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Some remarkable structures of CNN like LeNet, AlexNet, VGG, Inception, and ResNet Convolutional Neural Networks

What is a CNN?

their applications

A convolutional neural network (CNN, or ConvNet) is a class of deep neural networks ( a simple neural network with more than one hidden layer). They are also known as shift invariant or space invariant artificial neural networks (SIANN), based on their shared-weights architecture and translation invariance characteristics.

A Convolutional Neural Network (CNN) is a Deep Learning algorithm which takes in an input image, assigns importance (learnable weights and biases) to various aspects/objects in the image and is able to differentiate one from the other. The preprocessing required in a CNN is much lower as compared to other classification algorithms. While in primitive methods filters are hand-engineered, with enough training, CNN has the ability to learn these filters/characteristics.

CNN performs incredibly when it comes to analyzing a single image, but it lacks one essential quality - they only consider spatial features and visual data ignoring the temporal and time features i.e., how a frame is related to the previous frame. This is where Recurrent Neural Networks or RNN come into play. The term ‘recurrent’ suggests that the neural network repeats the same tasks for every sequence. RNN can also be used in Natural Language Processing

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The primary tasks of convolutional neural networks are the following:

Classify visual content (describe what they “see”),
Recognize objects within is scenery (for example, eyes, nose, lips, ears on the face),
Gather recognized objects into clusters (for example, eyes with eyes, noses with noses);
Understanding patterns in visual content such as images
Understanding patterns in Natural Language Processing data such as raw text.

Different types of layers used on Convolution Neural Networks:

Conv1D layer
Conv2D layer
Conv3D layer
SeparableConv1D layer
SeparableConv2D layer
DepthwiseConv2D layer
Conv2DTranspose layer
Conv3DTranspose layer

CNN Architecture

The architecture of a CNN is analogous to that of the connectivity pattern of Neurons in the Human Brain and was inspired by the organization of the Visual Cortex. Individual neurons respond to stimuli only in a restricted region of the visual field known as the Receptive Field. A collection of such fields overlap to cover the entire visual area.

A CNN is designed to mimic the connectivity pattern of neurons within the human brain. The neurons within a CNN are split into a three-dimensional structure, with each set of neurons analyzing a small region or feature of the input. In other words, each group of neurons specializes in identifying one part of the image. CNNs use the predictions from the layers to produce a final output that presents a vector of probability scores to represent the likelihood that a specific feature belongs to a certain class.

Every CNN is made up of multiple layers, the three main types of layers are:

Convolutional layer: It creates a feature map to predict the class probabilities for each feature by applying a filter that scans the whole image, few pixels at a time,
Pooling layer (downsampling): It scales down the amount of information the convolutional layer generated for each feature and maintains the most essential information (the process of the convolutional and pooling layers usually repeats several times); and
Fully-connected layer: applies weights over the input generated by the feature analysis to predict an accurate label.

In addition, the fully connected layers can be generalised into two more layers:

Fully connected input layer- this layer “flattens” the outputs generated by previous layers to turn them into a single vector that can be used as an input for the next layer.
Fully connected output layer- this layer generates the final probabilities to determine a class for the image.