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This comprehensive chapter provides a systematic overview of 27 major neural network architectures, presenting comparative implementations in both MATLAB and Python for each type. Beginning with fundamental models like Perceptrons and Feed Forward Networks, it progresses through advanced architectures including Recurrent Neural Networks (RNN, LSTM, GRU), Autoencoders (AE, DAE, VAE), and specialized networks like Deep Residual Networks and Generative Adversarial Networks. The chapter covers probabilistic models (Boltzmann Machines, Markov Chains), self-organizing networks (Kohonen, Hopfield), and modern architectures such as Neural Turing Machines. Each network type is accompanied by practical code examples demonstrating implementation specifics, parameter configurations, and application scenarios. The dual-language approach facilitates understanding of both MATLAB’s specialized neural network toolbox and Python’s deep learning ecosystems (TensorFlow, PyTorch), making this an essential reference for practitioners working across different computational environments in neural network development and deployment.

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A Summary of Different Type of Neural Networks in Matlab and Python

  • 章节

  • 发表日期：

    2026年

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  • Chunwei Zhang,
  • Tianpeng Li,
  • Ying Dai,
  • Li Sun,
  • Ardashir Mohammadzadeh

摘要

This comprehensive chapter provides a systematic overview of 27 major neural network architectures, presenting comparative implementations in both MATLAB and Python for each type. Beginning with fundamental models like Perceptrons and Feed Forward Networks, it progresses through advanced architectures including Recurrent Neural Networks (RNN, LSTM, GRU), Autoencoders (AE, DAE, VAE), and specialized networks like Deep Residual Networks and Generative Adversarial Networks. The chapter covers probabilistic models (Boltzmann Machines, Markov Chains), self-organizing networks (Kohonen, Hopfield), and modern architectures such as Neural Turing Machines. Each network type is accompanied by practical code examples demonstrating implementation specifics, parameter configurations, and application scenarios. The dual-language approach facilitates understanding of both MATLAB’s specialized neural network toolbox and Python’s deep learning ecosystems (TensorFlow, PyTorch), making this an essential reference for practitioners working across different computational environments in neural network development and deployment.