תוכן מסופק על ידי Erik Partridge. כל תוכן הפודקאסטים כולל פרקים, גרפיקה ותיאורי פודקאסטים מועלים ומסופקים ישירות על ידי Erik Partridge או שותף פלטפורמת הפודקאסט שלהם. אם אתה מאמין שמישהו משתמש ביצירה שלך המוגנת בזכויות יוצרים ללא רשותך, אתה יכול לעקוב אחר התהליך המתואר כאן https://he.player.fm/legal.
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Diego Pavia and Paul Finebaum are this week’s Netflix Sports Club guests. Paul Finebaum, the voice of the SEC, drops a bold national championship prediction that might give the Big Tenners pause, and he reveals which SEC quarterback has him starstruck. Vanderbilt quarterback, Diego Pavia, gives a standout performance on and off the field in SEC Football: Any Given Saturday. He relives that improbable ‘Bama victory, including pre-game routine and why he thinks this victory changed the conversation about Vandy football - sorry Nick Saban. Paul Fineman and Diego Pavia gaze into the crystal ball of the 2025 season, and what do they see? Heisman Trophies, making Auburn pay, and LSU’s chances for glory. In this interview, Vandy QB, Diego Pavia: 🟥 Details the 2024 Vandy vs. ‘Bama Buildup 🟥 Discusses playing for Coach Lea and with his bestie 🟥 Reveals his true height In this interview, SEC Expert, Paul Finebaum answers: 🟥 Why the SEC’s staying power is unrivaled? 🟥 Who’s almost a bigger star than Taylor Swift? 🟥 Which team will win the 2025 National Championship? 00:00 Intro 01:24 Vandy QB Diego Pavia Is a Star 01:37 Beating Bama 03:19 Choosing Vandy And Coach Lea 04:58 Give Me The Ball 06:20 Dude Put In The Work 07:38 Nick Saban Said What?? 08:20 Underdogs 10:22 Watch Out Auburn 12:33 Recruits: Come To Vandy! 13:29 Kay and Dani Talk SEC 15:54 Paul Finebaum’s SEC Picks 16:40 All About Arch Manning 17:23 SEC: The Only Game In Town 18:25 South Carolina Should Be In The Playoffs 19:40 Shane Beamer Bounce Back? 20:14 Arch Manning Is No Taylor Swift 21:42 The Weight of Being a Manning 22:31 Finebaum: “Best Player Since Tim Tebow” 23:20 LSU QB1 + Championship Or Bust 25:39 Post Saban Alabama 27:42 Is Vanderbilt Legit? 28:44 Can Mississippi State Survive? 29:49 The Vols “Will Struggle This Year” 31:44 SEC: Natty. Little Tenners: Nothing 33:09 Outro 34:28 Up Next - America’s Team: The Gambler And His Cowboys 🏈 Diego Pavia Instagram - https://bit.ly/45uLND4 TikTok - http://bit.ly/4fsLY5p X - https://bit.ly/4ldwx2j 🏈 Vanderbilt Instagram - http://bit.ly/4lfo8eu X - http://bit.ly/4mCiaWt YouTube - @vucommodores 🏈 Paul Finebaum Instagram - https://bit.ly/45kVtPv X - https://bit.ly/46JQFWc 🎙️ Kay Adams Instagram - http://bit.ly/3GYp4Go TikTok - http://bit.ly/4m7KmR9 X - http://bit.ly/45nI2Ou 🎙️ Dani Klupenger Instagram - https://bit.ly/3HeGGxx TikTok - https://bit.ly/4lQSBkl X - https://bit.ly/4lWpufr 🟥 Netflix Sports Instagram - http://bit.ly/45CPAhL TikTok - http://bit.ly/4mti6Ia X - http://bit.ly/4mseqGH Facebook - http://bit.ly/45o5xqK YouTube - @NetflixSports We want to hear from you! Leave us a voice message at www.speakpipe.com/NetflixSportsClub Be sure to watch, listen, and subscribe to the Netflix Sports Club Podcast on YouTube, Spotify, Tudum, and wherever you get your podcasts. Hosted by Kay Adams, the Netflix Sports Club Podcast is an all-access deep dive into the Netflix Sports Universe. Join Kay as she speaks with athletes, coaches, and top sports correspondents to break down the latest Netflix Sports series. Expect bold opinions, insightful analysis, and candid conversations you won’t find anywhere else.…
תוכן מסופק על ידי Erik Partridge. כל תוכן הפודקאסטים כולל פרקים, גרפיקה ותיאורי פודקאסטים מועלים ומסופקים ישירות על ידי Erik Partridge או שותף פלטפורמת הפודקאסט שלהם. אם אתה מאמין שמישהו משתמש ביצירה שלך המוגנת בזכויות יוצרים ללא רשותך, אתה יכול לעקוב אחר התהליך המתואר כאן https://he.player.fm/legal.
