Automatic differentiation is a computational technique used in numerical optimization and machine learning to efficiently and accurately compute the derivatives of mathematical functions. It is an alternative to traditional methods such as symbolic differentiation and finite differencing, offering advantages in terms of accuracy and speed. Automatic differentiation exploits the chain rule of calculus to break down complex functions into a sequence of elementary functions and operations, which simplifies the process of computing derivatives. It can be applied to both scalar and vector functions, making it a versatile tool for various applications in scientific computing. Overall, automatic differentiation plays a crucial role in optimizing models and algorithms in fields such as optimization, machine learning, and computational physics. Its ability to provide accurate gradients enables faster and more efficient training of neural networks, optimization of parameters, and solving differential equations.