Clean up repository: remove temp files, organize modules, prepare for PyPI publication

- Removed temporary test files and audit reports
- Deleted backup and temp_holding directories
- Reorganized module structure (07->09 spatial, 09->07 dataloader)
- Added new modules: 11-14 (tokenization, embeddings, attention, transformers)
- Updated examples with historical ML milestones
- Cleaned up documentation structure

modules/05_losses/README.md

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+# Module 05: Loss Functions - Learning Objectives for Neural Networks
+
+**Essential loss functions that define learning objectives and enable neural networks to learn from data through gradient-based optimization.**
+
+## 🎯 Learning Objectives
+
+By the end of this module, you will understand:
+
+- **Mathematical Foundation**: How loss functions translate learning problems into optimization objectives
+- **Numerical Stability**: Why proper implementation prevents catastrophic training failures in production
+- **Problem Matching**: When to use each loss function based on problem structure and data characteristics
+- **Production Integration**: How loss functions integrate with neural network training pipelines
+
+## 🏗️ What You'll Build
+
+### Core Loss Functions
+- **MeanSquaredError**: Regression loss for continuous value prediction
+- **CrossEntropyLoss**: Multi-class classification with numerically stable softmax
+- **BinaryCrossEntropyLoss**: Optimized binary classification loss
+
+### Key Features
+- ✅ Numerically stable implementations that handle edge cases
+- ✅ Efficient batch processing for scalable training  
+- ✅ Clean interfaces that integrate with neural networks
+- ✅ Comprehensive testing with real-world scenarios
+
+## 🚀 Quick Start
+
+```python
+from tinytorch.core.losses import MeanSquaredError, CrossEntropyLoss, BinaryCrossEntropyLoss
+
+# Regression: Predicting house prices
+mse = MeanSquaredError()
+regression_loss = mse(predicted_prices, actual_prices)
+
+# Multi-class classification: Image recognition
+ce_loss = CrossEntropyLoss() 
+classification_loss = ce_loss(model_logits, class_indices)
+
+# Binary classification: Spam detection
+bce_loss = BinaryCrossEntropyLoss()
+binary_loss = bce_loss(spam_logits, spam_labels)
+```
+
+## 📚 Usage Examples
+
+### When to Use Each Loss Function
+
+**Mean Squared Error (MSE)**
+- **Best for**: Regression problems (house prices, temperatures, ages)
+- **Output**: Any real number
+- **Activation**: Linear (no activation)
+
+**Cross-Entropy Loss**  
+- **Best for**: Multi-class classification (image classification, text categorization)
+- **Output**: Class probabilities (sum to 1)
+- **Activation**: Softmax
+
+**Binary Cross-Entropy Loss**
+- **Best for**: Binary classification (spam detection, medical diagnosis)
+- **Output**: Single probability (0 to 1) 
+- **Activation**: Sigmoid
+
+## 🧪 Testing Your Implementation
+
+Run the module to test all loss functions:
+
+```bash
+# Test implementations
+python modules/05_losses/losses_dev.py
+
+# Export to package
+tito module complete 05_losses
+```
+
+Expected output:
+```
+🧪 Testing Mean Squared Error Loss...
+✅ Perfect predictions test passed
+✅ All MSE loss tests passed!
+
+🧪 Testing Cross-Entropy Loss... 
+✅ Perfect predictions test passed
+✅ All Cross-Entropy loss tests passed!
+
+🎉 Complete loss function foundation ready!
+```
+
+## 🔗 Integration Examples
+
+### Training Loop Integration
+```python
+from tinytorch.core.layers import Sequential, Linear
+from tinytorch.core.activations import ReLU, Softmax
+from tinytorch.core.losses import CrossEntropyLoss
+
+# Build classifier
+model = Sequential([
+    Linear(784, 128), ReLU(),
+    Linear(128, 10), Softmax()
+])
+
+# Set up training
+loss_fn = CrossEntropyLoss()
+
+# Training step
+predictions = model(batch_inputs)
+loss = loss_fn(predictions, batch_targets)
+# loss.backward()  # Triggers gradient computation (with autograd)
+```
+
+## 🎯 Module Structure
+
+```
+05_losses/
+├── losses_dev.py          # Main implementation
+├── README.md              # This file
+└── module.yaml           # Module configuration
+```
+
+## 🔬 Key Implementation Details
+
+### Numerical Stability Features
+- **Cross-Entropy**: Uses log-sum-exp trick and probability clipping
+- **Binary Cross-Entropy**: Stable logits formulation prevents overflow
+- **All Losses**: Robust handling of edge cases and extreme values
+
+### Performance Optimizations
+- Efficient batch processing across multiple samples
+- Vectorized operations using NumPy
+- Memory-efficient computation for large datasets
+
+## 🚀 What's Next
+
+With loss functions implemented, you're ready for:
+- **Training Loops**: Complete end-to-end neural network training
+- **Optimizers**: Gradient-based parameter updates  
+- **Advanced Training**: Monitoring, checkpointing, and convergence analysis
+
+## 💡 Key Insights
+
+1. **Loss functions are the interface between business objectives and mathematical optimization**
+2. **Numerical stability is critical for reliable production training**
+3. **Different problem types require different loss functions for optimal performance**
+4. **Proper batch processing enables scalable training on large datasets**
+
+---
+
+**Next Module**: Training Infrastructure - Build complete training loops that bring all components together!