Linear Digressions

Deep learning is a field that’s growing quickly. That’s good! There are lots of new deep learning papers put out every day. That’s good too… right? What if not every paper out there is particularly good? What even makes a paper good in the first place? It’s an interesting thing to think about, and debate, since there’s no clean-cut answer and there are worthwhile arguments both ways. Wherever you...

Ordinary least squares (OLS) is often used synonymously with linear regression. If you’re a data scientist, machine learner, or statistician, you bump into it daily. If you haven’t had the opportunity to build up your understanding from the foundations, though, listen up: there are a number of assumptions underlying OLS that you should know and love. They’re interesting, force you to think about d...

Linear regression is a great tool if you want to make predictions about the mean value that an outcome will have given certain values for the inputs. But what if you want to predict the median? Or the 10th percentile? Or the 90th percentile. You need quantile regression, which is similar to ordinary least squares regression in some ways but with some really interesting twists that make it unique....

When data scientists use a linear regression to look for causal relationships between a treatment and an outcome, what they’re usually finding is the so-called average treatment effect. In other words, on average, here’s what the treatment does in terms of making a certain outcome more or less likely to happen. But there’s more to life than averages: sometimes the relationship works one way in som...

When you build a model for natural language processing (NLP), such as a recurrent neural network, it helps a ton if you’re not starting from zero. In other words, if you can draw upon other datasets for building your understanding of word meanings, and then use your training dataset just for subject-specific refinements, you’ll get farther than just using your training dataset for everything. This...

Facial recognition being used in everyday life seemed far-off not too long ago. Increasingly, it’s being used and advanced widely and with increasing speed, which means that our technical capabilities are starting to outpace (if they haven’t already) our consensus as a society about what is acceptable in facial recognition and what isn’t. The threats to privacy, fairness, and freedom are real, and...

Bringing you another old classic this week, as we gear up for 2019! See you next week with new content. Word2Vec is probably the go-to algorithm for vectorizing text data these days.  Which makes sense, because it is wicked cool.  Word2Vec has it all: neural networks, skip-grams and bag-of-words implementations, a multiclass classifier that gets swapped out for a binary classifier, made-up dummy w...

We’re taking a break for the holidays, chilling with the dog and an eggnog (Katie) and the cat and some spiced cider (Ben). Here’s an episode from a while back for you to enjoy. See you again in 2019! You might sometimes find that it's hard to get started doing something, but once you're going, it gets easier. Turns out machine learning algorithms, and especially recommendation engines, feel the s...

Convex optimization is one of the keys to data science, both because some problems straight-up call for optimization solutions and because popular algorithms like a gradient descent solution to ordinary least squares are supported by optimization techniques. But there are all kinds of subtleties, starting with convex and non-convex functions, why gradient descent is really an optimization problem,...

When you think about it, it’s pretty amazing that we can draw conclusions about huge populations, even the whole world, based on datasets that are comparatively very small (a few thousand, or a few hundred, or even sometimes a few dozen). That’s the power of statistics, though. This episode is kind of a two-for-one but we’re excited about it—first we’ll talk about the Normal or Gaussian distributi...

Neural nets are a way you can model a system, sure, but if you take a step back, squint, and tilt your head, they can also be called… software? Not in the sense that they’re written in code, but in the sense that the neural net itself operates under the same set of general requirements as does software that a human would write. Namely, neural nets take inputs and create outputs from them according...

Deep neural nets have a deserved reputation as the best-in-breed solution for computer vision problems. But there are many aspects of human vision that we take for granted but where neural nets struggle—this episode covers an eye-opening paper that summarizes some of the interesting weak spots of deep neural nets. Relevant links: https://arxiv.org/abs/1805.04025

At many places, data scientists don’t work solo anymore—it’s a team sport. But data science teams aren’t simply teams of data scientists working together. Instead, they’re usually cross-functional teams with engineers, managers, data scientists, and sometimes others all working together to build tools and products around data science. This episode talks about some of those roles on a typical data...

Last week’s episode, about methods for optimized web crawling logic, left off on a bit of a cliffhanger: the data scientists had found a solution to the problem, but it wasn’t something that the engineers (who own the search codebase, remember) liked very much. It was black-boxy, hard to parallelize, and introduced a lot of complexity to their code. This episode takes a second crack, where we form...

Got a fun optimization problem for you this week! It’s a two-for-one: how do you optimize the web crawling logic of an operation like Google search so that the results are, on average, as up-to-date as possible, and how do you optimize your solution of choice so that it’s maintainable by software engineers in a huge distributed system? We’re following an excellent post from the Unofficial Google D...

The Poisson distribution is a probability distribution function used to for events that happen in time or space. It’s super handy because it’s pretty simple to use and is applicable for tons of things—there are a lot of interesting processes that boil down to “events that happen in time or space.” This episode is a quick introduction to the distribution, and then a focus on two of our favorite app...

If you wanted to find a dataset of jokes, how would you do it? What about a dataset of podcast episodes? If your answer was “I’d try Google,” you might have been disappointed—Google is a great search engine for many types of web data, but it didn’t have any special tools to navigate the particular challenges of, well, dataset data. But all that is different now: Google recently announced Google Da...

We started Linear Digressions 4 years ago… this isn’t a technical episode, just two buddies shooting the breeze about something we’ve somehow built together.

This week, we’re dusting off the ol’ particle physics PhD to bring you an episode about ambitious new model-agnostic searches for new particles happening at CERN. Traditionally, new particles have been discovered by “targeted searches,” where scientists have a hypothesis about the particle they’re looking for and where it might be found. However, with the huge amounts of data coming out of CERN, a...

If you’re a data scientist, you know how important it is to keep your data orderly, clean, moving smoothly between different systems, well-documented… there’s a ton of work that goes into building and maintaining databases and data pipelines. This job, that of owner and maintainer of the data being used for analytics, is often the realm of data engineers. From data extraction, transform and loadin...

A very intriguing op-ed was published in the NY Times recently, in which the author (a senior official in the Trump White House) claimed to be a minor saboteur of sorts, acting with his or her colleagues to undermine some of Donald Trump’s worst instincts and tendencies. Pretty stunning, right? So who is the author? It’s a mystery—the op-ed was published anonymously. That hasn’t stopped people fro...

If you've been in data science for more than a year or two, chances are you've noticed changes in the field as it's grown and matured. And if you're newer to the field, you may feel like there's a disconnect between lots of different stories about what data scientists should know, or do, or expect from their job. This week, we cover two thought pieces, one that arose from interviews with 35(!) dat...

There's just too much interesting stuff at the intersection of agile software development and data science for us to be able to cover it all in one episode, so this week we're picking up where we left off last time. We'll give a quick overview of agile for those who missed last week or still have some questions, and then cover some of the aspects of agile that don't work well out-of-the-box when a...

If you're a data scientist at a firm that does a lot of software building, chances are good that you've seen or heard engineers sometimes talking about "agile software development." If you don't work at a software firm, agile practices might be newer to you. In either case, we wanted to go through a great series of blog posts about some of the practices from agile that are relevant for how data sc...

We've got a classic for you this week as we take a week off for the dog days of summer. See you again next week! Competing in a machine learning competition on Kaggle is a kind of rite of passage for data scientists. Losing unexpectedly at the very end of the contest is also something that a lot of us have experienced. It's not just bad luck: a very specific combination of overfitting on popular c...