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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Justin Manly, BCG’s lead on growth and innovation, explains how businesses can choose the right growth strategy given their aspirations and position in the market. Whether a company is gaining or losing share, in a booming or sluggish industry, profitable growth depends on knowing your starting point. Learn More: Justin Manly: https://on.bcg.com/4kGYyPH BCG’s Latest Thinking on Growth and Innovation Analytics: https://on.bcg.com/4kjTEIB BCG’s Latest Thinking on Corporate Finance and Strategy: https://on.bcg.com/3ZeAMlX Your Growth Strategy Depends on Your Starting Point: https://on.bcg.com/43YpzZm The Vitality Code: How Growth Leaders Master Strategy, Technology, People, and Culture: https://on.bcg.com/4dMKjH1 This podcast uses the following third-party services for analysis: Podtrac - https://analytics.podtrac.com/privacy-policy-gdrp…
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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