Linear Digressions

By Ben Jaffe and Katie Malone

Technology

What's Linear Digressions about?

Linear Digressions is a podcast about machine learning and data science. Machine learning is being used to solve a ton of interesting problems, and to accomplish goals that were out of reach even a few short years ago.

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LAST EPISODE

Unsupervised Dimensionality...

01.13.2020

Dimensionality reduction redux: this episode covers UMAP, an unsupervised algorithm designed to make high-dimensional data easier to visualize, cluster, etc. It’s similar to t-SNE but has some advantages. This episode gives a quick recap of t-SNE, especially the connection it ...

Linear Digressions episodes:

01.28.2019 Quantile Regression 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? ...
01.20.2019 Heterogeneous Treatment Effects 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 ...
01.14.2019 Pre-training language models for natural language processing problems 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 ...
01.07.2019 Facial Recognition, Society, and the Law 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 ...
12.31.2018 Re-release: Word2Vec 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. ...
12.23.2018 Re - Release: The Cold Start Problem 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 ...
12.17.2018 Convex (and non-convex) Optimization 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 ...
12.09.2018 The Normal Distribution and the Central Limit Theorem 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 ...
12.02.2018 Software 2.0 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 ...
11.18.2018 Limitations of Deep Nets for Computer Vision 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 ...
11.12.2018 Building Data Science Teams 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 ...
11.04.2018 Optimized Optimized Web Crawling 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, ...
10.28.2018 Optimized Web Crawling 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 ...
10.22.2018 Better Know a Distribution: The Poisson Distribution 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 ...
10.15.2018 Searching for Datasets with Google 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 ...
10.08.2018 It's our fourth birthday 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.
09.30.2018 Gigantic Searches in Particle Physics 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 ...
09.24.2018 Data Engineering 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, ...
09.16.2018 Text Analysis for Guessing the NYTimes Op-Ed Author 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 ...
09.09.2018 The Three Types of Data Scientists, and What They Actually Do 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 ...
08.26.2018 Agile Development for Data Scientists, Part 2: Where Modifications Help 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. ...
08.19.2018 Agile Development for Data Scientists, Part 1: The Good 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 ...
08.13.2018 Re - Release: How To Lose At Kaggle 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 ...
08.06.2018 Troubling Trends In Machine Learning Scholarship There's a lot of great machine learning papers coming out every day--and, if we're being honest, some papers that are not as great as we'd wish. In some ways this is symptomatic of a field that's growing really quickly, but ...
07.29.2018 Can Fancy Running Shoes Cause You To Run Faster? The stars aligned for me (Katie) this past weekend: I raced my first half-marathon in a long time and got to read a great article from the NY Times about a new running shoe that Nike claims can make its ...
07.22.2018 Compliance Bias When you're using an AB test to understand the effect of a treatment, there are a lot of assumptions about how the treatment (and control, for that matter) get applied. For example, it's easy to think that everyone who was ...
07.15.2018 AI Winter Artificial Intelligence has been widely lauded as a solution to almost any problem. But as we justapose the hype in the field against the real-world benefits we see, it raises the question: Are we coming up on an AI winter
07.08.2018 Rerelease: How to Find New Things to Learn We like learning on vacation. And we're on vacation, so we thought we'd re-air this episode about how to learn. Original Episode: https://lineardigressions.com/episodes/2017/5/14/how-to-find-new-things-to-learn Original Summary: If you're anything like us, you a) always are curious to learn more about data ...
07.02.2018 Rerelease: Space Codes We're on vacation on Mars, so we won't be communicating with you all directly this week. Though, if we wanted to, we could probably use this episode to help get started. Original Episode: http://lineardigressions.com/episodes/2017/3/19/space-codes Original Summary: It's hard to get ...
06.25.2018 Rerelease: Anscombe's Quartet We're on vacation, so we hope you enjoy this episode while we each sip cocktails on the beach. Original Episode: http://lineardigressions.com/episodes/2017/6/18/anscombes-quartet Original Summary: Anscombe's Quartet is a set of four datasets that have the same mean, variance and correlation but ...
06.18.2018 Rerelease: Hurricanes Produced Now that hurricane season is upon us again (and we are on vacation), we thought a look back on our hurricane forecasting episode was prudent. Stay safe out there.
