Procella, the system they built, is the topic of our episode today: by deconstructing the system, we dig into the four motivating uses of this system, the complexity they had to introduce to service all four uses simultaneously, and the impressive engineering that has to go into building something that “just works.”, Open source software is ubiquitous throughout data science, and enables the work of nearly every data scientist in some way or another. IMPORTANT NON-DATA SCIENCE CHICAGO MARATHON RACE RESULT FROM KATIE… Linear Digressions. Procella, the system they built, is the topic of our episode today: by deconstructing the system, we dig into the four motivating uses of this system, the complexity they had to introduce to service all four uses simultaneously, and the impressive engineering that has to go into building something that âjust works.â, Open source software is ubiquitous throughout data science, and enables the work of nearly every data scientist in some way or another. Since most episodes are in the format of Katie teaching Ben about some aspect of data science, Iâve been able to listen and learn about tons of different aspects of data science. Thanks, best wishes, and good night!—Katie and Ben, A Reality Check on AI-Driven Medical Assistants. Linear Digressions is a podcast about machine learning and data science. 14:00 A Reality Check on AI-Driven Medical Assistants Jul 19, 2020. This episode of Linear Digressions is a little different from most, as we’ll be interviewing a guest, one of my (Katie’s) friends from particle physics, Alex Radovic. Likewise, an algorithm getting a prediction mostly correct might not be an overall benefit if it introduces more dramatic failures when the prediction happens to be wrong. 10/10 Definitely would recommend you check out this podcast if youâre interested in learning (more) about data science. Open source projects, however, are disproportionately maintained by a small number of individuals, some of whom are institutionally supported, but many of whom do this maintenance on a purely volunteer basis. Technology; Episodes. Data science, machine learning, artificial intelligence are topics that are big, messy, and full of things to discover. Linear Digressions is a podcast about machine learning and data science. Latest Episodes. The data science and artificial intelligence community has made amazing strides in the past few years to algorithmically automate portions of the healthcare process. ... katie@lineardigressions.com . Linear Digressions is a podcast about machine learning and data science. Very well presented, and the hosts are ace. ), and marveling at how this thing that started out as a side project grew into a huge part of our lives for over 5 years.It’s been a ride, and a real pleasure and privilege to talk to you each week. – Ouça o Linear Digressions instantaneamente no seu tablet, telefone ou navegador - sem fazer qualquer download. Followers Plays. Linear Digressions is a podcast about machine learning and data science. In each episode, your hosts explore machine learning and data science through interesting (and often very unusual) applications. When a Case Field is specified, the input line features are first grouped according to case field values, and then an output line feature is created for each group. 35:44 So long, and thanks for all the fish Jul 26, 2020. Inside a Data Analysis: Fraud Hunting at Enron by Linear Digressions published on 2016-05-16T02:36:10Z It's storytime this week--the story, from beginning to end, of how Katie designed and built the main project for Udacity's Intro to Machine … This episode looks at two computer vision algorithms, one that diagnoses diabetic retinopathy and another that classifies liver cancer, and asks the questionâare patients now getting better care, and achieving better outcomes, with these algorithms in the mix? At least one NEW video is posted each week. Linear Digressions is a podcast about machine learning and data science. Follow Share. This is the last episode we plan to release, and it doesnât cover data scienceâitâs mostly reminiscing, thanking our wonderful audience (thatâs you! A Reality Check on AI-Driven Medical Assistants. Free videos about Excel, Power Pivot, Power Query and Power BI. Machine learning is being used to solve a ton of interesting problems, and to accomplish goals that … Linear Digressions. That problem has motivated the study of differential privacy, a set of techniques and definitions for … In each episode, your hosts explore machine learning and data science through interesting (and often very unusual) applications. No signup or install needed. On a recent episode of the Linear Digressions podcast, Katie and Ben talked about a situation in Uber where it might make sense to model a conditional quantile function. Machine learning is being used to solve a ton of interesting problems, and to accomplish goals that were out … Episodes cover a wide variety of interesting topics and applicationsânot exclusively academic or esoteric. Stream Tracks and Playlists from Linear Digressions on your desktop or mobile device. ), and marveling at how this thing that started out as a side project grew into a huge part of our lives for over 5 years.Itâs been a ride, and a real pleasure and privilege