Lec-2: Convolution, Max pool , Flattening , Dense layer in CNN
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3 سال پیش
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In this Convolutional Neural Network
In this Convolutional Neural Network (CNN) Bangla tutorial i will discuss four layers of CNN. How these layers work. How the convolutional layer is formed. This is a basic introductory video of CNN.
There are three types of layers that make up the CNN which are the convolutional layers, pooling layers, and fully-connected (FC) layers.
In deep learning, a convolutional neural network is a class of deep neural network, most commonly applied to analyze visual imagery. Convolutional Neural Networks are very similar to ordinary Neural Networks from the previous chapter: they are made up of neurons that have learnable weights and biases. Each neuron receives some inputs, performs a dot product and optionally follows it with a non-linearity.
Max pooling is a pooling operation that selects the maximum element from the region of the feature map covered by the filter. Thus, the output after max-pooling layer would be a feature map containing the most prominent features of the previous feature map.
Each neuron in a layer receives an input from all the neurons present in the previous layer—thus, they're densely connected. In other words, the dense layer is a fully connected layer, meaning all the neurons in a layer are connected to those in the next layer.
check out the playlists for new update:
Basic of CNN: Lec-1: introduction to CNN || Image t...
Implementation Of CNN with python code:
1. CNN tutorial || Image classification ... (binary image classification)
2.CNN tutorial |Multiclass image Classi...(Multiclass image classification)
#neural_network #CNN #Deep_leaning
There are three types of layers that make up the CNN which are the convolutional layers, pooling layers, and fully-connected (FC) layers.
In deep learning, a convolutional neural network is a class of deep neural network, most commonly applied to analyze visual imagery. Convolutional Neural Networks are very similar to ordinary Neural Networks from the previous chapter: they are made up of neurons that have learnable weights and biases. Each neuron receives some inputs, performs a dot product and optionally follows it with a non-linearity.
Max pooling is a pooling operation that selects the maximum element from the region of the feature map covered by the filter. Thus, the output after max-pooling layer would be a feature map containing the most prominent features of the previous feature map.
Each neuron in a layer receives an input from all the neurons present in the previous layer—thus, they're densely connected. In other words, the dense layer is a fully connected layer, meaning all the neurons in a layer are connected to those in the next layer.
check out the playlists for new update:
Basic of CNN: Lec-1: introduction to CNN || Image t...
Implementation Of CNN with python code:
1. CNN tutorial || Image classification ... (binary image classification)
2.CNN tutorial |Multiclass image Classi...(Multiclass image classification)
#neural_network #CNN #Deep_leaning
3 سال پیش
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