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WHY Are Probability And Stats Foundational to ML and DL?
Manage episode 445331385 series 3605861
Вміст надано Brian Carter. Весь вміст подкастів, включаючи епізоди, графіку та описи подкастів, завантажується та надається безпосередньо компанією Brian Carter або його партнером по платформі подкастів. Якщо ви вважаєте, що хтось використовує ваш захищений авторським правом твір без вашого дозволу, ви можете виконати процедуру, описану тут https://uk.player.fm/legal.
Probability and statistics are fundamental components of machine learning (ML) and deep learning (DL) because they provide the mathematical framework for understanding and analyzing data, which is crucial for making predictions and decisions.
This excerpt from the "Dive into Deep Learning" documentation explains the essential concepts of probability and statistics, which are crucial for understanding machine learning. The text introduces fundamental ideas like sample space, events, probability functions, and random variables, highlighting the distinction between discrete and continuous variables. It then delves into the relationships between multiple random variables, emphasizing the importance of conditional probabilities, Bayes' Theorem, and independence. The excerpt also covers expectations and variances, illustrating how they can be used to measure the average value and the spread of data. Finally, it explores the concepts of aleatoric and epistemic uncertainty, providing a framework for understanding the limitations of machine learning models and the role of data in improving their accuracy.
Read more: https://d2l.ai/chapter_preliminaries/probability.html
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