Podcasts about linear digressions

Ben Jaffe deciding to end his fantastic podcast Linear Digressions, Lyle celebrating 20 years hosting GeekSpeak, and geeking out on playing instruments that do not have “frets” like the Cello and Trombone.

This episode features Prof. Andrew Lo, the author of a paper that we discussed recently on Linear Digressions, in which Prof. Lo uses data to predict whether a medicine in the development pipeline will eventually go on to win FDA approval. This episode gets into the story behind that paper: how the approval prospects of different drugs inform the investment decisions of pharma companies, how to stitch together siloed and incomplete datasts to form a coherent picture, and how the academics building some of these models think about when and how their work can make it out of academia and into industry. Professor Lo is an expert in business (he teaches at the MIT Sloan School of Management) and work like his shows how data science can open up new ways of doing business. Relevant links: https://hdsr.mitpress.mit.edu/pub/ct67j043

This is a re-release of an episode that first ran on April 9, 2017. In our follow-up episode to last week's introduction to the first self-driving car, we will be doing a technical deep dive this week and talking about the most important systems for getting a car to drive itself 140 miles across the desert. Lidar? You betcha! Drive-by-wire? Of course! Probabilistic terrain reconstruction? Absolutely! All this and more this week on Linear Digressions.

Generative Adversarial Networks are a pattern of Machine Learning that can do some amazing things – in this episode we chat about them effecting our concepts of truth. And we include an episode of Linear Digressions from Ben and Katie to really explain how GANs really work.This Person Does Not Exist Is the Best One-Off Website of 2019How to recognize fake AI-generated imagesNeural Nets Play Cops and Robbers (aka generative adversarial networks) — Linear Digressions

You’d be hard-pressed to find a field with bigger, richer, and more scientifically valuable data than particle physics. Years before “data scientist” was even a term, particle physicists were inventing technologies like the world wide web and cloud computing grids to help them distribute and analyze the datasets required to make particle physics discoveries. Somewhat counterintuitively, though, deep learning has only really debuted in particle physics in the last few years, although it’s making up for lost time with many exciting new advances. 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. Alex and his colleagues have been at the forefront of machine learning in physics over the last few years, and his perspective on the strengths and shortcomings of those two fields together is a fascinating one.

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.

Katie Malone (@multiarmbandit) works in data science, has podcast about machine learning, and has a Phd in Physics. We mostly talked about machine learning, ways to kill people, mathematics, and impostor syndrome. Katie is the host of the Linear Digressionspodcast (@LinDigressions). She recommended the Linear Digressions interview with Matt Mightas something Embedded listeners might enjoy. Katie and Ben also recently did a show about git. Katie taught Udacity’s Intro to Machine Learningcourse (free!). She also recommends the Andrew Ng Machine Learning Coursera course. Neural nets can be fooled in hilarious ways: Muffins vs dogs, Labradoodles vs chicken, and more. Intentional, adversarial attacks are also possible. Impostor syndromeis totally a thing. We’ve talked about it before. You might recognize the discussion methodology from Embedded #24: I’m a Total Fraud. Katie works at Civis Analyticsand they are hiring.

An end-of-year episode chock full of topics! Roger and Hilary discuss using Excel, detecting serial killers, counting hurricane-related deaths, and whether it's possible to evaluate a data analysis without knowing who the author is.  If you are a Spotify user, you can now listen to the podcast on Spotify! Just use the search function and subscribe to Not So Standard Deviations in the app. Show notes: The serial killer detector: https://www.newyorker.com/magazine/2017/11/27/the-serial-killer-detector Puerto Rico death toll: https://www.nytimes.com/interactive/2017/12/08/us/puerto-rico-hurricane-maria-death-toll.html Machine learning is alchemy? https://twitter.com/haldaume3/status/938478425777377280 Tyler Schnoebelen on GANS https://twitter.com/tschnoebelen/status/939128421719961600 Excel follow up: https://www.wsj.com/articles/finance-pros-say-youll-have-to-pry-excel-out-of-their-cold-dead-hands-1512060948 LHC on Linear Digressions: http://lineardigressions.com/episodes/2016/2/23/hunting-for-the-higgs Support us through our Patreon page: https://www.patreon.com/NSSDeviations Roger on Twitter: https://twitter.com/rdpeng Hilary on Twitter: https://twitter.com/hspter Get the Not So Standard Deviations book: https://leanpub.com/conversationsondatascience/ Subscribe to the podcast on Apple Podcasts: https://itunes.apple.com/us/podcast/not-so-standard-deviations/id1040614570 Subscribe to the podcast on Google Play: https://play.google.com/music/listen?u=0#/ps/Izfnbx6tlruojkfrvhjfdj3nmna Find past episodes: http://nssdeviations.com Contact us at nssdeviations@gmail.com

