Computational Graphs
A computational graph is a way to represent mathematical operations as a directed graph where nodes correspond to operations or variables, and edges represent the flow of data or dependencies between operations. This representation is fundamental in deep learning frameworks because it allows for efficient calculation of derivatives, crucial for optimization algorithms like gradient descent. Each operation's output becomes the input for subsequent operations, defining the entire model's forward and backward passes.
Think of it as a recipe flowchart where each box is a cooking step, and the arrows show which ingredient or partially cooked dish goes into the next step.
This structure provides an efficient and organized way to build and train complex AI models, especially deep neural networks, by automating derivative computations.
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