Autoencoder

This project implements a Convolutional Autoencoder using TensorFlow/Keras to perform image compression and reconstruction on the MNIST handwritten digits dataset.

🧠 Convolutional Autoencoder on MNIST

This project implements a Convolutional Autoencoder using TensorFlow/Keras to perform image compression and reconstruction on the MNIST handwritten digits dataset.

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🚀 Features

• Implemented Encoder–Decoder CNN architecture with Conv2D, MaxPooling2D, and UpSampling2D layers.
• Trained on 50,000+ MNIST images for unsupervised feature learning.
• Visualized reconstructed images vs original images for performance evaluation.
• Demonstrates understanding of deep learning, data preprocessing, and model evaluation.

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📂 Tech Stack

• Python, NumPy, Matplotlib
• TensorFlow / Keras
• Scikit-learn

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Run the model

python autoencoder_mnist.py