深度学习：NiN（Network In Network）详细讲解与代码实现
深度学习：NiN（Network In Network）详细讲解与代码实现网络核心思想LeNet、AlexNet和VGG都有一个共同的设计模式：通过一系列的卷积层与汇聚层来提取空间结构特征；然后通过全连接层对特征的表征进行处理。AlexNet和VGG对LeNet的改进主要在于如何扩大和加深这两个模块...
DL：深度学习算法(神经网络模型集合)概览之《THE NEURAL NETWORK ZOO》的中文解释和感悟（九）
DRN Deep residual networks (DRN) are very deep FFNNs with extra connections passing input from one layer to a later layer (often ...
DL：深度学习算法(神经网络模型集合)概览之《THE NEURAL NETWORK ZOO》的中文解释和感悟（八）
ELM Extreme learning machines (ELM) are basically FFNNs but with random connections. They look very similar to LSMs and ESNs, but...
DL：深度学习算法(神经网络模型集合)概览之《THE NEURAL NETWORK ZOO》的中文解释和感悟（七）
DCIGN Deep convolutional inverse graphics networks (DCIGN) have a somewhat misleading name, as they are actually VAEs but ...
DL：深度学习算法(神经网络模型集合)概览之《THE NEURAL NETWORK ZOO》的中文解释和感悟（六）
DBN Deep belief networks (DBN) is the name given to stacked architectures of mostly RBMs or VAEs. These networks have been shown t...
DL：深度学习算法(神经网络模型集合)概览之《THE NEURAL NETWORK ZOO》的中文解释和感悟(五)
MC Markov chains (MC or discrete time Markov Chain, DTMC) are kind of the predecessors to BMs and HNs. They can be understood as ...
DL：深度学习算法(神经网络模型集合)概览之《THE NEURAL NETWORK ZOO》的中文解释和感悟(三)
AE Autoencoders (AE) are somewhat similar to FFNNs as AEs are more like a different use of FFNNs than a fundamentally different ar...
DL：深度学习算法(神经网络模型集合)概览之《THE NEURAL NETWORK ZOO》的中文解释和感悟(二)
Feed forward neural networks (FF or FFNN) and perceptrons (P) are very straight forward, they feed information from the front to the back (input and o...
DL：深度学习算法(神经网络模型集合)概览之《THE NEURAL NETWORK ZOO》的中文解释和感悟(一)
THE NEURAL NETWORK ZOOPOSTED ON SEPTEMBER 14, 2016 BY FJODOR VAN VEEN With new neural network architectures popping up every now ...
推荐系统遇上深度学习(五)--Deep&Cross Network模型理论和实践
1、原理 Deep&Cross Network模型我们下面将简称DCN模型： 一个DCN模型从嵌入和堆积层开始，接着是一个交叉网络和一个与之平行的深度网络，之后是最后的组合层，它结合了两个网络的输出。完整的网络模型如图： 嵌入和堆叠层 我们考虑具有离散和连续特征的输入数据。在网络规模推荐系统...
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