How do recurrent neural networks compare to other deep learning networks?
循环神经网络与其他深度学习网络相比如何？
RNNs are one of several different neural network architectures.
Recurrent neural network vs. feed-forward neural network
Like RNNs, feed-forward neural networks are artificial neural networks that pass information from one end to the other end of the architecture. A feed-forward neural network can perform simple classification, regression, or recognition tasks, but it can’t remember the previous input that it has processed. For example, it forgets Apple by the time its neuron processes the word is. The RNN overcomes this memory limitation by including a hidden memory state in the neuron.
Recurrent neural network vs. convolutional neural networks
Convolutional neural networks are artificial neural networks that are designed to process spatial data. You can use convolutional neural networks to extract spatial information from videos and images by passing them through a series of convolutional and pooling layers in the neural network. RNNs are designed to capture long-term dependencies in sequential data
RNN 是多种不同神经网络架构中的一种。
循环神经网络与前馈神经网络
与 RNN 一样，前馈神经网络是将信息从架构的一端传递到另一端的人工神经网络。前馈神经网络可以执行简单的分类、回归或识别任务，但无法记住之前处理的输入。例如，其神经元在处理 is 这个词时，它会忘记 Apple。RNN 通过在神经元中加入隐藏内存状态克服了这种内存限制。
循环神经网络与卷积神经网络
卷积神经网络是用于处理时序数据的人工神经网络。使用卷积神经网络，您可以通过神经网络中的一系列卷积层和池化层，从视频和图像中提取空间信息。RNN 旨在捕获顺序数据中的长期依赖关系