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神经网络模式识别 | Classify data by training a two-layer feed-forward network |
Autoencoder | Autoencoder class |
使用神经网络进行分类。
Deploy Shallow Neural Network Functions
Simulate and deploy trained shallow neural networks using MATLAB® tools.
Deploy Training of Shallow Neural Networks
Learn how to deploy training of shallow neural networks.
使用并行和分布式计算,可以加快神经网络训练和仿真以及处理大量数据的速度。
Automatically Save Checkpoints During Neural Network Training
Save intermediate results to protect the value of long training runs.
对输入和目标进行预处理,以提高训练效率。
了解如何在训练前使用 configure
函数手动配置网络。
使用函数将数据分为训练集、验证集和测试集。
不同问题类型的训练算法比较。
了解提高泛化能力和防止过拟合的方法。
Train Neural Networks with Error Weights
Learn how to use error weighting when training neural networks.
Normalize Errors of Multiple Outputs
Learn how to fit output elements with different ranges of values.
了解神经网络设计过程中的主要步骤。
Four Levels of Neural Network Design
Learn the different levels of using neural network functionality.
设计用于函数拟合和模式识别的多层浅层前馈神经网络的工作流。
了解多层浅层神经网络的架构。
Understanding Shallow Network Data Structures
Learn how the format of input data structures affects the simulation of networks.
试验浅层神经网络时要使用的样本数据集列表。
了解定义网络基本特征的属性。
Neural Network Subobject Properties
Learn properties that define network details such as inputs, layers, outputs, targets, biases, and weights.