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This post includes notations for deep learning.
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This post covers the representation and vectorization of neural networks.
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This post covers the basics about the concept of perceptron in deep learning.
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This post explains the types of machine learning and how the supervised learning works.
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This post covers the Central Limit Theorem (CLT) in probability and statistics.
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This post covers the basics of probability and statistics, including the classical approach and Bayes' theorem.
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This post covers the basics of descriptive statistics, including central tendency, dispersion, and quartiles.
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This post covers the basics of discrete probability distribution, including the probability mass function, cumulative distribution function, expected value, variance, and standard deviation.
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This post covers the basics of descriptive statistics, including central tendency, dispersion, and quartiles.
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Builder pattern in Python