to talk to you each week. This episode looks at two computer vision algorithms, one that diagnoses diabetic retinopathy and another that classifies liver cancer, and asks the question—are patients now getting better care, and achieving better outcomes, with these algorithms in the mix? Richard M. Golden, Ph.D., M.S.E.E., B.S.E.E. That’s not really a resource, sorry, but I think it’s important. The answer isnât no, exactly, but itâs not a resounding yes, because these algorithms interact with a very complex system (the healthcare system) and other shortcomings of that system are proving hard to automate away. ~ Katie Malone, thank you! Great fun listening to, and learning from, well researched discussions on interesting data science topics. Procella: YouTube's super-system for analytics data storage, This is a re-release of an episode that originally ran in October 2019.If youâre trying to manage a project that serves up analytics data for a few very distinct uses, youâd be wise to consider having custom solutions for each use case that are optimized for the needs and constraints of that use cases. Play Episode. A Data Science Take on Open Policing Data. Machine learning is being used to solve a ton of interesting problems, and to accompl… Linear Digressions (पॉडकास्ट) - Ben Jaffe and Katie Malone | Listen Notes 328 21286 Linear Digressions is a podcast about machine learning and data science. Linear Digressions is a podcast about machine learning and data science. The data science and artificial intelligence community has made amazing strides in the past few years to algorithmically automate portions of the healthcare process. It's quite common for survey respondents not to be representative of the larger population from which they are drawn. Linear Digressions by Ben Jaffe and Katie Malone Podcast by Ben Jaffe and Katie Malone. Linear Digressions Udacity Technology 4.9 • 38 Ratings; Listen on Apple Podcasts. Ben Jaffe and Katie Malone. Details. Machine learning is being used to solve a ton of interesting problems, and to accompl… Linear Digressions (podcast) - Ben Jaffe and Katie Malone | Listen Notes HTML5 audio not supported. 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. Drop us a line! 95 Followers 335 Plays. 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. Richard M. Golden, Ph.D., M.S.E.E., B.S.E.E. 95 Followers 335 Plays. Linear Digressions. Copyright © 2020 Apple Inc. All rights reserved. OVERVIEW EPISODES YOU MAY ALSO LIKE. Data science management isn’t easy, and many data scientists are finding themselves learning on the job how to manage data science teams as they get promoted into more formal leadership roles. Linear Digressions. Linear Digressions is a podcast about machine learning and data science. All-In with Chamath, Jason, Sacks & Friedberg. Linear Digressions by Ben Jaffe and Katie Malone. This is a re-release of an episode that first ran on January 29, 2017.This week: everybody's favorite WWII-era classifier metric! A few weeks ago, we put out a call for data scientists interested in issues of race and racism, or people studying how those topics can be studied with data science methods, should get in touch to come talk to our audience about their work. Follow Share. A great podcast, and I highly recommend. Details. 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. I love listening to this show. You also wouldn’t be YouTube, which found themselves with this problem (gigantic data needs and several very different use cases of what they needed to do with that data) and went a different way: they built one analytics data system to serve them all. Ben Jaffe and Katie Malone. – Listen to Linear Digressions instantly on your tablet, phone or browser - no downloads needed. Share on Facebook Share on Twitter Share by Email. 291 EpisodesProduced by Ben Jaffe and Katie MaloneWebsite. Linear Digressions is a podcast about machine learning and data science. Linear Digressions. 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. Linear Digressions. Linear Digressions Ben Jaffe and Katie Malone. 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. Explorations in Machine Learning and Data Science. Let’s make this more concrete. Love the puns! 1936 Followers. Ben Jaffe and Katie Malone. Follow Share. In 10 minutes, each of these podcasts teaches me what would've taken days of reading to learn on my ownThe friendly, funny, conversational tone makes easy listening while the content still makes you think deeply about problems and learn a lot along the wayThese two make a great team between Katie's knowledge and experience in the field of data, and Ben's remarkable ability to spot curiosities in descriptions and pluck insightful metaphors or examples from the airI'm thoroughly enjoying these, listening to them in my spare time for pleasure, while also finding them incredibly relevant to my past studies and current work, All-In with Chamath, Jason, Sacks & Friedberg. Linear Digressions. Itâs very informative but the tone is still pretty conversational, so itâs easy to follow along without having a PhD and 10 years of experience. Ben Jaffe and Katie Malone. 