Let's Descend: Why Calculus is Important in Deep Learning

OVERFIT: AI, Machine Learning, and Deep Learning Made Simple

An introduction to the fundamental concepts of calculus, explaining how they are essential for understanding deep learning. It begins by illustrating the concept of a limit using the calculation of a circle's area, before introducing the concept of a derivative, which describes a function's rate of change. It then extends these concepts to multivariate functions, discussing partial derivatives and gradients, which are crucial for optimizing models in deep learning. The chain rule, a powerful tool for calculating gradients in complex function compositions, is also explained in detail. The text concludes by highlighting the significance of automatic gradient computation, emphasizing its role in optimizing deep learning models and paving the way for the backpropagation algorithm, which will be elaborated on in later chapters.

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