The convolutional Networks

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• convolutional Neural Network
• The Unknown Word

convolutional Neural Network

• Use Python to impliment a simple network for Hanndwritten numeral classification.

• At some-your daily life,you may have seen some practical application of the target recognition Algorithm,such as Face detection on your mobile phone camera. But what is the principle of it work? The core of these computer vision Solutions is the convolution neural network (CNN). Simply Speaking,these Networks is particularly good at constructing complex features based on less complex features. A Classic example is the face detector. The face detector. The early layers is responsible for identifying vertical and horizontal Lines,and the later stage was responsible for find ing the nose and mouth.

  Primary Neural Network

  This article wil isn't introduce the general working principle of neural networkin detail,but you need some background knowl Edge to deal with convolution network. There is a layered architecture for neural networks. Each layer is composed of some nodes. Each of the node performs some mathematical operations on a input effectively,and obtains an output by calculation. The input is provided to any given node is the weighted sum of the output of the previous layer (and usually the Bias item of 1 or 0). The algorithm would learn these weights during training. In order to learn these parameters,the output of running training can is compared with the real value and the weights can be updated by propagating errors in the network.

The Unknown Word The first The

Column	Second Column
Horizontal	Perpendicular to the
Is reponsible for	Responsible for
Convolution	Convolution
layed architecture	Layered architecture
Architecture	Architecture
Mathematical operations	Mathematical operations
Concise	[Ken ' sais] Concise
by calculation	by calculating
Weights	Weight
Propagating	Reverse propagation [' Pra:pegeit]
An error to be raised	Raise an error
Raveling	disassemble [ravling]
Reshape	Refactoring

The convolutional Networks