Machine learning prototypes, demos, and code by
Cloudera Fast Forward
NLP for Question Answering
Ongoing posts and code documenting the process of building a question answering model.
Interpretability Revisited: SHAP and LIME
Explore how to use LIME and SHAP for interpretability.
Learn about using interpretability techniques on deep learning models.
Refractor predicts churn probabilities for telecom customers and shows which customer attributes contribute to those predictions.
An interactive visualization tool for exploring how a deep learning model can be applied to the task of anomaly detection.
Deep Learning for Anomaly Detection
Learn how to use deep learning approaches (including sequence models, VAEs, and GANS) for anomaly detection.
Blip visualizes how four different anomaly detection algorithms perform at detecting network attacks.
A semantic search engine that takes some input text and returns relevant famous quotes.
Learn about how we used transfer learning and a pretrained BERT model to build our sentiment analysis prototype.
Textflix uses movie reviews to show how machine learning can unlock the data embedded in large amounts of unstructured text.
With ConvNet Playground you can explore how a convolutional neural network does semantic image search.
Weak supervision with Snorkel
A notebook showing how to train a complaint classifier with Snorkel. Using data from the Consumer Financial Protection Bureau.
An interactive visualization of active learning data labeling strategies for supervised machine learning.
Handtrack.js is a library for prototyping realtime hand detection (bounding box), directly in the browser.
An interactive UMAP visualization of the MNIST data set.
Active Learning with Logistic Regression
A toy example about logistic regression and different active learning strategies.
See if you have what it takes to make it as a turbofan factory owner in our federated learning prototype.
Probabilistic Real Estate
A probabilistic programming prototype that predicts future real estate prices across New York City boroughs and neighborhoods.
Brief uses neural networks to score and highlight the most interesting sentences within any article.
Using three.js for 2D Data Visualization
An interactive notebook about using three.js to render tens of thousands of points.
Encartopedia visualizes Wikipedia topic clusters and plots your journey through them.
Visualizing the Taste of a Community of Cinephiles
An interactive visualization that uses T-SNE to cluster movies together based on user ratings.
Luhn Method Demo
Luhn's method, from 1958, provides a foundation for understanding modern auto-summarization techniques.
Cloudera Fast Forward is an applied machine learning research group.