docs(KDP): reorganising documentation for better UX

piotrlaczkowski · piotrlaczkowski · commit a67c6341e0ec · 2025-01-31T15:01:19.000+01:00
diff --git a/docs/contributing.md b/docs/contributing.md
@@ -69,7 +69,7 @@ If you wan't to request a new feature or you have detected an issue, please use
         - `init repo structure`
         - `adding pre-processing unit-tests`
 
-- [x] Once the code is ready create a Merge Request (MR) into the DEV branch with a proper naming convention
+- [x] Once the code is ready create a Merge Request (MR) into the MAIN branch with a proper naming convention
 
   > The name of your MR should follow the same exact convention as your commits (we have a dedicated check for this in the CI):
 
diff --git a/docs/kdp_overview.md b/docs/kdp_overview.md
@@ -0,0 +1,142 @@
+# 💡 Understanding KDP
+
+## 🎯 What is KDP?
+
+KDP (Keras Data Processor) is a powerful preprocessing library designed to streamline and enhance data preparation for deep learning models. It combines modern deep learning techniques with traditional preprocessing methods to create a flexible and efficient data processing pipeline.
+
+## 🌟 Key Features
+
+### 1. 🔄 Unified Preprocessing
+- Single interface for all preprocessing needs
+- Seamless integration with Keras models
+- End-to-end differentiable pipeline
+
+### 2. 🎛️ Advanced Feature Processing
+- **Numerical Features**
+  - Multiple scaling options
+  - Automatic outlier handling
+  - Missing value imputation
+
+- **Categorical Features**
+  - Learned embeddings
+  - Automatic vocabulary management
+  - Handling of unknown categories
+
+### 3. 🧠 Deep Learning Enhancements
+- **Tabular Attention**
+  - Feature interaction modeling
+  - Adaptive feature importance
+  - Multi-head attention support
+
+- **Feature Selection**
+  - Automatic importance learning
+  - Dynamic feature filtering
+  - Interpretable weights
+
+## 🏗️ Architecture Overview
+
+```mermaid
+graph TD
+    A[Raw Data] --> B[Feature Definition]
+    B --> C[Preprocessing Model]
+    C --> D[Feature Processing]
+    D --> E[Deep Learning Extensions]
+    E --> F[Processed Features]
+
+    subgraph "Feature Processing"
+    D1[Numerical Processing]
+    D2[Categorical Processing]
+    end
+
+    subgraph "Extensions"
+    E1[Tabular Attention]
+    E2[Feature Selection]
+    E3[Transformer Blocks]
+    end
+```
+
+## 💪 Why Choose KDP?
+
+### 1. 🎯 Simplicity
+- Intuitive API design
+- Minimal boilerplate code
+- Clear documentation
+
+### 2. 🚀 Performance
+- Optimized for large datasets
+- GPU acceleration support
+- Memory-efficient processing
+
+### 3. 🔧 Flexibility
+- Customizable preprocessing
+- Extensible architecture
+- Framework agnostic
+
+### 4. 🤝 Integration
+- Seamless Keras integration
+- Easy model export/import
+- Cloud platform support
+
+## 🛠️ Core Components
+
+### 1. Feature Definitions
+- Define data types and processing
+- Configure feature-specific parameters
+- Set preprocessing strategies
+
+### 2. Preprocessing Model
+- Manages feature transformations
+- Handles data flow
+- Maintains state
+
+### 3. Extensions
+- Add advanced capabilities
+- Enhance preprocessing
+- Improve model performance
+
+## 📈 Use Cases
+
+### 1. 📊 Tabular Data
+- Financial data processing
+- Customer analytics
+- Time series analysis
+
+### 2. 🎯 Feature Engineering
+- Automatic feature selection
+- Feature interaction modeling
+- Dimensionality reduction
+
+### 3. 🔄 Model Integration
+- Deep learning pipelines
+- AutoML systems
+- Production deployments
+
+## 🚀 Getting Started
+
+1. Check out our [Quick Start Guide](quick_start.md)
+2. Explore [Key Features](features.md)
+3. Try [Complex Examples](complex_examples.md)
+
+## 📚 Learning Path
+
+1. 🎓 **Beginner**
+   - Basic feature definition
+   - Simple preprocessing
+   - Data transformation
+
+2. 🏃 **Intermediate**
+   - Advanced features
+   - Custom preprocessing
+   - Performance optimization
+
+3. 🚀 **Advanced**
+   - Extension development
+   - Pipeline optimization
+   - Production deployment
+
+## 🔗 Next Steps
+
+- [🛠️ Key Features](features.md)
+- [🚀 Quick Start](quick_start.md)
+- [📚 Complex Examples](complex_examples.md)
+- [🤝 Contributing Guide](contributing.md)
diff --git a/docs/quick_start.md b/docs/quick_start.md
@@ -0,0 +1,42 @@
+# 🚀 Quick Start Guide
+
+## 📦 Installation
+
+```bash
+pip install keras-data-processor
+```
+
+## 🎯 Basic Usage
+
+### 1️⃣ Define Your Features
+
+```python
+from kdp.processor import PreprocessingModel
+from kdp.features import NumericalFeature, CategoricalFeature
+
+# Define features
+features = {
+    "age": NumericalFeature(),
+    "income": NumericalFeature(scaling="standard"),
+    "occupation": CategoricalFeature(embedding_dim=32),
+    "education": CategoricalFeature(embedding_dim=16)
+}
+```
+
+### 2️⃣ Create Preprocessing Model
+
+```python
+# Initialize the model
+model = PreprocessingModel(
+    features=features,
+    tabular_attention=True,  # Enable attention mechanism
+    feature_selection=True   # Enable feature selection
+)
+```
+
+
+## 🔗 Useful Links
+
+- [📚 Full Documentation](https://kdp.readthedocs.io)
+- [💻 GitHub Repository](https://github.com/piotrlaczkowski/keras-data-processor)
+- [🐛 Issue Tracker](https://github.com/piotrlaczkowski/keras-data-processor/issues)
diff --git a/mkdocs.yml b/mkdocs.yml
@@ -37,7 +37,7 @@ theme:
 
 nav:
   - 🏠 Home: index.md
-  # - 💡 Understanding KDP: kdp_overview.md
+  - 💡 Understanding KDP: kdp_overview.md
   - 🛠️ Key Features: features.md
   - 🏭 Factory System:
       - 🏗️ Layers Factory: layers_factory.md
