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Blog on Machine Learning, Statistics & Software Development

How to get around Dropbox’s symlink limitations on Linux

As of mid-2019, Dropbox announced that they no longer support symlinks that point outside of the main Dropbox folder. In this tutorial, we show a workaround on Linux that enables us to store in Dropbox any file, even if it is not located within the main Dropbox folder. What is the limitation and why it’s […]

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Dan Luu

Suspicious discontinuities

If you read any personal finance forums late last year, there's a decent chance you ran across a question from someone who was desperately trying to lose money before the end of the year. There are a number of ways someone could do this; one commonly suggested scheme was to buy put options that were expected to expire worthless , allowing the buyer to (probably) take a loss. One reason people were looking for ways to lose money was

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John Egan

How Pinterest Supercharged its Growth Team With Experiment Idea Review

Written by Jeff Chang & John Egan Growth teams need to be organized bottom-up to scale well The Pinterest Growth team has over 100 members, and we’ve run thousands of experiments over the years. It’s difficult to run that many experiments and still maintain a high success rate over time. We’ve found the traditional growth […] The post How Pinterest Supercharged its Growth Team With Experiment Idea Review appeared first on John Egan .

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How Pinterest Supercharged its Growth Team With Experiment Idea Review
Distill — Latest articles about machine learning

Growing Neural Cellular Automata

Training an end-to-end differentiable, self-organising cellular automata model of morphogenesis, able to both grow and regenerate specific patterns.

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Dan Luu

95%-ile isn't that good

Reaching 95%-ile isn't very impressive because it's not that hard to do. I think this is one of my most ridiculable ideas. It doesn't help that, when stated nakedly, that sounds elitist. But I think it's just the opposite: most people can become (relatively) good at most things. Note that when I say 95%-ile, I mean 95%-ile among people who participate, not all people (for many activities, just doing it at all makes you 99%-ile or above across all people). I'm also not referring to 95%-

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Machine, Think!

Upsampling in Core ML

Resizing feature maps is a common operation in many neural networks, especially those that perform some kind of image segmentation task. One issue I ran into recently while converting a neura

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Unsupervised Thinking

Unsupervised Thinking is on hiatus!

As some listeners may know, UST co-host Grace Lindsay is writing a book. That book---a popular science take on the science, history, and philosophy of many different topics in computational neuroscience---is very nearly due to the publisher. As a result, time for podcasting (or really anything other than book writing...) is rapidly disappearing! So until further notice, there will be no new episodes of Unsupervised Thi

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John Egan

Managing Your Growth Team’s Portfolio: A Step-by-Step Guide

Managing a Growth team in many ways can be like managing an investment portfolio. Each individual experiment or project is an investment and the goal is to maximize the long-term return (i.e. impact) of your portfolio. Having worked in Growth for almost 10 years I’ve seen how the portfolio of a Growth team can evolve […] The post Managing Your Growth Team’s Portfolio: A Step-by-Step Guide appeared first on John Egan .

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Managing Your Growth Team’s Portfolio: A Step-by-Step Guide
Next.js BlogDev & Frontend

Next.js 9.1.7

Next.js 9.1.7 improves upon a solid foundation, improving the enterprise-ready 9.1 release-channel. Upgrade for smaller client-side JavaScript bundles, redesigned CLI output, faster FCP/TTI, and more!

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Dan Luu

Algorithms interviews: theory vs. practice

When I ask people at trendy big tech companies why algorithms quizzes are mandatory, the most common answer I get is something like "we have so much scale, we can't afford to have someone accidentally write an O(n^2) algorithm and bring the site down" 1 . One thing I find funny about this is, even though a decent fraction of the value I've provided for companies has been solving phone-screen level algor

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Eric Jang

Selected Quotes from "The Dark Ages of AI Panel Discussion"

In 1984, a panel at the AAAI conference discussed whether the field was approaching an "AI Winter" . Mitch Waldrop wrote a transcript of the discussion , and much of it reads exactly like something written 35 years into the future.<br /&gt

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Unsupervised Thinking

Episode 51: Motor Control

To some neuroscientists, the brain exists to produce movement and everything else it does should be understood in light of this goal. On this episode, we talk about these "motor chauvinists" and the broader topic of how motor control is studied in neuroscience and artificial intelligence. First we briefly discuss the tangled anatomy of motor control in animals. Then we get into how artificial motor control is done, including optimal feedback control, reinforcement learning, and the six core prin

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Machine, Think!