06.11.2018 GDPR By now, you have probably heard of GDPR, the EU's new data privacy law. It's the reason you've been getting so many emails about everyone's updated privacy policy. In this episode, we talk about some of the potential ramifications of ...
06.03.2018 Git for Data Scientists If you're a data scientist, chances are good that you've heard of git, which is a system for version controlling code. Chances are also good that you're not quite as up on git as you want to be--git has a ...
05.20.2018 Analytics Maturity Data science and analytics are hot topics in business these days, but for a lot of folks looking to bring data into their organization, it can be hard to know where to start and what it looks like when they're ...
05.13.2018 SHAP: Shapley Values in Machine Learning Shapley values in machine learning are an interesting and useful enough innovation that we figured hey, why not do a two-parter? Our last episode focused on explaining what Shapley values are: they define a way of assigning credit for outcomes ...
05.07.2018 Game Theory for Model Interpretability: Shapley Values As machine learning models get into the hands of more and more users, there's an increasing expectation that black box isn't good enough: users want to understand why the model made a given prediction, not just what the prediction itself ...
04.30.2018 AutoML If you were a machine learning researcher or data scientist ten years ago, you might have spent a lot of time implementing individual algorithms like decision trees and neural networks by hand. If you were doing that work five years ...
04.23.2018 CPUs, GPUs, TPUs: Hardware for Deep Learning A huge part of the ascent of deep learning in the last few years is related to advances in computer hardware that makes it possible to do the computational heavy lifting required to build models with thousands or even millions ...
04.16.2018 A Technical Introduction to Capsule Networks Last episode we talked conceptually about capsule networks, the latest and greatest computer vision innovation to come out of Geoff Hinton's lab. This week we're getting a little more into the technical details, for those of you ready to have ...
04.09.2018 A Conceptual Introduction to Capsule Networks Convolutional nets are great for image classification... if this were 2016. But it's 2018 and Canada's greatest neural networker Geoff Hinton has some new ideas, namely capsule networks. Capsule nets are a completely new type of neural net architecture designed ...
04.02.2018 Convolutional Neural Nets If you've done image recognition or computer vision tasks with a neural network, you've probably used a convolutional neural net. This episode is all about the architecture and implementation details of convolutional networks, and the tricks that make them so ...
03.26.2018 Google Flu Trends It's been a nasty flu season this year. So we were remembering a story from a few years back (but not covered yet on this podcast) about when Google tried to predict flu outbreaks faster than the Centers for Disease ...
03.19.2018 How to pick projects for a professional data science team This week's episodes is for data scientists, sure, but also for data science managers and executives at companies with data science teams. These folks all think very differently about the same question: what should a data science team be working ...
03.12.2018 Autoencoders Autoencoders are neural nets that are optimized for creating outputs that... look like the inputs to the network. Turns out this is a not-too-shabby way to do unsupervised machine learning with neural nets.
03.05.2018 When Private Data Isn't Private Anymore After all the back-patting around making data science datasets and code more openly available, we figured it was time to also dump a bucket of cold water on everyone's heads and talk about the things that can go wrong when ...
02.26.2018 What makes a machine learning algorithm "superhuman"? A few weeks ago, we podcasted about a neural network that was being touted as "better than doctors" in diagnosing pneumonia from chest x-rays, and how the underlying dataset used to train the algorithm raised some serious questions. We're back ...
02.19.2018 Open Data and Open Science One interesting trend we've noted recently is the proliferation of papers, articles and blog posts about data science that don't just tell the result--they include data and code that allow anyone to repeat the analysis. It's far from universal (for ...
02.12.2018 Defining the quality of a machine learning production system Building a machine learning system and maintaining it in production are two very different things. Some folks over at Google wrote a paper that shares their thoughts around all the items you might want to test or check for your ...
02.04.2018 Auto-generating websites with deep learning We've already talked about neural nets in some detail (links below), and in particular we've been blown away by the way that image recognition from convolutional neural nets can be fed into recurrent neural nets that generate descriptions and captions ...
01.29.2018 The Case for Learned Index Structures, Part 2: Hash Maps and Bloom Filters Last week we started the story of how you could use a machine learning model in place of a data structure, and this week we wrap up with an exploration of Bloom Filters and Hash Maps. Just like last week, ...

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