Machine learning prototypes and interactive notebooks
A usable library for question answering on large datasets.
Tensorflow 2.0 notebook to explain and visualize a HuggingFace BERT for Question Answering model.
Ongoing posts and code documenting the process of building a question answering model.
Explore how to use LIME and SHAP for interpretability.
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.
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.
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.
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.
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.
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.
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.
An interactive visualization that uses T-SNE to cluster movies together based on user ratings.
Luhn's method, from 1958, provides a foundation for understanding modern auto-summarization techniques.
Cloudera Fast Forward Labs
Making the recently possible useful.
Cloudera Fast Forward Labs is an applied machine learning research group. Our mission is to empower enterprise data science practitioners to apply emergent academic research to production machine learning use cases in practical and socially responsible ways, while also driving innovation through the Cloudera ecosystem. Our team brings thoughtful, creative, and diverse perspectives to deeply researched work. In this way, we strive to help organizations make the most of their ML investment as well as educate and inspire the broader machine learning and data science community.
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