How the sausage gets made

FromLinear Digressions

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How the sausage gets made

FromLinear Digressions

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Length:

29 minutes

Released:

Jun 20, 2016

Format:

Podcast episode

Description

Something a little different in this episode--we'll be talking about the technical plumbing that gets our podcast from our brains to your ears. As it turns out, it's a multi-step bucket brigade process of RSS feeds, links to downloads, and lots of hand-waving when it comes to trying to figure out how many of you (listeners) are out there.

Released:

Jun 20, 2016

Format:

Podcast episode

Titles in the series (100)

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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Hunting for the Higgs: Machine learning and particle physics go together… by Linear Digressions
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Introducing Hidden Markov Models (HMM Part 1): Wikipedia says, "A hidden Markov model (HMM) is a… by Linear Digressions
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That's "Dr Katie" to You: Katie successfully defended her thesis! We celebr… by Linear Digressions
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Computers Try to Tell Jokes: Computers are capable of many impressive feats, b… by Linear Digressions
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The [Expletive Deleted] Problem: The town of [expletive deleted], England, is resp… by Linear Digressions
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How Outliers Helped Defeat Cholera: In the 1850s, there were a lot of things we didn’… by Linear Digressions
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Random Kanye: Ever feel like you could randomly assemble words … by Linear Digressions
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A Criminally Short Introduction to Semi Supervised Learning: Because there are more interesting problems than … by Linear Digressions
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Stats of World Series Streaks: Baseball is characterized by a high level of equa… by Linear Digressions
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Statistical Mistakes and the Challenger Disaster: After the Challenger exploded in 1986, killing al… by Linear Digressions
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Modeling Particles in Atomic Bombs: In a fun historical journey, Katie and Ben explor… by Linear Digressions
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Electoral Insights (Part 1): The first of our two-parter discussing the recent… by Linear Digressions
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Facial Recognition with Eigenfaces: A true classic topic in ML: Facial recognition is… by Linear Digressions
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Lie Detectors: Often machine learning discussions center around … by Linear Digressions
9 min listen
The State of Data Science: How many data scientists are there, where do they… by Linear Digressions
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Kalman Runners: The Kalman Filter is an algorithm for taking nois… by Linear Digressions
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Um Detector 1: So, um... what about machine learning for audio a… by Linear Digressions
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Troll Detection: Ever found yourself wasting time reading online c… by Linear Digressions
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Monte Carlo For Physicists: This is another physics-centered podcast, about a… by Linear Digressions
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The Enron Dataset: In 2000, Enron was one of the largest and compani… by Linear Digressions
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Better Facial Recognition with Fisherfaces: Now that we know about eigenfaces (if you don't, … by Linear Digressions
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Data Science for Making the World a Better Place: There's a good chance that great data science is … by Linear Digressions
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Labels and Where To Find Them: Supervised classification is built on the backs o… by Linear Digressions
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Genetics and Um Detection (HMM Part 2): In part two of our series on Hidden Markov Models… by Linear Digressions
15 min listen
PFun with P Values: Doing some science, and want to know if you might… by Linear Digressions
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Inferring Authorship (Part 1): This episode is inspired by one of our projects f… by Linear Digressions
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Neural Nets (Part 1): There is no known learning algorithm that is more… by Linear Digressions
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Watson: This machine learning algorithm beat the human ch… by Linear Digressions
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Falsifying Data: In the first of a few episodes on fraud in electi… by Linear Digressions
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Inferring Authorship (Part 2): Now that we’re up to speed on the classic author … by Linear Digressions
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Racism, the criminal justice system, and data science: As protests sweep across the United States in the… by Linear Digressions
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An interstitial word from Ben: A message from Ben around algorithmic bias, and h… by Linear Digressions
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Protecting Individual-Level Census Data with Differential Privacy: The power of finely-grained, individual-level dat… by Linear Digressions
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Causal Trees: What do you get when you combine the causal infer… by Linear Digressions
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Gaussian Processes: It’s pretty common to fit a function to a dataset… by Linear Digressions
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Keeping ourselves honest when we work with observational healthcare data: The abundance of data in healthcare, and the valu… by Linear Digressions
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Changing our formulation of AI to avoid runaway risks: Interview with Prof. Stuart Russell: AI is evolving incredibly quickly, and thinking n… by Linear Digressions
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Putting machine learning into a database: Most data scientists bounce back and forth regula… by Linear Digressions
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The work-from-home episode: Many of us have the privilege of working from hom… by Linear Digressions
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Understanding Covid-19 transmission: what the data suggests about how the disease spreads: Covid-19 is turning the world upside down right n… by Linear Digressions
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Network effects re-release: when the power of a public health measure lies in widespread adoption: This week’s episode is a re-release of a recent e… by Linear Digressions
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Causal inference when you can't experiment: difference-in-differences and synthetic controls: When you need to untangle cause and effect, but y… by Linear Digressions
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Better know a distribution: the Poisson distribution: This is a re-release of an episode that originall… by Linear Digressions
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The Lottery Ticket Hypothesis: Recent research into neural networks reveals that… by Linear Digressions
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Interesting technical issues prompted by GDPR and data privacy concerns: Data privacy is a huge issue right now, after yea… by Linear Digressions
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Thinking of data science initiatives as innovation initiatives: Put yourself in the shoes of an executive at a bi… by Linear Digressions
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Building a curriculum for educating data scientists: Interview with Prof. Xiao-Li Meng: As demand for data scientists grows, and it remai… by Linear Digressions
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Running experiments when there are network effects: Traditional A/B tests assume that whether or not … by Linear Digressions
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Zeroing in on what makes adversarial examples possible: Adversarial examples are really, really weird: pi… by Linear Digressions
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Unsupervised Dimensionality Reduction: UMAP vs t-SNE: Dimensionality reduction redux: this episode cove… by Linear Digressions
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Discover this podcast and so much more

How the sausage gets made

How the sausage gets made

Description

Titles in the series (100)

More Episodes from Linear Digressions

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