Project Overview:

In this project, I explored the capabilities of denoising autoencoders by reconstructing and denoising images from the MNIST dataset. My goal was to develop a robust model that not only reduced dimensionality but also maintained the integrity of the images. I implemented a 4-layer Multilayer Perceptron (MLP) architecture using PyTorch, optimizing it to reduce the input features from 784 to just 8 latent variables—achieving an impressive 98% reduction in feature space. By incorporating noise into the training process, I enhanced the model’s ability to learn meaningful representations and interpolate between different latent encodings. This approach resulted in a low reconstruction error of less than 0.005 MSE.

Reflection:

This project was such an exciting journey for me! It felt amazing to take my theoretical knowledge of neural networks—especially perceptrons—and transform it into actual working code. Jumping into PyTorch for the first time was a bit daunting, but I loved the challenge of learning on the fly without any formal lessons. Building the autoencoder model was an exciting learning journey, and I even programmed a program that took user inputs for the model. This project deepened my understanding of how these models function and made me even more passionate about integrating AI solutions into software.

Optimizing hyperparameters to minimize loss allowed me to learn the practical impact of different hyperparameters on a machine-learning model. One of the coolest parts was adding noise to the input data; it was fascinating to see through visualizations how such a simple technique could really boost the model’s robustness.

Looking ahead, I’m eager to adapt this autoencoder for other datasets, particularly in the medical imaging field. This intersection of healthcare and artificial intelligence is a space I’m keen to explore further. Each new dataset presents unique challenges that will help me grow my skills in developing machine-learning models and using various frameworks. My next goal is to combine my software development expertise with my passion for AI, creating interactive software solutions that genuinely solve problems for users.

Skills Acquired:

Data Science, Data Visualization, Neural Network Architecture, Dimensionality Reduction, Model Evaluation, PyTorch