Blog

Apr 25, 2018 · newsletter

JavaScript eats the world: deep learning and notebooks edition

Google recently announced TensorFlow.js, an open-source library for running machine learning in the browser, and a successor to the deeplearn.js library. While the majority of machine learning work is unlikely to shift to JavaScript anytime soon, the examples included on the TensorFlow.js site do a good job of showing the promise of machine learning models that run in the browser.

A short GIF of the Teachable Machines demo showing Grant raising his hand and the model responding with a GIF of a cat waving.

Teachable Machine lets you train a model to help you wave at cats.

Our favorite example is Teachable Machine, which walks you through a training process using images from your webcam to trigger response GIFs. It shows how training in the browser can help the model adapt to different contexts. For example, if you want your model to spot when a user raises their hand, a pre-trained model might have trouble if the user is sitting in a room surrounded by mannequins. Because you can train the Teachable Machine model with specific examples of both “raised hand” and “unraised hand,” there’s a good chance it will perform well in your weird mannequin room (a very specific situation). A related webcam-based example is the work Oz Ramos is doing to build a system for navigation using facial gestures, to help people with mobility impairments use the web.

Another advantage of having deep learning code running in the browser is that you can open up the code itself for people to interact with. Better JavaScript deep-learning tools mean more web-based interactive deep-learning explainers: explainers like Minsuk Kahng’s Deep Learning Tutorial, which shows how to use deeplearn.js to build a model for the Iris dataset. Because all of the code for the system is exposed and editable, viewers cans tweak different parameters and easily view the results.

A screenshot form a section of Minsuk Kahng’s Deep Learning Tutorial. It shows the model code and a slider for specifying the number of neurons in the hidden layer.

Minsuk Kahng’s deeplearning.js node lets you edit the code and immediately view the results.

Kahng’s tutorial is built on the new JavaScript notebook site Observable. Made by D3.js creator Mike Bostock and others, Observable makes it easy to build interactive and modifiable JavaScript examples of all types. Along with sites like Codepen and Glitch, Observable is leading a renaissance of interactive code examples and explainers. Grant has used Observable for everything from using three.js for 2D data visualization to finding out how long it takes a browser to say different numbers out loud.

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Nov 15, 2022 · newsletter

CFFL November Newsletter

November 2022 Perhaps November conjures thoughts of holiday feasts and festivities, but for us, it’s the perfect time to chew the fat about machine learning! Make room on your plate for a peek behind the scenes into our current research on harnessing synthetic image generation to improve classification tasks. And, as usual, we reflect on our favorite reads of the month. New Research! In the first half of this year, we focused on natural language processing with our Text Style Transfer blog series.
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Nov 14, 2022 · post

Implementing CycleGAN

by Michael Gallaspy · Introduction This post documents the first part of a research effort to quantify the impact of synthetic data augmentation in training a deep learning model for detecting manufacturing defects on steel surfaces. We chose to generate synthetic data using CycleGAN,1 an architecture involving several networks that jointly learn a mapping between two image domains from unpaired examples (I’ll elaborate below). Research from recent years has demonstrated improvement on tasks like defect detection2 and image segmentation3 by augmenting real image data sets with synthetic data, since deep learning algorithms require massive amounts of data, and data collection can easily become a bottleneck.
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Oct 20, 2022 · newsletter

CFFL October Newsletter

October 2022 We’ve got another action-packed newsletter for October! Highlights this month include the re-release of a classic CFFL research report, an example-heavy tutorial on Dask for distributed ML, and our picks for the best reads of the month. Open Data Science Conference Cloudera Fast Forward Labs will be at ODSC West near San Fransisco on November 1st-3rd, 2022! If you’ll be in the Bay Area, don’t miss Andrew and Melanie who will be presenting our recent research on Neutralizing Subjectivity Bias with HuggingFace Transformers.
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Sep 21, 2022 · newsletter

CFFL September Newsletter

September 2022 Welcome to the September edition of the Cloudera Fast Forward Labs newsletter. This month we’re talking about ethics and we have all kinds of goodies to share including the final installment of our Text Style Transfer series and a couple of offerings from our newest research engineer. Throw in some choice must-reads and an ASR demo, and you’ve got yourself an action-packed newsletter! New Research! Ethical Considerations When Designing an NLG System In the final post of our blog series on Text Style Transfer, we discuss some ethical considerations when working with natural language generation systems, and describe the design of our prototype application: Exploring Intelligent Writing Assistance.
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Sep 8, 2022 · post

Thought experiment: Human-centric machine learning for comic book creation

by Michael Gallaspy · This post has a companion piece: Ethics Sheet for AI-assisted Comic Book Art Generation I want to make a comic book. Actually, I want to make tools for making comic books. See, the problem is, I can’t draw too good. I mean, I’m working on it. Check out these self portraits drawn 6 months apart: Left: “Sad Face”. February 2022. Right: “Eyyyy”. August 2022. But I have a long way to go until my illustrations would be considered professional quality, notwithstanding the time it would take me to develop the many other skills needed for making comic books.
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Aug 18, 2022 · newsletter

CFFL August Newsletter

August 2022 Welcome to the August edition of the Cloudera Fast Forward Labs newsletter. This month we’re thrilled to introduce a new member of the FFL team, share TWO new applied machine learning prototypes we’ve built, and, as always, offer up some intriguing reads. New Research Engineer! If you’re a regular reader of our newsletter, you likely noticed that we’ve been searching for new research engineers to join the Cloudera Fast Forward Labs team.
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Reports

In-depth guides to specific machine learning capabilities

Prototypes

Machine learning prototypes and interactive notebooks
Notebook

ASR with Whisper

Explore the capabilities of OpenAI's Whisper for automatic speech recognition by creating your own voice recordings!
https://colab.research.google.com/github/fastforwardlabs/whisper-openai/blob/master/WhisperDemo.ipynb
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

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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