Partial derivatives play a critical role in the field of machine learning, specifically in the context of backpropagation and training neural networks. This paper provides an in-depth examination of the concept of partial derivatives and their applications in machine learning. We summarize the key findings of this paper, which include the use of partial derivatives in gradient computation, activation functions, normalization techniques, optimization algorithms, and regularization techniques. Additionally, we discuss the challenges associated with the use of partial derivatives, such as the vanishing gradient problem and numerical instabilities during backpropagation. Our analysis highlights the importance of choosing appropriate activation functions, normalization techniques, and optimization algorithms to enhance the efficiency and accuracy of partial derivative computations. Furthermore, we explore the impact of newer activation functions and regularization techniques on neural network performance. The insights provided in this review paper can assist researchers and practitioners in designing and implementing more effective machine learning models.

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