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Dog Dog
Bird Bird
Cat Cat
Pixels of image fed as input Input Layer Hidden Layer Output Layer
Input Image Image is processed Output probability
(Blue Jay) using Convolutional Beagel - 10%
Neural Network Blue Jay - 70 %
Maine Coon - 20%
Brainy Fact
Convolutional neural networks (ConvNets) were first introduced in the 1980s by Yann LeCun, a computer science
researcher. Its early version called LeNet (after LeCun), were used to recognize handwritten digits. It found its
use in postal services to read zip codes on envelopes and in banking/financial sectors to read digits on cheques.
Layers of Convolutional Neural Network
Convolutional Neural Network is made up of multiple layers of artificial neurons. It uses mathematical functions
to calculate the weighted sum of multiple inputs and generate the desired outputs. Let us discuss in detail about
the different layers present in Convolutional Neural Network.
Convolution Layer
Convolution is the first layer of a CNN and is also known as Feature Extractor Layer. The main purpose of this
layer is to extract the high-level features from the input image to perform operations such as edge detection,
blur and sharpen by applying filters.
Depending on the type of AI model, a Convolution Layer may be made up of two or more layers. In that case
the first Convolution Layer is responsible for capturing the Low-Level features such as edges, colour, gradient
orientation, etc. With added layers, the architecture adapts to the High-Level features and a whole network of
understanding of images in the dataset.
This layer deals with the Convolution process of handling an image with several types of kernels to provide
different features to the whole system. Each convolutional kernel is used to generate a feature map based on
input provided. Feature map is also known as the activation map. Feature maps have multiple uses like:
• The output of the filter applied to the previous layer is trapped by the feature map.
• It helps in reducing the size of the image so that it can be processed easily.
• It helps in focusing on the important features of the images like eyes, nose etc. so that it can be processed
efficiently.
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