Hilary and Roger discuss when to hire a data scientist, the Kaggle State of Data Science and Machine Learning Survey, and the lack of tools for tracking changes to data. Show notes: Stickers at Sonos: https://twitter.com/jahilliar/status/926153450857082880 John Carmack on git: https://twitter.com/id_aa_carmack/status/929389759624916992 Linear Digressions episode on “Data Lineage”: http://lineardigressions.com/episodes/2017/9/3/data-lineage Kaggle State of Data Science Survey: https://www.kaggle.com/surveys/2017 Support us through our Patreon page: https://www.patreon.com/NSSDeviations Roger on Twitter: https://twitter.com/rdpeng Hilary on Twitter: https://twitter.com/hspter Get the Not So Standard Deviations book: https://leanpub.com/conversationsondatascience/ Subscribe to the podcast on Apple Podcasts: https://itunes.apple.com/us/podcast/not-so-standard-deviations/id1040614570 Subscribe to the podcast on Google Play: https://play.google.com/music/listen?u=0#/ps/Izfnbx6tlruojkfrvhjfdj3nmna Find past episodes: http://nssdeviations.com Contact us at nssdeviations@gmail.com  

In our follow-up episode to last week's introduction to the first self-driving car, we will be doing a technical deep dive this week and talking about the most important systems for getting a car to drive itself 140 miles across the desert.  Lidar?  You betcha!  Drive-by-wire?  Of course!  Probabilistic terrain reconstruction?  Absolutely!  All this and more this week on Linear Digressions.

Show host Edaena Salinas talks with Katie Malone about Machine Learning.  Katie Malone is a Data Scientist in the Research and Development department at Civis Analytics. She is also an instructor of the Intro to Machine Learning online course from Udacity and host of Linear Digressions, a podcast about machine learning. Topics include: machine learning, data science, a career in machine learning.

We talk to Shirley Wu and Nadieh Bremer, long-time members of the D3 and data visualization communities, about their latest collaboration DataSketches and building data visualization using web technologies.   Panelists Shirley Wu @sxywu http://sxywu.com/ Nadieh Bremer @NadiehBremer http://www.visualcinnamon.com/   Hosts Tracy Lee @ladyleet Ray Shan @rayshan https://shan.io   Links Data Sketches http://www.datasketch.es/ Bay Area d3 User Group http://www.meetup.com/Bay-Area-d3-User-Group/ D3.unconf, an annual D3-focused conference http://visfest.com/d3unconf-2016/ Using Pinterest to collect inspiration https://www.pinterest.com/nadiehbremer/ R for data processing https://www.r-project.org/ Linear Digressions podcast - What's the biggest #bigdata? http://lineardigressions.com/episodes/2016/7/30/whats-the-biggest-bigdata Create React App to quickly bootstrap a data visualization project using React https://github.com/facebookincubator/create-react-app Mike Bostock, author of D3, and his visualization work on New York Times https://bost.ocks.org/mike/ D3 4.0 with improved force layout and modularity https://github.com/d3/d3/releases/tag/v4.0.0 Charting libraries Highcharts http://www.highcharts.com/ NVD3, built on top of D3 http://nvd3.org/

The town of [expletive deleted], England, is responsible for the clbuttic [expletive deleted] problem. This week on Linear Digressions: we try really hard not to swear too much. Related links: https://en.wikipedia.org/wiki/Scunthorpe_problem https://www.washingtonpost.com/news/worldviews/wp/2016/01/05/where-is-russia-actually-mordor-in-the-world-of-google-translate/

We've gone indie! Which shouldn't change anything about the podcast that you know and love, but we're super excited to keep bringing you Linear Digressions as a fully independent podcast. Some links mentioned in the show: https://twitter.com/lindigressions https://twitter.com/benjaffe https://twitter.com/multiarmbandit https://soundcloud.com/linear-digressions http://lineardigressions.com/