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neural_network/optimizers/IMPLEMENTATION_SUMMARY.md

@@ -2,8 +2,8 @@
 
 ## 🎯 Feature Request Implementation
 
-**Issue:** "Add neural network optimizers module to enhance training capabilities"  
-**Requested by:** @Adhithya-Laxman  
+**Issue:** "Add neural network optimizers module to enhance training capabilities"
+**Requested by:** @Adhithya-Laxman
 **Status:** ✅ **COMPLETED**
 
 ## 📦 What Was Implemented
@@ -15,7 +15,7 @@ neural_network/optimizers/
 ├── base_optimizer.py        # Abstract base class for all optimizers
 ├── sgd.py                  # Stochastic Gradient Descent
 ├── momentum_sgd.py         # SGD with Momentum
-├── nag.py                  # Nesterov Accelerated Gradient  
+├── nag.py                  # Nesterov Accelerated Gradient
 ├── adagrad.py              # Adaptive Gradient Algorithm
 ├── adam.py                 # Adaptive Moment Estimation
 ├── README.md               # Comprehensive documentation
@@ -28,7 +28,7 @@ neural_network/optimizers/
    - Basic gradient descent: `θ = θ - α * g`
    - Foundation for understanding optimization
 
-2. **MomentumSGD** 
+2. **MomentumSGD**
    - Adds momentum for acceleration: `v = β*v + (1-β)*g; θ = θ - α*v`
    - Reduces oscillations and speeds convergence
 
@@ -52,7 +52,7 @@ neural_network/optimizers/
 - **Type Safety**: Full type hints throughout (`typing`, `Union`, `List`)
 - **Educational Focus**: Clear mathematical formulations in docstrings
 - **Comprehensive Testing**: Doctests + example scripts
-- **Consistent Interface**: All inherit from `BaseOptimizer` 
+- **Consistent Interface**: All inherit from `BaseOptimizer`
 - **Error Handling**: Proper validation and meaningful error messages
 
 ### 📝 Code Quality Features
@@ -80,21 +80,21 @@ The implementation was validated on multiple test problems:
 - Momentum accelerates convergence but can overshoot
 - Adam provides robust performance with adaptive learning
 
-### Multi-dimensional (f(x,y) = x² + 10y²)  
+### Multi-dimensional (f(x,y) = x² + 10y²)
 - Tests adaptation to different parameter scales
 - Adagrad and Adam handle scale differences well
 - Momentum methods show improved stability
 
 ### Rosenbrock Function (Non-convex)
-- Classic challenging optimization benchmark  
+- Classic challenging optimization benchmark
 - Adam significantly outperformed other methods
 - Demonstrates real-world applicability
 
 ## 🎯 Educational Value
 
 ### Progressive Complexity
 1. **SGD**: Foundation - understand basic gradient descent
-2. **Momentum**: Build intuition for acceleration methods  
+2. **Momentum**: Build intuition for acceleration methods
 3. **NAG**: Learn about lookahead and overshoot correction
 4. **Adagrad**: Understand adaptive learning rates
 5. **Adam**: See how modern optimizers combine techniques
@@ -106,7 +106,7 @@ The implementation was validated on multiple test problems:
 
 ### Code Patterns
 - Abstract base classes and inheritance
-- Recursive algorithms for nested data structures  
+- Recursive algorithms for nested data structures
 - State management in optimization algorithms
 - Type safety in scientific computing
 
@@ -126,7 +126,7 @@ from neural_network.optimizers import SGD, Adam, Adagrad
 
 optimizers = {
     "sgd": SGD(0.01),
-    "adam": Adam(0.001),  
+    "adam": Adam(0.001),
     "adagrad": Adagrad(0.01)
 }
 
@@ -147,7 +147,7 @@ updated = optimizer.update(params_2d, grads_2d)
 
 ### For the Repository
 - **Gap Filled**: Addresses missing neural network optimization algorithms
-- **Educational Value**: High-quality learning resource for ML students  
+- **Educational Value**: High-quality learning resource for ML students
 - **Code Quality**: Demonstrates best practices in scientific Python
 - **Completeness**: Makes the repo more comprehensive for ML learning
 
@@ -163,7 +163,7 @@ The modular design makes it easy to add more optimizers:
 
 ### Future Additions Could Include
 - **RMSprop**: Another popular adaptive optimizer
-- **AdamW**: Adam with decoupled weight decay  
+- **AdamW**: Adam with decoupled weight decay
 - **LAMB**: Layer-wise Adaptive Moments optimizer
 - **Muon**: Advanced Newton-Schulz orthogonalization method
 - **Learning Rate Schedulers**: Time-based adaptation
@@ -185,7 +185,7 @@ class NewOptimizer(BaseOptimizer):
 - ✅ **Incremental Complexity**: SGD → Momentum → NAG → Adagrad → Adam
 - ✅ **Documentation**: Comprehensive docstrings and README
 - ✅ **Type Hints**: Full type safety throughout
-- ✅ **Testing**: Doctests + comprehensive test suite  
+- ✅ **Testing**: Doctests + comprehensive test suite
 - ✅ **Educational Value**: Clear explanations and examples
 
 ### Additional Value Delivered

neural_network/optimizers/README.md

@@ -14,7 +14,7 @@ The most basic optimizer that updates parameters in the direction opposite to th
 ### 2. MomentumSGD (SGD with Momentum)
 Adds a momentum term that accumulates past gradients to accelerate convergence and reduce oscillations.
 
-**Update Rule:** 
+**Update Rule:**
 ```
 v = β * v + (1-β) * g
 θ = θ - α * v
@@ -97,10 +97,10 @@ x_adam = [5.0]
 for i in range(20):
     grad_sgd = [gradient_quadratic(x_sgd[0])]
     grad_adam = [gradient_quadratic(x_adam[0])]
-    
+
     x_sgd = sgd.update(x_sgd, grad_sgd)
     x_adam = adam.update(x_adam, grad_adam)
-    
+
     print(f"Step {i+1}: SGD={x_sgd[0]:.4f}, Adam={x_adam[0]:.4f}")
 ```
 
@@ -153,7 +153,7 @@ for step in range(100):
         x, y = positions[name]
         grad = rosenbrock_grad(x, y)
         positions[name] = optimizer.update(positions[name], grad)
-    
+
     if step % 20 == 19:
         print(f"\\nStep {step + 1}:")
         for name, pos in positions.items():
@@ -209,7 +209,7 @@ where `f(θ)` is typically a loss function and `θ` represents the parameters of
 The optimizers differ in how they use gradient information `g = ∇f(θ)` to update parameters:
 
 1. **SGD** uses gradients directly
-2. **Momentum** accumulates gradients over time  
+2. **Momentum** accumulates gradients over time
 3. **NAG** uses lookahead to reduce overshooting
 4. **Adagrad** adapts learning rates based on gradient history
 5. **Adam** combines momentum with adaptive learning rates

neural_network/optimizers/__init__.py

@@ -8,7 +8,7 @@
 Available optimizers:
     - SGD: Stochastic Gradient Descent
     - MomentumSGD: SGD with momentum
-    - NAG: Nesterov Accelerated Gradient 
+    - NAG: Nesterov Accelerated Gradient
     - Adagrad: Adaptive Gradient Algorithm
     - Adam: Adaptive Moment Estimation
 
@@ -21,4 +21,4 @@
 from .adagrad import Adagrad
 from .adam import Adam
 
-__all__ = ["SGD", "MomentumSGD", "NAG", "Adagrad", "Adam"]
+__all__ = ["SGD", "MomentumSGD", "NAG", "Adagrad", "Adam"]