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This chapter provides an overview of essential libraries for codingCoding neural networksNeural network, as well as tools for managing experiment logging and visualizationVisualization in deepLearningdeep learning learningDeepdeep learning. It introduces popular neural networkNeural network codingCoding frameworks and tools for tracking and visualizing training progress. Additionally, the chapter presents practicalPracticepractical sample PyTorch code implementations for common neural networkNeural network architecturesArchitecture and machine learningLearningmachine learning tasksTask, including feedforward networks for regressionRegression and Convolutional Neural NetworksConvolutionconvolutional [neural] network (CNNs) for classificationClassification. These examples serve as practicalPracticepractical guides to complement the theoretical concepts discussed in earlier chapters.

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Coding Neural Networks for Deep Learning

  • 章节

  • 发表日期：

    2026年

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  • Benyamin Ghojogh,
  • Ali Ghodsi

摘要

This chapter provides an overview of essential libraries for codingCoding neural networksNeural network, as well as tools for managing experiment logging and visualizationVisualization in deepLearningdeep learning learningDeepdeep learning. It introduces popular neural networkNeural network codingCoding frameworks and tools for tracking and visualizing training progress. Additionally, the chapter presents practicalPracticepractical sample PyTorch code implementations for common neural networkNeural network architecturesArchitecture and machine learningLearningmachine learning tasksTask, including feedforward networks for regressionRegression and Convolutional Neural NetworksConvolutionconvolutional [neural] network (CNNs) for classificationClassification. These examples serve as practicalPracticepractical guides to complement the theoretical concepts discussed in earlier chapters.