Convolutional Neural Network

Convolutional Neural Network

Student: Suyunqulov Jaloliddin

Group: 315-19

Definition

So what is Convolutional Neural Network?

A convolutional neural network, or CNN, is a deep learning neural network

designed for processing structured arrays of data such as images.

Convolutional neural networks are widely used in computer vision and have

become the state of the art for many visual applications such as image

classification, and have also found success in natural language processing for text

classification.

Use CNNs For

• Image data
• Classification prediction problems
• Regression prediction problems

CNN architecture

A convolutional neural network consists of an input layer, hidden layers and an output layer. In any feed-forward neural network, any middle layers are called hidden because their inputs and outputs are masked by the activation function and final convolution.

CNN architecture types

• LeNet-5
• AlexNet
• VGG-16
• Inception-v1
• Inception-v3
• ResNet-50
• Xception
• Inception-v4
• Inception-ResNets
• ResNeXt-50

How it works?

Filtering: The match behind the match

Filtering: The match behind the match

• Line up the feature and image patch.
• Multiply each image pixel by the corresponding feature pixel.
• Add them up.
• Divide by the total number of pixels in the feature.

Pooling: Shrinking the image stack

• Pick a window size (usually 2 or 3).
• Pick a stride (usually 2).
• Walk your window across your filtered images.
• From each window, take the maximum value.

Normalization

Keep the math from breaking by tweaking each of the values just a bit

Change everything negative to zero