How to convert images to MLMultiArray

I see the following questions come up a lot on Stack Overflow, the Apple developer forums, and various Slack groups: My neural network works on images but the Core ML model expects an MLMultiArray object. How do I convert my UIImage to an MLMultiArray? My neural network outputs an MLMultiArray but how do I convert this back into a UIImage? My model outputs an image but the UIImage is all black. People run into these issues because most tr

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Joel on Software

So, how’s that retirement thing going, anyway?

For the last couple of months, Prashanth Chandrasekar has been getting settled in as the new CEO of Stack Overflow. I’m still going on some customer calls… Read more "So, how’s that retirement thing going, anyway?"

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So, how’s that retirement thing going, anyway?
Machine, Think!

On-device training with Core ML - part 4

In this series of blog posts we’re taking a deep dive into the new on-device model personalization features from Core ML 3. I’ll show how to create a customizable image classifier using k-Nearest Neighbors as well as a deep neural network, right from inside an iOS app. This is the last part of a four-part series: Introduction to on-device training <a href="https://machinethink.net/b

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Eric Jang

Differentiable Path Tracing on the GPU/TPU

You can download a PDF (typset in LaTeX) of this blog post here . Jupyter Notebook Code on GitHub: https://github.com/ericjang/pt-jax</

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Differentiable Path Tracing on the GPU/TPU
Machine, Think!

Core ML and Combine

Over the weekend I was reading Combine: Asynchronous Programming with Swift from my friends at raywenderlich.com when it occured on me that Combine and Core ML might make a nice couple. Combine lets you build reactive event processing chains . Doing inference with a Core ML model can be one of the stages in such a chain, as this is nothing more than another data transforma

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Eric Jang

Robinhood, Leverage, and Lemonade

DISCLAIMER: NO INVESTMENT OR LEGAL ADVICE The Content is for informational purposes only, you should not construe any such information or other material as legal, tax, investment, financial, or other advice. Investing involves risk, please consult a financial professional before making an investment. Robinhood is a zero-commission brokerage that was founded in 2013. It ha

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On GATE, Text and Social Media Analysis, and Detecting Misinformation Online

Which MPs changed party affiliation, 2017-2019

As part of our work tracking Twitter abuse towards MPs and candidates going into the December 12th general election I've been updating our

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Unsupervised Thinking

Episode 50: Brain Organoids

Most neuroscience research takes place in a full, live animal. But brain organoids are different. Brain organoids are three-dimensional blobs of brain grown from human stem cells and they offer novel access to the study of human brain development. On this episode we go beyond our computational comfort zone to talk about the history of stem cells, the potion of chemicals needed to get these little blobs to grow, and the extent to which they mimic features of the human brain when they do. We also

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Next.js BlogDev & Frontend

Introducing Create Next App

We're delighted today to introduce the new Create Next App. Create Next App sets up a modern React application powered by Next.js in one command.

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Unsupervised Thinking

Episode 49: How Important is Learning?

The age-old debate of nature versus nurture is now being played out between artificial intelligence and neuroscience. The dominant approach in AI, machine learning , puts an emphasis on adapting processing to fit the data at hand. Animals, on the other hand, seem to have a lot of built in structure and tendencies, that mean they function well right out of the womb. So are most of our abilities the result of genetically-encoded instructions, honed over generations of evolution? Or are our i

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Next.js BlogDev & Frontend

Next.js 9.0.7

Next.js 9.0 was released approximately two months ago. Since then, we’ve been busy with 7 smaller but quite important releases. Let’s dive into what these releases have brought to your websites and applications, with absolutely no breaking changes.

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Joel on Software

Welcome, Prashanth!

Last March, I shared that we were starting to look for a new CEO for Stack Overflow. We were looking for that rare combination of someone who… Read more "Welcome, Prashanth!"

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Blog on Text Analytics - Provalis Research

Training Workshop in Sienna, Italy Oct 28 – Oct 30, 2019

This workshop is sold out! Provalis Research is holding a three-day training workshop for QDA Miner 5 and WordStat 8 in Siena, Italy. The training will be held from October 28 to October 30, 2019 (from 9:00 am to 5:00 pm), at University of Siena. The workshop will have a greater focus on WordStat but there will […]

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Machine, Think!

On-device training with Core ML - part 3

In this series of blog posts we’re taking a deep dive into the new on-device model personalization features from Core ML 3. I’ll show how to create a customizable image classifier using k-Nearest Neighbors as well as a deep neural network, right from inside an iOS app. This is part 3 of a four-part series: Introduction to on-device training <a href="https://machinethink.net/blog/cor

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