Blog

Prototypes

Machine learning prototypes and interactive notebooks
Library

NeuralQA

A usable library for question answering on large datasets.
https://neuralqa.fastforwardlabs.com
Notebook

Explain BERT for Question Answering Models

Tensorflow 2.0 notebook to explain and visualize a HuggingFace BERT for Question Answering model.
https://colab.research.google.com/drive/1tTiOgJ7xvy3sjfiFC9OozbjAX1ho8WN9?usp=sharing
Notebooks

NLP for Question Answering

Ongoing posts and code documenting the process of building a question answering model.
https://qa.fastforwardlabs.com
Notebook

Interpretability Revisited: SHAP and LIME

Explore how to use LIME and SHAP for interpretability.
https://colab.research.google.com/drive/1pjPzsw_uZew-Zcz646JTkRDhF2GkPk0N
Prototype

Refractor

Refractor predicts churn probabilities for telecom customers and shows which customer attributes contribute to those predictions.
https://refractor.fastforwardlabs.com
Prototype

Anomagram

An interactive visualization tool for exploring how a deep learning model can be applied to the task of anomaly detection.
https://anomagram.fastforwardlabs.com
Prototype

Blip

Blip visualizes how four different anomaly detection algorithms perform at detecting network attacks.
https://blip.fastforwardlabs.com
Demo

S-quote

A semantic search engine that takes some input text and returns relevant famous quotes.
https://github.com/cjwallace/squote
Prototype

Textflix

Textflix uses movie reviews to show how machine learning can unlock the data embedded in large amounts of unstructured text.
https://textflix.fastforwardlabs.com
Prototype

ConvNet Playground

With ConvNet Playground you can explore how a convolutional neural network does semantic image search.
https://convnetplayground.fastforwardlabs.com
Notebook

Weak supervision with Snorkel

A notebook showing how to train a complaint classifier with Snorkel. Using data from the Consumer Financial Protection Bureau.
https://github.com/fastforwardlabs/snorkel-demo-colab/blob/master/snorkel_demo.ipynb
Prototype

Active Learner

An interactive visualization of active learning data labeling strategies for supervised machine learning.
https://activelearner.fastforwardlabs.com
Library

Handtrack.js

Handtrack.js is a library for prototyping realtime hand detection (bounding box), directly in the browser.
https://victordibia.github.io/handtrack.js
Prototype

UMAP Explorer

An interactive UMAP visualization of the MNIST data set.
https://grantcuster.github.io/umap-explorer
Notebook

Active Learning with Logistic Regression

A toy example about logistic regression and different active learning strategies.
https://observablehq.com/@cjwallace/an-invitation-to-active-learning
Prototype

Turbofan Tycoon

See if you have what it takes to make it as a turbofan factory owner in our federated learning prototype.
https://turbofan.fastforwardlabs.com
Prototype

Probabilistic Real Estate

A probabilistic programming prototype that predicts future real estate prices across New York City boroughs and neighborhoods.
http://fastforwardlabs.github.io/pre
Prototype

Brief Preview

Brief uses neural networks to score and highlight the most interesting sentences within any article.
http://fastforwardlabs.github.io/brief/
Notebook

Using three.js for 2D Data Visualization

An interactive notebook about using three.js to render tens of thousands of points.
https://observablehq.com/@grantcuster/using-three-js-for-2d-data-visualization
Prototype

Encartopedia

Encartopedia visualizes Wikipedia topic clusters and plots your journey through them.
http://encartopedia.fastforwardlabs.com
Prototype

Visualizing the Taste of a Community of Cinephiles

An interactive visualization that uses T-SNE to cluster movies together based on user ratings.
http://fastforwardlabs.github.io/cinephile_tsne/
Prototype

Luhn Method Demo

Luhn's method, from 1958, provides a foundation for understanding modern auto-summarization techniques.
http://fastforwardlabs.github.io/luhn/

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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