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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

 

Codersarts has a team of seasoned professionals working with artificial intelligence technologies including machine learning and deep learning to build next-gen solutions. We have hands-on expertise in building and deploying deep learning models like CNN and RNN models for applications such as the image caption generating model.

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