291 EpisodesProduced by Ben Jaffe and Katie MaloneWebsite. This is the best Data Science Podcast right now. The answer isn’t no, exactly, but it’s not a resounding yes, because these algorithms interact with a very complex system (the healthcare system) and other shortcomings of that system are proving hard to automate away. A few weeks ago, we put out a call for data scientists interested in issues of race and racism, or people studying how those topics can be studied with data science methods, should get in touch to come talk to our audience about their work. Getting a faster diagnosis from an image might not be an improvement if the image is now harder to capture (because of strict data quality requirements associated with the algorithm that wouldn’t stop a human doing the same job). The health of the data science ecosystem depends on the support of open source projects, on an individual and institutional level.https://hdsr.mitpress.mit.edu/pub/xsrt4zs2/release/2. 30 min 2020 JUN 15. So long, and thanks for all the fish. Procella: YouTube's super-system for analytics data storage, This is a re-release of an episode that originally ran in October 2019.If you’re trying to manage a project that serves up analytics data for a few very distinct uses, you’d be wise to consider having custom solutions for each use case that are optimized for the needs and constraints of that use cases. Linear Digressions By Ben Jaffe and Katie Malone. Katie and Ben, keep it up! The Kalman Filter is an algorithm for taking noisy measurements of dynamic systems and using them to get a better idea of the underlying dynamics than you could get from a simple extrapolation. Ben Jaffe and Katie Malone. Ben Jaffe and Katie Malone. Open in Pocket Casts. But it's not just for winning wars, it's a fantastic go-to metric for all your classifier quality needs. Ben Jaffe and Katie Malone. 27 min 2020 MAY 25. Technology. This is the last episode we plan to release, and it doesn’t cover data science—it’s mostly reminiscing, thanking our wonderful audience (that’s you! lineardigressions.com. Ben and Katie seem like would be excellent colleagues to chat data science with, but this podcast is the next best thing. O’Reilly recently release a report, written by yours truly (Katie) and another experienced data science manager, Michelangelo D’Agostino, where we lay out the most important tasks of … Linear Digressions is a podcast about machine learning and data science. The power of finely-grained, individual-level data comes with a drawback: it compromises the privacy of potentially anyone and everyone in the dataset. Linear Digressions Episodes; Contact; We make this podcast for you. Subscribe to this podcast. Linear Digressions is a podcast about machine learning and data science. Name * Email Address * Subject * Message Thanks for your message! For every data scientist whose work is deployed into some kind of product, and is being used to solve real-world problems, these papers underscore how important and difficult it is to consider all the context around those problems. Details About Us. Even for de-identified datasets, there can be ways to re-identify the records or otherwise figure out sensitive personal information. A Data Science Take on Open Policing Data. Linear Digressions. Linear Digressions is a podcast about machine learning and data science. Thanks, best wishes, and good night!âKatie and Ben, A Reality Check on AI-Driven Medical Assistants. Beginner … Her explanations are always clean and easy to understand, and Benâs questions are always illuminating and clarifying. This is a re-release of an episode that first ran on January 29, 2017.This week: everybody's favorite WWII-era classifier metric! Play Episode. The case field can be of integer, date, or string type. We love hearing from you. Linear Digressions is a podcast about machine learning and data science. Linear Digressions is a podcast about machine learning and data science. This is a re-release of an episode that was originally released on February 26, 2017. ... which is something that Katie does a lot for her day job at Civis Analytics. This week weâre excited to bring on Todd Hendricks, Bay Area data scientist and a volunteer who reached out to tell us about his studies with the Stanford Open Policing dataset. Welcome to Katie Analytics. The Case Field is used to group features for separate linear directional mean computations. 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. For every data scientist whose work is deployed into some kind of product, and is being used to solve real-world problems, these papers underscore how important and difficult it is to consider all the context around those problems. Linear Digressions is a podcast about machine learning and data science. All good things must come to an end, including this podcast. Linear Digressions. Technologie; Linear Digressions is a podcast about machine learning and data science. Follow Share. This week we’re excited to bring on Todd Hendricks, Bay Area data scientist and a volunteer who reached out to tell us about his studies with the Stanford Open Policing dataset. Listen to A Data Scientist's View Of The Fight Against Cancer and 290 more episodes by Linear Digressions, free! In each episode, your hosts explore machine learning and data science through interesting (and often very unusual) applications. 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