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Lecture Notes: Neural Network Architectures

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arxiv 2304.05133 v2 pith:HE4CTBPU submitted 2023-04-11 cs.LG math.OC

Evelyn Herberg This is my paper · ORCID
classification cs.LGmath.OC
keywords neuralnetworkarchitectureslecturenetworksnotesconvolutionalcovered
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These lecture notes provide an overview of Neural Network architectures from a mathematical point of view. Especially, Machine Learning with Neural Networks is seen as an optimization problem. Covered are an introduction to Neural Networks and the following architectures: Feedforward Neural Network, Convolutional Neural Network, ResNet, and Recurrent Neural Network.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Dissecting a Small Artificial Neural Network

    cond-mat.dis-nn 2025-01 conditional novelty 4.0 of 10

    The microcanonical entropy of the XOR network's loss landscape peaks at discrete loss values, and these entropic barriers vanish when more hidden neurons are added.

  2. An introduction to Neural Networks for Physicists

    physics.ed-ph 2025-05 conditional novelty 3.0 of 10

    A teaching paper demonstrates perceptron training, PINNs, autoencoders, and SINDy on pendulum examples, with code notebooks provided online.

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