Empirical Risk Minimization Machine Learning Bytes podcast

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הוסף לפני four שנים

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.

World of Secrets

1
The Killing Call: 1. The death of a star 35:12

לפני 14 ימים35:12

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35:12

Sidhu Moose Wala explodes onto the Canadian music scene. His sound is a fusion of two worlds - hip-hop with the poetic language of rural Punjab, where he is from. After years of struggle he’s making it. But with the spotlight comes a dark side. As his fame grows, so do the threats. "We will kill you." Presented by broadcaster and DJ Bobby Friction and investigative journalist Ishleen Kaur. Season 8 of World of Secrets, The Killing Call, is a BBC Eye investigation for the BBC World Service. Archive audio credits: Lovepreet Waraich, Malwa TV, BritAsia TV, MPHONE Canteeni Mandeer, GK Digital, Thakur Media, Capital Extra, Famous Punjab TV, ModernSings, Dheeth.jeha, RealRohitBlogs, Mirror Now, India Today. Here’s a link to the BBC Eye two-part documentary films, which we recommend you watch after listening to this podcast: https://bit.ly/thekillingcall If you are in the UK, you can watch on iPlayer: https://www.bbc.co.uk/programmes/m002f18y…

Machine Learning Bytes «
Empirical Risk Minimization

לפני 6 שנים 0:38

שתפו

M4A•בית הפרקים

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).

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6 פרקים

#Tech #Erik Partridge

Empirical Risk Minimization

Machine Learning Bytes

published לפני 6 שנים

שתפו

M4A•בית הפרקים

Lecture notes from Carnegie Mellon University (no affiliation).

--- Send in a voice message: https://podcasters.spotify.com/pod/show/mlbytes/message

6 פרקים

#Tech #Erik Partridge

כל הפרקים

Machine Learning Bytes

1
K-Fold Cross Validation 0:55

לפני 6 years0:55

0:55

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…

Machine Learning Bytes

1
Stratified Sampling 0:59

לפני 6 years0:59

0:59

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…

Machine Learning Bytes

1
Boosting 0:51

לפני 6 years0:51

0:51

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…

Machine Learning Bytes

1
Bagging 0:50

לפני 6 years0:50

0:50

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…

Machine Learning Bytes

1
Bias, Variance, and the Bias-Variance Tradeoff 1:57

לפני 6 years1:57

1:57

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…

Machine Learning Bytes

1
Empirical Risk Minimization 0:38

לפני 6 years0:38

0:38

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פודקאסטים ששווה להאזין

1
The Killing Call: 1. The death of a star 35:12

Machine Learning Bytes «
Empirical Risk Minimization

Empirical Risk Minimization

פודקאסטים ששווה להאזין

כל הפרקים

1
K-Fold Cross Validation 0:55

1
Stratified Sampling 0:59

1
Boosting 0:51

1
Bagging 0:50

1
Bias, Variance, and the Bias-Variance Tradeoff 1:57

1
Empirical Risk Minimization 0:38

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פודקאסטים ששווה להאזין

Machine Learning Bytes « Empirical Risk Minimization

Empirical Risk Minimization

פודקאסטים ששווה להאזין

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Machine Learning Bytes «
Empirical Risk Minimization