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Modern deep-learning stacks rely on a computation graph to represent a neural network as a directed acyclic graph (DAG) whose nodes are tensor operations and whose edges carry multi-dimensional arrays (tensors). This abstraction allows the framework to reason about the entire model ahead of time, insert missing backward operations, schedule ker ...

Automatic differentiation (AD) is a fundamental technique in deep learning frameworks. This article demonstrates how to implement forward-mode automatic differentiation using Python operator overloading. The implemantation is remarkably concise—you can build a working system in just a few lines of code. Understanding Forward-Mode Automatic Diff ...

Automatic differentiation (AD) computes exact derivatives efficiently by applying the chain rule during program execution. Two primary strategies exist: forward mode and reverse mode. Their suitability depends on the shape of the function being differentiated—specifically, the number of inputs versus outputs. Intuitive Analogy: Manufacturing Wo ...

Modern AI frameworks rely on computation graphs as the fundamental abstraction for representing and executing neural network models. By using universal data structures like tensors to interpret and perform neural network operations, computation graphs enable systematic analysis and optimization of AI systems. Motivation: Challenges in AI Engine ...