🖼️ Task 4: Image Classification with CNN

An intermediate-level Deep Learning project developed during my internship at VedGrow. This project implements a Convolutional Neural Network (CNN) using TensorFlow & Keras to classify handwritten digits from the classic MNIST dataset.

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🏢 Project Overview

The primary goal of this project is to build a highly accurate computer vision pipeline capable of recognizing and categorizing handwritten digits (0-9) from image pixel matrices. The engine optimizes mathematical parameter layers via deep convolutional networks to recognize abstract structural features.

🚀 Key Features Implemented:

• 📥 Instant Dataset Ingestion: Configured integrated loader matrices to download and stream MNIST array sets seamlessly within seconds.
• ⚙️ Data Augmentation Filters: Implemented sequential transformation pipelines mapping random rotations and zoom ratios to handle variations and prevent model overfitting.
• 🧠 CNN Layer Architecture: Structured an optimal deep neural network layout combining multiple Convolutional layers, Pooling components, Flatten operations, and a 10-node Softmax classification layer.
• 📈 Performance Logs Verification: Visualized network metric tracking baselines by plotting separate standalone training vs. validation loss/accuracy curves.
• 🎯 Custom Predictive Inference: Integrated verification sandbox workflows that select unseen images and extract correct classifications alongside computed probability confidence indicators.

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🛠️ Tech Stack & Frameworks

• Language: Python 3.x
• Deep Learning Framework: TensorFlow 2.x / Keras
• Matrix Operations: NumPy
• Data Visualization: Matplotlib, Seaborn

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📂 Project Structure

📂 image_classification_cnn/
├── 📄 cnn_classifier.ipynb                 # Structured Jupyter Notebook with 6 Core Sections
├── 📄 cnn_model_accuracy_curves.png         # Saved visualization chart for model accuracy trends
├── 📄 cnn_model_loss_curves.png             # Saved visualization chart for loss minimization
└── 📄 cnn_live_custom_prediction_test.png   # Output proof plot verifying successful live inference

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⚙️ Setup and Execution

1. Open the project root folder space inside your active system command terminal.