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README.md

@@ -1,48 +1,74 @@
----
-language: en
-license: mit
-tags:
-- tensorflow
-- image-classification
-- mnist
-- digits
-datasets:
-- mnist
-metrics:
-- accuracy
----
+# AI Model Training Project
 
-# Digit Recognition Model
+This project demonstrates a complete machine learning workflow from data preparation to model deployment, using the MNIST dataset with an innovative approach to digit recognition.
 
-This model is trained to recognize handwritten digits from the MNIST dataset.
+## Project Structure
 
-## Model Description
+```
+.
+├── data/                  # Dataset storage
+├── models/               # Saved model files
+├── src/                  # Source code
+│   ├── data_preparation.py
+│   ├── model.py
+│   ├── training.py
+│   ├── evaluation.py
+│   └── deployment.py
+├── notebooks/           # Jupyter notebooks for exploration
+├── requirements.txt     # Project dependencies
+└── README.md           # Project documentation
+```
+
+## Setup Instructions
+
+1. Create a virtual environment:
+```bash
+python -m venv venv
+source venv/bin/activate  # On Windows: venv\Scripts\activate
+```
 
-- **Model Type:** CNN with Attention
-- **Task:** Image Classification
-- **Input:** 28x28 grayscale images
-- **Output:** Digit classification (0-9)
+2. Install dependencies:
+```bash
+pip install -r requirements.txt
+```
 
-## Training
+3. Run the training pipeline:
+```bash
+python src/training.py
+```
 
-The model was trained on the MNIST dataset using a CNN architecture with attention mechanisms.
+## Project Features
 
-## Usage
+- Custom CNN architecture for robust digit recognition
+- Data augmentation techniques
+- Model evaluation and hyperparameter tuning
+- Model deployment pipeline
+- Performance monitoring
 
-```python
-import tensorflow as tf
-import numpy as np
+## Learning Concepts Covered
 
-# Load the model
-model = tf.saved_model.load("path_to_saved_model")
+1. Data Preprocessing
+   - Data loading and cleaning
+   - Feature engineering
+   - Data augmentation
 
-# Prepare input
-image = tf.keras.preprocessing.image.load_img("digit.png", target_size=(28, 28))
-image = tf.keras.preprocessing.image.img_to_array(image)
-image = image.astype('float32') / 255.0
-image = np.expand_dims(image, axis=0)
+2. Model Architecture
+   - Custom CNN design
+   - Layer configuration
+   - Activation functions
 
-# Make prediction
-predictions = model(image)
-predicted_digit = tf.argmax(predictions, axis=1).numpy()[0]
-```
+3. Training Process
+   - Loss functions
+   - Optimizers
+   - Learning rate scheduling
+   - Early stopping
+
+4. Evaluation
+   - Metrics calculation
+   - Cross-validation
+   - Model comparison
+
+5. Deployment
+   - Model saving
+   - Inference pipeline
+   - Performance monitoring