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src/data/roadmaps/machine-learning/content/back-propagation@0meihv22e11GwqnRdSJ9g.md
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# 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.
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)
src/data/roadmaps/machine-learning/content/probability@tP0oBkjvJC9hrtARkgLon.md
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# Linear Algebra
# 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)
src/data/roadmaps/machine-learning/content/validation-techniques@0hi0LdCtj9Paimgfc-l1O.md
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- [@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))
- [@video@Validating Machine Learning Model and Avoiding Common Challenges](https://www.youtube.com/watch?v=TnIh2b2Rw6%5D(https://www.youtube.com/watch?v=TnIh2b2Rw6Y))