement sigmoid function (be careful with numerical stability!)
+        """
+        raise NotImplementedError("Student implementation required")
+    
+    def __call__(self, x: Tensor) -> Tensor:
+        return self.forward(x)
+
+# %% ../../modules/layers/layers_dev.ipynb 12
+class Sigmoid:
+    """Sigmoid Activation: f(x) = 1 / (1 + e^(-x))"""
+    
+    def forward(self, x: Tensor) -> Tensor:
+        """Apply Sigmoid with numerical stability"""
+        # Use the numerically stable version to avoid overflow
+        # For x >= 0: sigmoid(x) = 1 / (1 + exp(-x))
+        # For x < 0: sigmoid(x) = exp(x) / (1 + exp(x))
+        x_data = x.data
+        result = np.zeros_like(x_data)
+        
+        # Stable computation
+        positive_mask = x_data >= 0
+        result[positive_mask] = 1.0 / (1.0 + np.exp(-x_data[positive_mask]))
+        result[~positive_mask] = np.exp(x_data[~positive_mask]) / (1.0 + np.exp(x_data[~positive_mask]))
+        
+        return Tensor(result)
+    
+    def __call__(self, x: Tensor) -> Tensor:
+        return self.forward(x)
+
+# %% ../../modules/layers/layers_dev.ipynb 13
+class Tanh:
+    """
+    Tanh Activation: f(x) = tanh(x)
+    
+    Squashes input to range (-1, 1). Zero-centered output.
+    
+    TODO: Implement Tanh activation function.
+    """
+    
+    def forward(self, x: Tensor) -> Tensor:
+        """
+        Apply Tanh: f(x) = tanh(x)
+        
+        Args:
+            x: Input tensor
+            
+        Returns:
+            Output tensor with Tanh applied element-wise
+            
+        TODO: Implement tanh function
+        """
+        raise NotImplementedError("Student implementation required")
+    
+    def __call__(self, x: Tensor) -> Tensor:
+        return self.forward(x)
+
+# %% ../../modules/layers/layers_dev.ipynb 14
+class Tanh:
+    """Tanh Activation: f(x) = tanh(x)"""
+    
+    def forward(self, x: Tensor) -> Tensor:
+        """Apply Tanh"""
+        return Tensor(np.tanh(x.data))
+    
+    def __call__(self, x: Tensor) -> Tensor:
+        return self.forward(x)