Empirical Risk Minimization Machine Learning Bytes podcast

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محتوای ارائه شده توسط Erik Partridge. تمام محتوای پادکست شامل قسمت‌ها، گرافیک‌ها و توضیحات پادکست مستقیماً توسط Erik Partridge یا شریک پلتفرم پادکست آن‌ها آپلود و ارائه می‌شوند. اگر فکر می‌کنید شخصی بدون اجازه شما از اثر دارای حق نسخه‌برداری شما استفاده می‌کند، می‌توانید روندی که در اینجا شرح داده شده است را دنبال کنید.https://fa.player.fm/legal

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Antoni Porowski and the Right Time to Leave a Party 46:55

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"Queer Eye" and "No Taste Like Home" star Antoni Porowski joins Tinfoil Swans live at the Food & Wine Classic in Aspen for a heartfelt and humorous conversation about identity, vulnerability, and finding meaning through food. He opens up about his path from actor and caterer to TV host and storyteller, the emotional layers of cooking, navigating fame, and the gentle art of knowing when to leave the party. Along the way, he shares reflections on therapy, self-care, family dynamics — and what turning 40 taught him. Sponsor: Old Fitzgerald® Kentucky Straight Bourbon Whiskey. Bardstown, KY. 50% Alc./Vol. Think Wisely. Drink Wisely. Learn more about your ad choices. Visit podcastchoices.com/adchoices…

Machine Learning Bytes «
Empirical Risk Minimization

۶ سال پیش 0:38

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M4A•خانه قسمت

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

6 قسمت

#Tech #Erik Partridge

Empirical Risk Minimization

Machine Learning Bytes

published ۶ سال پیش

اشتراک گذاری

M4A•خانه قسمت

Lecture notes from Carnegie Mellon University (no affiliation).

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

6 قسمت

#Tech #Erik Partridge

همه قسمت ها

1
K-Fold Cross Validation 0:55

۶ years پیش0:55

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…

1
Stratified Sampling 0:59

۶ years پیش0:59

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…

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Boosting 0:51

۶ years پیش0:51

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…

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Bagging 0:50

۶ years پیش0:50

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…

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

۶ years پیش1:57

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…

1
Empirical Risk Minimization 0:38

۶ years پیش0:38

0:38

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