CNN Autoencoder Explained with Code | MNIST Deep Learning Project
Learn how to build a CNN-based Autoencoder using TensorFlow and Keras with a complete step-by-step explanation.
In this video, I explain:
What is an Autoencoder
Encoder and Decoder architecture
CNN layers (Conv2D, MaxPooling, UpSampling)
Training on MNIST dataset
Output visualization (Original vs Reconstructed images)
This project is perfect for:
Students (AI / ML / Data Science)
College assignments and viva
Beginners in Deep Learning
💡 Applications:
Image Compression
Noise Reduction
Feature Extraction
📌 Tools used:
Python
TensorFlow / Keras
NumPy
Matplotlib