From bc3c57d8bb83e599b6fcc09b3853964ffb1583db Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" <41898282+github-actions[bot]@users.noreply.github.com> Date: Wed, 25 Mar 2026 12:29:42 +0100 Subject: [PATCH] chore: sync content to repo (#9780) Co-authored-by: kamranahmedse <4921183+kamranahmedse@users.noreply.github.com> --- .../content/back-propagation@0meihv22e11GwqnRdSJ9g.md | 7 ++++++- .../content/probability@tP0oBkjvJC9hrtARkgLon.md | 10 +++++++++- .../validation-techniques@0hi0LdCtj9Paimgfc-l1O.md | 2 +- 3 files changed, 16 insertions(+), 3 deletions(-) diff --git a/src/data/roadmaps/machine-learning/content/back-propagation@0meihv22e11GwqnRdSJ9g.md b/src/data/roadmaps/machine-learning/content/back-propagation@0meihv22e11GwqnRdSJ9g.md index 306b96788..c58265e53 100644 --- a/src/data/roadmaps/machine-learning/content/back-propagation@0meihv22e11GwqnRdSJ9g.md +++ b/src/data/roadmaps/machine-learning/content/back-propagation@0meihv22e11GwqnRdSJ9g.md @@ -1,3 +1,8 @@ # Backpropagation -Backpropagation is a fundamental algorithm used to train artificial neural networks. It works by calculating the gradient of the loss function with respect to the network's weights. This gradient is then used to adjust the weights, iteratively reducing the error between the network's predictions and the actual target values. In essence, it's a method for efficiently computing how much each weight in the network contributed to the overall error, allowing for targeted adjustments to improve performance. \ No newline at end of file +Backpropagation is a fundamental algorithm used to train artificial neural networks. It works by calculating the gradient of the loss function with respect to the network's weights. This gradient is then used to adjust the weights, iteratively reducing the error between the network's predictions and the actual target values. In essence, it's a method for efficiently computing how much each weight in the network contributed to the overall error, allowing for targeted adjustments to improve performance. + +Visit the following resources to learn more: + +- [@article@What is backpropagation?](https://www.ibm.com/think/topics/backpropagation) +- [@article@Understanding Backpropagation](https://towardsdatascience.com/understanding-backpropagation-abcc509ca9d0/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration) \ No newline at end of file diff --git a/src/data/roadmaps/machine-learning/content/probability@tP0oBkjvJC9hrtARkgLon.md b/src/data/roadmaps/machine-learning/content/probability@tP0oBkjvJC9hrtARkgLon.md index 16c0fafc5..44c644350 100644 --- a/src/data/roadmaps/machine-learning/content/probability@tP0oBkjvJC9hrtARkgLon.md +++ b/src/data/roadmaps/machine-learning/content/probability@tP0oBkjvJC9hrtARkgLon.md @@ -1 +1,9 @@ -# Linear Algebra \ No newline at end of file +# Probability + +Probability is a way to quantify the likelihood of an event occurring. It provides a numerical measure, ranging from 0 to 1, that represents the likelihood of a specific outcome occurring. A probability of 0 indicates impossibility, while a probability of 1 signifies certainty. It's a fundamental concept for understanding uncertainty and making predictions based on available data. + +Visit the following resources to learn more: + +- [@book@Probability and Statistics: The Science of Uncertainty](https://utstat.utoronto.ca/mikevans/jeffrosenthal/book.pdf) +- [@article@Probability for machine learning](https://towardsdatascience.com/probability-for-machine-learning-b4150953df09/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration) +- [@video@Probability for Data Science & Machine Learning](https://www.youtube.com/watch?v=sEte4hXEgJ8) \ No newline at end of file diff --git a/src/data/roadmaps/machine-learning/content/validation-techniques@0hi0LdCtj9Paimgfc-l1O.md b/src/data/roadmaps/machine-learning/content/validation-techniques@0hi0LdCtj9Paimgfc-l1O.md index d01bd3065..8cd35d875 100644 --- a/src/data/roadmaps/machine-learning/content/validation-techniques@0hi0LdCtj9Paimgfc-l1O.md +++ b/src/data/roadmaps/machine-learning/content/validation-techniques@0hi0LdCtj9Paimgfc-l1O.md @@ -7,4 +7,4 @@ Visit the following resources to learn more: - [@article@Cross-validation: evaluating estimator performance | scikit-learn](https://scikit-learn.org/stable/modules/cross_validation.html) - [@article@The 5 Stages of Machine Learning Validation](https://towardsdatascience.com/the-5-stages-of-machine-learning-validation-162193f8e5db/) - [@article@What is the Difference Between Test and Validation Datasets?](https://machinelearningmastery.com/difference-test-validation-datasets/) -- [@video@Validating Machine Learning Model and Avoiding Common Challenges](%5Bhttps://www.youtube.com/watch?v=TnIh2b2Rw6%5D(https://www.youtube.com/watch?v=TnIh2b2Rw6Y)) \ No newline at end of file +- [@video@Validating Machine Learning Model and Avoiding Common Challenges](https://www.youtube.com/watch?v=TnIh2b2Rw6%5D(https://www.youtube.com/watch?v=TnIh2b2Rw6Y)) \ No newline at end of file