The maximum pooling operation performs downsampling by dividing
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Learn long-term dependencies between time steps in time series and sequence data. The long short-term memory (LSTM) operation allows a network to Performs a nonlinear threshold operation, where any input value less than zero is multiplied by The leaky rectified linear unit (ReLU) activation operation Layer normalization after the learnable operations, such as LSTM and fully connect Multilayer perceptron neural networks and reduce the sensitivity to network initialization, use The layer normalization operation normalizes the input dataĪcross all channels for each observation independently. "batch-size", the computed value is known as the mean squared error "sum" and the NormalizationFactor option is L 2 loss (based on the squared L 2 norm) given NormalizationFactor option is "batch-size", theĬomputed value is known as the mean absolute error (MAE). L 1 loss given network predictions and target values. Instance normalization between convolution and nonlinear operations such as relu. The convolutional neural network and reduce the sensitivity to network hyperparameters, use The instance normalization operation normalizes the input dataĪcross each channel for each observation independently. When the 'TransitionPoint' option is 1, this is also known as smooth L 1 loss. The Huber operation computes the Huber loss between network predictions and target values for regression tasks. The gated recurrent unit (GRU) operation allows a network to learn dependencies between time steps in time series and sequence data. The convolutional neural network and reduce the sensitivity to network initialization, use group The group normalization operation normalizes the input dataĪcross grouped subsets of channels for each observation independently. Weights the input by its probability under a Gaussian distribution. The Gaussian error linear unit (GELU) activation operation The fully connect operation multiplies the input by a weight matrix and then adds a bias vector. Use embeddings to map discrete data suchĪs categorical values or words to numeric vectors.
Vectors, where the indices correspond to discrete data. The embed operation converts numeric indices to numeric The transposed convolution operation upsamples feature maps. The neural ordinary differential equation (ODE) operation returns the solution of a specified ODE. Use the dlconv function for deep learning convolution, groupedĬonvolution, and channel-wise separable convolution. The convolution operation applies sliding filters to the inputĭata. The CTC operation computes the connectionist temporal classification (CTC) loss between unaligned sequences. Cross-channel normalization typicallyĬross-channel normalization is also known as local response normalization. In different channels to normalize each activation. The cross-channel normalization operation uses local responses The cross-entropy operation computes the cross-entropy lossīetween network predictions and target values for single-label and multi-label classification Normalization between convolution and nonlinear operations such as relu. To speed up training of theĬonvolutional neural network and reduce the sensitivity to network initialization, use batch The batch normalization operation normalizes the input dataĪcross all observations for each channel independently. The input into pooling regions and computing the average value of each region. The average pooling operation performs downsampling by dividing The attention operation focuses on parts of the input using
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