A computational graph, also known as a computational network or a neural network, is a graphical representation of a mathematical model or a computational process. It is commonly used in the field of machine learning and artificial intelligence.

  In a computational graph, nodes represent mathematical operations or computations, while edges represent the flow of data between those operations. Each node takes inputs from other nodes and produces an output, which can then be passed as input to other nodes. This creates a directed acyclic graph (DAG) structure, where the flow of data is unidirectional and loops are not allowed.

  The computational graph allows for the efficient execution of complex calculations by breaking them down into smaller, more manageable operations. By decomposing the computation into a series of interconnected nodes, it becomes easier to understand and optimize the underlying mathematical model.

  The computational graph also enables automatic differentiation, a technique used for computing the derivatives of functions. By applying the chain rule of calculus to the operations in the graph, it is possible to calculate the gradients of the model's parameters with respect to a given loss function. This is essential for training machine learning models using gradient-based optimization algorithms like backpropagation.

  Overall, the computational graph provides a visual representation of the computations involved in a mathematical model or a computational process. It helps in understanding and optimizing complex calculations and is a fundamental building block in many machine learning algorithms.