Tag: layer
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The Total Derivative: Correcting the Misconception of Backpropagation’s Chain Rule
The Total Derivative: Correcting the Misconception of Backpropagation’s Chain Rule This article uses concepts from this brilliant paper. For a deeper understanding of the mathematics please refer to the paper. Here we try to present the math in a more intuitive and explicit way, with some important nuances highlighted. 1 Introduction Discussions about Backpropagation often…
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Layers of the AI Stack, Explained Simply
Layers of the AI Stack, Explained Simply This is the first in a multi-part series on creating web applications with Generative Ai integration. Table of Contents Introduction The Virtues of the Application Layer Thick Wrappers The Return of Clippy Getting Stuff Done While You Sleep Introduction The AI space is a vast and complicated landscape. Matt…
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Guiding Two-Layer Neural Network Lipschitzness via Gradient Descent Learning Rate Constraints
Guiding Two-Layer Neural Network Lipschitzness via Gradient Descent Learning Rate Constraints arXiv:2502.03792v1 Announce Type: new Abstract: We demonstrate that applying an eventual decay to the learning rate (LR) in empirical risk minimization (ERM), where the mean-squared-error loss is minimized using standard gradient descent (GD) for training a two-layer neural network with Lipschitz activation functions, ensures…