Anomaly Detection using Autoencoders in PyTorch
This notebook presents a framework for anomaly detection using autoencoders implemented in PyTorch. The approach leverages a neural network autoencoder for both dimensionality reduction and reconst...
Stats-IQ
Causal Inference for Treatment Effects in Clinical Data: A Double Machine Learning Approach
Research Question: Does quitting smoking cause weight gain? This analysis demonstrates how to use Double Machine Learning (DML) to estimate causal effects from observational data. We analyze data ...
The Mathematics of Breakthroughs: Why Small Teams Drive Scientific Revolution
🔍 Interactive Visualizations Explore the data behind the collaboration paradox through these interactive infographics: 📊 View Full Infographic: The Collaboration Paradox 📈 Interactive Analysis: Th...
Array Variate Normal Distribution for Missing Data
Unveiling the Unseen: A Guide to Array Variate Normal Distribution and Recovering Missing Data Ever stared at a dataset with gaping holes and wondered how to make sense of it? Data, especially in ...
Anomaly Detection using GANs
TabularGAN for Anomaly Detection in Clinical Data This notebook demonstrates an application of Generative Adversarial Networks (GANs) for anomaly detection in structured clinical data using: A...