Feb 14, 2018 · fast forward food labs

Probabilistic Cookies!

In the spirit of Valentine’s Day, we at Fast Forward Labs thought it would be fun to bake cookies for our sweethearts. Being DIY nerds, we thought we’d math it up a bit.

We used python to generate probability distributions and matplotlib to check our distributions. Then we wrote a python function to generate a SCAD file defining three-dimensional shapes from the distributions. Using OpenSCAD, an open-source CAD program, we checked the 3D models and exported them to STL files for printing. We used a 5th Generation MakerBot Replicator to print our 3D models. And we baked cookies. Here’s one of our office dogs (and my best friend), Dogface, admiring the results.

There were a number of challenges involved in generating 3D models and printing them. Here’s how the basic process went.

We chose a beta distribution for our first prototype because it’s well behaved for purposes of making a 3D printed object.

  • A beta distribution only has values from 0 to 1. This gives us a fixed-width shape to work with. (Compare a Gaussian distribution, which has long tails on both sides and thus may not normalize to a good shape across 0 to 1.)
  • The area under the curve of a beta distribution is necessarily 1, which helps keep the shape from getting too eccentric while allowing flexibility in choice of parameters, and thus a wider range of shapes.
  • If you take two beta distributions, put them x-axis to x-axis, and squint, they look a bit like a heart.

We created a beta distribution in python.

import numpy as np
from scipy.stats import beta
import matplotlib.pyplot as plt

# choose enough points to have a relatively smooth curve without 
# creating so many facets that the 3D printer is slow
# choose an odd number so there is a definitive peak to the curve
numpoints = 35

# set up linspace
X = np.linspace(0, 1, numpoints)

# beta distribution parameters
a, b = 2, 1.6

# get beta distribution array
betadist = beta.pdf(X, a, b)

And plotted that distribution with some suitable parameters.

# turn off the axes so we only see the curve

# plot the curve itself
plt.plot(X, betadist)

Once we found a set of parameters that we liked, we used the points in the distribution in python to create an OpenSCAD-formatted SCAD file. A SCAD file is a readable text file that contains a combination of points in space that define shapes and instructions to manipulate those shapes. We made two 2D copies of the distribution, sized one up a bit, and centered them together. We extruded them both into 3D, one linear, and the other with a slight “cone” projection. This gave us one relatively thin edge for piercing cookie dough. Then we subtracted one shape from the other to make a hollow in the larger shape. Rendered in 3D in OpenSCAD, it looks like this:

Note that the 3D model looks more eccentric than the python-generated plot. This is because the plot had axes of differing scales.

With a 3D model in hand, it was time to print a physical object. We exported our SCAD object into a stereolithography (STL) file, which we then imported into MakerBot’s printing software.

We made a final print file and extruded our first prototypes at NYC Resistor our friendly neighborhood hackerspace. 3D printing can take a while for large shapes, so we started out small.

Here are the first results (we had begun tinkering with a Gaussian distribution at that point):

Of course these first test shapes are too small for cookies. After a lot of tinkering and refinement, we ended up with beta, Gaussian, and Poisson distribution shapes scaled up for cookie size (about 100mm high).

We made some cookie dough and got to business.

Note that the Poisson distribution (printed in white) has big solid areas at the top. This makes it more of a cookie-dough perturber than a cookie cutter. Those solid areas are an artifact of my own ignorance of OpenSCAD. The “clever” OpenSCAD scale() approach I had been using was a hack; I later learned that the offset() function is the correct solution.

OpenSCAD issues aside, the cookies turned out fine. Here are some of the results, some decorated with axes and histograms.

If you feel inclined to give it a shot yourself, here’s a jupyter notebook with some code to get you started.

Happy Valentine’s Day from Fast Forward Labs!

Read more

Feb 28, 2018 · newsletter
Jan 26, 2018 · newsletter

Latest posts

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. more
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. more
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. more
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. more
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. more
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. more

Popular posts

Oct 30, 2019 · newsletter
Exciting Applications of Graph Neural Networks
Nov 14, 2018 · post
Federated learning: distributed machine learning with data locality and privacy
Apr 10, 2018 · post
PyTorch for Recommenders 101
Oct 4, 2017 · post
First Look: Using Three.js for 2D Data Visualization
Aug 22, 2016 · whitepaper
Under the Hood of the Variational Autoencoder (in Prose and Code)
Feb 24, 2016 · post
"Hello world" in Keras (or, Scikit-learn versus Keras)


In-depth guides to specific machine learning capabilities


Machine learning prototypes and interactive notebooks

ASR with Whisper

Explore the capabilities of OpenAI's Whisper for automatic speech recognition by creating your own voice recordings!


A usable library for question answering on large datasets.

Explain BERT for Question Answering Models

Tensorflow 2.0 notebook to explain and visualize a HuggingFace BERT for Question Answering model.

NLP for Question Answering

Ongoing posts and code documenting the process of building a question answering model.

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.

Cloudera   Blog   Twitter

©2022 Cloudera, Inc. All rights reserved.