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FromLinear Digressions

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FromLinear Digressions

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Length:

20 minutes

Released:

Jun 5, 2017

Format:

Podcast episode

Description

The year: 1998. The size of the web: 150 million pages. The problem: information retrieval. How do you find the "best" web pages to return in response to a query? A graduate student named Larry Page had an idea for how it could be done better and created a search engine as a research project. That search engine was called Google.

Released:

Jun 5, 2017

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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Facial Recognition with Eigenfaces: A true classic topic in ML: Facial recognition is… by Linear Digressions
10 min listen
Inferring Authorship (Part 1): This episode is inspired by one of our projects f… by Linear Digressions
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Random Number Generation: Let's talk about randomness! Although randomness … 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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Careers in Data Science: Let’s talk money. As a “hot” career right now, da… 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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Labels and Where To Find Them: Supervised classification is built on the backs o… by Linear Digressions
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Hunting for the Higgs: Machine learning and particle physics go together… 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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Introducing Hidden Markov Models (HMM Part 1): Wikipedia says, "A hidden Markov model (HMM) is a… by Linear Digressions
15 min listen
Um Detector 1: So, um... what about machine learning for audio a… by Linear Digressions
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Yiddish Translation: Imagine a language that is mostly spoken rather t… 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
11 min listen
The Enron Dataset: In 2000, Enron was one of the largest and compani… by Linear Digressions
12 min listen
Lie Detectors: Often machine learning discussions center around … by Linear Digressions
9 min listen
Better Facial Recognition with Fisherfaces: Now that we know about eigenfaces (if you don't, … by Linear Digressions
12 min listen
Falsifying Data: In the first of a few episodes on fraud in electi… by Linear Digressions
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Bayesian Psychics: Come get a little "out there" with us this week, … by Linear Digressions
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Indie Announcement: We've gone indie! Which shouldn't change anythin… by Linear Digressions
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Neural Nets (Part 2): In the last episode, we zipped through neural net… by Linear Digressions
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Random Kanye: Ever feel like you could randomly assemble words … 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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Computers Try to Tell Jokes: Computers are capable of many impressive feats, b… by Linear Digressions
9 min listen
Troll Detection: Ever found yourself wasting time reading online c… by Linear Digressions
13 min listen
Sold! Auctions (Part 2): The Google ads auction is a special kind of aucti… by Linear Digressions
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The State of Data Science: How many data scientists are there, where do they… by Linear Digressions
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PFun with P Values: Doing some science, and want to know if you might… 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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A Criminally Short Introduction to Semi Supervised Learning: Because there are more interesting problems than … by Linear Digressions
9 min listen
Thresholdout: Down with Overfitting: Overfitting to your training data can be avoided … by Linear Digressions
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Procella: YouTube's super-system for analytics data storage: This is a re-release of an episode that originall… by Linear Digressions
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Rock the ROC Curve: This is a re-release of an episode that first ran… by Linear Digressions
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Criminology and Data Science: This episode features Zach Drake, a working data … 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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Convolutional Neural Networks: This is a re-release of an episode that originall… 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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The Grammar Of Graphics: You may not realize it consciously, but beautiful… 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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Titles in the series (100)

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