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The mathematics of machine learning

Practical AI

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Mathematics Behind Neural Networks

This chapter explores the mathematical foundations necessary for understanding neural networks, focusing on calculus, linear algebra, and optimization techniques. It highlights the significance of gradient descent and its application in minimizing errors in machine learning models, while also addressing the role of probability theory in classification.

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