2.4 CapsNet 胶囊神经网络(Capsule Neural Network - CapsNet)于2017年被Hinton团队提出,其通过在CNN中引入胶囊结构以获得更稳定的输出,更好的解决模型的层次连接问题和“Picass problem”(识别图片中拥有所有正确的part,但是它们间location relationship却并不正确) 图15. 毕加索问题 在传统的CNN网络中,浅层的神经元主...
unique concept unit的数量开始急剧减少，因此CNN网络的可解释性是受到unit的排序的影响的。（写者注，这...
目前经常使用的深度神经网络模型主要有卷积神经网络(CNN) 、递归神经网络(RNN)、深信度网络(DBN) 、深度自动编码器(AutoEncoder) 和生成对抗网络(GAN) 等。 递归神经网络实际.上包含了两种神经网络。一种是循环神经网络(Recurrent NeuralNetwork) ;另一种是结构递归神经网络(Recursive Neural Network),它使用相似的...
4.1 深层神经网络(Deep L-layer neural network) 4.2 前向传播和反向传播(Forward and backward propagation) 4.3 深层网络中的前向传播(Forward propagation in a Deep Network) 4.4 核对矩阵的维数(Getting your matrix dimensions right) 4.5 ...
1. 深层神经网络(Deep L-layer Neural network) 什么是深度神经网络呢? 首先,我们之前已经学习了Logistic回归、单隐层神经网络,图1中还给出了一个双隐层神经网络和五隐层的神经网络。 图1 不同层级神经网络计算流程图示例 Logistic回归是最简单的一种神经网络,是一个浅层模型,而图1中的最后一个神经网络的模型...
This is undesirable since neurons in later layers of processing in a Neural Network (more on this soon) would be receiving data that isnot zero-centered. This has implications on the dynamics duringgradient descent, because if the data coming into a neuron is always positive(e.g., ...
【原创】深度神经网络(Deep Neural Network, DNN) 线性模型通过特征间的现行组合来表达“结果-特征集合”之间的对应关系。由于线性模型的表达能力有限,在实践中,只能通过增加“特征计算”的复杂度来优化模型。比如,在广告CTR预估应用中,除了“标题长度、描述长度、位次、广告id,cookie“等这样的简单原始特征,还有大量的...
The NVIDIA CUDA® Deep Neural Network library (cuDNN) is a GPU-accelerated library of primitives fordeep neural networks. cuDNN provides highly tuned implementations for standard routines such as forward and backward convolution, attention, matmul, pooling, and normalization. ...
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The Intel® oneAPI Deep Neural Network Library (oneDNN) provides highly optimized implementations of deep learning building blocks. With this open source, cross-platform library, deep learning application and framework developers can use the same API for CPUs, GPUs, or both—it abstracts out instru...