Stratified sampling provides a mechanism by which to split a larger dataset into smaller pieces. While random approaches are commonly used, stratified sampling ensures a relatively consistent distribution. This can result in an unwanted loss of variance, but can also beneficially reduce variance. --- Send in a voice message: https://podcasters.spotify.com/pod/show/mlbytes/message
תוכן מסופק על ידי Erik Partridge. כל תוכן הפודקאסטים כולל פרקים, גרפיקה ותיאורי פודקאסטים מועלים ומסופקים ישירות על ידי Erik Partridge או שותף פלטפורמת הפודקאסט שלהם. אם אתה מאמין שמישהו משתמש ביצירה שלך המוגנת בזכויות יוצרים ללא רשותך, אתה יכול לעקוב אחר התהליך המתואר כאן https://he.player.fm/legal.
Stratified sampling provides a mechanism by which to split a larger dataset into smaller pieces. While random approaches are commonly used, stratified sampling ensures a relatively consistent distribution. This can result in an unwanted loss of variance, but can also beneficially reduce variance. --- Send in a voice message: https://podcasters.spotify.com/pod/show/mlbytes/message
K-fold cross validation is the practice by which we separate a large data set into smaller pieces, independently process each data set, and then train our models on some number of the segments, and validate it on the rest. This is generally considered a best practice, or at least good practice, in machine learning, as it helps ensure the correct characterization of your model on the validation set. Machine Learning Mastery has a great post on the topic . --- Send in a voice message: https://podcasters.spotify.com/pod/show/mlbytes/message…
Stratified sampling provides a mechanism by which to split a larger dataset into smaller pieces. While random approaches are commonly used, stratified sampling ensures a relatively consistent distribution. This can result in an unwanted loss of variance, but can also beneficially reduce variance. --- Send in a voice message: https://podcasters.spotify.com/pod/show/mlbytes/message…
Boosting is also an ensemble meta-algorithm, like boosting. However, in boosting we teach a large number of weak, but specialized learners, and combine them according to their strengths. For more information on boosting, consider watching the University of Washington's great lecture on the topic . --- Send in a voice message: https://podcasters.spotify.com/pod/show/mlbytes/message…
Bagging is an ensemble meta-algorithm. Basically, we take some number of estimators (usually dozens-ish), train them each on some random subset of the training data. Then, we average the predictions of each individual estimator in order to make the resulting prediction. While this reduces the variance of your predictions (indeed, that is the core purpose of bagging), it may come at the trade off of bias. For a more academic basis, see slide #13 of this lecture by Joëlle Pineau at McGill University. --- Send in a voice message: https://podcasters.spotify.com/pod/show/mlbytes/message…
The bias-variance trade-off is a key problem in your model search. While bias represents how well your model can capture the salient details of a problem, and generally correlates with more complex algorithms, it comes at the trade off of variance. Variance is the degree to which on individual predictions your estimators stray from the mean output on those values. High variance means that a model has overfit, and incorrectly or incompletely learned the problem from the training set. Most commonly, high bias = underfitting, high variance = overfitting. Please consider joining the conversation on Twitter . I also blog from time to time. You can find me at erikpartridge.com . For more academic sources, consider reading the slides from this fantastic Carnegie Mellon lecture . --- Send in a voice message: https://podcasters.spotify.com/pod/show/mlbytes/message…
The concept of empirical risk minimization drives modern approaches to training many machine learning algorithms, including deep neural networks. Today's thirty second summary covers the basics of what you need to know, but the concept goes well beyond just the simple case we discuss today. If you are looking to discuss the topic further, please consider joining the conversation on Twitter . Lecture notes from Carnegie Mellon University (no affiliation). --- Send in a voice message: https://podcasters.spotify.com/pod/show/mlbytes/message…
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