Convolutional neural network in MATLAB

Description

The convolutional neural network hereinafter referred to as CNN, refers to a type of artificial neural network that has the main features of a neurocognitive network. CNN’s structure uses three main techniques: weight sharing, local receptor fields, and spatial downsampling. This network is somewhat similar to the biological vision system model in which raw data is applied to it as network input without the need for initial processing or feature extraction. In fact, on CNN, feature extraction and identification are performed in a common structure.

 

 

In the structure of this network, in order to apply mapping to the image, it is considered that each neural connection applies the same local conversion to all spatial transformations. With this provision, the ratio between the degree of freedom of the system and the number of samples required for learning is significantly increased and causes the power of generalization of the system to be strengthened. This feature shows its efficiency in the field of image processing, in which we usually encounter the problem that the large size of the input leads to an ill posed problem.

 

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In the structure of this network, network weights are repeated on spatial arrays, which makes this type of network inherently insensitive to input transfer. This feature is also useful for image processing. In addition, the implementation of this network on the hardware is easy to do, which makes it possible to use it in uninterrupted applications. CNN is currently used in matters such as handwriting processing and face recognition.

 

 

Heading:

Deep Learning

Differences between conventional pattern recognition models and deep learning

What is a Trainable feature extractor?

The overall structure of a pattern recognition system

The structure of an object detection system

Features visualization in the stages of a deep learning system (convolutional network)

Low-Level feature, Mid-Level feature, high-level feature

Visual Cortex (Where did the idea of ​​deep learning come from?)

Introduction of three deep learning structures (feed-forward, feed-back, bi-directional)

Why do we need deep learning?

Which models are deep? Are MLP and SVM and dual-layer and decision tree models deep?

In which applications are deep learning used today?

Compare models

(boosting, perceptron, SVM, AE, decision tree, RBM, DBN, DBM, GMM, spare coding, BayesNP, Conventional Net, RNN, Neural Net)

The overall architecture of a convolutional neural network

Introduction of Normalization Block

Introducing Filter Bank block

Introducing Non-Linear Block

Introducing Pooling or subsampling block

Introducing changes to an image as it passes through different layers of a convolutional neural network

Introducing the structure of a convolutional neural network

What changes the image after convolution?

What changes the image after pooling?

The concept of convolution in the image

Window in convolution

The concept of pooling in the image

An example of a local average operator

Show the image of each stage of a convolutional neural network

Error pre-propagation algorithm in convolutional neural network

A numerical example of a convolutional operator

Two uses of identifying traffic signs and identifying house numbers

An example of a convolutional neural network identifying the subject of a scene in a video

An example of a convolutional neural network in object detection

 

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