Many of us are entering the new year with a similar goal — to build community and connect more with others. To kick off season five, Priya Parker shares ideas on how to be the host with the most. An expert on building connection, Priya is the author of “The Art of Gathering: How We Meet and Why It Matters.” Whether it's a book club, wedding, birthday or niche-and-obscurely themed party, Priya and Chris talk about how to create meaningful and fun experiences for all of your guests — including yourself. For the full text transcript, visit go.ted.com/BHTranscripts . For the full text transcript, visit go.ted.com/BHTranscripts Want to help shape TED’s shows going forward? Fill out our survey here ! Learn more about TED Next at ted.com/futureyou Hosted on Acast. See acast.com/privacy for more information.…
Player FM - Internet Radio Done Right
Checked 1+ y ago
اضافه شده در four سال پیش
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
Player FM - برنامه پادکست
با برنامه Player FM !
با برنامه Player FM !
))
پادکست هایی که ارزش شنیدن دارند
حمایت شده
Boosting
Manage episode 311996328 series 3211418
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
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/message6 قسمت
اشتراک گذاریManage episode 311996328 series 3211418
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
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/message6 قسمت
همه قسمت ها
×
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…
به Player FM خوش آمدید!
Player FM در سراسر وب را برای یافتن پادکست های با کیفیت اسکن می کند تا همین الان لذت ببرید. این بهترین برنامه ی پادکست است که در اندروید، آیفون و وب کار می کند. ثبت نام کنید تا اشتراک های شما در بین دستگاه های مختلف همگام سازی شود.
Daily Deals
Player FM - برنامه پادکست
با برنامه Player FM !
با برنامه Player FM !
))
